Split (2)

train · 44.6k rows

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36fc32aa-fe47-48f5-8900-acd7f8cbfd1f	integration-of-3d-object-recognition-and	1307.7466	null	http://arxiv.org/abs/1307.7466v1	http://arxiv.org/pdf/1307.7466v1.pdf	Integration of 3D Object Recognition and Planning for Robotic Manipulation: A Preliminary Report	We investigate different approaches to integrating object recognition and planning in a tabletop manipulation domain with the set of objects used in the 2012 RoboCup@Work competition. Results of our preliminary experiments show that, with some approaches, close integration of perception and planning improves the quality of plans, as well as the computation times of feasible plans.	['Esra Erdem', 'Damien Jade Duff', 'Volkan Patoglu']	2013-07-29	null	null	null	null	['3d-object-recognition']	['computer-vision']	[ 2.09813878e-01 1.95974976e-01 -1.46762967e-01 -3.20247471e-01 -4.43331152e-01 -7.61290967e-01 4.75951701e-01 4.87131745e-01 -3.16942543e-01 8.36445332e-01 3.84997994e-01 1.29716188e-01 -6.98564768e-01 -7.65632391e-01 -7.59711564e-01 1.61427543e-01 -3.83879751e-01 9.76684332e-01 8.37828040e-01 -6.38164282e-01 6.89212322e-01 6.97582722e-01 -1.93811309e+00 6.05297446e-01 4.92637873e-01 1.05495167e+00 4.42793161e-01 9.89256561e-01 3.39047670e-01 8.58596027e-01 -6.08748257e-01 6.13060147e-02 5.25023937e-01 2.36263368e-02 -1.33758295e+00 4.63244438e-01 3.13291430e-01 -3.48617196e-01 -4.89782780e-01 7.13711619e-01 2.64390230e-01 7.70600319e-01 3.06323111e-01 -1.14565146e+00 -3.13005857e-02 7.20164895e-01 2.36863524e-01 2.11009890e-01 1.28604352e+00 1.33419946e-01 6.75689340e-01 -4.28232282e-01 1.01520586e+00 1.35145664e+00 1.90108359e-01 -8.07630457e-03 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11968b1c-7494-426b-a876-329e5526e7d5	visual-textual-association-with-hardest-and	1912.03083	null	https://arxiv.org/abs/1912.03083v1	https://arxiv.org/pdf/1912.03083v1.pdf	Visual-Textual Association with Hardest and Semi-Hard Negative Pairs Mining for Person Search	Searching persons in large-scale image databases with the query of natural language description is a more practical important applications in video surveillance. Intuitively, for person search, the core issue should be visual-textual association, which is still an extremely challenging task, due to the contradiction between the high abstraction of textual description and the intuitive expression of visual images. However, for this task, while positive image-text pairs are always well provided, most existing methods doesn't tackle this problem effectively by mining more reasonable negative pairs. In this paper, we proposed a novel visual-textual association approach with visual and textual attention, and cross-modality hardest and semi-hard negative pair mining. In order to evaluate the effectiveness and feasibility of the proposed approach, we conduct extensive experiments on typical person search datasdet: CUHK-PEDES, in which our approach achieves the top1 score of 55.32% as a new state-of-the-art. Besides, we also evaluate the semi-hard pair mining approach in COCO caption dataset, and validate the effectiveness and complementarity of the methods.	['Guangyu Gao', 'Zhen Liu', 'Jing Ge']	2019-12-06	null	null	null	null	['person-search']	['computer-vision']	[-2.33451158e-01 -3.34573388e-01 -1.19727805e-01 -2.21617982e-01 -7.02468932e-01 -2.30057463e-01 6.06186807e-01 -1.15318298e-02 -6.31982863e-01 6.94546819e-01 2.12610334e-01 5.12677580e-02 -2.23494276e-01 -3.77508104e-01 -5.40065408e-01 -5.75634241e-01 1.83610529e-01 6.88444138e-01 3.32014412e-01 -1.57845870e-01 -2.81330705e-01 -2.76445031e-01 -1.49391842e+00 4.62965757e-01 7.97211289e-01 1.20376790e+00 1.86062813e-01 2.38436759e-01 3.47592570e-02 6.77841187e-01 -4.13625598e-01 -1.02633846e+00 2.44997740e-01 -2.60088533e-01 -6.09239399e-01 2.58431494e-01 5.84999859e-01 -2.50953108e-01 -3.87568384e-01 1.29088116e+00 6.34070694e-01 -1.24434225e-01 5.85067332e-01 -1.48939145e+00 -7.24605978e-01 2.15964198e-01 -1.00051260e+00 3.93740565e-01 5.71403146e-01 2.13668272e-01 1.07246006e+00 -8.29635978e-01 4.64938819e-01 1.38647795e+00 4.58770335e-01 5.32686889e-01 -7.37783015e-01 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9ee048ed-d502-45d9-8a6a-53ccc98b76d0	dynamic-programming-on-a-quantum-annealer	2306.04285	null	https://arxiv.org/abs/2306.04285v1	https://arxiv.org/pdf/2306.04285v1.pdf	Dynamic Programming on a Quantum Annealer: Solving the RBC Model	We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all states and generating candidate global solutions in milliseconds, irrespective of problem size. Using existing quantum hardware, we achieve an order-of-magnitude speed-up in solving the real business cycle model over benchmarks in the literature. We also provide a detailed introduction to quantum annealing and discuss its potential use for more challenging economic problems.	['Isaiah Hull', 'Jesús Fernández-Villaverde']	2023-06-07	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 1.26478270e-01 2.39104941e-01 -2.04544753e-01 -1.58994541e-01 -9.12341058e-01 -6.84268475e-01 3.59298378e-01 2.78008670e-01 -5.77961385e-01 7.62102962e-01 -4.13135111e-01 -5.57374835e-01 -2.41837338e-01 -1.39926863e+00 -5.83896935e-01 -8.22497845e-01 -1.54895902e-01 1.15317810e+00 -2.27049589e-01 -7.47154236e-01 6.79332078e-01 1.03575170e-01 -1.57376301e+00 -1.10240310e-01 6.41671062e-01 7.89277434e-01 -1.36468619e-01 9.01991010e-01 3.30354571e-01 1.43115669e-01 -5.46265304e-01 -4.93129820e-01 9.83740807e-01 -5.72469115e-01 -1.07851171e+00 -3.44152629e-01 -4.53027040e-01 -4.97142896e-02 -6.38437927e-01 1.41884267e+00 4.47160870e-01 3.55006725e-01 1.76758111e-01 -1.15980113e+00 -4.95520830e-01 6.59641802e-01 -2.75267899e-01 4.67769086e-01 4.54586178e-01 6.61623776e-01 1.57212639e+00 1.20681360e-01 5.53381026e-01 1.24079025e+00 2.53659099e-01 4.89236474e-01 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e6096282-fdeb-4be7-abe2-d6dc871ec006	early-stage-diabetes-prediction-via-extreme	2202.11216	null	https://arxiv.org/abs/2202.11216v1	https://arxiv.org/pdf/2202.11216v1.pdf	Early Stage Diabetes Prediction via Extreme Learning Machine	Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe diseases that can lead to patient death. Developing and rural areas suffer the most due to the limited medical providers and financial situations. This paper proposed a novel approach based on an extreme learning machine for diabetes prediction based on a data questionnaire that can early alert the users to seek medical assistance and prevent late diagnoses and severe illness development.	['Murat Ozer', 'Zag ElSayed', 'Nelly Elsayed']	2022-02-22	null	null	null	null	['diabetes-prediction']	['medical']	[-3.14235747e-01 8.77686441e-02 -4.87321734e-01 -6.94093168e-01 -6.28724247e-02 7.74371102e-02 4.25752662e-02 8.93325567e-01 -2.07757607e-01 1.01570499e+00 1.02588512e-01 -1.58847600e-01 -3.12883444e-02 -9.92940962e-01 1.49241790e-01 -4.42342192e-01 -2.78615028e-01 8.59349310e-01 -1.66662872e-01 -3.09440017e-01 2.87414789e-01 3.76023024e-01 -1.28232670e+00 2.55169749e-01 1.66767633e+00 9.41630423e-01 -2.15444520e-01 4.45426852e-01 -5.67681849e-01 7.47058690e-01 -5.30558944e-01 -3.25704873e-01 7.15833381e-02 -5.65760016e-01 -1.71826392e-01 -1.61780909e-01 -1.33885797e-02 -5.94442666e-01 7.89743736e-02 9.52036679e-01 7.91157961e-01 -5.13769388e-01 7.41639018e-01 -1.35602808e+00 -7.08331347e-01 8.87435377e-02 -6.01707339e-01 -8.43108539e-03 7.10763514e-01 6.16866723e-03 -1.05822653e-01 -7.09708571e-01 2.44002551e-01 1.22244143e+00 9.77160037e-01 7.18098462e-01 -1.02007377e+00 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4eac2c85-dc48-4f9d-86cb-be4fe403bd75	deep-neural-object-analysis-by-interactive	1807.01035	null	http://arxiv.org/abs/1807.01035v2	http://arxiv.org/pdf/1807.01035v2.pdf	Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot	We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.	['Stefan Wermter', 'Matthias Kerzel', 'Manfred Eppe', 'Erik Strahl']	2018-07-03	null	null	null	null	['material-classification']	['computer-vision']	[ 2.69393474e-02 2.36390471e-01 5.92720270e-01 1.75169408e-01 -4.56685364e-01 -7.49186575e-01 -1.08113438e-01 -6.08264878e-02 -3.44472468e-01 2.08623543e-01 1.03352062e-01 2.69191504e-01 5.31928800e-02 -5.01994848e-01 -1.31914735e+00 -8.46231580e-01 -4.68293995e-01 2.41530865e-01 2.07196489e-01 2.85849392e-01 9.24968943e-02 5.63267827e-01 -1.82527101e+00 5.02493382e-01 1.39161065e-01 1.71376026e+00 6.23022079e-01 9.20913398e-01 5.94486058e-01 9.54348266e-01 -8.23141694e-01 3.11699599e-01 1.08358413e-01 4.19735998e-01 -6.50318027e-01 -1.51839763e-01 -3.92860085e-01 -5.52082956e-01 -1.86199918e-01 9.12513971e-01 5.48576534e-01 1.95114966e-02 9.32335854e-01 -1.16607082e+00 -6.65831208e-01 1.07551718e+00 9.40756947e-02 -4.33253586e-01 9.89570081e-01 -4.78757359e-02 7.63094068e-01 -1.07792580e+00 2.29127899e-01 1.31586289e+00 8.61257434e-01 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-1.08752799e+00 -1.09199417e+00 3.74856242e-03 4.50212687e-01 -9.44074750e-01 -6.98626876e-01 3.02503616e-01]	[5.762899875640869, -0.6819994449615479]
94df6ad3-92ca-492d-8730-d4e424c69287	the-cube-illumination-estimation-dataset	2011.10028	null	https://arxiv.org/abs/2011.10028v1	https://arxiv.org/pdf/2011.10028v1.pdf	The Cube++ Illumination Estimation Dataset	Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets have problems such as too few images, inappropriate image quality, lack of scene diversity, absence of version tracking, violation of various assumptions, GDPR regulation violation, lack of additional shooting procedure info, etc. In this paper, a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research. It consists of 4890 images with known illumination colors as well as with additional semantic data that can further make the learning process more accurate. Due to the usage of the SpyderCube color target, for every image there are two ground-truth illumination records covering different directions. Because of that, the dataset can be used for training and testing of methods that perform single or two-illuminant estimation. This makes it superior to many similar existing datasets. The datasets, it's smaller version SimpleCube++, and the accompanying code are available at https://github.com/Visillect/CubePlusPlus/.	['Sven Lončarić', 'Marko Subašić', 'Karlo Koscević', 'Daria Senshina', 'Alexander Belokopytov', 'Nikola Banić', 'Illya Semenkov', 'Alex Savchik', 'Egor Ershov']	2020-11-19	null	null	null	null	['color-constancy']	['computer-vision']	[ 1.48544073e-01 -6.48786783e-01 1.42472044e-01 -4.04694259e-01 -3.21366578e-01 -7.30042636e-01 4.00552839e-01 -1.89297825e-01 -2.72876084e-01 6.77853227e-01 -2.56299794e-01 -2.35566676e-01 8.32576975e-02 -4.83450800e-01 -6.86876774e-01 -8.91596079e-01 5.07485390e-01 3.93987373e-02 2.08269194e-01 3.15004587e-02 4.31175083e-01 5.30788720e-01 -2.02068520e+00 -1.10158799e-02 8.41971397e-01 1.24316847e+00 2.54822910e-01 5.45794845e-01 -1.45420253e-01 4.71360296e-01 -5.54051101e-01 -2.86117285e-01 5.57792723e-01 -4.45052743e-01 -2.56710172e-01 2.34263957e-01 5.59439242e-01 -4.55417812e-01 1.47680074e-01 1.25870419e+00 5.60353875e-01 9.30313393e-02 4.08883899e-01 -1.68884456e+00 -4.30562586e-01 -1.68610275e-01 -6.87252343e-01 -2.10582659e-01 1.05066314e-01 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bd6f1921-9c0a-4dca-8708-ce4173fdcaba	from-paraphrasing-to-semantic-parsing	2106.06228	null	https://arxiv.org/abs/2106.06228v1	https://arxiv.org/pdf/2106.06228v1.pdf	From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding	Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammar constrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterance and meaning representation. During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve competitive unsupervised semantic parsing performance on multiple datasets.	['Xunliang Cai', 'Fan Yang', 'Jiansong Chen', 'Weipeng Zhang', 'Le Sun', 'Xianpei Han', 'Chunlei Xin', 'Bo Chen', 'Shan Wu']	2021-06-11	null	https://aclanthology.org/2021.acl-long.397	https://aclanthology.org/2021.acl-long.397.pdf	acl-2021-5	['unsupervised-semantic-parsing']	['natural-language-processing']	[ 8.21708918e-01 7.52412498e-01 -2.32062340e-01 -7.90486872e-01 -5.61468780e-01 -6.64848030e-01 2.35567600e-01 1.36065349e-01 3.38142700e-02 2.93208003e-01 3.25362027e-01 -4.34921443e-01 3.55827302e-01 -8.50712597e-01 -8.45289707e-01 -2.65672445e-01 5.63953936e-01 4.93870676e-01 1.51280224e-01 -4.49474081e-02 1.45273745e-01 -3.71782631e-01 -1.34378290e+00 4.16105539e-01 1.03632760e+00 7.97275722e-01 8.51576328e-01 5.61011970e-01 -1.02773786e+00 7.57317483e-01 -5.47071874e-01 -2.00583369e-01 -1.86658651e-01 -1.03833473e+00 -1.33079123e+00 1.91090241e-01 -1.85123235e-02 -1.04103222e-01 1.50344163e-01 1.38016224e+00 -2.30406269e-01 -6.01174869e-03 -2.49350853e-02 -1.23210943e+00 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7c129fd1-1988-4ae3-bf59-c241c2d3a566	fairgan-gans-based-fairness-aware-learning	null	null	https://dl.acm.org/doi/10.1145/3485447.3511958	https://dl.acm.org/doi/10.1145/3485447.3511958	FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback	Ranking algorithms in recommender systems influence people to make decisions. Conventional ranking algorithms based on implicit feedback data aim to maximize the utility to users by capturing users’ preferences over items. However, these utility-focused algorithms tend to cause fairness issues that require careful consideration in online platforms. Existing fairness-focused studies does not explicitly consider the problem of lacking negative feedback in implicit feedback data, while previous utility-focused methods ignore the importance of fairness in recommendations. To fill this gap, we propose a Generative Adversarial Networks (GANs) based learning algorithm FairGAN mapping the exposure fairness issue to the problem of negative preferences in implicit feedback data. FairGAN does not explicitly treat unobserved interactions as negative, but instead, adopts a novel fairness-aware learning strategy to dynamically generate fairness signals. This optimizes the search direction to make FairGAN capable of searching the space of the optimal ranking that can fairly allocate exposure to individual items while preserving users’ utilities as high as possible.	['Ke Deng', 'Yongli Ren', 'Jie Li']	2022-04-25	null	null	null	proceedings-of-the-acm-web-conference-2022-4	['exposure-fairness']	['adversarial']	[ 5.24004782e-03 2.12450102e-01 -5.60856521e-01 -6.82175577e-01 -2.78501511e-01 -5.36517441e-01 5.36341012e-01 -3.83096308e-01 -3.97693187e-01 9.50884521e-01 5.68660736e-01 -1.84745237e-01 -2.30363086e-01 -1.21364319e+00 -1.29303381e-01 -4.20821041e-01 5.30091114e-02 3.73531729e-01 -6.00560963e-01 -3.85564715e-01 2.62564987e-01 -6.03414923e-02 -1.22713065e+00 1.82706192e-01 1.32071924e+00 6.27274930e-01 -3.58359963e-01 6.15870655e-01 -2.63323244e-02 1.12023914e+00 -7.75499642e-01 -1.11887920e+00 7.81483650e-01 -8.21468532e-01 -4.57901090e-01 -3.91817838e-01 4.59190696e-01 -8.72350872e-01 -3.02999347e-01 1.13369501e+00 8.25675011e-01 4.89665508e-01 5.92406034e-01 -1.60416079e+00 -1.53317320e+00 8.57422352e-01 -2.28497967e-01 -2.89773643e-02 2.92321891e-02 1.58448264e-01 1.38577068e+00 -1.98678061e-01 3.50790858e-01 1.22177255e+00 4.48387533e-01 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48633a2b-d021-432f-86fe-9f91adee2d97	token-level-serialized-output-training-for	2307.03354	null	https://arxiv.org/abs/2307.03354v1	https://arxiv.org/pdf/2307.03354v1.pdf	Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments	In real-world applications, users often require both translations and transcriptions of speech to enhance their comprehension, particularly in streaming scenarios where incremental generation is necessary. This paper introduces a streaming Transformer-Transducer that jointly generates automatic speech recognition (ASR) and speech translation (ST) outputs using a single decoder. To produce ASR and ST content effectively with minimal latency, we propose a joint token-level serialized output training method that interleaves source and target words by leveraging an off-the-shelf textual aligner. Experiments in monolingual (it-en) and multilingual (\{de,es,it\}-en) settings demonstrate that our approach achieves the best quality-latency balance. With an average ASR latency of 1s and ST latency of 1.3s, our model shows no degradation or even improves output quality compared to separate ASR and ST models, yielding an average improvement of 1.1 WER and 0.4 BLEU in the multilingual case.	['Yashesh Gaur', 'Jinyu Li', 'Jian Xue', 'Junkun Chen', 'Peidong Wan', 'Sara Papi']	2023-07-07	null	null	null	null	['speech-recognition', 'automatic-speech-recognition']	['speech', 'speech']	[ 6.02888882e-01 2.54931480e-01 2.28567179e-02 -2.53673017e-01 -1.62879264e+00 -8.56443226e-01 6.62237763e-01 2.63031572e-01 -4.12251055e-01 6.01925313e-01 4.18826371e-01 -8.57601166e-01 6.48485601e-01 -1.93874195e-01 -8.89541864e-01 -2.06689656e-01 3.42010945e-01 5.06625950e-01 2.08875239e-01 -4.26215410e-01 -1.63028404e-01 1.20344935e-02 -1.18065608e+00 5.01586735e-01 1.10524952e+00 6.18341267e-01 5.96216261e-01 1.18682766e+00 -2.20447406e-01 5.94737113e-01 -9.65732396e-01 -4.82825369e-01 1.71363831e-01 -5.99417448e-01 -7.03602612e-01 -1.77588060e-01 2.54713923e-01 -4.12569463e-01 -4.00018185e-01 7.57122457e-01 7.74215519e-01 -1.52129248e-01 2.10669175e-01 -8.88414562e-01 -3.51101816e-01 1.15316272e+00 -4.27613348e-01 2.26768225e-01 7.91022718e-01 1.41487181e-01 9.75002646e-01 -8.47850502e-01 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e3a6f554-4ddb-42c8-91e8-d08459083b07	decomposing-normal-and-abnormal-features-of-1	2103.12328	null	https://arxiv.org/abs/2103.12328v1	https://arxiv.org/pdf/2103.12328v1.pdf	Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval	In medical imaging, the characteristics purely derived from a disease should reflect the extent to which abnormal findings deviate from the normal features. Indeed, physicians often need corresponding images without abnormal findings of interest or, conversely, images that contain similar abnormal findings regardless of normal anatomical context. This is called comparative diagnostic reading of medical images, which is essential for a correct diagnosis. To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful. Therefore, we propose a neural network architecture to decompose the semantic components of medical images into two latent codes: normal anatomy code and abnormal anatomy code. The normal anatomy code represents normal anatomies that should have existed if the sample is healthy, whereas the abnormal anatomy code attributes to abnormal changes that reflect deviation from the normal baseline. These latent codes are discretized through vector quantization to enable binary hashing, which can reduce the computational burden at the time of similarity search. By calculating the similarity based on either normal or abnormal anatomy codes or the combination of the two codes, our algorithm can retrieve images according to the selected semantic component from a dataset consisting of brain magnetic resonance images of gliomas. Our CBIR system qualitatively and quantitatively achieves remarkable results.	['Ryuji Hamamoto', 'Tatsuya Harada', 'Akiko Nakagawa', 'Masamichi Takahashi', 'Mototaka Miyake', 'Yusuke Kurose', 'Ryuichiro Hataya', 'Kazuma Kobayashi']	2021-03-23	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[ 5.22448838e-01 6.00056984e-02 -3.46710563e-01 -4.25601691e-01 -7.60994673e-01 -3.52702647e-01 3.77650887e-01 7.29926169e-01 -2.75434554e-01 3.44576508e-01 3.64465386e-01 -1.93230569e-01 -4.24402565e-01 -8.99241149e-01 -1.39707446e-01 -9.55878496e-01 -9.93551835e-02 5.79163909e-01 1.00120649e-01 1.05102971e-01 3.54361296e-01 5.57105601e-01 -1.68863082e+00 5.68680406e-01 5.47324836e-01 1.32326186e+00 4.32093680e-01 4.25921679e-01 -2.96139330e-01 7.16389477e-01 -6.14798844e-01 8.51195008e-02 1.23542681e-01 -6.36959970e-01 -1.02628064e+00 4.63150181e-02 1.55845135e-01 -4.15856779e-01 -3.79457414e-01 1.61111331e+00 4.26045805e-01 -1.78378709e-02 8.91401052e-01 -9.98073518e-01 -1.00887632e+00 1.75141364e-01 -3.57561916e-01 4.13458675e-01 4.04037058e-01 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f3046db4-a837-4c10-a772-4dfea2aab962	assessing-evaluation-metrics-for-speech-to	2110.13877	null	https://arxiv.org/abs/2110.13877v1	https://arxiv.org/pdf/2110.13877v1.pdf	Assessing Evaluation Metrics for Speech-to-Speech Translation	Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speech-to-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.	['Severin Klinger', 'Julian Mäder', 'Elizabeth Salesky']	2021-10-26	null	null	null	null	['speech-to-speech-translation']	['speech']	[ 4.55376774e-01 4.38609123e-02 -9.39346775e-02 -3.71787310e-01 -1.37591410e+00 -9.39422369e-01 8.27410340e-01 -1.41309455e-01 -4.46777433e-01 9.39410090e-01 5.96298575e-01 -7.82726943e-01 2.29867160e-01 -2.65773058e-01 -3.32526207e-01 -2.24853933e-01 7.22268999e-01 8.39768469e-01 3.32116820e-02 -6.47228122e-01 -1.85119689e-01 3.36754888e-01 -1.19178593e+00 3.62750798e-01 9.81674552e-01 2.12225392e-01 2.25156471e-01 7.57453620e-01 -3.61851871e-01 3.19334149e-01 -9.69134867e-01 -6.13223016e-01 9.81334820e-02 -9.58791077e-01 -8.86156082e-01 -2.58009266e-02 5.64074636e-01 -1.18173817e-02 8.62031654e-02 1.03902197e+00 6.33520901e-01 -7.83071443e-02 5.08022249e-01 -7.46555746e-01 -8.34030151e-01 8.33045900e-01 2.90820926e-01 2.96624035e-01 7.93126822e-01 1.88276336e-01 8.95636737e-01 -9.74440753e-01 8.85176659e-01 1.43495846e+00 2.57375002e-01 5.62529922e-01 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e6c698c8-40ed-4638-bf69-b541a19ef545	towards-live-3d-reconstruction-from-wearable	2211.11836	null	https://arxiv.org/abs/2211.11836v1	https://arxiv.org/pdf/2211.11836v1.pdf	Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques	Mixed reality (MR) is a key technology which promises to change the future of warfare. An MR hybrid of physical outdoor environments and virtual military training will enable engagements with long distance enemies, both real and simulated. To enable this technology, a large-scale 3D model of a physical environment must be maintained based on live sensor observations. 3D reconstruction algorithms should utilize the low cost and pervasiveness of video camera sensors, from both overhead and soldier-level perspectives. Mapping speed and 3D quality can be balanced to enable live MR training in dynamic environments. Given these requirements, we survey several 3D reconstruction algorithms for large-scale mapping for military applications given only live video. We measure 3D reconstruction performance from common structure from motion, visual-SLAM, and photogrammetry techniques. This includes the open source algorithms COLMAP, ORB-SLAM3, and NeRF using Instant-NGP. We utilize the autonomous driving academic benchmark KITTI, which includes both dashboard camera video and lidar produced 3D ground truth. With the KITTI data, our primary contribution is a quantitative evaluation of 3D reconstruction computational speed when considering live video.	['Andreas Spanias', 'Suren Jayasuriya', 'David Ramirez']	2022-11-21	null	null	null	null	['mixed-reality']	['computer-vision']	[-9.10524204e-02 -5.12784600e-01 -1.29180253e-01 -3.80981714e-01 -9.32385504e-01 -1.05272841e+00 7.18198001e-01 -3.30902994e-01 -7.83438265e-01 5.65825462e-01 -9.56931524e-03 -4.51865643e-01 -2.60998875e-01 -7.29846716e-01 -7.43600070e-01 -2.87988007e-01 -5.24449110e-01 9.08695161e-01 3.59268010e-01 -8.59221756e-01 2.94459552e-01 1.07998455e+00 -1.36517918e+00 3.69361276e-03 3.28255981e-01 7.73702681e-01 2.42019296e-01 1.09978092e+00 2.82265216e-01 4.91192669e-01 -2.56481558e-01 -5.77540696e-03 9.20277894e-01 1.78824291e-01 -1.10159121e-01 -2.39762440e-01 7.23213494e-01 -7.15364814e-01 -7.76490927e-01 6.86587811e-01 6.90533936e-01 3.93180966e-01 -1.96522493e-02 -1.24849117e+00 2.29281411e-01 -1.42399356e-01 -3.90919417e-01 3.05337936e-01 1.03173649e+00 5.82236052e-01 3.86882305e-01 -9.51341808e-01 1.13948929e+00 1.05878389e+00 9.45988476e-01 3.42638552e-01 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d028dbd6-3542-48a8-a6d9-c3a1cd7e7e78	docsynth-a-layout-guided-approach-for	2107.02638	null	https://arxiv.org/abs/2107.02638v1	https://arxiv.org/pdf/2107.02638v1.pdf	DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis	Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.	['Umapada Pal', 'Josep Lladós', 'Pau Riba', 'Sanket Biswas']	2021-07-06	null	null	null	null	['document-layout-analysis']	['computer-vision']	[ 6.31800354e-01 1.47465333e-01 2.50347167e-01 -3.00434828e-01 -6.40007913e-01 -7.94162214e-01 1.04746068e+00 -1.58952009e-02 -4.71447688e-03 7.78869689e-01 1.66160375e-01 -1.40648693e-01 -2.79185660e-02 -6.20750964e-01 -8.97488832e-01 -4.20272201e-01 4.23638225e-01 6.84124768e-01 2.76706535e-02 -1.03679597e-01 6.93137467e-01 5.97925007e-01 -1.84211671e+00 9.18770909e-01 8.80509794e-01 5.05767047e-01 6.31017685e-01 9.79681790e-01 -4.32278335e-01 6.27750337e-01 -1.41097808e+00 -3.91640872e-01 1.37616828e-01 -6.62474751e-01 -7.59829104e-01 7.15528250e-01 9.63963985e-01 -3.12733352e-01 5.79527505e-02 7.00097501e-01 6.22735679e-01 6.26972467e-02 9.66678143e-01 -1.37518394e+00 -1.16958380e+00 7.79105663e-01 -4.10623789e-01 -5.28732538e-01 5.75778067e-01 2.86867976e-01 6.47992253e-01 -9.84494746e-01 1.14190495e+00 1.42658949e+00 1.94898844e-01 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a2bb23bd-e925-45be-ac4f-0c45148e718e	what-is-the-ground-truth-reliability-of-multi	2104.04214	null	https://arxiv.org/abs/2104.04214v1	https://arxiv.org/pdf/2104.04214v1.pdf	What is the ground truth? Reliability of multi-annotator data for audio tagging	Crowdsourcing has become a common approach for annotating large amounts of data. It has the advantage of harnessing a large workforce to produce large amounts of data in a short time, but comes with the disadvantage of employing non-expert annotators with different backgrounds. This raises the problem of data reliability, in addition to the general question of how to combine the opinions of multiple annotators in order to estimate the ground truth. This paper presents a study of the annotations and annotators' reliability for audio tagging. We adapt the use of Krippendorf's alpha and multi-annotator competence estimation (MACE) for a multi-labeled data scenario, and present how MACE can be used to estimate a candidate ground truth based on annotations from non-expert users with different levels of expertise and competence.	['Annamaria Mesaros', 'Irene Martin-Morato']	2021-04-09	null	null	null	null	['audio-tagging']	['audio']	[-3.48903611e-02 5.56758404e-01 4.97716546e-01 -3.71564180e-01 -1.37809896e+00 -9.08871174e-01 -5.49946800e-02 8.23911369e-01 -7.80645370e-01 7.76097536e-01 4.35384303e-01 2.10597560e-01 -9.40158218e-02 -1.50179893e-01 -2.38455296e-01 -4.33476299e-01 5.37349284e-01 7.67579496e-01 6.32120907e-01 -1.98057905e-01 1.17427595e-01 8.41730684e-02 -1.72628760e+00 5.88699341e-01 8.17937315e-01 8.23174119e-01 2.94899613e-01 7.30951846e-01 -1.05537735e-01 9.19499636e-01 -1.07375836e+00 -8.91893625e-01 2.84035951e-02 -1.40674561e-01 -1.12566185e+00 -9.23843756e-02 5.30127943e-01 1.17480159e-01 4.35418516e-01 8.52764308e-01 1.04951453e+00 1.09724060e-01 2.02019066e-01 -1.32010829e+00 -3.55742723e-01 5.95232725e-01 -2.02789471e-01 9.34331790e-02 9.87322628e-01 -3.50602686e-01 8.57035518e-01 -7.90601909e-01 5.23750246e-01 9.34525192e-01 1.32778537e+00 3.31523329e-01 -8.49239945e-01 -3.73264790e-01 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1f32707e-2965-44cd-bf0b-3f6aad4451ac	determining-the-veracity-of-rumours-on	1611.06314	null	http://arxiv.org/abs/1611.06314v1	http://arxiv.org/pdf/1611.06314v1.pdf	Determining the Veracity of Rumours on Twitter	While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.	['Danica Vukadinovic Greetham', 'Colin Singleton', 'Alan Pilgrim', 'Ioannis Agrafiotis', 'Jason R. C. Nurse', 'Georgios Giasemidis', 'Chris Willis']	2016-11-19	null	null	null	null	['rumour-detection']	['natural-language-processing']	[-4.35433090e-01 3.02157253e-01 -2.12851226e-01 -3.82730246e-01 -2.73860395e-01 -4.09483492e-01 1.02587652e+00 8.43000472e-01 -2.49714881e-01 7.10069239e-01 3.54252100e-01 -3.14330637e-01 1.33770891e-02 -8.92249048e-01 -2.30341434e-01 -1.91914514e-01 -5.01175582e-01 2.25784868e-01 2.75044888e-01 -6.01346254e-01 6.72840297e-01 2.82444507e-01 -1.34747934e+00 2.27776602e-01 6.20365024e-01 8.19012642e-01 -5.17650306e-01 4.32786971e-01 -3.75475250e-02 1.23132014e+00 -8.35758746e-01 -8.04194033e-01 -2.28633642e-01 -2.39022762e-01 -9.10677373e-01 -2.14672685e-01 -5.31652719e-02 -2.90903479e-01 -7.12925196e-02 8.65252972e-01 2.78183389e-02 -2.24404186e-01 4.14880097e-01 -1.63649380e+00 -5.96206903e-01 9.39886630e-01 -3.30048323e-01 5.51156759e-01 8.60883355e-01 3.46521512e-02 1.00181746e+00 -6.48905218e-01 8.82310450e-01 1.10207093e+00 1.04515672e+00 2.14856207e-01 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28cf926b-fd1e-48e3-8411-47b6448fe9b2	flexhdr-modelling-alignment-and-exposure	2201.02625	null	https://arxiv.org/abs/2201.02625v3	https://arxiv.org/pdf/2201.02625v3.pdf	FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging	High dynamic range (HDR) imaging is of fundamental importance in modern digital photography pipelines and used to produce a high-quality photograph with well exposed regions despite varying illumination across the image. This is typically achieved by merging multiple low dynamic range (LDR) images taken at different exposures. However, over-exposed regions and misalignment errors due to poorly compensated motion result in artefacts such as ghosting. In this paper, we present a new HDR imaging technique that specifically models alignment and exposure uncertainties to produce high quality HDR results. We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map that robustly merges the frames into a single high quality HDR image. Further, we introduce a progressive, multi-stage image fusion approach that can flexibly merge any number of LDR images in a permutation-invariant manner. Experimental results show our method can produce better quality HDR images with up to 1.1dB PSNR improvement to the state-of-the-art, and subjective improvements in terms of better detail, colours, and fewer artefacts.	['Eduardo Pérez-Pellitero', 'Gregory Slabaugh', 'Aleš Leonardis', 'Lucas Vandroux', 'Thomas Tanay', 'Sibi Catley-Chandar']	2022-01-07	null	null	null	null	['models-alignment']	['knowledge-base']	[ 6.75324500e-01 -2.37137467e-01 5.34622729e-01 -5.22853851e-01 -1.12423277e+00 -5.60582280e-01 4.42628652e-01 -3.25718790e-01 -2.42442757e-01 7.32917011e-01 2.21139953e-01 2.96127331e-03 -2.48081490e-01 -5.33985913e-01 -1.00553524e+00 -8.19384754e-01 1.69305012e-01 -1.50225729e-01 2.90018052e-01 -4.16732989e-02 1.63108200e-01 4.58712280e-01 -1.60378575e+00 1.49623975e-01 1.12411356e+00 9.00377333e-01 7.02633321e-01 1.03449941e+00 5.35293341e-01 7.18410611e-01 -6.64556623e-01 -4.76695746e-01 7.39432752e-01 -4.58587706e-01 -5.87628305e-01 5.78886211e-01 9.96863723e-01 -9.29739237e-01 -4.80315030e-01 1.17889738e+00 5.49366176e-01 9.66743827e-02 1.08714424e-01 -9.80061650e-01 -7.89843798e-01 2.22834215e-01 -9.36168075e-01 1.37895212e-01 3.77215743e-01 7.21414089e-01 6.13635242e-01 -4.64942396e-01 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3b9c49d7-b6a0-412d-ab90-b81d4fba7ce3	wider-vision-enriching-convolutional-neural	2102.11132	null	https://arxiv.org/abs/2102.11132v1	https://arxiv.org/pdf/2102.11132v1.pdf	Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases	Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, the understanding of hidden feature map activations is limited by the discriminative knowledge gleaned during training. The aim of our work is to explain and expand CNNs models via the mirroring or alignment of CNN to an external knowledge base. This will allow us to give a semantic context or label for each visual feature. We can match CNN feature activations to nodes in our external knowledge base. This supports knowledge-based interpretation of the features associated with model decisions. To demonstrate our approach, we build two separate graphs. We use an entity alignment method to align the feature nodes in a CNN with the nodes in a ConceptNet based knowledge graph. We then measure the proximity of CNN graph nodes to semantically meaningful knowledge base nodes. Our results show that in the aligned embedding space, nodes from the knowledge graph are close to the CNN feature nodes that have similar meanings, indicating that nodes from an external knowledge base can act as explanatory semantic references for features in the model. We analyse a variety of graph building methods in order to improve the results from our embedding space. We further demonstrate that by using hierarchical relationships from our external knowledge base, we can locate new unseen classes outside the CNN training set in our embeddings space, based on visual feature activations. This suggests that we can adapt our approach to identify unseen classes based on CNN feature activations. Our demonstrated approach of aligning a CNN with an external knowledge base paves the way to reason about and beyond the trained model, with future adaptations to explainable models and zero-shot learning.	['Susan Mckeever', 'Sarah Jane Delany', 'Xuehao Liu']	2021-02-22	null	null	null	null	['explainable-models']	['computer-vision']	[ 7.94188827e-02 8.05407584e-01 -3.17734838e-01 -4.88070965e-01 2.28899598e-01 -7.06186056e-01 7.13080108e-01 5.43373287e-01 -1.93016142e-01 2.74943978e-01 3.47824723e-01 -4.96946871e-02 -3.58408868e-01 -1.17983913e+00 -8.74447405e-01 -4.26062256e-01 5.94819374e-02 2.40483493e-01 5.23268163e-01 -2.58086234e-01 7.26797655e-02 6.12700820e-01 -1.75342095e+00 6.25087202e-01 1.25008449e-01 8.20258439e-01 1.89469919e-01 3.69244009e-01 -5.27093649e-01 6.87001526e-01 -5.38411319e-01 -5.20982862e-01 1.97410733e-02 -3.58122289e-01 -1.04203665e+00 -2.01085567e-01 5.23812175e-01 1.46968558e-01 -2.46024415e-01 8.90295565e-01 -1.49558246e-01 1.65461197e-01 6.28825843e-01 -1.71367371e+00 -9.82551277e-01 6.40189826e-01 1.69297382e-01 5.87355867e-02 1.13758855e-01 -2.86452621e-01 1.30861676e+00 -9.08963203e-01 1.12257850e+00 1.25689805e+00 7.23849893e-01 7.34597266e-01 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3bd863cb-8da7-4944-b6a0-d86771ba3e61	self-supervised-classification-of-dynamic	1910.09094	null	https://arxiv.org/abs/1910.09094v2	https://arxiv.org/pdf/1910.09094v2.pdf	Self-supervised classification of dynamic obstacles using the temporal information provided by videos	Nowadays, autonomous driving systems can detect, segment, and classify the surrounding obstacles using a monocular camera. However, state-of-the-art methods solving these tasks generally perform a fully supervised learning process and require a large amount of training labeled data. On another note, some self-supervised learning approaches can deal with detection and segmentation of dynamic obstacles using the temporal information available in video sequences. In this work, we propose to classify the detected obstacles depending on their motion pattern. We present a novel self-supervised framework consisting of learning offline clusters from temporal patch sequences and considering these clusters as labeled sets to train a real-time image classifier. The presented model outperforms state-of-the-art unsupervised image classification methods on large-scale diverse driving video dataset BDD100K.	['Mohamed-Cherif Rahal', 'Sid Ali Hamideche', 'Florent Chiaroni']	2019-10-21	null	null	null	null	['unsupervised-image-classification']	['computer-vision']	[ 2.59798557e-01 -2.80645400e-01 -5.67944646e-01 -4.58862782e-01 -5.16849279e-01 -5.45930088e-01 4.93775666e-01 1.45979807e-01 -7.56076097e-01 5.86792648e-01 -5.64146519e-01 -1.71268731e-01 1.23953946e-01 -7.45177388e-01 -8.67003322e-01 -7.24897504e-01 -6.71936153e-03 4.69504535e-01 9.73265648e-01 -1.03482753e-01 3.89268041e-01 2.86918461e-01 -2.32097459e+00 3.35891157e-01 9.96478021e-01 9.70357537e-01 5.62315345e-01 6.72824740e-01 -7.70818144e-02 1.13810396e+00 -3.32845569e-01 3.12902957e-01 2.95177847e-01 -2.17540145e-01 -3.83138150e-01 4.27305967e-01 5.46299219e-01 -5.81413731e-02 -7.41779268e-01 1.01390314e+00 6.80570006e-02 2.54313290e-01 7.43867159e-01 -1.39077139e+00 2.41151825e-01 1.21148370e-01 -8.21058154e-02 6.14404917e-01 1.71862468e-01 2.01758072e-01 4.40939456e-01 -6.35340095e-01 8.73841465e-01 6.74350679e-01 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e84c90c0-fb73-4275-a568-fb6e4cd7a37b	statistics-and-deep-learning-based-hybrid	2202.12720	null	https://arxiv.org/abs/2202.12720v1	https://arxiv.org/pdf/2202.12720v1.pdf	Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection	Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One example is Multivariate Exponential Smoothing Long Short-Term Memory (MES-LSTM), a hybrid between a multivariate statistical forecasting model and a Recurrent Neural Network variant, Long Short-Term Memory. It has also been shown that a model that ($i$) produces accurate forecasts and ($ii$) is able to quantify the associated predictive uncertainty satisfactorily, can be successfully adapted to a model suitable for anomaly detection tasks. With the increasing ubiquity of multivariate data and new application domains, there have been numerous anomaly detection methods proposed in recent years. The proposed methods have largely focused on deep learning techniques, which are prone to suffer from challenges such as ($i$) large sets of parameters that may be computationally intensive to tune, $(ii)$ returning too many false positives rendering the techniques impractical for use, $(iii)$ requiring labeled datasets for training which are often not prevalent in real life, and ($iv$) understanding of the root causes of anomaly occurrences inhibited by the predominantly black-box nature of deep learning methods. In this article, an extension of MES-LSTM is presented, an interpretable anomaly detection model that overcomes these challenges. With a focus on renewable energy generation as an application domain, the proposed approach is benchmarked against the state-of-the-art. The findings are that MES-LSTM anomaly detector is at least competitive to the benchmarks at anomaly detection tasks, and less prone to learning from spurious effects than the benchmarks, thus making it more reliable at root cause discovery and explanation.	['Terence L van Zyl', 'Thabang Mathonsi']	2022-02-25	null	null	null	null	['prediction-intervals']	['miscellaneous']	[-3.16099301e-02 1.84534833e-01 1.16715789e-01 -3.19627732e-01 -4.78547037e-01 -1.62450001e-01 7.67514825e-01 4.68391806e-01 -1.39404491e-01 7.39864707e-01 -2.37267479e-01 -6.90766335e-01 -3.65033239e-01 -9.19575036e-01 -7.35205591e-01 -8.07847857e-01 -7.57150710e-01 3.22524846e-01 -1.20783858e-01 -2.84923375e-01 3.51534933e-01 5.58401167e-01 -1.86133802e+00 5.62443621e-02 1.07191873e+00 1.75169075e+00 -3.51235539e-01 3.32863927e-01 -4.91576016e-01 7.60521710e-01 -7.12404430e-01 -1.89514399e-01 1.13502875e-01 -9.38952640e-02 -3.75973612e-01 -4.39673603e-01 3.06006581e-01 -2.23105639e-01 6.33996874e-02 1.00111556e+00 2.46332660e-01 4.18297797e-01 8.21567476e-01 -1.43769908e+00 -5.07045269e-01 5.41209340e-01 -3.83008271e-01 5.21675408e-01 5.80820255e-02 7.65381977e-02 7.61228025e-01 -9.81940985e-01 -2.62409039e-02 1.09814382e+00 9.74218547e-01 3.22921038e-01 -1.03019989e+00 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3.11744094e-01 5.35197139e-01 -8.62784863e-01 -7.81736493e-01 -4.48010355e-01]	[7.127496242523193, 2.6982502937316895]
d8f45e10-8df5-4ae5-8617-6bfe8c95d9cc	multimodal-sensor-fusion-for-real-time	2305.13596	null	https://arxiv.org/abs/2305.13596v1	https://arxiv.org/pdf/2305.13596v1.pdf	Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition	Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.	['Seung Ki Moon', 'Youxiang Chew', 'Wenhe Feng', 'Xiling Yao', 'Lequn Chen']	2023-05-23	null	null	null	null	['defect-detection']	['computer-vision']	[ 2.38938242e-01 -3.44702274e-01 3.82224828e-01 1.90282196e-01 -7.36658931e-01 3.94098684e-02 1.39664948e-01 3.30287874e-01 -1.92852885e-01 -1.08845852e-01 -5.49464464e-01 1.45716414e-01 -1.67861164e-01 -1.00580633e+00 -4.64327246e-01 -1.02506089e+00 2.83258647e-01 2.24863008e-01 2.89410144e-01 -1.83264866e-01 4.53081697e-01 5.63383579e-01 -2.06151628e+00 2.23765120e-01 7.00158477e-01 1.61993814e+00 8.67394805e-01 4.97070879e-01 -3.08999047e-02 2.35768899e-01 -7.18526900e-01 4.00438100e-01 5.27918451e-02 -1.30473271e-01 1.18537813e-01 9.09205079e-02 -1.16091065e-01 -4.11357343e-01 -2.80747265e-01 8.64128768e-01 6.37319684e-01 -1.46650195e-01 6.01420641e-01 -1.06388783e+00 -6.15318894e-01 9.60855111e-02 -4.82463300e-01 -2.04073325e-01 4.29896772e-01 3.20288897e-01 5.22356033e-01 -1.20539606e+00 4.94333446e-01 9.50286865e-01 6.62379682e-01 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ab68add8-5272-4136-b94b-648fc0e7ce29	tempsal-uncovering-temporal-information-for	2301.02315	null	https://arxiv.org/abs/2301.02315v1	https://arxiv.org/pdf/2301.02315v1.pdf	TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction	Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark. Our code will be publicly available on GitHub.	['Sabine Süsstrunk', 'Mathieu Salzmann', 'Tong Zhang', 'Ludo Hoffstetter', 'Bahar Aydemir']	2023-01-05	null	null	null	null	['saliency-prediction']	['computer-vision']	[ 3.83941114e-01 -7.66630843e-02 -6.32743299e-01 -5.53088009e-01 -2.72209585e-01 -2.62186110e-01 5.25319815e-01 9.84424800e-02 -2.61270314e-01 4.90728706e-01 2.87267745e-01 -7.42040249e-03 -1.08651944e-01 -1.45800292e-01 -7.58122325e-01 -3.56330574e-01 -8.43113065e-02 -8.02492723e-02 1.10910511e+00 -2.58050621e-01 9.34990287e-01 6.89249719e-03 -1.98330879e+00 3.53576124e-01 9.23243165e-01 1.12393677e+00 6.70751929e-01 5.06246626e-01 2.41824239e-01 9.45875823e-01 -1.49964854e-01 4.14600819e-02 -1.43815741e-01 -6.61139667e-01 -8.32131565e-01 -1.95544332e-01 3.22362244e-01 -7.22987354e-02 -3.80135208e-01 9.50875759e-01 2.33309224e-01 2.80937940e-01 2.90688664e-01 -1.41976976e+00 -1.16169977e+00 4.94455814e-01 -5.42445183e-01 9.20771122e-01 3.32799226e-01 2.38181189e-01 1.13486588e+00 -9.45499003e-01 6.41147852e-01 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a46abe98-c23d-402c-a6ce-4d587e1022be	meltr-meta-loss-transformer-for-learning-to	2303.13009	null	https://arxiv.org/abs/2303.13009v1	https://arxiv.org/pdf/2303.13009v1.pdf	MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models	Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase, a naive strategy to minimize a single task-specific loss is adopted for fine-tuning. However, such fine-tuning methods do not fully leverage other losses that are potentially beneficial for the target task. Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning. We formulate the auxiliary learning as a bi-level optimization problem and present an efficient optimization algorithm based on Approximate Implicit Differentiation (AID). For evaluation, we apply our framework to various video foundation models (UniVL, Violet and All-in-one), and show significant performance gain on all four downstream tasks: text-to-video retrieval, video question answering, video captioning, and multi-modal sentiment analysis. Our qualitative analyses demonstrate that MELTR adequately `transforms' individual loss functions and `melts' them into an effective unified loss. Code is available at https://github.com/mlvlab/MELTR.	['Hyunwoo J. Kim', 'Byungseok Roh', 'Kyoung-Woon On', 'Hyeong Kyu Choi', 'Joonmyung Choi', 'Dohwan Ko']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ko_MELTR_Meta_Loss_Transformer_for_Learning_To_Fine-Tune_Video_Foundation_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ko_MELTR_Meta_Loss_Transformer_for_Learning_To_Fine-Tune_Video_Foundation_CVPR_2023_paper.pdf	cvpr-2023-1	['video-captioning', 'video-question-answering', 'video-retrieval', 'multimodal-sentiment-analysis', 'auxiliary-learning', 'multimodal-sentiment-analysis']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'natural-language-processing']	[ 5.32945171e-02 -1.79627299e-01 -4.49607790e-01 -5.82715333e-01 -1.34894168e+00 -5.25182366e-01 4.64349866e-01 6.76340088e-02 -4.79448795e-01 5.80420971e-01 3.36275131e-01 -3.55020016e-01 -8.22578669e-02 -4.07575876e-01 -8.50763261e-01 -3.39482397e-01 3.00454378e-01 2.40175098e-01 -7.26514682e-03 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711557f1-1d98-45ee-a4ca-be0ad7e4dcf4	partslip-low-shot-part-segmentation-for-3d	2212.01558	null	https://arxiv.org/abs/2212.01558v2	https://arxiv.org/pdf/2212.01558v2.pdf	PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models	Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps.	['Hao Su', 'Fatih Porikli', 'Zhan Ling', 'Shizhong Han', 'Hong Cai', 'Yinhao Zhu', 'Minghua Liu']	2022-12-03	null	http://openaccess.thecvf.com//content/CVPR2023/html/Liu_PartSLIP_Low-Shot_Part_Segmentation_for_3D_Point_Clouds_via_Pretrained_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_PartSLIP_Low-Shot_Part_Segmentation_for_3D_Point_Clouds_via_Pretrained_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-part-segmentation']	['computer-vision']	[ 1.58492535e-01 1.53223291e-01 -3.85629833e-01 -4.88478154e-01 -1.10520959e+00 -6.48933828e-01 4.50945944e-01 -1.88768089e-01 -9.71023440e-02 -1.34710409e-02 -2.31142119e-01 -8.89643505e-02 3.40497881e-01 -5.32208204e-01 -1.16224325e+00 -1.68026268e-01 2.17885941e-01 9.50054228e-01 7.99313188e-01 -1.76041648e-01 -3.01150754e-02 8.17490816e-01 -1.76622534e+00 -1.84968412e-01 5.96705616e-01 9.75510001e-01 2.95507431e-01 3.32562387e-01 -6.89947307e-01 1.76611319e-01 -1.06749624e-01 -1.99136868e-01 9.28441346e-01 2.54478365e-01 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3deaf337-61a4-42a4-a294-e082dc8ae74a	motion-correction-and-volumetric	2202.05863	null	https://arxiv.org/abs/2202.05863v1	https://arxiv.org/pdf/2202.05863v1.pdf	Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data	Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.	['Roxane Licandro', 'Georg Langs', 'Daniela Prayer', 'Gregor Kasprian', 'Sebastien Ourselin', 'Tom Vercauteren', 'Athena Taymourtash', 'Karl-Heinz Nenning', 'Ernst Schwartz', 'Michael Ebner', 'Daniel Sobotka']	2022-02-11	null	null	null	null	['l2-regularization']	['methodology']	[ 2.06120715e-01 -3.96100013e-03 1.64548010e-01 -5.14008939e-01 -4.06471908e-01 -4.38272923e-01 3.03489447e-01 1.03879496e-01 -6.65178359e-01 5.75218201e-01 2.65110344e-01 -2.06869870e-01 -5.98855555e-01 -4.02631789e-01 -7.59191871e-01 -6.85479045e-01 -6.26394153e-01 2.79853940e-01 1.94534034e-01 3.33423793e-01 2.55823284e-01 8.77860010e-01 -9.09178019e-01 1.24702878e-01 9.07378852e-01 6.56115890e-01 2.57641524e-01 4.97362345e-01 1.49408758e-01 5.04455268e-01 -6.94594905e-02 -1.24874525e-01 9.68525559e-02 -6.35659277e-01 -9.60304260e-01 -2.16518864e-01 1.60542950e-01 -3.06822240e-01 -7.35949352e-02 8.81610334e-01 7.67693400e-01 4.81613368e-01 5.81494451e-01 -5.21736145e-01 -1.44812450e-01 7.08714247e-01 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2.87178636e-01 9.41598237e-01 1.04222679e+00 1.64790049e-01 6.44099593e-01 4.01562899e-01 -1.09648371e+00 1.97940335e-01 -1.08430529e+00 -5.48695743e-01 1.77993223e-01 3.81265938e-01 -5.49122334e-01 -2.48279884e-01 -7.61350663e-03]	[13.915407180786133, -2.4270694255828857]
4a15a2ec-6cc7-4a86-b524-2cf2f858a0eb	automatic-liver-segmentation-from-ct-images	2101.09987	null	https://arxiv.org/abs/2101.09987v1	https://arxiv.org/pdf/2101.09987v1.pdf	Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study	Medical imaging has been employed to support medical diagnosis and treatment. It may also provide crucial information to surgeons to facilitate optimal surgical preplanning and perioperative management. Essentially, semi-automatic organ and tumor segmentation has been studied by many researchers. Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers. This paper addresses to propose the most efficient DL architectures for Liver segmentation by adapting and comparing state-of-the-art DL frameworks, studied in different disciplines. These frameworks are implemented and adapted into a Commercial software, 'LiverVision'. It is aimed to reveal the most effective and accurate DL architecture for fully automatic liver segmentation. Equal conditions were provided to all architectures in the experiments so as to measure the effectiveness of algorithms accuracy, and Dice coefficient metrics were also employed to support comparative analysis. Experimental results prove that 'U-Net' and 'SegNet' have been superior in line with the experiments conducted considering the concepts of time, cost, and effectiveness. Considering both architectures, 'SegNet' was observed to be more successful in eliminating false-positive values. Besides, it was seen that the accuracy metric used to measure effectiveness in image segmentation alone was not enough. Results reveal that DL algorithms are able to automate organ segmentation from DICOM images with high accuracy. This contribution is critical for surgical preplanning and motivates author to apply this approach to the different organs and field of medicine.	['E. Bostanci', 'S. Can', 'M. S Guzel', 'Y. T. Cetin', 'K. E. Sengun']	2021-01-25	null	null	null	null	['liver-segmentation']	['medical']	[-4.68562931e-01 6.13990659e-03 -1.09370552e-01 -2.59236634e-01 -1.66570812e-01 -4.78293300e-01 3.73462975e-01 4.13031489e-01 -4.58087444e-01 7.96419263e-01 6.07485250e-02 -3.12941641e-01 -3.41280073e-01 -6.64220393e-01 -1.31167933e-01 -8.46755207e-01 -2.98072249e-01 4.91711944e-01 -1.55685917e-02 3.99514474e-02 2.05502540e-01 1.01526642e+00 -1.01328111e+00 -2.73203664e-02 9.14920688e-01 7.11113095e-01 7.48701245e-02 5.65236688e-01 -3.15697849e-01 5.57901740e-01 -3.53781730e-01 -3.00653458e-01 5.53053081e-01 -6.73970103e-01 -8.67997169e-01 1.06402859e-01 1.21584557e-01 -2.84747183e-01 -7.08820149e-02 1.00653267e+00 9.15507436e-01 -2.88276255e-01 6.73530698e-01 -6.95506513e-01 -2.91883945e-01 6.47430897e-01 -1.77037105e-01 4.34130490e-01 2.17954531e-01 3.04304123e-01 2.45113119e-01 -5.47376692e-01 5.78502357e-01 8.00840020e-01 7.65362382e-01 3.63540620e-01 -8.37286770e-01 -2.62650251e-01 -5.34216166e-01 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89a73fe1-3349-4a62-b7cd-7255bf031f19	wbi-ddi-drug-drug-interaction-extraction	null	null	https://aclanthology.org/S13-2105	https://aclanthology.org/S13-2105.pdf	WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting	null	['Tim Rockt{\\"a}schel', 'Ulf Leser', 'Philippe Thomas', 'Mariana Neves']	2013-06-01	null	null	null	semeval-2013-6	['drug-drug-interaction-extraction']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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7357b7e2-3b34-454e-b600-21566616d838	stylegan-as-a-utility-preserving-face-de	2212.02611	null	https://arxiv.org/abs/2212.02611v1	https://arxiv.org/pdf/2212.02611v1.pdf	StyleGAN as a Utility-Preserving Face De-identification Method	Several face de-identification methods have been proposed to preserve users' privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, e.g., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN, have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identified face that carries the utilities of the target face. We examined this de-identification method with respect to preserving utility and privacy, by implementing several face detection, verification, and identification attacks. Through extensive experiments and also comparing with two state-of-the-art face de-identification methods, we show that StyleGAN preserves the quality and utility of the faces much better than the other approaches and also by choosing the style mixing levels correctly, it can preserve the privacy of the faces much better than other methods.	['Shirin Nilizadeh', 'Seyyed Mohammad Sadegh Moosavi Khorzooghi']	2022-12-05	null	null	null	null	['face-detection', 'de-identification']	['computer-vision', 'natural-language-processing']	[ 2.64974803e-01 1.89330563e-01 3.88932973e-01 -3.89008194e-01 -1.83842003e-01 -8.03253829e-01 4.89597529e-01 -6.88358545e-01 -3.05329375e-02 7.52806842e-01 7.47093558e-03 2.60166764e-01 3.13206762e-01 -9.19407010e-01 -4.95580286e-01 -1.06393945e+00 3.12428437e-02 6.70515969e-02 -2.70981699e-01 -1.72220796e-01 8.38800445e-02 7.07640409e-01 -1.69233966e+00 2.22829223e-01 6.59006596e-01 9.49629962e-01 -7.51257777e-01 4.32069778e-01 1.48045152e-01 2.06697240e-01 -9.48842108e-01 -1.10880971e+00 6.66143417e-01 -6.41420424e-01 -3.32011014e-01 -3.28767905e-03 6.33626223e-01 -6.94299757e-01 -5.69770932e-01 1.42915738e+00 6.31400943e-01 -3.00239563e-01 5.64269900e-01 -1.67339194e+00 -9.86164927e-01 3.28513443e-01 -6.19496465e-01 -4.31758881e-01 3.81677747e-01 7.14389756e-02 2.10367292e-01 -5.84102571e-01 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1.22270596e+00 -6.50345683e-01 -4.69511390e-01 -6.77931249e-01 -9.27891910e-01 6.42299235e-01 3.27875078e-01 -5.25315642e-01 -4.27369803e-01 6.80225343e-02]	[12.744172096252441, 0.871919572353363]
5f028721-e0d9-477b-a939-4d4951363946	a-knowledge-distillation-ensemble-framework	2011.09361	null	https://arxiv.org/abs/2011.09361v2	https://arxiv.org/pdf/2011.09361v2.pdf	A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data	The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$%$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$%$ CI: 0.870-0.935) in predicting ICU admission.	['Richard JB Dobson', 'James T Teo', 'Sam Norton', 'James Galloway', 'Zeljko Kraljevic', 'Anthony Shek', 'Honghan Wu', 'Thomas Searle', 'Daniel Bean', 'Zina M Ibrahim']	2020-11-18	null	null	null	null	['outlier-ensembles']	['methodology']	[ 2.28654146e-01 -2.60482639e-01 -1.36780180e-02 -3.02122086e-01 -6.68498039e-01 -3.67060244e-01 9.23754424e-02 8.85108173e-01 -3.95759821e-01 7.43118525e-01 6.47152364e-01 -7.15786695e-01 -7.25193322e-01 -7.36129522e-01 -1.80653289e-01 -7.05722988e-01 -8.14639747e-01 6.15543067e-01 -3.33959311e-01 1.18575275e-01 6.76779747e-02 4.68415409e-01 -1.13487089e+00 7.16900826e-01 5.42595148e-01 1.19471061e+00 -2.21100613e-01 9.71969366e-01 2.36325026e-01 1.21513391e+00 -5.38155675e-01 3.76907624e-02 5.04075661e-02 -5.32748401e-01 -6.63522422e-01 -5.54388106e-01 -3.70824456e-01 -6.11350715e-01 -8.30142945e-02 1.28425181e-01 8.49814951e-01 -2.39029434e-03 7.96149254e-01 -9.68520582e-01 1.19980676e-02 5.97218633e-01 5.52774295e-02 5.19378781e-01 1.94182992e-01 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8e3e63e2-8c14-4873-9c1b-265b1bda6321	mining-mathematical-documents-for-question	2211.06664	null	https://arxiv.org/abs/2211.06664v1	https://arxiv.org/pdf/2211.06664v1.pdf	Mining Mathematical Documents for Question Answering via Unsupervised Formula Labeling	The increasing number of questions on Question Answering (QA) platforms like Math Stack Exchange (MSE) signifies a growing information need to answer math-related questions. However, there is currently very little research on approaches for an open data QA system that retrieves mathematical formulae using their concept names or querying formula identifier relationships from knowledge graphs. In this paper, we aim to bridge the gap by presenting data mining methods and benchmark results to employ Mathematical Entity Linking (MathEL) and Unsupervised Formula Labeling (UFL) for semantic formula search and mathematical question answering (MathQA) on the arXiv preprint repository, Wikipedia, and Wikidata, which is part of the Wikimedia ecosystem of free knowledge. Based on different types of information needs, we evaluate our system in 15 information need modes, assessing over 7,000 query results. Furthermore, we compare its performance to a commercial knowledge-base and calculation-engine (Wolfram Alpha) and search-engine (Google). The open source system is hosted by Wikimedia at https://mathqa.wmflabs.org. A demovideo is available at purl.org/mathqa.	['Bela Gipp', 'Moritz Schubotz', 'Philipp Scharpf']	2022-11-12	null	null	null	null	['mathematical-question-answering']	['natural-language-processing']	[-6.12211347e-01 3.24017733e-01 2.12602973e-01 -3.43225062e-01 -6.58404410e-01 -8.57450426e-01 5.58138788e-01 6.61822677e-01 -1.72157019e-01 7.03564405e-01 1.20088093e-01 -6.71089590e-01 -9.30227876e-01 -1.71990478e+00 -6.81525171e-01 2.89154381e-01 4.13662344e-01 1.01990497e+00 5.80933630e-01 -8.19326222e-01 6.00456655e-01 3.46489847e-01 -1.87989080e+00 4.94590461e-01 1.48507059e+00 1.31745565e+00 1.22683726e-01 7.66333759e-01 -1.14485788e+00 1.38387549e+00 -3.50482106e-01 -1.04580069e+00 -1.05633944e-01 -2.53760219e-01 -1.60688663e+00 -1.21786332e+00 1.13350320e+00 2.48848066e-01 -5.11008203e-01 1.32395971e+00 2.12103248e-01 6.53516129e-02 2.73574591e-01 -1.25445688e+00 -1.04989445e+00 6.12519264e-01 5.33288300e-01 5.25808752e-01 1.00746059e+00 -3.23962808e-01 1.22192192e+00 -6.04405403e-01 1.06252718e+00 1.17391086e+00 6.61217809e-01 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2.95082778e-01 -4.86474961e-01 -7.18125343e-01 -2.17476517e-01 2.88768888e-01 -3.66265029e-01 -8.58765662e-01 -3.35407555e-01 -9.95302275e-02]	[10.537640571594238, 7.959228515625]
dbb2efeb-8a29-476c-85d1-8db6bc8d63dc	disentangled-high-quality-salient-object	2108.03551	null	https://arxiv.org/abs/2108.03551v2	https://arxiv.org/pdf/2108.03551v2.pdf	Disentangled High Quality Salient Object Detection	Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to the era of high resolution, SOD methods are facing new challenges. The major limitation of previous methods is that they try to identify the salient regions and estimate the accurate objects boundaries simultaneously with a single regression task at low-resolution. This practice ignores the inherent difference between the two difficult problems, resulting in poor detection quality. In this paper, we propose a novel deep learning framework for high-resolution SOD task, which disentangles the task into a low-resolution saliency classification network (LRSCN) and a high-resolution refinement network (HRRN). As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions. HRRN is a regression task, which aims at accurately refining the saliency value of pixels in the uncertain region to preserve a clear object boundary at high-resolution with limited GPU memory. It is worth noting that by introducing uncertainty into the training process, our HRRN can well address the high-resolution refinement task without using any high-resolution training data. Extensive experiments on high-resolution saliency datasets as well as some widely used saliency benchmarks show that the proposed method achieves superior performance compared to the state-of-the-art methods.	['Mofei Song', 'Shouhong Ding', 'Bo Li', 'Lv Tang']	2021-08-08	null	http://openaccess.thecvf.com//content/ICCV2021/html/Tang_Disentangled_High_Quality_Salient_Object_Detection_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Tang_Disentangled_High_Quality_Salient_Object_Detection_ICCV_2021_paper.pdf	iccv-2021-1	['salient-object-detection']	['computer-vision']	[ 5.18174231e-01 -1.37580633e-01 -1.54370204e-01 -1.52061984e-01 -8.98475826e-01 4.65070307e-02 3.50206077e-01 -3.41432951e-02 -4.19339567e-01 7.05718458e-01 4.88882698e-02 1.11280672e-01 1.44918561e-02 -7.27098227e-01 -7.05659151e-01 -6.75898194e-01 2.12043226e-01 5.15375063e-02 1.02390015e+00 -2.62747675e-01 4.53980148e-01 2.92638034e-01 -1.90278065e+00 1.36806890e-01 1.26657546e+00 1.23682857e+00 8.02787185e-01 2.44527608e-01 5.15856631e-02 7.76270688e-01 -3.12005728e-01 -1.20720165e-02 1.85063139e-01 -9.83405113e-02 -6.80699706e-01 -1.44632254e-02 5.42517424e-01 -3.16837519e-01 3.85522358e-02 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40f283c8-f5bf-4c2c-8eaf-fabe1d93d93d	sact-self-aware-multi-space-feature	2006.14262	null	https://arxiv.org/abs/2006.14262v1	https://arxiv.org/pdf/2006.14262v1.pdf	SACT: Self-Aware Multi-Space Feature Composition Transformer for Multinomial Attention for Video Captioning	Video captioning works on the two fundamental concepts, feature detection and feature composition. While modern day transformers are beneficial in composing features, they lack the fundamental problems of selecting and understanding of the contents. As the feature length increases, it becomes increasingly important to include provisions for improved capturing of the pertinent contents. In this work, we have introduced a new concept of Self-Aware Composition Transformer (SACT) that is capable of generating Multinomial Attention (MultAtt) which is a way of generating distributions of various combinations of frames. Also, multi-head attention transformer works on the principle of combining all possible contents for attention, which is good for natural language classification, but has limitations for video captioning. Video contents have repetitions and require parsing of important contents for better content composition. In this work, we have introduced SACT for more selective attention and combined them for different attention heads for better capturing of the usable contents for any applications. To address the problem of diversification and encourage selective utilization, we propose the Self-Aware Composition Transformer model for dense video captioning and apply the technique on two benchmark datasets like ActivityNet and YouCookII.	['Chiranjib Sur']	2020-06-25	null	null	null	null	['dense-video-captioning']	['computer-vision']	[ 1.73873141e-01 -2.76666701e-01 -2.61586219e-01 -2.59860873e-01 -5.66101670e-01 -4.21690822e-01 3.64385635e-01 -1.67932317e-01 -9.44486558e-02 7.75500417e-01 7.14058578e-01 -1.30192563e-01 1.84169356e-02 -6.97573125e-01 -8.37986827e-01 -6.93787932e-01 1.04650825e-01 3.47067535e-01 4.25522149e-01 -3.84805620e-01 2.39716858e-01 1.08949117e-01 -1.90832257e+00 7.19658911e-01 9.92116451e-01 1.12511241e+00 7.38138974e-01 6.00561023e-01 -7.27867246e-01 1.03237271e+00 -6.23157382e-01 -5.60073614e-01 6.15325905e-02 -7.97739267e-01 -7.61193633e-01 1.86298922e-01 3.80186826e-01 -2.98926055e-01 -3.10465932e-01 9.20672238e-01 4.35500979e-01 1.06045790e-01 5.80080211e-01 -1.58096623e+00 -9.30086434e-01 1.14786291e+00 -6.32765830e-01 7.37159789e-01 3.94510597e-01 9.89448205e-02 1.24960744e+00 -6.33393109e-01 2.69548506e-01 1.23563480e+00 3.72964829e-01 7.11452961e-01 -5.26711941e-01 -6.66545153e-01 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6.14052236e-01 4.43899035e-01 -7.33399332e-01 -3.48669410e-01 1.99888870e-01]	[10.504915237426758, 0.6543228030204773]
b02bca17-c51c-44e2-a8c5-52c6038e8b56	mq-coder-inspired-arithmetic-coder-for	2306.12708	null	https://arxiv.org/abs/2306.12708v1	https://arxiv.org/pdf/2306.12708v1.pdf	MQ-Coder inspired arithmetic coder for synthetic DNA data storage	Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are now considered as serious candidates for this new kind of storage. This paper introduces a novel arithmetic coder for DNA data storage, and presents some results on a lossy JPEG 2000 based image compression method adapted for DNA data storage that uses this novel coder. The DNA coding algorithms presented here have been designed to efficiently compress images, encode them into a quaternary code, and finally store them into synthetic DNA molecules. This work also aims at making the compression models better fit the problematic that we encounter when storing data into DNA, namely the fact that the DNA writing, storing and reading methods are error prone processes. The main take away of this work is our arithmetic coder and it's integration into a performant image codec.	['Marc Antonini', 'Eva Gil San Antonio', 'Melpomeni Dimopoulou', 'Xavier Pic']	2023-06-22	null	null	null	null	['image-compression']	['computer-vision']	[ 5.91585577e-01 -2.55907569e-02 -1.06738277e-01 -1.47963166e-01 -1.10564165e-01 -2.86824077e-01 6.27649128e-01 8.15123498e-01 -7.43243694e-01 7.68582880e-01 1.84812009e-01 -3.33658814e-01 1.44668490e-01 -1.08284009e+00 -5.95844150e-01 -9.20900643e-01 9.64575261e-02 4.36281919e-01 4.66212392e-01 -2.33328253e-01 7.95536101e-01 8.28570485e-01 -2.18296599e+00 3.13395411e-01 7.35926449e-01 7.49504566e-01 7.33674943e-01 1.08902276e+00 -2.99774051e-01 7.82337248e-01 -5.51224053e-01 -5.23536384e-01 -2.10508779e-02 -5.91612637e-01 -6.95287228e-01 -1.16368897e-01 -1.06313400e-01 -2.72990465e-01 -3.39202404e-01 9.16567743e-01 4.66501176e-01 -2.01907381e-01 8.03214192e-01 -5.71248055e-01 -7.02477515e-01 3.03722292e-01 8.44283998e-02 2.24423453e-01 3.45957309e-01 9.63295251e-02 1.83661222e-01 -5.80617249e-01 8.53092551e-01 1.04107964e+00 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01eb067c-610c-4dda-85c4-77a1cac3b959	learning-goal-conditioned-policies-offline	2301.02099	null	https://arxiv.org/abs/2301.02099v1	https://arxiv.org/pdf/2301.02099v1.pdf	Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping	Developing agents that can execute multiple skills by learning from pre-collected datasets is an important problem in robotics, where online interaction with the environment is extremely time-consuming. Moreover, manually designing reward functions for every single desired skill is prohibitive. Prior works targeted these challenges by learning goal-conditioned policies from offline datasets without manually specified rewards, through hindsight relabelling. These methods suffer from the issue of sparsity of rewards, and fail at long-horizon tasks. In this work, we propose a novel self-supervised learning phase on the pre-collected dataset to understand the structure and the dynamics of the model, and shape a dense reward function for learning policies offline. We evaluate our method on three continuous control tasks, and show that our model significantly outperforms existing approaches, especially on tasks that involve long-term planning.	['Karteek Alahari', 'Alessandro Lazaric', 'Piotr Bojanowski', 'Sainbayar Sukhbaatar', 'Lina Mezghani']	2023-01-05	null	null	null	null	['continuous-control']	['playing-games']	[ 1.51932627e-01 1.47821978e-01 -3.32508892e-01 -1.58252239e-01 -6.27900362e-01 -7.07780659e-01 5.06512821e-01 1.67313904e-01 -7.71073341e-01 1.05405331e+00 1.42441422e-01 -2.72319078e-01 -4.06458855e-01 -4.83312964e-01 -9.38871980e-01 -4.80752975e-01 -3.95443797e-01 8.94880176e-01 2.61823952e-01 -3.79065663e-01 2.70453155e-01 4.06988770e-01 -1.41306365e+00 -1.98327467e-01 1.04353499e+00 8.74066472e-01 6.33476257e-01 6.85837150e-01 4.92586881e-01 1.15069604e+00 -2.66553849e-01 2.80628204e-01 6.18996441e-01 -2.40626365e-01 -8.41207922e-01 1.86180547e-01 -6.09720983e-02 -6.85194790e-01 -3.57219607e-01 1.02253497e+00 1.59264848e-01 5.10219336e-01 4.33908850e-01 -1.32549059e+00 -2.25805834e-01 7.11988807e-01 -1.77863598e-01 -2.45475128e-01 7.97763467e-02 5.66089928e-01 9.85933423e-01 -2.89545298e-01 5.18173754e-01 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3b8a911e-0fe2-46ff-884e-08cdf2f7cac3	offline-handwritten-chinese-text-recognition	2006.15619	null	https://arxiv.org/abs/2006.15619v1	https://arxiv.org/pdf/2006.15619v1.pdf	Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks	Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios. The offline handwritten Chinese text recognition (HCTR) is one of the most challenging tasks because it involves thousands of characters, variant writing styles and complex data collection process. Recently, the recurrent-free architectures for text recognition appears to be competitive as its highly parallelism and comparable results. In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. To reduce the overfitting, we apply dropout after each max-pooling layer and with extreme high rate on the last one before the linear layer. The CASIA-HWDB database is selected to tune and evaluate the proposed models. With the existing text samples as templates, we randomly choose isolated character samples to synthesis more text samples for training. We finally achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the best published result without language model correction.	['Xianchao Xu', 'Brian Liu', 'Yu Zhang']	2020-06-28	null	null	null	null	['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition']	['computer-vision', 'natural-language-processing']	[ 1.09491535e-01 -6.70208275e-01 6.39694557e-02 -4.82988149e-01 -6.35381699e-01 -4.04986113e-01 5.24867058e-01 -9.72759426e-02 -9.55222905e-01 4.71938252e-01 -6.28360286e-02 -3.34521055e-01 5.37624061e-01 -2.97768295e-01 -5.76488674e-01 -7.40815163e-01 4.83890772e-01 5.20537257e-01 3.19296896e-01 -2.45026313e-02 4.45901453e-01 6.49317861e-01 -1.06978929e+00 6.51657104e-01 9.85317588e-01 8.73394907e-01 2.86721617e-01 9.61320519e-01 -2.94703364e-01 1.15150344e+00 -8.60439241e-01 -8.00795913e-01 8.33631456e-02 -2.07595319e-01 -4.39769268e-01 7.98951276e-03 4.91113693e-01 -5.64580619e-01 -8.01488578e-01 8.01981926e-01 7.96858191e-01 -6.14445508e-02 5.91324031e-01 -5.47222435e-01 -7.87023664e-01 9.93278861e-01 -4.80456382e-01 -1.49277989e-02 -3.40622701e-02 -1.20544739e-01 6.16829515e-01 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a6de0fb5-8f68-426b-88ba-8555a3f192d4	estimating-predictive-uncertainty-for-rumour	2005.07174	null	https://arxiv.org/abs/2005.07174v1	https://arxiv.org/pdf/2005.07174v1.pdf	Estimating predictive uncertainty for rumour verification models	The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous, so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.	['Maria Liakata', 'Elena Kochkina']	2020-05-14	estimating-predictive-uncertainty-for-rumour-1	https://aclanthology.org/2020.acl-main.623	https://aclanthology.org/2020.acl-main.623.pdf	acl-2020-6	['rumour-detection']	['natural-language-processing']	[-4.1001420e-02 5.8685178e-01 -4.7842029e-01 -6.3667661e-01 -6.6602242e-01 -5.4860264e-01 7.8726929e-01 9.3634617e-01 -1.4471523e-01 1.2163688e+00 4.4513902e-01 -6.4159608e-01 -1.1971829e-01 -8.7061822e-01 -5.8064067e-01 9.5992021e-02 -2.7620497e-01 8.2031322e-01 2.2357517e-01 -1.1604535e-01 8.1177360e-01 4.4010732e-01 -1.2652469e+00 5.5615890e-01 8.4763396e-01 6.3635796e-01 -4.3339720e-01 6.2196141e-01 -2.9575902e-01 1.7471440e+00 -1.0209574e+00 -3.5738203e-01 -3.3486899e-02 -3.2123226e-01 -9.5829755e-01 6.0713086e-03 -2.4008997e-01 -5.8980882e-01 2.1778534e-01 1.2404398e+00 -1.3046321e-01 1.9500920e-01 6.6779256e-01 -1.3477334e+00 -2.5146073e-01 1.0373316e+00 -7.7759728e-02 4.1186336e-01 8.4215462e-01 -1.7394630e-02 7.0257771e-01 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3d06b4a7-577d-4b65-87c0-1e5ccae7a54e	achieving-counterfactual-fairness-with	2303.14665	null	https://arxiv.org/abs/2303.14665v1	https://arxiv.org/pdf/2303.14665v1.pdf	Achieving Counterfactual Fairness with Imperfect Structural Causal Model	Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i.e., what if the individual belongs to other sensitive groups). The existing studies need to pre-define the structural causal model that captures the correlations among variables for counterfactual inference; however, the underlying causal model is usually unknown and difficult to be validated in real-world scenarios. Moreover, the misspecification of the causal model potentially leads to poor performance in model prediction and thus makes unfair decisions. In this research, we propose a novel minimax game-theoretic model for counterfactual fairness that can produce accurate results meanwhile achieve a counterfactually fair decision with the relaxation of strong assumptions of structural causal models. In addition, we also theoretically prove the error bound of the proposed minimax model. Empirical experiments on multiple real-world datasets illustrate our superior performance in both accuracy and fairness. Source code is available at \url{https://github.com/tridungduong16/counterfactual_fairness_game_theoretic}.	['Guandong Xu', 'Qian Li', 'Tri Dung Duong']	2023-03-26	null	null	null	null	['counterfactual-inference']	['miscellaneous']	[-1.27573055e-03 2.83380777e-01 -6.79531097e-01 -2.99438536e-01 -3.05778831e-01 -3.62004787e-01 2.90667623e-01 1.70921925e-02 -4.18347865e-01 1.40457022e+00 2.54695803e-01 -6.32708371e-01 -5.35385191e-01 -1.00236499e+00 -5.67247570e-01 -6.10306323e-01 -2.63879716e-01 3.38329196e-01 -4.29025948e-01 -2.73535736e-02 3.50872338e-01 -1.98064908e-01 -1.13666737e+00 -1.63881406e-01 1.38815320e+00 7.19336987e-01 -5.20351194e-02 2.51630396e-01 3.08066070e-01 8.39815974e-01 -3.69227707e-01 -1.00003481e+00 6.89563811e-01 -5.17323434e-01 -6.09215856e-01 -5.71907043e-01 5.91148138e-02 -5.51528037e-01 -3.71933520e-01 1.54542363e+00 5.80440044e-01 2.14870319e-01 4.87145394e-01 -1.63098431e+00 -6.82177901e-01 9.79499936e-01 -9.10435379e-01 9.47402939e-02 1.20392367e-01 2.97205001e-01 1.16606128e+00 -1.97147597e-02 3.67888212e-01 1.45952380e+00 3.90077710e-01 8.30440342e-01 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182c2f0d-6d60-4ce1-9b88-759d9fea0345	dino-mc-self-supervised-contrastive-learning	2303.06670	null	https://arxiv.org/abs/2303.06670v1	https://arxiv.org/pdf/2303.06670v1.pdf	DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops	Due to the costly nature of remote sensing image labeling and the large volume of available unlabeled imagery, self-supervised methods that can learn feature representations without manual annotation have received great attention. While prior works have explored self-supervised learning in remote sensing tasks, pretext tasks based on local-global view alignment remain underexplored. Inspired by DINO, which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for use in self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. Moreover, we extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state of the art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models and results are available at https://github.com/WennyXY/DINO-MC.	['Michael Kirley', 'Shuchang Shen', 'Sachith Seneviratne', 'Xinye Wanyan']	2023-03-12	null	null	null	null	['change-detection', 'multi-label-image-classification']	['computer-vision', 'computer-vision']	[ 5.30149102e-01 -3.00951619e-02 -4.72680092e-01 -6.11723840e-01 -8.38661373e-01 -9.10795212e-01 7.18143284e-01 1.16802193e-01 -1.89774692e-01 5.61626077e-01 1.35140911e-01 -6.05572343e-01 -2.19403014e-01 -8.84845436e-01 -5.53556085e-01 -7.21002340e-01 -1.41950235e-01 8.11875537e-02 -1.00901701e-01 -6.86077103e-02 -1.44536048e-01 3.98643017e-01 -1.71125317e+00 1.50608793e-01 9.01819408e-01 7.81738400e-01 5.82300186e-01 4.58154351e-01 2.13674441e-01 7.21656203e-01 1.79487001e-02 2.20305726e-01 6.35804594e-01 -3.69746804e-01 -1.01817298e+00 5.59011161e-01 7.91037679e-01 -4.04843301e-01 -1.09684929e-01 1.08305597e+00 4.93311077e-01 3.11083853e-01 6.36267185e-01 -8.65777016e-01 -8.76765728e-01 8.59774411e-01 -6.64027810e-01 2.63315082e-01 -1.72430769e-01 -9.52878743e-02 1.36458898e+00 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d30a4469-4ff6-4c49-a84c-752f261a6778	an-improved-coarse-to-fine-method-for-solving	null	null	https://aclanthology.org/U19-1024	https://aclanthology.org/U19-1024.pdf	An Improved Coarse-to-Fine Method for Solving Generation Tasks	The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model.	['Wenyv Guan', 'Qianying Liu', 'Bin Wang', 'Sujian Li', 'Guangzhi Han']	2019-04-01	null	null	null	alta-2019-4	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[ 1.65436957e-02 2.44068369e-01 1.79224700e-01 -5.75085521e-01 -5.33265054e-01 -4.12568063e-01 5.45002699e-01 -1.16308890e-01 -1.37305737e-01 6.56045616e-01 1.70788080e-01 -4.70694155e-02 1.41521648e-01 -1.22017658e+00 -7.48797655e-01 -3.69313210e-01 6.98736548e-01 4.09304172e-01 4.54299718e-01 -1.62926987e-01 6.91867173e-01 5.18071018e-02 -1.25730062e+00 5.87035596e-01 1.22871912e+00 6.22098863e-01 5.56682348e-01 5.64161539e-01 -8.63160849e-01 7.06243098e-01 -8.53835285e-01 -5.54761708e-01 1.95199490e-01 -5.21857500e-01 -7.20152020e-01 -1.69445813e-01 4.03210461e-01 -5.12630403e-01 1.07327126e-01 1.35083449e+00 2.99534082e-01 3.03110946e-02 3.84632200e-01 -1.03625488e+00 -9.79820967e-01 1.05523086e+00 -5.40909410e-01 -1.36457711e-01 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37d57313-a9da-46ba-8b78-cf057529303d	efficient-action-recognition-using-confidence	2109.02137	null	https://arxiv.org/abs/2109.02137v2	https://arxiv.org/pdf/2109.02137v2.pdf	Efficient Action Recognition Using Confidence Distillation	Modern neural networks are powerful predictive models. However, when it comes to recognizing that they may be wrong about their predictions, they perform poorly. For example, for one of the most common activation functions, the ReLU and its variants, even a well-calibrated model can produce incorrect but high confidence predictions. In the related task of action recognition, most current classification methods are based on clip-level classifiers that densely sample a given video for non-overlapping, same-sized clips and aggregate the results using an aggregation function - typically averaging - to achieve video level predictions. While this approach has shown to be effective, it is sub-optimal in recognition accuracy and has a high computational overhead. To mitigate both these issues, we propose the confidence distillation framework to teach a representation of uncertainty of the teacher to the student sampler and divide the task of full video prediction between the student and the teacher models. We conduct extensive experiments on three action recognition datasets and demonstrate that our framework achieves significant improvements in action recognition accuracy (up to 20%) and computational efficiency (more than 40%).	['Rong Zheng', 'Fei Chiang', 'Shervin Manzuri Shalmani']	2021-09-05	null	null	null	null	['video-prediction']	['computer-vision']	[ 4.87553120e-01 1.09269477e-01 -4.88201082e-01 -6.92361355e-01 -1.30992961e+00 -2.70086557e-01 3.65611523e-01 1.09677333e-02 -2.67417580e-01 8.65370810e-01 2.55709831e-02 -3.11197728e-01 1.06748268e-01 -4.99971926e-01 -1.04314196e+00 -6.96868420e-01 1.18891746e-01 4.25573409e-01 5.22534966e-01 5.34323752e-01 1.64495140e-01 3.28380764e-01 -1.78608799e+00 6.71299338e-01 9.62462008e-01 1.31971014e+00 -2.78191626e-01 8.71615827e-01 1.65846363e-01 1.45748842e+00 -5.93259513e-01 -5.53301096e-01 1.00088596e-01 -3.72043997e-01 -6.46818697e-01 4.29769829e-02 8.49300802e-01 -6.67340815e-01 -2.15379521e-01 1.08352959e+00 -2.91111227e-02 2.07316399e-01 7.64206171e-01 -1.33159757e+00 -2.82003999e-01 5.41243315e-01 -5.12623906e-01 2.20734552e-01 2.80880690e-01 4.89599537e-03 8.85079980e-01 -8.26620460e-01 1.53243631e-01 1.21220195e+00 8.40251505e-01 5.02411008e-01 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-5.06948233e-01 2.98474371e-01 2.01929763e-01 -6.51736021e-01 -3.94455194e-01 6.63951784e-02]	[8.509333610534668, 0.647853434085846]
9c5bed5d-90ab-42e1-9c31-f6b70218687d	a-data-augmented-approach-to-transfer	2108.02870	null	https://arxiv.org/abs/2108.02870v1	https://arxiv.org/pdf/2108.02870v1.pdf	A Data Augmented Approach to Transfer Learning for Covid-19 Detection	Covid-19 detection at an early stage can aid in an effective treatment and isolation plan to prevent its spread. Recently, transfer learning has been used for Covid-19 detection using X-ray, ultrasound, and CT scans. One of the major limitations inherent to these proposed methods is limited labeled dataset size that affects the reliability of Covid-19 diagnosis and disease progression. In this work, we demonstrate that how we can augment limited X-ray images data by using Contrast limited adaptive histogram equalization (CLAHE) to train the last layer of the pre-trained deep learning models to mitigate the bias of transfer learning for Covid-19 detection. We transfer learned various pre-trained deep learning models including AlexNet, ZFNet, VGG-16, ResNet-18, and GoogLeNet, and fine-tune the last layer by using CLAHE-augmented dataset. The experiment results reveal that the CLAHE-based augmentation to various pre-trained deep learning models significantly improves the model efficiency. The pre-trained VCG-16 model with CLAHEbased augmented images achieves a sensitivity of 95% using 15 epochs. AlexNet works show good sensitivity when trained on non-augmented data. Other models demonstrate a value of less than 60% when trained on non-augmented data. Our results reveal that the sample bias can negatively impact the performance of transfer learning which is significantly improved by using CLAHE-based augmentation.	['Aparna Reji', 'Shagufta Henna']	2021-08-05	null	null	null	null	['covid-19-detection']	['medical']	[ 8.86404067e-02 -1.67816103e-01 1.76706277e-02 -1.94039553e-01 -6.90332532e-01 -2.36781180e-01 2.98126310e-01 -3.21271867e-02 -8.19282115e-01 6.88157380e-01 -1.01806298e-01 -4.85444188e-01 1.25438929e-01 -8.97585571e-01 -6.87876344e-01 -5.34998298e-01 -2.04196930e-01 4.37920213e-01 3.64594042e-01 -2.72589196e-02 -1.88732311e-01 5.29526055e-01 -9.51500595e-01 2.66809583e-01 7.81240702e-01 9.55495179e-01 2.60004371e-01 8.52296114e-01 2.49616072e-01 5.48318565e-01 -6.23717070e-01 -1.01964369e-01 3.02121431e-01 -2.64310420e-01 -4.63768333e-01 -4.96804565e-01 3.65596175e-01 -9.38444793e-01 -3.06983948e-01 7.61117101e-01 6.34282649e-01 -2.30392992e-01 9.91003752e-01 -1.09346282e+00 -4.90549415e-01 7.18573853e-02 -6.97437882e-01 6.03622139e-01 -1.64839923e-01 4.39436108e-01 4.26891148e-01 -7.83113062e-01 4.84818459e-01 8.52029085e-01 1.15759254e+00 6.51008368e-01 -5.60428858e-01 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2.01574981e-01 5.91841400e-01 -6.48262501e-01 -1.48994267e-01 -1.23822344e-02]	[15.515303611755371, -1.760079264640808]
88a64a2e-1bf8-47f3-b35a-455905a447fa	using-semantic-role-knowledge-for-relevance	1908.03313	null	https://arxiv.org/abs/1908.03313v1	https://arxiv.org/pdf/1908.03313v1.pdf	Using Semantic Role Knowledge for Relevance Ranking of Key Phrases inDocuments: An Unsupervised Approach	In this paper, we investigate the integration of sentence position and semantic role of words in a PageRank system to build a key phrase ranking method. We present the evaluation results of our approach on three scientific articles. We show that semantic role information, when integrated with a PageRank system, can become a new lexical feature. Our approach had an overall improvement on all the data sets over the state-of-art baseline approaches.	['Gargi B. Dasgupta', 'Prateeti Mohapatra', 'Neelamadhav Gantayat']	2019-08-09	null	null	null	null	['phrase-ranking']	['natural-language-processing']	[ 1.31703496e-01 1.90999374e-01 -7.52043009e-01 -2.50055552e-01 -8.43168080e-01 -8.31272781e-01 1.07100129e+00 8.92922580e-01 -8.87124002e-01 1.08055258e+00 1.07697260e+00 -1.26513794e-01 -3.82434011e-01 -6.19894564e-01 -2.98216701e-01 -1.06846437e-01 6.21921271e-02 6.12493813e-01 1.03109992e+00 -7.76428938e-01 1.15804839e+00 5.20821176e-02 -1.61074269e+00 5.64558268e-01 8.89403045e-01 4.97723907e-01 4.42150295e-01 4.96904135e-01 -6.83868468e-01 9.63621497e-01 -8.91243696e-01 -2.88904250e-01 -1.84105486e-01 -1.43681884e-01 -1.13892007e+00 -6.27276897e-01 5.88538945e-01 1.06537469e-01 -6.94222823e-02 1.14562392e+00 6.03323042e-01 7.57494196e-02 6.89538717e-01 -7.01258063e-01 -3.96320134e-01 1.03984463e+00 -4.19248044e-01 5.31109095e-01 9.82243419e-01 -9.84982371e-01 1.98768651e+00 -9.89901304e-01 1.15663624e+00 1.36639023e+00 2.51403987e-01 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b6896e5d-3a18-43d3-8163-9a30ca0d8a7c	entropy-minimisation-framework-for-event	null	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2988_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500154.pdf	Entropy Minimisation Framework for Event-based Vision Model Estimation	We propose a novel EMin framework for event-based vision model estimation. The framework extends previous event-based motion compensation algorithms to handle models whose outputs have arbitrary dimensions. The main motivation comes from estimating motion from events directly in 3D space (e. g. events augmented with depth), without projecting them onto an image plane. This is achieved by modelling the event alignment according to candidate parameters and minimising the resultant dispersion. We provide a family of suitable entropy loss functions and an efficient approximation whose complexity is only linear with the number of events (e. g. the complexity does not depend on the number of image pixels). The framework is evaluated on several motion estimation problems, including optical flow and rotational motion. As proof of concept, we also test our framework on 6-DOF estimation by performing the optimisation directly in 3D space.	['Yiannis Demiris', 'Urbano Miguel Nunes']	null	null	null	null	eccv-2020-8	['event-based-vision']	['computer-vision']	[ 1.51302159e-01 -3.00504118e-02 4.14461643e-02 1.66707169e-02 -3.86133790e-01 -4.02506381e-01 8.19917738e-01 8.47769380e-02 -9.40190196e-01 5.16817749e-01 1.79894269e-01 1.13530777e-01 -1.18577681e-01 -5.10400414e-01 -7.42653131e-01 -6.93518281e-01 -5.68736233e-02 3.39970678e-01 4.94241267e-01 2.85613835e-01 5.07861733e-01 7.90585637e-01 -1.58658159e+00 -3.71337891e-01 6.20563686e-01 7.71973372e-01 1.37224182e-01 1.27108002e+00 2.11200163e-01 8.65665078e-01 -3.29235494e-01 -3.11197937e-01 3.01454961e-01 -4.47678059e-01 -7.53162861e-01 5.43007910e-01 4.76984382e-01 -5.60666740e-01 -4.65379417e-01 7.98015118e-01 5.40606558e-01 4.34785813e-01 6.81328356e-01 -1.15439677e+00 1.28170341e-01 -3.18713367e-01 -2.63406754e-01 3.40376854e-01 3.34621072e-01 4.69403863e-02 8.09917808e-01 -1.00057089e+00 1.12516630e+00 1.14783931e+00 4.55306560e-01 5.70402026e-01 -1.17875290e+00 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a5231088-d7f2-4d1d-8f18-0078da34a8fb	gantron-emotional-speech-synthesis-with	2110.03390	null	https://arxiv.org/abs/2110.03390v1	https://arxiv.org/pdf/2110.03390v1.pdf	GANtron: Emotional Speech Synthesis with Generative Adversarial Networks	Speech synthesis is used in a wide variety of industries. Nonetheless, it always sounds flat or robotic. The state of the art methods that allow for prosody control are very cumbersome to use and do not allow easy tuning. To tackle some of these drawbacks, in this work we target the implementation of a text-to-speech model where the inferred speech can be tuned with the desired emotions. To do so, we use Generative Adversarial Networks (GANs) together with a sequence-to-sequence model using an attention mechanism. We evaluate four different configurations considering different inputs and training strategies, study them and prove how our best model can generate speech files that lie in the same distribution as the initial training dataset. Additionally, a new strategy to boost the training convergence by applying a guided attention loss is proposed.	['Rodrigo Brechard Alarcia', 'Enrique Hortal']	2021-10-06	null	null	null	null	['emotional-speech-synthesis']	['speech']	[ 3.10954660e-01 3.64043921e-01 7.43869469e-02 -3.17167342e-02 -5.75008273e-01 -6.57917440e-01 6.30880952e-01 -3.42290074e-01 -1.58680290e-01 9.90489125e-01 3.73089239e-02 -1.91478387e-01 5.30165695e-02 -8.62248838e-01 -8.79112840e-01 -7.70404935e-01 4.87855017e-01 5.65725803e-01 1.45683661e-01 -5.77282608e-01 -3.41016315e-02 5.76858997e-01 -1.59381104e+00 2.00729862e-01 8.36716890e-01 7.10278153e-01 2.82163948e-01 7.81739593e-01 -2.74883330e-01 7.29335427e-01 -1.07046533e+00 -5.88625610e-01 1.53567776e-01 -9.30306017e-01 -5.43308973e-01 -1.45813450e-02 9.91991013e-02 1.43636167e-02 8.31732824e-02 1.05583334e+00 8.43146861e-01 1.89396068e-01 6.73541188e-01 -1.06287420e+00 -5.19272804e-01 8.67329657e-01 -1.04918815e-02 -1.32982478e-01 3.90855044e-01 2.52726376e-01 7.33594120e-01 -4.86581236e-01 6.03498220e-01 1.19070828e+00 4.33629721e-01 1.09668779e+00 -1.41212678e+00 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1f869b0d-39e6-47f5-8c65-4445782076a4	a-prospective-approach-for-human-to-human	2202.08146	null	https://arxiv.org/abs/2202.08146v4	https://arxiv.org/pdf/2202.08146v4.pdf	A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation	Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. ...	['Md. Mohsin Sarker Raihan', 'Abdullah Bin Shams', 'Md. Mohi Uddin Khan']	2022-02-16	null	null	null	null	['human-interaction-recognition']	['computer-vision']	[ 1.14439897e-01 -1.18562952e-01 -2.09643513e-01 -2.30542913e-01 -5.11718154e-01 -2.56640892e-02 3.44466306e-02 -1.49297267e-01 -3.62222940e-01 7.74380863e-01 1.20362498e-01 -3.03061157e-01 -2.32451335e-01 -8.48020494e-01 -4.38370317e-01 -5.41477561e-01 -3.94299954e-01 -1.09375551e-01 -1.68093473e-01 3.11278045e-01 -3.98089826e-01 2.74159044e-01 -1.62192285e+00 -3.84737067e-02 3.89142632e-01 1.26079595e+00 -2.74356067e-01 7.91578054e-01 4.22943354e-01 6.10640466e-01 -8.52535546e-01 5.07462658e-02 1.47083223e-01 -4.84833837e-01 -3.05982232e-01 -5.91202140e-01 6.61004260e-02 -3.48575145e-01 -7.69490123e-01 2.94857085e-01 9.13484335e-01 2.69204259e-01 8.63118619e-02 -1.57544553e+00 -3.24422210e-01 4.86801058e-01 -4.08052474e-01 3.08590949e-01 8.82685900e-01 -5.91790937e-02 4.17813838e-01 -2.44008586e-01 -3.32737528e-02 5.57306170e-01 8.25896025e-01 3.11075617e-02 -7.99771786e-01 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1.06720097e-01 1.73744231e-01 -7.81291425e-01 -3.40297759e-01 -3.77159715e-01]	[6.852363109588623, 0.6773692965507507]
0996c567-223a-44fb-b2f5-a14918d1f9d3	predicting-the-performance-of-multilingual	2110.08875	null	https://arxiv.org/abs/2110.08875v1	https://arxiv.org/pdf/2110.08875v1.pdf	Predicting the Performance of Multilingual NLP Models	Recent advancements in NLP have given us models like mBERT and XLMR that can serve over 100 languages. The languages that these models are evaluated on, however, are very few in number, and it is unlikely that evaluation datasets will cover all the languages that these models support. Potential solutions to the costly problem of dataset creation are to translate datasets to new languages or use template-filling based techniques for creation. This paper proposes an alternate solution for evaluating a model across languages which make use of the existing performance scores of the model on languages that a particular task has test sets for. We train a predictor on these performance scores and use this predictor to predict the model's performance in different evaluation settings. Our results show that our method is effective in filling the gaps in the evaluation for an existing set of languages, but might require additional improvements if we want it to generalize to unseen languages.	['Monojit Choudhury', 'Kalika Bali', 'Sandipan Dandapat', 'Tanuja Ganu', 'Sunayana Sitaram', 'Anirudh Srinivasan']	2021-10-17	null	null	null	null	['multilingual-nlp']	['natural-language-processing']	[ 1.90673426e-01 -3.80499177e-02 -2.17629239e-01 -8.16742003e-01 -1.27139044e+00 -1.06484282e+00 6.53637111e-01 9.32119489e-02 -5.93255758e-01 1.01996708e+00 2.20802754e-01 -8.41865897e-01 1.99911043e-01 -7.30295777e-01 -5.82421839e-01 3.97494249e-02 3.29689234e-01 1.00014448e+00 2.28749350e-01 -2.61709839e-01 -6.47735149e-02 3.96212667e-01 -1.36430049e+00 7.11744130e-01 9.14874017e-01 3.35033894e-01 2.12848663e-01 6.81335628e-01 -6.01371765e-01 5.35513341e-01 -9.37286854e-01 -5.14992416e-01 2.64364094e-01 -3.02371234e-01 -1.08400023e+00 4.15837616e-02 4.45150375e-01 1.46280959e-01 1.92452341e-01 6.95487916e-01 3.76812518e-01 -7.74916857e-02 5.18384993e-01 -1.25106394e+00 -6.38911963e-01 7.37780571e-01 -2.86648691e-01 1.71088353e-01 8.10704410e-01 -6.00611083e-02 8.84287119e-01 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d6eb9f95-4db8-4826-b978-336d4e4ce81d	alleviating-over-smoothing-for-unsupervised	2305.06154	null	https://arxiv.org/abs/2305.06154v1	https://arxiv.org/pdf/2305.06154v1.pdf	Alleviating Over-smoothing for Unsupervised Sentence Representation	Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language models (PLMs) and contrastive learning have achieved promising results on this task. Experimentally, we observe that the over-smoothing problem reduces the capacity of these powerful PLMs, leading to sub-optimal sentence representations. In this paper, we present a Simple method named Self-Contrastive Learning (SSCL) to alleviate this issue, which samples negatives from PLMs intermediate layers, improving the quality of the sentence representation. Our proposed method is quite simple and can be easily extended to various state-of-the-art models for performance boosting, which can be seen as a plug-and-play contrastive framework for learning unsupervised sentence representation. Extensive results prove that SSCL brings the superior performance improvements of different strong baselines (e.g., BERT and SimCSE) on Semantic Textual Similarity and Transfer datasets. Our codes are available at https://github.com/nuochenpku/SSCL.	['Jia Li', 'Daxin Jiang', 'Jianhui Chang', 'Bowen Cao', 'Jian Pei', 'Ming Gong', 'Linjun Shou', 'Nuo Chen']	2023-05-09	null	null	null	null	['semantic-textual-similarity']	['natural-language-processing']	[ 1.51692882e-01 9.73973516e-03 -3.63290280e-01 -6.16499782e-01 -8.65146101e-01 -2.13653952e-01 8.31636488e-01 4.54973131e-01 -5.26104152e-01 6.06310964e-01 6.20216727e-01 -2.42568091e-01 1.40524775e-01 -7.19252348e-01 -5.84120691e-01 -5.85946918e-01 3.18908334e-01 2.22720310e-01 3.20948273e-01 -6.47763014e-01 4.59879369e-01 5.74194677e-02 -1.23209214e+00 6.88636422e-01 1.12258387e+00 6.33288562e-01 5.28034389e-01 4.91468012e-01 -7.25582004e-01 1.09215295e+00 -4.16712105e-01 -5.44451773e-01 -2.10986778e-01 -4.90810335e-01 -9.97751355e-01 -2.98119634e-01 2.96097964e-01 -1.01894908e-01 -3.38676900e-01 1.12781107e+00 6.43951833e-01 2.22767845e-01 7.26828694e-01 -1.05272210e+00 -8.24033380e-01 9.39765334e-01 -7.20544159e-01 4.01166022e-01 2.45625526e-01 -6.62119165e-02 1.11591351e+00 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311dbf6b-5204-44e8-8bfc-9e7626e39930	doe2vec-deep-learning-based-features-for	2304.01219	null	https://arxiv.org/abs/2304.01219v1	https://arxiv.org/pdf/2304.01219v1.pdf	DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis	We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e.g., automated selection of optimization algorithms. Principally, using large training data sets generated with a random function generator, DoE2Vec self-learns an informative latent representation for any design of experiments (DoE). Unlike the classical exploratory landscape analysis (ELA) method, our approach does not require any feature engineering and is easily applicable for high dimensional search spaces. For validation, we inspect the quality of latent reconstructions and analyze the latent representations using different experiments. The latent representations not only show promising potentials in identifying similar (cheap-to-evaluate) surrogate functions, but also can significantly boost performances when being used complementary to the classical ELA features in classification tasks.	['Thomas Bäck', 'Markus Gitterle', 'Peter Krause', 'Moritz Frenzel', 'Fu Xing Long', 'Bas van Stein']	2023-03-31	null	null	null	null	['feature-engineering']	['methodology']	[-1.68101951e-01 -2.51213551e-01 -1.45208120e-01 4.11365479e-02 -9.37465250e-01 -7.31417894e-01 9.25589085e-01 3.18612039e-01 -3.53527963e-01 8.28192413e-01 3.90552223e-01 -2.58898944e-01 -5.74882805e-01 -1.01097715e+00 -7.21624613e-01 -1.13720953e+00 -3.78910601e-02 5.88608563e-01 -4.93321449e-01 -2.39383742e-01 2.28035107e-01 5.70066810e-01 -1.88048363e+00 1.93943962e-01 9.12542641e-01 8.75189841e-01 1.68540224e-01 4.53800321e-01 9.75718275e-02 3.60700905e-01 -5.23164868e-01 -1.46531552e-01 -3.05737592e-02 -6.44090474e-01 -5.10902166e-01 -2.58105904e-01 -9.51370895e-02 1.38521478e-01 -1.37917712e-01 8.28596652e-01 7.61622190e-01 4.64177698e-01 1.28816390e+00 -9.27956581e-01 -4.75215346e-01 4.79315430e-01 2.44495999e-02 1.34520292e-01 9.36267897e-02 4.51945871e-01 1.27591312e+00 -8.26368511e-01 8.74278247e-01 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6d97b99f-d3ff-491b-a3d9-23adec71176b	information-efficient-learning-of-complexly	2110.12879	null	https://arxiv.org/abs/2110.12879v2	https://arxiv.org/pdf/2110.12879v2.pdf	Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty	In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen, Schollmeyer and Augustin (2018, Int. J. Approx. Reason), we now design elicitation procedures and algorithms that enable decision makers to reveal their underlying preference system (i.e. two relations, one encoding the ordinal, the other the cardinal part of the preferences) while having to answer as few as possible simple ranking questions. Here, two different approaches are followed. The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker's consideration times. In contrast, the second approach explicitly elicits also the cardinal part of the decision maker's preference system, however, only an approximate version of it. This approximation is obtained by additionally collecting labels of preference strength during the elicitation procedure. For both approaches, we give conditions under which they produce the decision maker's true preference system and investigate how their efficiency can be improved. For the latter purpose, besides data-free approaches, we also discuss ways for effectively guiding the elicitation procedure if data from previous elicitation rounds is available. Finally, we demonstrate how the proposed elicitation methods can be utilized in problems of decision under (severe) uncertainty. Precisely, we show that under certain conditions optimal decisions can be found without fully specifying the preference system.	['Georg Schollmeyer', 'Thomas Augustin', 'Hannah Blocher', 'Christoph Jansen']	2021-10-19	null	null	null	null	['decision-making-under-uncertainty', 'decision-making-under-uncertainty']	['medical', 'reasoning']	[ 2.51685739e-01 5.55673540e-01 -2.66010374e-01 -6.16208494e-01 -6.47243738e-01 -1.12789953e+00 3.11643571e-01 4.00056660e-01 -4.86939996e-01 8.82362425e-01 1.50843874e-01 -6.09624267e-01 -7.42569983e-01 -8.85332048e-01 -3.09507012e-01 -5.74846804e-01 1.05338223e-01 9.68252361e-01 -2.21768156e-01 -1.30269870e-01 4.58813816e-01 5.00208199e-01 -1.72010422e+00 3.92263651e-01 1.00883353e+00 1.40002167e+00 -1.22246323e-02 4.95209098e-01 -1.68368388e-02 4.99047995e-01 -2.03412578e-01 -4.58369970e-01 5.42321682e-01 7.14477524e-02 -1.08165956e+00 2.84007996e-01 -1.62823454e-01 -5.37557244e-01 4.55178767e-01 1.08234310e+00 3.72897238e-01 1.46652907e-01 9.40633118e-01 -1.22128963e+00 -2.58051157e-01 1.00603557e+00 -1.20997481e-01 -3.43558252e-01 7.57983088e-01 -6.64570779e-02 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a656376e-60ee-4151-a71e-e621e4ae0ac8	from-rain-removal-to-rain-generation	2008.03580	null	https://arxiv.org/abs/2008.03580v2	https://arxiv.org/pdf/2008.03580v2.pdf	From Rain Generation to Rain Removal	For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper, to further improve the deraining performance, we novelly attempt to handle the SIRR task from the perspective of training datasets by exploring a more efficient way to synthesize rainy images. Specifically, we build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator with the input as some latent variables representing the physical structural rain factors, e.g., direction, scale, and thickness. To solve this model, we employ the variational inference framework to approximate the expected statistical distribution of rainy image in a data-driven manner. With the learned generator, we can automatically and sufficiently generate diverse and non-repetitive training pairs so as to efficiently enrich and augment the existing benchmark datasets. User study qualitatively and quantitatively evaluates the realism of generated rainy images. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution that not only helps significantly improve the deraining performance of current deep single image derainers, but also largely loosens the requirement of large training sample pre-collection for the SIRR task.	['Yefeng Zheng', 'Qi Xie', 'Qian Zhao', 'Hong Wang', 'Zongsheng Yue', 'Deyu Meng']	2020-08-08	null	http://openaccess.thecvf.com//content/CVPR2021/html/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.pdf	cvpr-2021-1	['single-image-deraining']	['computer-vision']	[ 3.60508151e-02 -1.85787484e-01 4.73892659e-01 -4.76966202e-01 -7.29992867e-01 -1.97826833e-01 3.12330246e-01 -6.88924909e-01 -4.56835143e-02 8.21974576e-01 -7.30700092e-03 -2.72181273e-01 1.19203761e-01 -9.87889826e-01 -6.85313463e-01 -1.34376419e+00 3.10376167e-01 3.36328685e-01 -1.06819630e-01 -3.84180427e-01 -5.72371744e-02 6.48158669e-01 -1.58362520e+00 -1.52883649e-01 1.53653026e+00 4.43113953e-01 7.57763386e-01 7.25639522e-01 1.32495351e-02 8.41137230e-01 -7.08245873e-01 -1.53708965e-01 1.73799649e-01 -6.43733263e-01 -8.25542212e-02 3.06705475e-01 4.71116275e-01 -7.66548514e-01 -4.08425510e-01 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96914383-b569-4f17-afcc-1a9710fc3b67	moving-the-eiffel-tower-to-rome-tracing-and	null	null	https://openreview.net/forum?id=mMECu_poAs	https://openreview.net/pdf?id=mMECu_poAs	Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT	We investigate the mechanisms underlying factual knowledge recall in auto-regressive transformer language models. To this end, we develop a method for identifying neuron activations that are capable of altering a model's factual predictions. Within GPT-2, this reveals two distinct sets of neurons that we hypothesize correspond to knowing an abstract fact and saying a concrete word, respectively. Based on this insight, we propose ROME, a simple and efficient rank-one model editing method for rewriting abstract facts in auto-regressive language models. For validation, we introduce CounterFact, a dataset of over twenty thousand rewritable facts, as well as tools to facilitate sensitive measurements of edit quality. Compared to previously-published knowledge editing methods, ROME achieves superior generalization and specificity.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['model-editing']	['natural-language-processing']	[ 2.48751566e-01 5.79986274e-01 -6.73985183e-02 -3.61315876e-01 -6.27710104e-01 -6.74013555e-01 1.25839400e+00 1.39288500e-01 -3.34304422e-01 1.05433834e+00 6.89778209e-01 -3.57905835e-01 -1.04646683e-01 -9.55924690e-01 -1.17912292e+00 -1.67590410e-01 2.06115291e-01 3.12385052e-01 -1.27463043e-01 -4.12002921e-01 5.86805463e-01 2.87829429e-01 -1.32491302e+00 8.11642706e-01 8.47657144e-01 5.16215384e-01 6.82210596e-03 1.60079360e-01 -2.01797754e-01 1.29688060e+00 -6.98202848e-01 -1.40871525e+00 -3.20030153e-02 -3.79147917e-01 -8.45458388e-01 -8.64837468e-01 7.29388416e-01 -1.83120713e-01 -6.82240844e-01 1.04182589e+00 1.62934572e-01 2.30876148e-01 8.98136139e-01 -8.63570690e-01 -1.51357806e+00 1.28172755e+00 -2.21608784e-02 7.74412453e-01 4.46401358e-01 4.88465019e-02 1.10950017e+00 -1.09475696e+00 7.78221309e-01 1.46146798e+00 7.48027086e-01 6.66838467e-01 -1.50609529e+00 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4.82068062e-01 4.22942936e-02 -8.47656071e-01 -2.75366217e-01 2.38288254e-01]	[10.26821231842041, 8.115128517150879]
53990195-e6b2-48f2-9738-50e51bec54a5	learning-and-compositionality-a-unification	2208.12789	null	https://arxiv.org/abs/2208.12789v1	https://arxiv.org/pdf/2208.12789v1.pdf	Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming	We consider learning and compositionality as the key mechanisms towards simulating human-like intelligence. While each mechanism is successfully achieved by neural networks and symbolic AIs, respectively, it is the combination of the two mechanisms that makes human-like intelligence possible. Despite the numerous attempts on building hybrid neuralsymbolic systems, we argue that our true goal should be unifying learning and compositionality, the core mechanisms, instead of neural and symbolic methods, the surface approaches to achieve them. In this work, we review and analyze the strengths and weaknesses of neural and symbolic methods by separating their forms and meanings (structures and semantics), and propose Connectionist Probabilistic Program (CPPs), a framework that connects connectionist structures (for learning) and probabilistic program semantics (for compositionality). Under the framework, we design a CPP extension for small scale sequence modeling and provide a learning algorithm based on Bayesian inference. Although challenges exist in learning complex patterns without supervision, our early results demonstrate CPP's successful extraction of concepts and relations from raw sequential data, an initial step towards compositional learning.	['Hai Li', 'Ximing Qiao']	2022-08-26	null	null	null	null	['probabilistic-programming']	['methodology']	[ 2.59027809e-01 6.16658688e-01 -4.32310939e-01 -5.88771045e-01 -3.55365187e-01 -5.10740340e-01 1.02229893e+00 5.48270112e-03 -3.39739680e-01 6.27734423e-01 3.40890475e-02 -6.55768275e-01 -5.03946543e-01 -8.13924670e-01 -8.57541978e-01 -4.34456676e-01 -2.34147891e-01 1.07300079e+00 3.73201907e-01 -5.47323450e-02 3.27288568e-01 5.74061632e-01 -1.74949419e+00 4.96632069e-01 8.44098210e-01 6.07385993e-01 6.01597726e-02 7.02008843e-01 -6.99707687e-01 1.56967056e+00 -4.40513283e-01 -5.07525682e-01 -2.85278112e-01 -5.49464345e-01 -9.64127839e-01 -6.04886293e-01 2.09222063e-01 -6.06466681e-02 -3.55996370e-01 9.60067332e-01 -6.14545345e-02 -9.42185000e-02 9.86826837e-01 -1.52296817e+00 -6.06183231e-01 1.32357311e+00 -2.01824438e-02 -1.42027602e-01 4.00571883e-01 -8.52668956e-02 1.03179204e+00 -4.45572615e-01 3.87819439e-01 1.46658528e+00 1.08456910e+00 7.50838101e-01 -1.57350385e+00 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48e6802b-e90d-4ca2-90e9-ffcda47f7718	a-conceptual-framework-for-inferring	null	null	https://aclanthology.org/W14-2625	https://aclanthology.org/W14-2625.pdf	A Conceptual Framework for Inferring Implicatures	null	['Lingjia Deng', 'Janyce Wiebe']	2014-06-01	null	null	null	ws-2014-6	['implicatures']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 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5fe9db81-f6dc-4531-93fb-a63267c0150c	ita-image-text-alignments-for-multi-modal	2112.06482	null	https://arxiv.org/abs/2112.06482v4	https://arxiv.org/pdf/2112.06482v4.pdf	ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition	Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representations. However, it is difficult to model such interactions as image and text representations are trained separately on the data of their respective modality and are not aligned in the same space. As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized. ITA first aligns the image into regional object tags, image-level captions and optical characters as visual contexts, concatenates them with the input texts as a new cross-modal input, and then feeds it into a pretrained textual embedding model. This makes it easier for the attention module of a pretrained textual embedding model to model the interaction between the two modalities since they are both represented in the textual space. ITA further aligns the output distributions predicted from the cross-modal input and textual input views so that the MNER model can be more practical in dealing with text-only inputs and robust to noises from images. In our experiments, we show that ITA models can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets, even without image information.	['Kewei Tu', 'Fei Huang', 'Zhongqiang Huang', 'Tao Wang', 'Nguyen Bach', 'Zixia Jia', 'Yong Jiang', 'Min Gui', 'Xinyu Wang']	2021-12-13	null	https://aclanthology.org/2022.naacl-main.232	https://aclanthology.org/2022.naacl-main.232.pdf	naacl-2022-7	['multi-modal-named-entity-recognition']	['natural-language-processing']	[ 1.36770993e-01 1.23865120e-02 -1.28599226e-01 -3.55680346e-01 -8.02897334e-01 -6.52070642e-01 7.85077214e-01 -2.58908451e-01 -5.89459836e-01 2.45855048e-01 1.90680549e-01 -1.91100687e-01 3.44241321e-01 -6.05125546e-01 -1.05911231e+00 -6.65352225e-01 6.10445440e-01 5.08762419e-01 3.16566795e-01 5.02003990e-02 -1.18938148e-01 4.71852154e-01 -1.36606896e+00 7.76974857e-01 5.81140697e-01 1.17620730e+00 5.89425802e-01 4.48031068e-01 -6.76251531e-01 6.70306683e-01 -4.23920125e-01 -6.22118831e-01 1.78497463e-01 -3.45090955e-01 -7.49219716e-01 2.46872544e-01 5.72801352e-01 -1.41133174e-01 -5.72780013e-01 9.39831674e-01 3.44767392e-01 3.04547530e-02 9.74750817e-01 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e339daf9-bf14-451e-b605-19bd01db3cef	an-empirical-exploration-of-skip-connections	1610.03167	null	http://arxiv.org/abs/1610.03167v1	http://arxiv.org/pdf/1610.03167v1.pdf	An Empirical Exploration of Skip Connections for Sequential Tagging	In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging. We investigate three kinds of skip connections connecting to LSTM cells: (a) skip connections to the gates, (b) skip connections to the internal states and (c) skip connections to the cell outputs. We present comprehensive experiments showing that skip connections to cell outputs outperform the remaining two. Furthermore, we observe that using gated identity functions as skip mappings works pretty well. Based on this novel skip connections, we successfully train deep stacked bidirectional LSTM models and obtain state-of-the-art results on CCG supertagging and comparable results on POS tagging.	['Cheng-qing Zong', 'Jiajun Zhang', 'Huijia Wu']	2016-10-11	an-empirical-exploration-of-skip-connections-2	https://aclanthology.org/C16-1020	https://aclanthology.org/C16-1020.pdf	coling-2016-12	['ccg-supertagging']	['natural-language-processing']	[ 7.51323551e-02 1.78236291e-01 -2.54685193e-01 -3.90136719e-01 -5.90059996e-01 -7.24674582e-01 6.03160739e-01 9.64926258e-02 -4.76975828e-01 9.94790137e-01 4.50711459e-01 -6.42701745e-01 5.91474116e-01 -7.28559554e-01 -7.05954134e-01 -7.74629712e-01 -2.75366545e-01 5.06070852e-01 7.95180082e-01 -3.82681072e-01 3.71652506e-02 3.61737669e-01 -7.65875280e-01 8.82916272e-01 4.87906605e-01 8.23399842e-01 -2.15368997e-02 5.88115156e-01 -6.35868371e-01 1.07425928e+00 -4.42442387e-01 -6.63854063e-01 -2.08417833e-01 -2.67232507e-01 -1.26199079e+00 -6.93963706e-01 2.12605551e-01 -8.16906914e-02 -2.36921161e-01 8.18491638e-01 2.89930224e-01 -8.04426800e-03 1.57473192e-01 -7.59469092e-01 -6.94096208e-01 1.24686503e+00 -2.22705096e-01 4.57730025e-01 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3.31285089e-01 7.09679350e-02 1.09192431e+00 3.17253917e-01 7.63766646e-01 8.33832324e-02 -5.79484880e-01 4.89525720e-02 -1.11043870e+00 -5.27096152e-01 5.67258485e-02 2.15962946e-01 -4.81891662e-01 1.55272530e-02 -1.73022822e-01]	[10.177583694458008, 9.720492362976074]
0dba3d01-a695-4e65-816b-63740806618d	anytime-stereo-image-depth-estimation-on	1810.11408	null	http://arxiv.org/abs/1810.11408v2	http://arxiv.org/pdf/1810.11408v2.pdf	Anytime Stereo Image Depth Estimation on Mobile Devices	Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process 1242$ \times $375 resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error -- using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at https://github.com/mileyan/AnyNet .	['Kilian Q. Weinberger', 'Zihang Lai', 'Gao Huang', 'Yan Wang', 'Mark Campbell', 'Laurens van der Maaten', 'Brian H. Wang']	2018-10-26	null	null	null	null	['stereo-depth-estimation']	['computer-vision']	[ 2.70502210e-01 1.34156764e-01 1.24991253e-01 -5.40921807e-01 -9.60047603e-01 -5.04388452e-01 4.43540215e-01 -1.26982719e-01 -6.20725036e-01 5.88496804e-01 -2.70444810e-01 -3.88526231e-01 4.16889578e-01 -8.63095403e-01 -7.10312426e-01 -2.68281072e-01 1.54254243e-01 5.89103639e-01 5.52993894e-01 5.93654439e-02 5.98721027e-01 3.68248582e-01 -1.94848359e+00 8.86033624e-02 8.81779075e-01 1.26278448e+00 5.21553636e-01 1.06628108e+00 1.18399650e-01 8.22935164e-01 -3.89815062e-01 -3.86649251e-01 6.52534604e-01 -8.72737095e-02 -5.55285156e-01 -1.41459331e-01 7.31829584e-01 -9.90546525e-01 -4.14511263e-01 1.03227246e+00 7.78409123e-01 -1.12373389e-01 2.32580066e-01 -1.01441991e+00 3.10151316e-02 -5.68841472e-02 -3.78824085e-01 2.19107494e-01 3.41396213e-01 5.68691194e-01 5.66889763e-01 -1.05282402e+00 6.62508190e-01 1.11087024e+00 4.34943378e-01 5.73390305e-01 -1.12343025e+00 -5.33635259e-01 1.23057123e-02 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7fb34d26-834b-4456-ae6f-4cdb66bb7b69	hybrid-neural-rendering-for-large-scale	2304.12652	null	https://arxiv.org/abs/2304.12652v2	https://arxiv.org/pdf/2304.12652v2.pdf	Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur	Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage.	['Xiaojuan Qi', 'Xiaoyang Lyu', 'Xin Yu', 'yinda zhang', 'Peng Dai']	2023-04-25	null	http://openaccess.thecvf.com//content/CVPR2023/html/Dai_Hybrid_Neural_Rendering_for_Large-Scale_Scenes_With_Motion_Blur_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Dai_Hybrid_Neural_Rendering_for_Large-Scale_Scenes_With_Motion_Blur_CVPR_2023_paper.pdf	cvpr-2023-1	['neural-rendering', 'novel-view-synthesis']	['computer-vision', 'computer-vision']	[ 2.39925295e-01 -2.89295256e-01 3.52710307e-01 -2.99207568e-01 -4.13580954e-01 -4.72901225e-01 4.50804949e-01 -6.10113442e-01 9.89633873e-02 6.79122627e-01 3.97378147e-01 -8.63047689e-02 6.07544668e-02 -8.00427198e-01 -9.28635895e-01 -5.85571766e-01 4.24856842e-01 -3.04607034e-01 -5.41220345e-02 -1.45694360e-01 2.38352597e-01 5.15809357e-01 -1.74445593e+00 3.73710126e-01 1.12245750e+00 8.44027638e-01 5.59359670e-01 6.33285284e-01 1.57395095e-01 7.06807196e-01 -3.91196370e-01 -3.11321169e-01 4.98922706e-01 -4.02946591e-01 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0a23ee8a-b047-492d-b8ab-2c0d49c18a1c	fused-depthwise-tiling-for-memory	2303.17878	null	https://arxiv.org/abs/2303.17878v1	https://arxiv.org/pdf/2303.17878v1.pdf	Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference	Memory optimization for deep neural network (DNN) inference gains high relevance with the emergence of TinyML, which refers to the deployment of DNN inference tasks on tiny, low-power microcontrollers. Applications such as audio keyword detection or radar-based gesture recognition are heavily constrained by the limited memory on such tiny devices because DNN inference requires large intermediate run-time buffers to store activations and other intermediate data, which leads to high memory usage. In this paper, we propose a new Fused Depthwise Tiling (FDT) method for the memory optimization of DNNs, which, compared to existing tiling methods, reduces memory usage without inducing any run time overhead. FDT applies to a larger variety of network layers than existing tiling methods that focus on convolutions. It improves TinyML memory optimization significantly by reducing memory of models where this was not possible before and additionally providing alternative design points for models that show high run time overhead with existing methods. In order to identify the best tiling configuration, an end-to-end flow with a new path discovery method is proposed, which applies FDT and existing tiling methods in a fully automated way, including the scheduling of the operations and planning of the layout of buffers in memory. Out of seven evaluated models, FDT achieved significant memory reduction for two models by 76.2% and 18.1% where existing tiling methods could not be applied. Two other models showed a significant run time overhead with existing methods and FDT provided alternative design points with no overhead but reduced memory savings.	['Ulf Schlichtmann', 'Daniel Mueller-Gritschneder', 'Rafael Stahl']	2023-03-31	null	null	null	null	['gesture-recognition']	['computer-vision']	[ 2.35184859e-02 1.21430822e-01 1.30888596e-01 -1.56059235e-01 -6.61737993e-02 -4.01534945e-01 2.50268221e-01 2.78491490e-02 -9.22825158e-01 5.72500944e-01 -2.46758997e-01 -7.45860279e-01 -1.99424967e-01 -1.08112371e+00 -7.70184457e-01 -4.41252381e-01 -5.10336868e-02 5.01577020e-01 6.51118934e-01 1.80654645e-01 1.47654504e-01 7.03719914e-01 -1.75151312e+00 2.04384521e-01 4.15442735e-01 1.15602624e+00 6.18313432e-01 6.77541852e-01 -5.59856534e-01 6.16757512e-01 -1.06033015e+00 9.19827148e-02 2.57990390e-01 -4.49011736e-02 -5.71356237e-01 -6.77702487e-01 4.08648193e-01 -5.25554538e-01 -1.42907962e-01 8.04433286e-01 8.57858896e-01 4.39336710e-02 2.39270199e-02 -1.25048172e+00 4.27052677e-01 8.25381458e-01 -1.84985951e-01 2.39484757e-02 -2.41178408e-01 8.77948031e-02 3.02505821e-01 -4.51348573e-01 5.01805544e-01 1.29780281e+00 5.83109915e-01 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1cb2125f-8cff-49f2-966d-36890ed8842e	listen-denoise-action-audio-driven-motion	2211.09707	null	https://arxiv.org/abs/2211.09707v2	https://arxiv.org/pdf/2211.09707v2.pdf	Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models	Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for synthesising human motion that co-occurs with audio, e.g., dancing and co-speech gesticulation, since motion is complex and highly ambiguous given audio, calling for a probabilistic description. Specifically, we adapt the DiffWave architecture to model 3D pose sequences, putting Conformers in place of dilated convolutions for improved modelling power. We also demonstrate control over motion style, using classifier-free guidance to adjust the strength of the stylistic expression. Experiments on gesture and dance generation confirm that the proposed method achieves top-of-the-line motion quality, with distinctive styles whose expression can be made more or less pronounced. We also synthesise path-driven locomotion using the same model architecture. Finally, we generalise the guidance procedure to obtain product-of-expert ensembles of diffusion models and demonstrate how these may be used for, e.g., style interpolation, a contribution we believe is of independent interest. See https://www.speech.kth.se/research/listen-denoise-action/ for video examples, data, and code.	['Gustav Eje Henter', 'Jonas Beskow', 'Rajmund Nagy', 'Simon Alexanderson']	2022-11-17	null	null	null	null	['gesture-generation']	['robots']	[ 0.00683315 0.17103559 0.20382404 -0.41655448 -0.7372093 -0.7453644 0.77414715 -0.52429086 -0.2933942 0.49503362 0.6342362 -0.10337753 -0.15353192 -0.629928 -0.6924202 -0.88104725 -0.12167802 0.42212617 0.09627973 -0.44655195 0.03150593 0.43730628 -1.3894923 0.63892925 0.5236112 0.6652464 0.20123027 1.1944478 -0.19717036 0.79626286 -0.7433574 -0.44288185 -0.02260436 -0.66338557 -0.6886519 0.22631997 0.1889227 -0.12152465 -0.23334748 0.70516026 0.6532876 0.31711435 0.92262447 -1.0845282 -0.731765 0.7708703 -0.1749638 -0.12597503 0.5480159 0.38100946 0.81001306 -0.7764927 0.7187147 1.532132 0.6995861 0.9783437 -1.299786 -0.41326115 -0.04759231 0.11180969 -1.2385662 -0.61043143 0.81466955 -0.4348174 0.5870483 0.5218069 0.835671 1.8224534 -0.14158314 1.1343167 0.9182242 -0.3984657 0.28109485 -0.09166768 -0.5114 0.69565046 -0.5337196 0.16920005 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c4fca45c-6c08-4a40-a4c9-4b225fe11762	describing-emotions-with-acoustic-property	2211.07737	null	https://arxiv.org/abs/2211.07737v1	https://arxiv.org/pdf/2211.07737v1.pdf	Describing emotions with acoustic property prompts for speech emotion recognition	Emotions lie on a broad continuum and treating emotions as a discrete number of classes limits the ability of a model to capture the nuances in the continuum. The challenge is how to describe the nuances of emotions and how to enable a model to learn the descriptions. In this work, we devise a method to automatically create a description (or prompt) for a given audio by computing acoustic properties, such as pitch, loudness, speech rate, and articulation rate. We pair a prompt with its corresponding audio using 5 different emotion datasets. We trained a neural network model using these audio-text pairs. Then, we evaluate the model using one more dataset. We investigate how the model can learn to associate the audio with the descriptions, resulting in performance improvement of Speech Emotion Recognition and Speech Audio Retrieval. We expect our findings to motivate research describing the broad continuum of emotion	['Rita Singh', 'Bhiksha Raj', 'Huaming Wang', 'Soham Deshmukh', 'Benjamin Elizalde', 'Hira Dhamyal']	2022-11-14	null	null	null	null	['speech-emotion-recognition']	['speech']	[ 2.57876694e-01 -5.83544150e-02 -6.69177026e-02 -9.11169231e-01 -1.07362616e+00 -7.83930480e-01 5.13970852e-01 3.14113498e-01 -3.23045030e-02 1.47804171e-01 6.32835746e-01 6.39806911e-02 1.48104690e-02 -2.98051953e-01 -5.45426190e-01 -3.48351717e-01 -2.07593679e-01 1.80978671e-01 -4.20556813e-01 -5.79158291e-02 1.45609053e-02 3.06523114e-01 -1.85290515e+00 6.15408301e-01 3.86871070e-01 1.34779096e+00 1.25641534e-02 9.94975805e-01 -1.44463107e-01 7.30128169e-01 -7.75553107e-01 7.50942230e-02 9.60177835e-03 -6.04261875e-01 -6.41142249e-01 7.38689303e-02 3.55275422e-01 -2.99054563e-01 1.39704406e-01 6.92253292e-01 4.96664643e-01 2.89647341e-01 1.07987988e+00 -1.30887961e+00 -5.42968869e-01 8.03195298e-01 -2.36924384e-02 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389daf25-e5de-4bcb-bd90-4a5c4981c93e	context-is-key-grammatical-error-detection	1906.06593	null	https://arxiv.org/abs/1906.06593v2	https://arxiv.org/pdf/1906.06593v2.pdf	Context is Key: Grammatical Error Detection with Contextual Word Representations	Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely supervised task can be challenging, as GED datasets are limited in size and the label distributions are highly imbalanced. Contextualized word representations offer a possible solution, as they can efficiently capture compositional information in language and can be optimized on large amounts of unsupervised data. In this paper, we perform a systematic comparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Devlin et al., 2018; Akbik et al., 2018) on a range of public GED datasets, and propose an approach to effectively integrate such representations in current methods, achieving a new state of the art on GED. We further analyze the strengths and weaknesses of different contextual embeddings for the task at hand, and present detailed analyses of their impact on different types of errors.	['Helen Yannakoudakis', 'Samuel Bell', 'Marek Rei']	2019-06-15	context-is-key-grammatical-error-detection-1	https://aclanthology.org/W19-4410	https://aclanthology.org/W19-4410.pdf	ws-2019-8	['grammatical-error-detection']	['natural-language-processing']	[-1.67036623e-01 1.69331044e-01 1.11212395e-01 -3.37336093e-01 -6.09487474e-01 -4.47221220e-01 3.05868685e-01 8.70840728e-01 -8.11591268e-01 6.80094659e-01 4.69328344e-01 -4.41963166e-01 -7.57432953e-02 -5.62775552e-01 -3.29264700e-01 8.64945352e-03 2.59665191e-01 4.82773483e-01 -1.44968808e-01 -2.52538294e-01 4.75471020e-01 2.69431353e-01 -1.62026751e+00 -1.25842020e-01 1.45607698e+00 5.62768877e-01 2.22412720e-01 7.43461192e-01 -4.53962266e-01 9.19568360e-01 -7.42134452e-01 -7.80899465e-01 -4.33879137e-01 -2.66485751e-01 -9.32516575e-01 -4.04750347e-01 8.24203730e-01 -1.41072676e-01 -3.58461291e-01 1.13057065e+00 7.94998825e-01 1.89854011e-01 9.95428503e-01 -9.26318109e-01 -1.38975108e+00 7.06105053e-01 1.25605101e-02 4.90393817e-01 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c4a57a46-1b7a-406c-a0e0-ce5d8cb2e048	mediated-multi-agent-reinforcement-learning	2306.08419	null	https://arxiv.org/abs/2306.08419v1	https://arxiv.org/pdf/2306.08419v1.pdf	Mediated Multi-Agent Reinforcement Learning	The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private information. This results in agents that forgo their individual goals in favour of social good, which can potentially be exploited by selfish defectors. We argue that cooperation also requires agents' identities and boundaries to be respected by making sure that the emergent behaviour is an equilibrium, i.e., a convention that no agent can deviate from and receive higher individual payoffs. Inspired by advances in mechanism design, we propose to solve the problem of cooperation, defined as finding socially beneficial equilibrium, by using mediators. A mediator is a benevolent entity that may act on behalf of agents, but only for the agents that agree to it. We show how a mediator can be trained alongside agents with policy gradient to maximize social welfare subject to constraints that encourage agents to cooperate through the mediator. Our experiments in matrix and iterative games highlight the potential power of applying mediators in MARL.	['Kirill Chernyshev', 'Ilya Zisman', 'Dmitry Ivanov']	2023-06-14	null	null	null	null	['multi-agent-reinforcement-learning']	['methodology']	[-1.63904577e-02 7.39562571e-01 -1.05031639e-01 7.26368055e-02 -2.00887218e-01 -5.66385448e-01 4.66103911e-01 1.21407695e-02 -9.38137233e-01 1.23412204e+00 2.13604376e-01 -7.45812710e-03 -4.26244229e-01 -8.95985961e-01 -2.26247430e-01 -1.01194215e+00 -5.30977130e-01 5.53560078e-01 -1.46426648e-01 -5.78850091e-01 1.95306793e-01 9.29449946e-02 -1.21992040e+00 -2.51523584e-01 7.66254127e-01 4.27662402e-01 5.72959296e-02 7.65154719e-01 4.49297160e-01 1.42871749e+00 -9.16380286e-01 -2.38942236e-01 5.74933469e-01 -8.50268781e-01 -7.82270670e-01 3.39045644e-01 -8.03661406e-01 -6.06220424e-01 1.97360098e-01 1.04275918e+00 4.88385946e-01 1.81470454e-01 6.46244287e-01 -1.85249400e+00 -6.64143443e-01 9.91995275e-01 -6.20193958e-01 -2.32344270e-01 4.16419119e-01 3.36367548e-01 1.41003525e+00 5.03893606e-02 7.45370686e-01 1.17887831e+00 1.35145694e-01 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b1f70684-62b5-406f-9c37-2b804fe49f9c	coverage-as-a-principle-for-discovering-1	2102.13515	null	https://arxiv.org/abs/2102.13515v3	https://arxiv.org/pdf/2102.13515v3.pdf	Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning	Designing agents that acquire knowledge autonomously and use it to solve new tasks efficiently is an important challenge in reinforcement learning. Knowledge acquired during an unsupervised pre-training phase is often transferred by fine-tuning neural network weights once rewards are exposed, as is common practice in supervised domains. Given the nature of the reinforcement learning problem, we argue that standard fine-tuning strategies alone are not enough for efficient transfer in challenging domains. We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights. Our experiments show that, when combined with large-scale pre-training in the absence of rewards, existing intrinsic motivation objectives can lead to the emergence of complex behaviors. These pre-trained policies can then be leveraged by BT to discover better solutions than without pre-training, and combining BT with standard fine-tuning strategies results in additional benefits. The largest gains are generally observed in domains requiring structured exploration, including settings where the behavior of the pre-trained policies is misaligned with the downstream task.	['Charles Blundell', 'Adrià Puigdomènech Badia', 'Alex Vitvitskyi', 'Steven Kapturowski', 'Andre Barreto', 'Steven Hansen', 'Pablo Sprechmann', 'Víctor Campos']	2021-02-24	coverage-as-a-principle-for-discovering	https://openreview.net/forum?id=INhwJdJtxn6	https://openreview.net/pdf?id=INhwJdJtxn6	null	['unsupervised-pre-training']	['methodology']	[ 4.32842910e-01 3.34024698e-01 -2.68866181e-01 -1.50589630e-01 -2.71836162e-01 -7.80861735e-01 6.01868510e-01 2.79724151e-01 -7.89552867e-01 1.07442391e+00 4.32937980e-01 -2.26658955e-01 -1.82301149e-01 -7.37710714e-01 -1.14925194e+00 -6.24688148e-01 -1.86384648e-01 5.17835081e-01 1.19184248e-01 -5.13647854e-01 2.55045384e-01 2.61230439e-01 -1.39814425e+00 -6.16088547e-02 1.12874615e+00 4.99629498e-01 3.64228159e-01 4.75246668e-01 -7.38009661e-02 8.09576273e-01 -4.54647213e-01 1.54482976e-01 5.10588288e-01 -5.77812493e-01 -9.47594047e-01 6.98819906e-02 -6.65367097e-02 -5.72393060e-01 -2.47757390e-01 8.06438923e-01 5.16840667e-02 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6dfe0849-78fb-4640-bc7a-c4108ae7404b	visual-programming-compositional-visual	2211.11559	null	https://arxiv.org/abs/2211.11559v1	https://arxiv.org/pdf/2211.11559v1.pdf	Visual Programming: Compositional visual reasoning without training	We present VISPROG, a neuro-symbolic approach to solving complex and compositional visual tasks given natural language instructions. VISPROG avoids the need for any task-specific training. Instead, it uses the in-context learning ability of large language models to generate python-like modular programs, which are then executed to get both the solution and a comprehensive and interpretable rationale. Each line of the generated program may invoke one of several off-the-shelf computer vision models, image processing routines, or python functions to produce intermediate outputs that may be consumed by subsequent parts of the program. We demonstrate the flexibility of VISPROG on 4 diverse tasks - compositional visual question answering, zero-shot reasoning on image pairs, factual knowledge object tagging, and language-guided image editing. We believe neuro-symbolic approaches like VISPROG are an exciting avenue to easily and effectively expand the scope of AI systems to serve the long tail of complex tasks that people may wish to perform.	['Aniruddha Kembhavi', 'Tanmay Gupta']	2022-11-18	null	http://openaccess.thecvf.com//content/CVPR2023/html/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Gupta_Visual_Programming_Compositional_Visual_Reasoning_Without_Training_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-reasoning', 'visual-reasoning']	['computer-vision', 'reasoning']	[ 2.16967165e-01 5.44000149e-01 1.89867496e-01 -6.00809276e-01 -6.56365335e-01 -7.46390522e-01 6.46367133e-01 -9.30306688e-03 -2.27304801e-01 4.60853547e-01 8.02574754e-02 -8.57113779e-01 1.98728174e-01 -6.25567913e-01 -9.50810671e-01 -2.29211092e-01 2.13296518e-01 4.82293636e-01 2.69085616e-01 -1.54514089e-01 2.88883477e-01 3.49081814e-01 -1.52373254e+00 7.73687720e-01 7.28541613e-01 5.26645958e-01 4.15258378e-01 9.59723592e-01 -5.17613769e-01 1.33068085e+00 -3.13599318e-01 -5.35700917e-01 2.88912784e-02 -3.59305352e-01 -9.43708479e-01 -8.86691585e-02 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e0a2cde5-a29d-4076-b709-5ac5c6e438d1	ga-net-guided-aggregation-net-for-end-to-end	1904.06587	null	http://arxiv.org/abs/1904.06587v1	http://arxiv.org/pdf/1904.06587v1.pdf	GA-Net: Guided Aggregation Net for End-to-end Stereo Matching	In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image cost dependencies respectively. The first is a semi-global aggregation layer which is a differentiable approximation of the semi-global matching, the second is the local guided aggregation layer which follows a traditional cost filtering strategy to refine thin structures. These two layers can be used to replace the widely used 3D convolutional layer which is computationally costly and memory-consuming as it has cubic computational/memory complexity. In the experiments, we show that nets with a two-layer guided aggregation block easily outperform the state-of-the-art GC-Net which has nineteen 3D convolutional layers. We also train a deep guided aggregation network (GA-Net) which gets better accuracies than state-of-the-art methods on both Scene Flow dataset and KITTI benchmarks.	['Ruigang Yang', 'Feihu Zhang', 'Philip H. S. Torr', 'Victor Prisacariu']	2019-04-13	ga-net-guided-aggregation-net-for-end-to-end-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_GA-Net_Guided_Aggregation_Net_for_End-To-End_Stereo_Matching_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_GA-Net_Guided_Aggregation_Net_for_End-To-End_Stereo_Matching_CVPR_2019_paper.pdf	cvpr-2019-6	['stereo-depth-estimation']	['computer-vision']	[-5.88129684e-02 -1.77405417e-01 1.32733956e-01 -3.93529296e-01 -3.50467384e-01 -1.51494965e-02 4.66873795e-01 -2.23402828e-01 -5.65320075e-01 5.27875900e-01 2.16995835e-01 -4.00947452e-01 1.49385601e-01 -1.18816376e+00 -8.94033968e-01 -3.46042097e-01 -3.60548347e-02 2.49052733e-01 5.78973711e-01 -3.77067834e-01 3.91487539e-01 5.31606317e-01 -1.71444190e+00 2.62734771e-01 1.26341689e+00 1.59060359e+00 3.24636213e-02 6.14154041e-01 -3.78992856e-01 1.05600893e+00 -1.56123012e-01 -4.79374409e-01 8.90708327e-01 -2.39599854e-01 -7.08849311e-01 -2.23392025e-01 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0ab1e75f-ceac-4e72-baff-50000710920c	double-embeddings-and-cnn-based-sequence	1805.04601	null	http://arxiv.org/abs/1805.04601v1	http://arxiv.org/pdf/1805.04601v1.pdf	Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction	One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.	['Philip S. Yu', 'Bing Liu', 'Hu Xu', 'Lei Shu']	2018-05-11	double-embeddings-and-cnn-based-sequence-1	https://aclanthology.org/P18-2094	https://aclanthology.org/P18-2094.pdf	acl-2018-7	['aspect-extraction']	['natural-language-processing']	[-1.63738996e-01 3.45018774e-01 -5.37465036e-01 -7.57360816e-01 -3.88841450e-01 -3.97980213e-01 7.28815734e-01 5.54894328e-01 -5.79425573e-01 4.18623507e-01 3.04073751e-01 -4.81977135e-01 2.97521979e-01 -9.93331313e-01 -3.79273444e-01 -3.62407327e-01 2.52023607e-01 2.85356790e-01 -2.35022470e-01 -4.79159147e-01 3.76658589e-01 2.39890590e-01 -1.43203902e+00 2.00748816e-01 2.51212329e-01 1.21474528e+00 -5.31839013e-01 6.03892148e-01 -4.70378846e-01 5.63610077e-01 -6.98318243e-01 -9.50618267e-01 1.43382162e-01 8.20795521e-02 -6.87905550e-01 3.70980680e-01 3.40212345e-01 -3.66813898e-01 1.83673948e-01 9.30136979e-01 5.53807318e-01 -6.77308813e-02 6.33901358e-01 -1.00832665e+00 -1.42488873e+00 5.41200280e-01 -5.91200113e-01 1.34457707e-01 3.00092429e-01 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a8ce3fe8-0d8a-4f2c-9b94-391da9b7928a	sse-a-metric-for-evaluating-search-system	2306.10175	null	https://arxiv.org/abs/2306.10175v1	https://arxiv.org/pdf/2306.10175v1.pdf	SSE: A Metric for Evaluating Search System Explainability	Explainable Information Retrieval (XIR) is a growing research area focused on enhancing transparency and trustworthiness of the complex decision-making processes taking place in modern information retrieval systems. While there has been progress in developing XIR systems, empirical evaluation tools to assess the degree of explainability attained by such systems are lacking. To close this gap and gain insights into the true merit of XIR systems, we extend existing insights from a factor analysis of search explainability to introduce SSE (Search System Explainability), an evaluation metric for XIR search systems. Through a crowdsourced user study, we demonstrate SSE's ability to distinguish between explainable and non-explainable systems, showing that systems with higher scores indeed indicate greater interpretability. Additionally, we observe comparable perceived temporal demand and performance levels between non-native and native English speakers. We hope that aside from these concrete contributions to XIR, this line of work will serve as a blueprint for similar explainability evaluation efforts in other domains of machine learning and natural language processing.	['Carsten Eickhoff', 'Catherine Chen']	2023-06-16	null	null	null	null	['information-retrieval']	['natural-language-processing']	[ 8.11556056e-02 6.45891249e-01 -4.01052982e-01 -6.44333959e-01 -9.21995580e-01 -8.12717140e-01 1.03666842e+00 3.56294900e-01 -4.06740665e-01 1.70320481e-01 5.78815639e-01 -8.87425959e-01 -3.35947901e-01 -5.38413785e-02 -4.16632116e-01 3.46527308e-01 2.65315771e-01 3.84270191e-01 -4.56726551e-01 -4.15136337e-01 6.75910473e-01 3.25238556e-01 -1.40406346e+00 6.37084723e-01 1.12885964e+00 5.43804407e-01 -1.23458683e-01 7.71722317e-01 5.27415350e-02 1.09441698e+00 -6.25088751e-01 -7.71218598e-01 3.36016685e-01 -3.76743972e-01 -1.16466892e+00 -3.20910990e-01 6.64819062e-01 -5.29208422e-01 -2.80164778e-01 8.85654271e-01 1.92816496e-01 5.85384369e-02 7.54159808e-01 -1.24798703e+00 -1.81330884e+00 8.05912673e-01 -1.08800344e-01 2.61871725e-01 7.40589619e-01 2.31203809e-01 1.54323637e+00 -1.01552558e+00 6.80180252e-01 1.37907755e+00 4.12166893e-01 5.95446825e-01 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90c6ca71-734a-47cf-a46b-4eb40a4b589c	domain-adaptive-faster-r-cnn-for-object	1803.03243	null	http://arxiv.org/abs/1803.03243v1	http://arxiv.org/pdf/1803.03243v1.pdf	Domain Adaptive Faster R-CNN for Object Detection in the Wild	Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.	['Luc van Gool', 'Yuhua Chen', 'Dengxin Dai', 'Christos Sakaridis', 'Wen Li']	2018-03-08	domain-adaptive-faster-r-cnn-for-object-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.pdf	cvpr-2018-6	['robust-object-detection']	['computer-vision']	[ 2.35764235e-01 -2.34419480e-01 1.75827280e-01 -2.75944650e-01 -6.84226573e-01 -6.13939822e-01 5.64199746e-01 -3.08928322e-02 -5.32858312e-01 6.58939898e-01 -2.35015184e-01 1.97521485e-02 4.74699661e-02 -7.20868409e-01 -9.59766686e-01 -9.15295541e-01 3.22437942e-01 2.92037368e-01 8.33857596e-01 -2.71122694e-01 2.98998803e-01 7.10641146e-01 -1.55306280e+00 2.50744641e-01 7.45525301e-01 1.08873820e+00 -2.03152671e-02 4.89146948e-01 -2.93322839e-02 5.05457342e-01 -7.48447299e-01 -2.84132123e-01 6.53621435e-01 -1.33561194e-01 -3.64129305e-01 2.86167860e-01 4.58462387e-01 -2.75849313e-01 -2.30257809e-01 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a695ea2d-1dcd-428f-ba14-7887a3ea6009	mastering-terra-mystica-applying-self-play-to	2102.10540	null	https://arxiv.org/abs/2102.10540v1	https://arxiv.org/pdf/2102.10540v1.pdf	Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games	In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi \cite{AlphaZero}. We directly apply these breakthroughs to a novel state-representation of TM with the goal of creating an AI that will rival human players. Specifically, we present the initial results of applying AlphaZero to this state-representation and analyze the strategies developed. A brief analysis is presented. We call this modified algorithm with our novel state-representation AlphaTM. In the end, we discuss the success and shortcomings of this method by comparing against multiple baselines and typical human scores. All code used for this paper is available at on \href{https://github.com/kandluis/terrazero}{GitHub}.	['Luis Perez']	2021-02-21	null	null	null	null	['board-games']	['playing-games']	[ 5.14883548e-02 7.89403245e-02 1.38840511e-01 1.11872278e-01 -9.35533404e-01 -8.82362902e-01 4.43402052e-01 -3.91208321e-01 -6.28626287e-01 7.62071788e-01 2.35787258e-01 -4.21401203e-01 -3.24395627e-01 -6.97721899e-01 -3.24135244e-01 -4.06607389e-01 -1.47389829e-01 8.61693323e-01 3.18417639e-01 -1.02827072e+00 4.91360664e-01 -5.58766313e-02 -1.59340334e+00 5.03967345e-01 5.95806003e-01 6.28248930e-01 1.21276617e-01 1.00611460e+00 7.14320242e-02 1.18847835e+00 -1.03073919e+00 -7.23161101e-01 5.68342328e-01 -7.31149197e-01 -1.16239572e+00 -5.29951096e-01 1.78217724e-01 -3.47248703e-01 -5.11199951e-01 8.03584337e-01 7.80527353e-01 5.30313671e-01 2.41291597e-01 -1.28413570e+00 1.56009449e-02 9.58208084e-01 -4.47921097e-01 5.69210887e-01 6.02383792e-01 3.76153588e-01 1.12729716e+00 -1.84204921e-01 6.24739170e-01 9.78508592e-01 6.80378616e-01 1.00114453e+00 -7.63800979e-01 -8.66335809e-01 4.86220121e-02 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aa545b69-77a7-4c2d-8f88-ba28cf19371a	r2d2-robust-data-to-text-with-replacement	2205.12467	null	https://arxiv.org/abs/2205.12467v1	https://arxiv.org/pdf/2205.12467v1.pdf	R2D2: Robust Data-to-Text with Replacement Detection	Unfaithful text generation is a common problem for text generation systems. In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any real-world applications. We introduce R2D2, a training framework that addresses unfaithful Data-to-Text generation by training a system both as a generator and a faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. To facilitate such training, we propose two methods for sampling unfaithful sentences. We argue that the poor entity retrieval capability of D2T systems is one of the primary sources of unfaithfulness, so in addition to the existing metrics, we further propose NER-based metrics to evaluate the fidelity of D2T generations. Our experimental results show that R2D2 systems could effectively mitigate the unfaithful text generation, and they achieve new state-of-the-art results on FeTaQA, LogicNLG, and ToTTo, all with significant improvements.	['Dragomir Radev', 'Weijin Zou', 'Luke Benson', 'Yixin Liu', 'Yilun Zhao', 'Lorenzo Jaime Yu Flores', 'Linyong Nan']	2022-05-25	null	null	null	null	['data-to-text-generation']	['natural-language-processing']	[ 5.00552468e-02 4.06377524e-01 -1.04244696e-02 -9.76151899e-02 -1.08597016e+00 -4.99579400e-01 1.09622705e+00 -9.94781181e-02 -2.13962138e-01 1.07906091e+00 4.08804864e-01 -3.89916450e-01 1.33841082e-01 -1.07789922e+00 -4.62397248e-01 -3.63985419e-01 4.64738578e-01 8.09208751e-01 1.37608811e-01 -7.44165361e-01 3.24086398e-01 5.40181249e-02 -1.35327148e+00 4.97403473e-01 1.62232506e+00 5.95542192e-01 1.54749095e-01 8.00688148e-01 -4.56637740e-01 8.61983120e-01 -1.18365848e+00 -8.38550568e-01 2.37298653e-01 -5.64543962e-01 -9.40639019e-01 -3.18268895e-01 3.42147827e-01 -5.16885698e-01 -3.51636559e-01 8.21756721e-01 1.00133538e+00 -1.14425771e-01 1.05150044e+00 -1.37865663e+00 -8.75699937e-01 1.29932034e+00 -3.18731457e-01 -4.85560708e-02 4.65892434e-01 1.35509238e-01 9.56469059e-01 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3468542c-4af0-4b95-8a4d-6d5aa94bcff2	towards-correlated-sequential-rules	2210.15637	null	https://arxiv.org/abs/2210.15637v1	https://arxiv.org/pdf/2210.15637v1.pdf	Towards Correlated Sequential Rules	The goal of high-utility sequential pattern mining (HUSPM) is to efficiently discover profitable or useful sequential patterns in a large number of sequences. However, simply being aware of utility-eligible patterns is insufficient for making predictions. To compensate for this deficiency, high-utility sequential rule mining (HUSRM) is designed to explore the confidence or probability of predicting the occurrence of consequence sequential patterns based on the appearance of premise sequential patterns. It has numerous applications, such as product recommendation and weather prediction. However, the existing algorithm, known as HUSRM, is limited to extracting all eligible rules while neglecting the correlation between the generated sequential rules. To address this issue, we propose a novel algorithm called correlated high-utility sequential rule miner (CoUSR) to integrate the concept of correlation into HUSRM. The proposed algorithm requires not only that each rule be correlated but also that the patterns in the antecedent and consequent of the high-utility sequential rule be correlated. The algorithm adopts a utility-list structure to avoid multiple database scans. Additionally, several pruning strategies are used to improve the algorithm's efficiency and performance. Based on several real-world datasets, subsequent experiments demonstrated that CoUSR is effective and efficient in terms of operation time and memory consumption.	['Chien-Ming Chen', 'Wensheng Gan', 'Lili Chen']	2022-10-27	null	null	null	null	['product-recommendation', 'sequential-pattern-mining']	['miscellaneous', 'natural-language-processing']	[ 4.53456640e-01 -1.65229216e-01 -5.82246184e-01 -2.84411103e-01 2.97476619e-01 -1.39986649e-01 1.45609900e-01 2.02593654e-01 1.28104007e-02 7.69249439e-01 -2.93738633e-01 -6.21523201e-01 -4.74040717e-01 -1.19461012e+00 -1.01908237e-01 -4.29574162e-01 -3.38341922e-01 2.36905918e-01 5.98909080e-01 -5.70401773e-02 2.68472344e-01 4.21336234e-01 -1.81518781e+00 5.86681902e-01 1.05854690e+00 1.23814046e+00 4.69261348e-01 1.23205110e-01 -2.81058043e-01 1.01018322e+00 -4.22381163e-01 -1.81368496e-02 2.96158195e-01 -7.07305551e-01 -4.80738759e-01 -6.14815168e-02 -7.48392940e-01 -8.41196179e-02 2.83366501e-01 8.10701728e-01 -2.05599487e-01 1.84260100e-01 1.68373108e-01 -1.28767347e+00 -8.10803100e-02 6.98282361e-01 -8.90657723e-01 2.85915732e-01 5.56617022e-01 -1.57370076e-01 1.16570151e+00 -6.41446412e-01 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1f06ad82-687f-4cf6-9fe9-15147038b0f2	human-in-the-loop-through-chain-of-thought	2306.07932	null	https://arxiv.org/abs/2306.07932v2	https://arxiv.org/pdf/2306.07932v2.pdf	Human-in-the-Loop through Chain-of-Thought	While the emergence of powerful language models along with Chain-of-thought prompting has made automation more and more omnipresent, it sometimes demonstrates its weakness in long-term or multi-step logical reasoning. For example, users don't always get desirable answers for complex mathematical problems without human involvement. Against this background, we present the Manual Correction System (MCS) -- a human-in-the-loop system enhanced by Chain-of-Thought prompting, which explores how manual correction of sub-logics in rationales can improve LLM's reasoning performance. Moving one step forward, considering a system with human-in-the-loop involves more than having humans improve performance but also controlling the cost. Therefore, we post a Cost-utility Analysis Model for Human-in-the-Loop systems (CAMLOP) based on classical economics theory to analyze, quantify and balance the utility and the corresponding cost. We conduct experiments of MCS and CAMLOP with twelve datasets. A significant advantage w.r.t cost and utility proves its superiority over strong baselines.	['Wenjuan Han', 'Baobao Chang', 'Zefan Cai']	2023-06-10	null	null	null	null	['logical-reasoning']	['reasoning']	[-4.80602771e-01 5.85310400e-01 -2.99947798e-01 -5.25375247e-01 -4.37789023e-01 -5.11110008e-01 8.65115285e-01 5.26631117e-01 -5.75143158e-01 5.31080246e-01 3.09229940e-01 -9.70641673e-01 -4.83312994e-01 -8.24792147e-01 -5.06942570e-01 1.83878154e-01 2.58285463e-01 6.27097070e-01 -8.25085938e-02 -5.37868083e-01 3.62846076e-01 5.73249646e-02 -1.06950116e+00 3.28880727e-01 9.48953927e-01 4.84747678e-01 -7.38753304e-02 5.43001592e-01 -1.63941637e-01 1.74805319e+00 -7.19321817e-02 -1.00457835e+00 3.08087140e-01 -2.71892637e-01 -1.10816669e+00 -4.56047118e-01 1.53837949e-01 -2.63035625e-01 2.71094348e-02 1.01981819e+00 2.72933036e-01 1.02642439e-01 7.07932591e-01 -1.50337029e+00 -1.02825415e+00 1.16026294e+00 -5.02617657e-01 5.18616065e-02 7.28414297e-01 4.53804851e-01 1.44738412e+00 -7.07857490e-01 7.53915548e-01 1.60496593e+00 5.62833965e-01 2.04192266e-01 -1.28224158e+00 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3.54227930e-01 1.73796445e-01 -7.03082919e-01 -2.74719387e-01 -9.40187797e-02]	[9.584860801696777, 7.372693061828613]
c52986c9-6fea-4a2b-ab17-d1cf078b93c2	bandana-using-non-volatile-memory-for-storing	1811.05922	null	http://arxiv.org/abs/1811.05922v2	http://arxiv.org/pdf/1811.05922v2.pdf	Bandana: Using Non-volatile Memory for Storing Deep Learning Models	Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the required memory. We present Bandana, a storage system that reduces the DRAM footprint of embeddings, by using Non-volatile Memory (NVM) as the primary storage medium, with a small amount of DRAM as cache. The main challenge in storing embeddings on NVM is its limited read bandwidth compared to DRAM. Bandana uses two primary techniques to address this limitation: first, it stores embedding vectors that are likely to be read together in the same physical location, using hypergraph partitioning, and second, it decides the number of embedding vectors to cache in DRAM by simulating dozens of small caches. These techniques allow Bandana to increase the effective read bandwidth of NVM by 2-3x and thereby significantly reduce the total cost of ownership.	['Assaf Eisenman', 'Misha Smelyanskiy', 'Maxim Naumov', 'Darryl Gardner', 'Sergey Pupyrev', 'Asaf Cidon', 'Sachin Katti', 'Kim Hazelwood']	2018-11-14	null	null	null	null	['hypergraph-partitioning']	['graphs']	[-8.01190197e-01 -2.43075401e-01 -6.76052511e-01 8.55438858e-02 -4.56378400e-01 -5.45604765e-01 5.74422300e-01 3.78773451e-01 -4.78357762e-01 4.64988738e-01 2.97658324e-01 -5.34120440e-01 1.76604822e-01 -1.56487000e+00 -9.84227121e-01 -6.87027097e-01 -7.54234269e-02 7.48226285e-01 6.36018276e-01 1.13985039e-01 1.10067762e-01 4.56208289e-01 -1.73845327e+00 2.12076560e-01 3.13645959e-01 1.09171653e+00 -1.86199814e-01 3.79130095e-01 -4.51010734e-01 8.87096286e-01 -5.60046792e-01 1.62416190e-01 8.20984542e-02 1.71307564e-01 -6.66563749e-01 -9.48125005e-01 3.83508921e-01 -9.19775665e-01 -9.95784819e-01 4.97264177e-01 3.60653669e-01 4.84972447e-01 8.06536436e-01 -1.22132087e+00 -8.65554988e-01 9.93589818e-01 -3.39492351e-01 2.05472171e-01 -3.75561804e-01 3.56354192e-02 8.51563931e-01 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df026bcc-0b59-4701-be99-baa2977364ea	learning-hypergraph-regularized-attribute	1503.05782	null	http://arxiv.org/abs/1503.05782v1	http://arxiv.org/pdf/1503.05782v1.pdf	Learning Hypergraph-regularized Attribute Predictors	We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.	['Mohamed Elhoseiny', 'Sheng Huang', 'Dan Yang', 'Ahmed Elgammal']	2015-03-19	learning-hypergraph-regularized-attribute-1	http://openaccess.thecvf.com/content_cvpr_2015/html/Huang_Learning_Hypergraph-Regularized_Attribute_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_Learning_Hypergraph-Regularized_Attribute_2015_CVPR_paper.pdf	cvpr-2015-6	['hypergraph-embedding']	['graphs']	[ 2.22568467e-01 5.60441792e-01 -7.40196049e-01 -8.92570138e-01 -6.60658777e-01 -2.79172719e-01 6.65831625e-01 2.99058378e-01 2.52087377e-02 6.39596283e-01 1.28570095e-01 5.17305639e-03 -6.23241961e-01 -1.38349354e+00 -4.52111483e-01 -5.96638978e-01 -2.06317440e-01 1.00417566e+00 -1.94473982e-01 -4.64255549e-02 1.44643998e-02 3.13767493e-01 -1.60996675e+00 2.33129755e-01 7.67713785e-01 1.04599655e+00 -4.11982417e-01 2.73027718e-01 -3.83410722e-01 6.05246663e-01 1.97395369e-01 -8.70119631e-01 4.32695299e-01 -2.57524908e-01 -1.09878170e+00 3.48106772e-01 3.35329413e-01 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ce746562-bbb2-41a5-8a86-783bde9faae8	pcfgs-can-do-better-inducing-probabilistic	2104.13727	null	https://arxiv.org/abs/2104.13727v1	https://arxiv.org/pdf/2104.13727v1.pdf	PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols	Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols. In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. We further use neural parameterization for the new form to improve unsupervised parsing performance. We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols. Our code: https://github.com/sustcsonglin/TN-PCFG	['Kewei Tu', 'Yanpeng Zhao', 'Songlin Yang']	2021-04-28	null	https://aclanthology.org/2021.naacl-main.117	https://aclanthology.org/2021.naacl-main.117.pdf	naacl-2021-4	['constituency-grammar-induction']	['natural-language-processing']	[ 1.49069861e-01 1.22206420e-01 -6.95882365e-02 -3.94505918e-01 -9.99399006e-01 -8.42360437e-01 2.08387524e-01 9.21821035e-03 -2.92461872e-01 6.13450229e-01 1.46131337e-01 -1.14420521e+00 1.49198413e-01 -7.70533442e-01 -7.96343505e-01 -5.36510646e-01 -3.71583849e-01 5.38097680e-01 3.31960827e-01 -2.71540638e-02 1.85450371e-02 3.22396398e-01 -1.19219375e+00 6.78252578e-02 8.36789429e-01 1.85632184e-01 3.02134335e-01 9.46301460e-01 -4.64898676e-01 3.96517068e-01 -3.42419326e-01 -3.59103441e-01 2.02047110e-01 -2.05956638e-01 -9.18221951e-01 -1.33126631e-01 4.27679271e-01 -3.39639813e-01 1.96131747e-02 9.89039779e-01 1.67104989e-01 1.42231107e-01 2.25047603e-01 -7.91082382e-01 -4.64460582e-01 1.21347725e+00 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8a622bec-afee-4859-b118-df8f4d5a352c	semi-supervised-text-classification-with	null	null	https://aclanthology.org/2021.acl-long.391	https://aclanthology.org/2021.acl-long.391.pdf	Semi-Supervised Text Classification with Balanced Deep Representation Distributions	Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts. Naturally, their performance is largely affected by the accuracy of pseudo-labels for unlabeled texts. Unfortunately, they often suffer from low accuracy because of the margin bias problem caused by the large difference between representation distributions of labels in SSTC. To alleviate this problem, we apply the angular margin loss, and perform Gaussian linear transformation to achieve balanced label angle variances, i.e., the variance of label angles of texts within the same label. More accuracy of predicted pseudo-labels can be achieved by constraining all label angle variances balanced, where they are estimated over both labeled and pseudo-labeled texts during self-training loops. With this insight, we propose a novel SSTC method, namely Semi-Supervised Text Classification with Balanced Deep representation Distributions (S2TC-BDD). To evaluate S2TC-BDD, we compare it against the state-of-the-art SSTC methods. Empirical results demonstrate the effectiveness of S2TC-BDD, especially when the labeled texts are scarce.	['Jihong Ouyang', 'Ximing Li', 'Changchun Li']	2021-08-01	null	null	null	acl-2021-5	['semi-supervised-text-classification-1']	['natural-language-processing']	[ 1.76483393e-02 1.28477633e-01 -3.80617857e-01 -8.92388821e-01 -7.94442058e-01 -6.98919892e-01 6.19937122e-01 -5.84282391e-02 -3.27445894e-01 4.45337474e-01 1.83858320e-01 -1.49151057e-01 2.87205964e-01 -4.63126510e-01 -5.69001853e-01 -9.27739084e-01 6.16075695e-01 7.92285919e-01 -1.94609299e-01 -5.04159518e-02 6.15300015e-02 1.58388319e-03 -1.30573189e+00 2.52480507e-01 1.19754004e+00 1.04149437e+00 -4.16677855e-02 1.62140295e-01 -4.77149040e-01 8.21102560e-01 -8.04828048e-01 -4.21024829e-01 3.09698105e-01 -4.88853127e-01 -6.88135624e-01 4.47072417e-01 3.47697675e-01 -1.68075815e-01 -2.98100322e-01 1.23402297e+00 3.57082754e-01 7.80769289e-02 1.12442327e+00 -1.36883032e+00 -4.53503251e-01 9.63234127e-01 -1.03297627e+00 -5.47154129e-01 -3.03137656e-02 -2.14515805e-01 8.42851639e-01 -9.38321888e-01 2.19157755e-01 1.14835978e+00 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-1.54303515e+00 -8.40529442e-01 7.04157472e-01 8.98218825e-02 -7.46692955e-01 -7.32449353e-01 -1.12581290e-02]	[9.392217636108398, 3.8342771530151367]
bc96a2d1-5c21-4c18-9662-d02811d57bf1	light-field-intrinsics-with-a-deep-encoder	null	null	http://openaccess.thecvf.com/content_cvpr_2018/html/Alperovich_Light_Field_Intrinsics_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Alperovich_Light_Field_Intrinsics_CVPR_2018_paper.pdf	Light Field Intrinsics With a Deep Encoder-Decoder Network	We present a fully convolutional autoencoder for light fields, which jointly encodes stacks of horizontal and vertical epipolar plane images through a deep network of residual layers. The complex structure of the light field is thus reduced to a comparatively low-dimensional representation, which can be decoded in a variety of ways. The different pathways of upconvolution we currently support are for disparity estimation and separation of the lightfield into diffuse and specular intrinsic components. The key idea is that we can jointly perform unsupervised training for the autoencoder path of the network, and supervised training for the other decoders. This way, we find features which are both tailored to the respective tasks and generalize well to datasets for which only example light fields are available. We provide an extensive evaluation on synthetic light field data, and show that the network yields good results on previously unseen real world data captured by a Lytro Illum camera and various gantries.	['Michael Strecke', 'Bastian Goldluecke', 'Ole Johannsen', 'Anna Alperovich']	2018-06-01	null	null	null	cvpr-2018-6	['lightfield']	['computer-vision']	[ 2.43083566e-01 -1.48556590e-01 4.84499693e-01 -4.32711750e-01 -1.56289786e-01 -5.51809430e-01 6.91689610e-01 -7.19409406e-01 -3.59710842e-01 7.16805995e-01 2.07935676e-01 -1.85108576e-02 6.49693310e-02 -8.71388793e-01 -7.97453105e-01 -9.99404848e-01 1.90754220e-01 3.21918398e-01 1.43621758e-01 -3.20065796e-01 2.04672918e-01 6.68398499e-01 -1.84933555e+00 2.95802921e-01 2.80495912e-01 8.26817453e-01 2.65992075e-01 1.05134416e+00 2.65984803e-01 1.05720747e+00 -2.99672455e-01 -4.16827142e-01 6.37373507e-01 -2.19374016e-01 -6.10755205e-01 3.80499095e-01 8.61169577e-01 -8.30569029e-01 -7.52494752e-01 9.97001469e-01 3.53601247e-01 -8.05128440e-02 7.08784640e-01 -7.63110459e-01 -4.40807313e-01 -4.90881652e-02 -3.50690514e-01 2.29407221e-01 1.85300902e-01 2.82653838e-01 9.38010633e-01 -8.38776112e-01 8.55060756e-01 9.09879327e-01 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-1.22777820e+00 -1.31648839e-01 -5.10906637e-01 -6.96212173e-01 4.12133694e-01 1.08034265e+00 -5.34381449e-01 -6.25285208e-01 4.61237013e-01]	[9.551238059997559, -2.7825684547424316]
47ff6134-6d79-4eef-80ae-c5bd2341eb3d	aerorit-a-new-scene-for-hyperspectral-image	1912.08178	null	https://arxiv.org/abs/1912.08178v3	https://arxiv.org/pdf/1912.08178v3.pdf	AeroRIT: A New Scene for Hyperspectral Image Analysis	We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various sub-categories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the performance of three popular architectures - SegNet, U-Net, and Res-U-Net, for scene understanding and object identification via the task of dense semantic segmentation to establish a benchmark for the scene. To further strengthen the network, we add squeeze and excitation blocks for better channel interactions and use self-supervised learning for better encoder initialization. Aerial hyperspectral image analysis has been restricted to small datasets with limited train/test splits capabilities and we believe that AeroRIT will help advance the research in the field with a more complex object distribution to perform well on. The full dataset, with flight lines in radiance and reflectance domain, is available for download at https://github.com/aneesh3108/AeroRIT. This dataset is the first step towards developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks like vehicle tracking and occlusion handling.	['Matthew J. Hoffman', 'Nilay Mokashi', 'Emmett Ientilucci', 'Christopher Kanan', 'Aneesh Rangnekar']	2019-12-17	null	null	null	null	['occlusion-handling']	['computer-vision']	[ 7.48925745e-01 -3.65390748e-01 2.83252671e-02 -4.05767977e-01 -6.17814481e-01 -8.23072255e-01 1.30135909e-01 -1.82425231e-01 -5.10496758e-02 5.97679734e-01 -4.18260634e-01 -6.82032824e-01 -4.19482023e-01 -1.16307640e+00 -6.96003199e-01 -7.06488371e-01 -7.11152107e-02 3.40942234e-01 -1.11856513e-01 -4.57370609e-01 -2.98181742e-01 6.27829134e-01 -1.67100191e+00 4.97999817e-01 8.57737303e-01 1.21041381e+00 3.54934722e-01 6.87786639e-01 4.14024472e-01 3.71498317e-01 -2.18396872e-01 4.26298380e-02 9.49884653e-01 -1.62398443e-01 -9.31058228e-01 2.34864771e-01 9.21695292e-01 -4.95928764e-01 -2.71643311e-01 1.25771189e+00 6.17060184e-01 9.73946080e-02 3.49347383e-01 -1.08318400e+00 -5.37170827e-01 7.86154628e-01 -6.21453285e-01 1.74233437e-01 -3.16339612e-01 4.21385109e-01 1.02499282e+00 -4.97558177e-01 3.03605139e-01 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-1.21762729e+00 -3.20939541e-01 -1.00859749e+00 -9.48172688e-01 -3.58977318e-02 3.49534392e-01 -2.89232135e-01 -3.42737824e-01 3.73164415e-02]	[9.584031105041504, -1.4867440462112427]
6705feab-408b-456d-a854-44389ef685af	how-do-we-use-our-hands-discovering-a-diverse	null	null	http://openaccess.thecvf.com/content_cvpr_2015/html/Huang_How_Do_We_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Huang_How_Do_We_2015_CVPR_paper.pdf	How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps	Our aim is to show how state-of-the-art computer vision techniques can be used to advance prehensile analysis (i.e., understanding the functionality of human hands). Prehensile analysis is a broad field of multi-disciplinary interest, where researchers painstakingly manually analyze hours of hand-object interaction videos to understand the mechanics of hand manipulation. In this work, we present promising empirical results indicating that wearable cameras and unsupervised clustering techniques can be used to automatically discover common modes of human hand use. In particular, we use a first-person point-of-view camera to record common manipulation tasks and leverage its strengths for reliably observing human hand use. To learn a diverse set of hand-object interactions, we propose a fast online clustering algorithm based on the Determinantal Point Process (DPP). Furthermore, we develop a hierarchical extension to the DPP clustering algorithm and show that it can be used to discover appearance-based grasp taxonomies. Using a purely data-driven approach, our proposed algorithm is able to obtain hand grasp taxonomies that roughly correspond to the classic Cutkosky grasp taxonomy. We validate our approach on over 10 hours of first-person point-of-view videos in both choreographed and real-life scenarios.	['Wei-Chiu Ma', 'De-An Huang', 'Minghuang Ma', 'Kris M. Kitani']	2015-06-01	null	null	null	cvpr-2015-6	['online-clustering']	['computer-vision']	[-2.12847721e-02 -5.72056353e-01 -2.84990579e-01 1.74074143e-01 -1.81607187e-01 -8.58318567e-01 2.86380500e-01 -2.80185044e-01 -5.39371669e-02 7.35987499e-02 1.84291244e-01 7.08785802e-02 -7.60231912e-01 -1.66480854e-01 -7.26710558e-01 -4.68059808e-01 -2.49204710e-01 6.83509111e-01 5.96163385e-02 -1.39501125e-01 5.52733839e-01 7.48241663e-01 -1.95298767e+00 3.29804808e-01 4.27794993e-01 4.65572357e-01 4.71338272e-01 7.62638927e-01 4.31932539e-01 4.67607766e-01 -1.42713144e-01 -1.85414463e-01 6.40972674e-01 -3.91574986e-02 -1.00190461e+00 5.73706985e-01 6.31919265e-01 -4.40528542e-01 -6.27344847e-01 6.37178957e-01 3.53683352e-01 3.79574150e-01 6.13827884e-01 -1.24017894e+00 -2.30200648e-01 4.65823978e-01 -6.03782654e-01 1.17448950e-03 8.51925731e-01 3.67266715e-01 1.09276283e+00 -7.03528166e-01 8.68835330e-01 1.23899627e+00 4.34489727e-01 4.09852356e-01 -1.39559245e+00 -3.51695180e-01 3.85444909e-02 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19a06fd4-6332-4ec0-923a-e35cc234cce6	semi-supervised-learning-for-hyperspectral	2306.10955	null	https://arxiv.org/abs/2306.10955v1	https://arxiv.org/pdf/2306.10955v1.pdf	Semi-Supervised Learning for hyperspectral images by non parametrically predicting view assignment	Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images. However, these images suffer from the problem of curse of dimensionality and usually require a large number samples for tasks such as classification, especially in supervised setting. Recently, to effectively train the deep learning models with minimal labelled samples, the unlabeled samples are also being leveraged in self-supervised and semi-supervised setting. In this work, we leverage the idea of semi-supervised learning to assist the discriminative self-supervised pretraining of the models. The proposed method takes different augmented views of the unlabeled samples as input and assigns them the same pseudo-label corresponding to the labelled sample from the downstream task. We train our model on two HSI datasets, namely Houston dataset (from data fusion contest, 2013) and Pavia university dataset, and show that the proposed approach performs better than self-supervised approach and supervised training.	['Xiao Xiang Zhu', 'Biplab Banerjee', 'Conrad M Albrecht', 'Yi Wang', 'Nassim Ait Ali Braham', 'Shivam Pande']	2023-06-19	null	null	null	null	['pseudo-label']	['miscellaneous']	[ 7.94449270e-01 -1.19307525e-02 -1.85888201e-01 -7.90781796e-01 -9.34893310e-01 -5.04774094e-01 6.02600038e-01 1.23928366e-02 -2.12218642e-01 7.66566634e-01 1.43096104e-01 7.90799223e-03 -1.63982958e-01 -6.55220270e-01 -6.08735621e-01 -1.06909561e+00 2.23514661e-01 3.38863820e-01 -2.60559827e-01 1.94441736e-01 -3.48515585e-02 3.29837203e-01 -1.67252529e+00 3.10309201e-01 1.16743195e+00 1.32817388e+00 3.45125318e-01 3.30661863e-01 3.46475281e-02 8.97789359e-01 -8.70081112e-02 2.35293716e-01 6.65582478e-01 -4.99166548e-01 -6.44058406e-01 7.29290843e-01 5.79711854e-01 -2.30175644e-01 -3.72958928e-01 1.27948332e+00 4.69877243e-01 2.16559723e-01 7.75747180e-01 -1.35812783e+00 -5.23254037e-01 5.06267726e-01 -8.96136999e-01 -1.16973609e-01 -4.02159959e-01 7.31696859e-02 9.77140665e-01 -8.79049063e-01 2.58202165e-01 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c274505f-dca7-4b5f-a88f-2408cbbfd796	mteb-massive-text-embedding-benchmark	2210.07316	null	https://arxiv.org/abs/2210.07316v3	https://arxiv.org/pdf/2210.07316v3.pdf	MTEB: Massive Text Embedding Benchmark	Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb.	['Nils Reimers', 'Loïc Magne', 'Nouamane Tazi', 'Niklas Muennighoff']	2022-10-13	null	null	null	null	['text-clustering']	['natural-language-processing']	[-9.34326500e-02 -1.02466226e-01 -3.41972291e-01 -2.29817241e-01 -9.89857674e-01 -5.93761563e-01 1.08541858e+00 5.78653038e-01 -7.47331262e-01 2.04874083e-01 6.31907165e-01 -3.72443557e-01 -1.76653489e-01 -4.28966433e-01 -1.11557305e-01 -3.31358194e-01 7.21338391e-03 9.31764066e-01 3.21465820e-01 -4.48753119e-01 4.61157382e-01 -1.32698612e-02 -1.42348170e+00 2.82749861e-01 4.75617021e-01 6.52375996e-01 9.68884826e-02 6.77288115e-01 -5.04825115e-01 3.97873551e-01 -4.42005247e-01 -8.31487775e-01 5.56864552e-02 -1.22518092e-01 -1.21692967e+00 -5.03985643e-01 6.14070892e-01 -1.46081537e-01 -6.45021260e-01 8.14976752e-01 8.89820635e-01 5.97138815e-02 8.64439785e-01 -1.40276361e+00 -1.25476718e+00 6.98620200e-01 -3.47853929e-01 4.15014148e-01 1.95217863e-01 -1.22533657e-01 1.93338168e+00 -1.25402892e+00 6.60401165e-01 1.28793323e+00 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944cba14-8ce7-4970-afa4-0ffc61ab3bed	predicate-argument-based-bi-encoder-for-1	null	null	https://aclanthology.org/2022.acl-long.382	https://aclanthology.org/2022.acl-long.382.pdf	Predicate-Argument Based Bi-Encoder for Paraphrase Identification	Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings. While cross-encoders have achieved high performances across several benchmarks, bi-encoders such as SBERT have been widely applied to sentence pair tasks. They exhibit substantially lower computation complexity and are better suited to symmetric tasks. In this work, we adopt a bi-encoder approach to the paraphrase identification task, and investigate the impact of explicitly incorporating predicate-argument information into SBERT through weighted aggregation. Experiments on six paraphrase identification datasets demonstrate that, with a minimal increase in parameters, the proposed model is able to outperform SBERT/SRoBERTa significantly. Further, ablation studies reveal that the predicate-argument based component plays a significant role in the performance gain.	['Yekun Chai', 'Julie Weeds', 'David Weir', 'Qiwei Peng']	null	null	null	null	acl-2022-5	['paraphrase-identification']	['natural-language-processing']	[ 3.65579933e-01 -6.03657514e-02 -3.89795929e-01 -5.18850684e-01 -9.34726894e-01 -4.73897487e-01 5.89305758e-01 4.80067730e-01 -3.30289960e-01 4.92542952e-01 5.74565768e-01 -3.16231877e-01 -5.64580224e-02 -5.26877046e-01 -7.48973250e-01 -2.99541473e-01 3.96411270e-01 6.23901673e-02 -3.51124108e-02 -3.40579361e-01 3.19567025e-01 3.90653312e-02 -1.32649171e+00 5.23911595e-01 8.85653973e-01 9.93798494e-01 1.74425598e-02 2.41763622e-01 -1.52858779e-01 1.11716509e+00 -5.32394886e-01 -9.47359324e-01 2.22705305e-01 -5.35563886e-01 -1.11699641e+00 -2.44959071e-01 6.24809146e-01 -2.91105181e-01 -3.73299241e-01 9.69895601e-01 4.39015508e-01 -1.84775308e-01 3.86561364e-01 -1.11703587e+00 -8.42008352e-01 8.55527520e-01 -6.03370249e-01 4.62775886e-01 7.81069636e-01 -1.93903163e-01 1.70186913e+00 -1.01328862e+00 3.14263731e-01 1.09277034e+00 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914f5573-5706-4f54-b06c-d12da5c6e7c0	fourier-based-video-prediction-through	2110.05881	null	https://arxiv.org/abs/2110.05881v1	https://arxiv.org/pdf/2110.05881v1.pdf	Fourier-based Video Prediction through Relational Object Motion	The ability to predict future outcomes conditioned on observed video frames is crucial for intelligent decision-making in autonomous systems. Recently, deep recurrent architectures have been applied to the task of video prediction. However, this often results in blurry predictions and requires tedious training on large datasets. Here, we explore a different approach by (1) using frequency-domain approaches for video prediction and (2) explicitly inferring object-motion relationships in the observed scene. The resulting predictions are consistent with the observed dynamics in a scene and do not suffer from blur.	['Sven Behnke', 'Malte Mosbach']	2021-10-12	null	null	null	null	['video-prediction']	['computer-vision']	[ 4.53151762e-01 4.80462089e-02 -6.40353486e-02 -3.91497463e-01 -1.25888288e-01 -3.35096300e-01 8.85687649e-01 -3.68932813e-01 -1.66748077e-01 8.31322014e-01 3.82734478e-01 -4.28670980e-02 8.33449438e-02 -4.15267259e-01 -6.78076565e-01 -7.22248018e-01 -1.70482531e-01 2.06891112e-02 3.99471313e-01 1.55827224e-01 2.87117392e-01 3.81330341e-01 -1.64492738e+00 4.33235377e-01 6.12735689e-01 1.04789793e+00 6.18658602e-01 1.21160114e+00 3.33020806e-01 1.60680044e+00 -3.13830763e-01 -3.42389226e-01 1.05375074e-01 -6.64023101e-01 -7.60473132e-01 3.40640157e-01 4.24056947e-01 -6.45990968e-01 -1.04793692e+00 1.06901515e+00 -1.55724570e-01 3.62024754e-01 5.30111194e-01 -9.19662893e-01 -6.12479150e-01 2.48431470e-02 -1.42064273e-01 3.39931875e-01 4.63727087e-01 4.07812178e-01 7.79994130e-01 -7.39222944e-01 6.72108293e-01 1.15215492e+00 5.24796724e-01 7.55039990e-01 -1.32933021e+00 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dc1eed65-0aa4-4df9-b355-2b90b493716c	towards-domain-agnostic-depth-completion	2207.14466	null	https://arxiv.org/abs/2207.14466v1	https://arxiv.org/pdf/2207.14466v1.pdf	Towards Domain-agnostic Depth Completion	Existing depth completion methods are often targeted at a specific sparse depth type, and generalize poorly across task domains. We present a method to complete sparse/semi-dense, noisy, and potentially low-resolution depth maps obtained by various range sensors, including those in modern mobile phones, or by multi-view reconstruction algorithms. Our method leverages a data driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to our model. We propose an effective training scheme where we simulate various sparsity patterns in typical task domains. In addition, we design two new benchmarks to evaluate the generalizability and the robustness of depth completion methods. Our simple method shows superior cross-domain generalization ability against state-of-the-art depth completion methods, introducing a practical solution to high quality depth capture on a mobile device. Code is available at: https://github.com/YvanYin/FillDepth.	['Chunhua Shen', 'Simon Chen', 'Simon Niklaus', 'Oliver Wang', 'Jianming Zhang', 'Wei Yin']	2022-07-29	null	null	null	null	['depth-completion']	['computer-vision']	[ 4.40442502e-01 6.72814772e-02 -2.31750190e-01 -5.02272725e-01 -1.10067630e+00 -3.11143339e-01 3.77891839e-01 -5.03605843e-01 -2.61207104e-01 5.83959758e-01 6.56451702e-01 1.80442214e-01 1.88869208e-01 -8.00043702e-01 -8.77188623e-01 -4.42976862e-01 3.05847257e-01 5.30549288e-01 1.83788210e-01 9.06931460e-02 1.80791736e-01 2.19938472e-01 -1.54868269e+00 4.12070930e-01 6.63345397e-01 1.19874156e+00 6.55204237e-01 7.46295214e-01 1.50522083e-01 5.97383797e-01 -5.46948574e-02 -1.56799778e-01 5.27653158e-01 1.06573015e-01 -6.37564361e-01 1.78141251e-01 6.17997527e-01 -1.04441488e+00 -7.34531462e-01 7.53856957e-01 6.68813944e-01 2.11157482e-02 3.82520080e-01 -8.16900611e-01 -2.96814740e-01 5.91488853e-02 -6.03684485e-01 3.16875800e-02 8.73276114e-01 1.66205704e-01 5.93216896e-01 -1.01804018e+00 7.83286273e-01 1.07927275e+00 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dbb99c71-ccb7-4c30-b384-0b3cda6b6ed1	robust-table-detection-and-structure	2203.09056	null	https://arxiv.org/abs/2203.09056v2	https://arxiv.org/pdf/2203.09056v2.pdf	Robust Table Detection and Structure Recognition from Heterogeneous Document Images	We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from heterogeneous document images. For table detection, we propose to use CornerNet as a new region proposal network to generate higher quality table proposals for Faster R-CNN, which has significantly improved the localization accuracy of Faster R-CNN for table detection. Consequently, our table detection approach achieves state-of-the-art performance on three public table detection benchmarks, namely cTDaR TrackA, PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone network. Furthermore, we propose a new split-and-merge based table structure recognition approach, in which a novel spatial CNN based separation line prediction module is proposed to split each detected table into a grid of cells, and a Grid CNN based cell merging module is applied to recover the spanning cells. As the spatial CNN module can effectively propagate contextual information across the whole table image, our table structure recognizer can robustly recognize tables with large blank spaces and geometrically distorted (even curved) tables. Thanks to these two techniques, our table structure recognition approach achieves state-of-the-art performance on three public benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we have further demonstrated the advantages of our approach in recognizing tables with complex structures, large blank spaces, as well as geometrically distorted or even curved shapes on a more challenging in-house dataset.	['Qiang Huo', 'Lei Sun', 'WeiHong Lin', 'Chixiang Ma']	2022-03-17	null	null	null	null	['table-recognition', 'table-detection']	['computer-vision', 'miscellaneous']	[-1.17304854e-01 -1.40808448e-01 -2.22920299e-01 7.05186203e-02 -1.11109841e+00 -9.77773905e-01 3.81068647e-01 4.81449276e-01 -4.43505570e-02 4.57282096e-01 1.23148844e-01 -3.53341460e-01 2.29115531e-01 -1.20782280e+00 -9.70679045e-01 -3.81518364e-01 4.37325351e-02 8.07736218e-01 4.22337055e-01 -4.58378255e-01 2.70860791e-01 8.13418090e-01 -1.26051164e+00 7.84274757e-01 6.51696563e-01 1.27323973e+00 -1.66602910e-01 6.70140147e-01 -4.38628227e-01 1.03416383e+00 -7.56965637e-01 -4.78440166e-01 3.58948231e-01 2.86521297e-02 -7.73310244e-01 1.47610471e-01 5.99976122e-01 -2.94089317e-01 -6.18074536e-01 6.51795745e-01 3.94485921e-01 -1.50296703e-01 3.88807833e-01 -6.85134053e-01 -5.65588474e-01 1.00248861e+00 -9.79334295e-01 4.71170619e-02 5.55744231e-01 -3.72466221e-02 1.10686314e+00 -1.05443847e+00 9.28497791e-01 1.30908513e+00 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5a4ccf8d-f45f-47e2-802a-20a88c2ba989	indian-commercial-truck-license-plate	2211.13194	null	https://arxiv.org/abs/2211.13194v2	https://arxiv.org/pdf/2211.13194v2.pdf	Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation	Detection and recognition of a licence plate is important when automating weighbridge services. While many large databases are available for Latin and Chinese alphanumeric license plates, data for Indian License Plates is inadequate. In particular, databases of Indian commercial truck license plates are inadequate, despite the fact that commercial vehicle license plate recognition plays a profound role in terms of logistics management and weighbridge automation. Moreover, models to recognise license plates are not effectively able to generalise to such data due to its challenging nature, and due to the abundant frequency of handwritten license plates, leading to the usage of diverse font styles. Thus, a database and effective models to recognise and detect such license plates are crucial. This paper provides a database on commercial truck license plates, and using state-of-the-art models in real-time object Detection: You Only Look Once Version 7, and SceneText Recognition: Permuted Autoregressive Sequence Models, our method outperforms the other cited references where the maximum accuracy obtained was less than 90%, while we have achieved 95.82% accuracy in our algorithm implementation on the presented challenging license plate dataset. Index Terms- Automatic License Plate Recognition, character recognition, license plate detection, vision transformer.	['Keyur D. Joshi', 'Siddharth Agrawal']	2022-11-23	null	null	null	null	['license-plate-recognition', 'real-time-object-detection', 'license-plate-detection']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.03297636e-01 -8.72542858e-01 1.36886403e-01 1.13065653e-02 -7.83933103e-01 -1.00752497e+00 5.76531768e-01 -4.78229284e-01 -5.45620680e-01 6.72650933e-01 -3.16550732e-01 -4.10921127e-01 5.01876473e-02 -5.94027400e-01 -4.22917098e-01 -6.92173183e-01 4.74875897e-01 7.02958405e-01 7.32085764e-01 -3.16409588e-01 9.98045504e-01 9.31788325e-01 -1.48553979e+00 1.13246128e-01 6.04637146e-01 8.12003911e-01 2.41578415e-01 8.57415497e-01 -2.64009416e-01 6.30777836e-01 -6.53675020e-01 -5.88867307e-01 5.59777558e-01 8.22868664e-03 -2.67608259e-02 5.80055475e-01 1.69848591e-01 -3.43655080e-01 -5.15240967e-01 9.55332518e-01 3.32798481e-01 -1.80268586e-01 1.32274115e+00 -9.65159416e-01 -7.22150087e-01 -3.70558649e-01 -5.27734816e-01 2.07924172e-01 2.34208032e-01 1.85603753e-01 3.22285771e-01 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79e082a9-53fa-4f72-8b4e-ab4c378a204e	a-metric-to-compare-the-anatomy-variation	2302.11929	null	https://arxiv.org/abs/2302.11929v1	https://arxiv.org/pdf/2302.11929v1.pdf	A metric to compare the anatomy variation between image time series	Biological processes like growth, aging, and disease progression are generally studied with follow-up scans taken at different time points, i.e., with image time series (TS) based analysis. Comparison between TS representing a biological process of two individuals/populations is of interest. A metric to quantify the difference between TS is desirable for such a comparison. The two TS represent the evolution of two different subject/population average anatomies through two paths. A method to untangle and quantify the path and inter-subject anatomy(shape) difference between the TS is presented in this paper. The proposed metric is a generalized version of Fr\'echet distance designed to compare curves. The proposed method is evaluated with simulated and adult and fetal neuro templates. Results show that the metric is able to separate and quantify the path and shape differences between TS.	['Jayanthi Sivaswamy', 'Alphin J Thottupattu']	2023-02-23	null	null	null	null	['anatomy']	['miscellaneous']	[ 4.84147072e-02 -9.76890251e-02 2.50061899e-01 -1.45386219e-01 -1.42828315e-01 -3.19268703e-01 6.26218438e-01 6.85225904e-01 -5.85562170e-01 7.10934699e-01 -1.18197955e-01 1.47171319e-01 -4.01568234e-01 -6.16627395e-01 -2.10753769e-01 -7.52925754e-01 -5.77377617e-01 5.65012515e-01 4.35790420e-01 -7.92179778e-02 2.60876685e-01 5.97495258e-01 -1.48709774e+00 -3.56928468e-01 9.21554685e-01 7.30644524e-01 1.03359535e-01 5.45600355e-01 -1.97464079e-01 -2.70786881e-01 -6.45223618e-01 -1.82798952e-01 2.54488051e-01 -5.95180094e-01 -6.37675107e-01 -1.98847279e-02 5.07571518e-01 1.32791176e-01 2.40296260e-01 9.71766949e-01 5.36639273e-01 2.74257604e-02 9.54079688e-01 -1.14563465e+00 -1.84856519e-01 4.18231100e-01 -6.26448452e-01 6.17143273e-01 3.29217672e-01 3.49147283e-02 3.21295232e-01 -4.87410694e-01 7.36127079e-01 1.01260805e+00 6.94876015e-01 2.95253992e-01 -1.35071146e+00 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a9e09e7b-e45b-4b39-8b21-96572a4edc1d	deepfake-adapter-dual-level-adapter-for	2306.00863	null	https://arxiv.org/abs/2306.00863v1	https://arxiv.org/pdf/2306.00863v1.pdf	DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection	Existing deepfake detection methods fail to generalize well to unseen or degraded samples, which can be attributed to the over-fitting of low-level forgery patterns. Here we argue that high-level semantics are also indispensable recipes for generalizable forgery detection. Recently, large pre-trained Vision Transformers (ViTs) have shown promising generalization capability. In this paper, we propose the first parameter-efficient tuning approach for deepfake detection, namely DeepFake-Adapter, to effectively and efficiently adapt the generalizable high-level semantics from large pre-trained ViTs to aid deepfake detection. Given large pre-trained models but limited deepfake data, DeepFake-Adapter introduces lightweight yet dedicated dual-level adapter modules to a ViT while keeping the model backbone frozen. Specifically, to guide the adaptation process to be aware of both global and local forgery cues of deepfake data, 1) we not only insert Globally-aware Bottleneck Adapters in parallel to MLP layers of ViT, 2) but also actively cross-attend Locally-aware Spatial Adapters with features from ViT. Unlike existing deepfake detection methods merely focusing on low-level forgery patterns, the forgery detection process of our model can be regularized by generalizable high-level semantics from a pre-trained ViT and adapted by global and local low-level forgeries of deepfake data. Extensive experiments on several standard deepfake detection benchmarks validate the effectiveness of our approach. Notably, DeepFake-Adapter demonstrates a convincing advantage under cross-dataset and cross-manipulation settings. The source code is released at https://github.com/rshaojimmy/DeepFake-Adapter	['Ziwei Liu', 'Liqiang Nie', 'Tianxing Wu', 'Rui Shao']	2023-06-01	null	null	null	null	['deepfake-detection', 'face-swapping']	['computer-vision', 'computer-vision']	[ 1.14841312e-01 -2.71060675e-01 9.79241803e-02 -3.06116760e-01 -8.66817653e-01 -5.78900337e-01 4.20035064e-01 -3.09348971e-01 -2.74739027e-01 1.70029402e-01 2.97806740e-01 2.54230071e-02 5.00545949e-02 -6.38315260e-01 -1.05977166e+00 -6.68669581e-01 2.25568503e-01 -3.25740911e-02 3.87858570e-01 -1.84076801e-01 3.10911208e-01 4.90215659e-01 -1.59223425e+00 6.37391508e-01 8.22474360e-01 9.32286918e-01 5.05558908e-01 8.74260604e-01 2.44831324e-01 6.51970148e-01 -7.05079317e-01 -4.72522706e-01 2.69701421e-01 -3.19245875e-01 -8.27189326e-01 2.99764395e-01 8.47061515e-01 -8.17115903e-01 -6.32354677e-01 9.32937682e-01 4.43365186e-01 6.03589527e-02 2.83213288e-01 -1.03022814e+00 -9.64452624e-01 2.58651525e-01 -4.53057110e-01 3.96664590e-01 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43d97e6b-1875-4a49-9a4d-3225f5c5637c	maskgan-better-text-generation-via-filling-in	1801.07736	null	http://arxiv.org/abs/1801.07736v3	http://arxiv.org/pdf/1801.07736v3.pdf	MaskGAN: Better Text Generation via Filling in the______	Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.	['William Fedus', 'Ian Goodfellow', 'Andrew M. Dai']	2018-01-23	null	null	null	null	['multivariate-time-series-imputation']	['time-series']	[ 6.65812373e-01 6.13102913e-01 -3.22694704e-02 -8.65479931e-02 -1.33847368e+00 -6.10446095e-01 1.20324063e+00 -3.03171724e-01 -2.48067185e-01 1.32245696e+00 6.15354300e-01 -2.46514127e-01 6.06989861e-01 -9.58947957e-01 -1.01261902e+00 -6.38288617e-01 4.50934947e-01 9.10356045e-01 -4.99028862e-01 -1.50779307e-01 -7.09614903e-02 6.28032684e-02 -1.02646661e+00 2.30650127e-01 1.01728082e+00 2.36083969e-01 1.34357661e-01 1.22471154e+00 -2.12509125e-01 8.19735229e-01 -1.23092413e+00 -5.70290327e-01 6.59126490e-02 -1.27186275e+00 -7.01619864e-01 9.28693339e-02 3.16590786e-01 -4.76735771e-01 -2.07683071e-01 7.72135854e-01 5.44743299e-01 -1.54448837e-01 1.07822967e+00 -9.68236864e-01 -8.11881542e-01 8.70117724e-01 -3.05920780e-01 -1.67789549e-01 4.26597774e-01 4.84402746e-01 1.04196978e+00 -7.51457572e-01 7.73336351e-01 1.30252111e+00 3.33046854e-01 1.01489615e+00 -1.59457946e+00 -4.17847037e-01 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ab074322-5f8f-4d61-a1f8-2a946eb41ba2	universal-features-of-mountain-ridge-patterns	1804.03457	null	https://arxiv.org/abs/1804.03457v3	https://arxiv.org/pdf/1804.03457v3.pdf	Universal features of mountain ridge networks on Earth	Compared to the heavily studied surface drainage systems, the mountain ridge systems have been a subject of less attention even on the empirical level, despite the fact that their structure is richer. To reduce this deficiency, we analyze different mountain ranges by means of a network approach and grasp some essential features of the ridge branching structure. We also employ a fractal analysis as it is especially suitable for describing properties of rough objects and surfaces. As our approach differs from typical analyses that are carried out in geophysics, we believe that it can initialize a research direction that will allow to shed more light on the processes that are responsible for landscape formation and will contribute to the network theory by indicating a need for the construction of new models of the network growth as no existing model can properly describe the ridge formation. We also believe that certain features of our study can offer help in the cartographic generalization. Specifically, we study structure of the ridge networks based on the empirical elevation data collected by SRTM. We consider mountain ranges from different geological periods and geographical locations. For each mountain range, we construct a simple topographic network representation (the ridge junctions are nodes) and a ridge representation (the ridges are nodes and the junctions are edges) and calculate the parameters characterizing their topology. We observe that the topographic networks inherit the fractal structure of the mountain ranges but do not show any other complex features. In contrast, the ridge networks, while lacking the proper fractality, reveal the power-law degree distributions with the exponent $1.6\le \beta \le 1.7$. By taking into account the fact that the analyzed mountains differ in many properties, these values seem to be universal for the earthly mountainous terrain.	['Stanisław Drożdż', 'Paweł Zięba', 'Paweł Oświęcimka', 'Jarosław Kwapień', 'Rafał Rak']	2018-04-10	null	null	null	null	['geophysics']	['miscellaneous']	[ 3.06092985e-02 2.44183093e-01 -2.71087717e-02 -2.57965654e-01 3.30835372e-01 -3.54383707e-01 8.44139278e-01 4.23712283e-01 -2.77063161e-01 8.65399241e-01 8.34063441e-02 -5.74034214e-01 -8.98356259e-01 -1.53260052e+00 -3.78377169e-01 -7.50891745e-01 -7.10230291e-01 5.98561049e-01 4.88817155e-01 -8.59678566e-01 2.17819870e-01 7.95629621e-01 -1.67014074e+00 -2.88461447e-01 1.14817894e+00 5.29133976e-01 1.33677617e-01 2.15725631e-01 -2.06729367e-01 2.03929603e-01 -3.18060398e-01 -7.26742446e-02 5.08925095e-02 -4.37039196e-01 -7.63164997e-01 -1.93794891e-02 5.67023642e-03 1.85280144e-01 -2.38466904e-01 8.91209424e-01 -5.05439453e-02 3.33771184e-02 9.77301478e-01 -7.69045591e-01 -1.95139110e-01 5.97282529e-01 -5.14001131e-01 1.00724742e-01 1.37786388e-01 -2.13033140e-01 1.10013163e+00 -4.68031436e-01 8.45573604e-01 1.11116648e+00 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050fe7b7-5d6f-4a2b-b7fe-61973ea18215	simple-and-effective-knowledge-driven-query-1	2206.14264	null	https://arxiv.org/abs/2206.14264v1	https://arxiv.org/pdf/2206.14264v1.pdf	Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction	A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).	['Wei-Te Chen', 'Yandi Xia', 'Naoki Yoshinaga', 'Keiji Shinzato']	2022-06-28	simple-and-effective-knowledge-driven-query	https://aclanthology.org/2022.acl-short.25	https://aclanthology.org/2022.acl-short.25.pdf	acl-2022-5	['attribute-value-extraction']	['natural-language-processing']	[ 4.34781089e-02 1.06374502e-01 -5.59660852e-01 -7.67015815e-01 -1.52929103e+00 -9.69792724e-01 9.96584520e-02 4.47600663e-01 -5.64292431e-01 7.93379605e-01 -2.32712366e-02 -3.60529721e-01 -2.22435310e-01 -1.11398327e+00 -9.31430876e-01 -3.25498641e-01 1.66204851e-02 9.58690345e-01 1.71397135e-01 -3.62847626e-01 -8.58356152e-03 1.65181547e-01 -1.53402412e+00 6.14197910e-01 1.05144227e+00 1.26479673e+00 -8.46489370e-02 4.94891375e-01 -4.77183849e-01 6.17816031e-01 -6.94213808e-01 -1.08453774e+00 2.00110853e-01 -1.64571106e-01 -1.19670367e+00 -7.77640939e-02 2.96552926e-01 -2.29527086e-01 1.91308692e-01 9.54578161e-01 3.83125305e-01 1.59059197e-01 4.78989542e-01 -1.24421918e+00 -7.88524032e-01 8.12210917e-01 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6239d6fc-45fd-4981-b6cf-9c5190abd13f	we-used-neural-networks-to-detect-clickbaits	1612.01340	null	https://arxiv.org/abs/1612.01340v2	https://arxiv.org/pdf/1612.01340v2.pdf	We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!	Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user's curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.	['Tanmoy Chakraborty', 'Noseong Park', 'Ankesh Anand']	2016-12-05	null	null	null	null	['clickbait-detection']	['natural-language-processing']	[-2.72242069e-01 -2.17376292e-01 -8.03428173e-01 -2.26386741e-01 -1.07389414e+00 -6.59307659e-01 7.65479445e-01 5.73910058e-01 -4.14886206e-01 5.22652984e-01 2.87383676e-01 -5.60392201e-01 1.32167310e-01 -8.45806241e-01 -7.46656418e-01 9.26027745e-02 5.40620834e-02 1.57514751e-01 7.03945100e-01 -1.86885715e-01 7.97361910e-01 -6.19549341e-02 -1.27152669e+00 3.68439466e-01 8.50918055e-01 1.32349396e+00 -1.75611675e-01 8.29891622e-01 -4.48413014e-01 8.81735384e-01 -6.71198666e-01 -5.84735811e-01 2.23435549e-04 -8.38653818e-02 -5.73449314e-01 -2.81527787e-01 8.48961532e-01 -5.20493448e-01 -7.59304583e-01 1.12269652e+00 -1.82756335e-02 -1.67221755e-01 3.77329201e-01 -8.10025036e-01 -1.07792997e+00 5.55616081e-01 -5.06435096e-01 7.74087727e-01 2.48095438e-01 -8.67070705e-02 1.96062088e+00 -6.49435043e-01 5.89344978e-01 7.58200467e-01 7.58008957e-01 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ed95296e-b819-4814-91e2-1ca3592e24f1	linear-algebra-with-transformers-1	2112.01898	null	https://arxiv.org/abs/2112.01898v2	https://arxiv.org/pdf/2112.01898v2.pdf	Linear algebra with transformers	Transformers can learn to perform numerical computations from examples only. I study nine problems of linear algebra, from basic matrix operations to eigenvalue decomposition and inversion, and introduce and discuss four encoding schemes to represent real numbers. On all problems, transformers trained on sets of random matrices achieve high accuracies (over 90%). The models are robust to noise, and can generalize out of their training distribution. In particular, models trained to predict Laplace-distributed eigenvalues generalize to different classes of matrices: Wigner matrices or matrices with positive eigenvalues. The reverse is not true.	['François Charton']	2021-12-03	null	null	null	null	['automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'reasoning']	[ 1.87430978e-02 -4.24736924e-02 -2.32289642e-01 -2.90315777e-01 -7.57781386e-01 -7.54335046e-01 1.15117736e-01 -1.69558063e-01 -5.83820604e-02 9.21489000e-01 -4.63081375e-02 -8.81663620e-01 -3.30895334e-01 -9.09028292e-01 -6.02271736e-01 -6.08181894e-01 -9.52218533e-01 7.96040058e-01 -3.05404752e-01 -5.08905888e-01 1.65655464e-01 7.48991430e-01 -1.07170403e+00 4.09500390e-01 7.62888789e-01 1.17420363e+00 -5.94923437e-01 8.70591283e-01 5.44182397e-03 1.09070563e+00 -5.06072938e-01 -2.91086048e-01 5.35511076e-01 -1.10646799e-01 -9.05017436e-01 -5.46096787e-02 1.44215912e-01 -8.84283632e-02 -4.53680843e-01 1.20871878e+00 1.64973274e-01 -5.37193976e-02 1.18897212e+00 -1.67021298e+00 -8.87593925e-01 9.72329617e-01 -4.74522203e-01 5.05131744e-02 7.48118639e-01 -5.87792695e-02 1.33733320e+00 -1.30410743e+00 2.55273223e-01 1.26189435e+00 1.09081626e+00 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4f2ffae9-7934-462c-b593-8800a421edf0	norm-aware-embedding-for-efficient-person	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.pdf	Norm-Aware Embedding for Efficient Person Search	Person Search is a practically relevant task that aims to jointly solve Person Detection and Person Re-identification (re-ID). Specifically, it requires to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. One major challenge comes from the contradictory goals of the two sub-tasks, i.e., person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two sub-tasks in a joint person search model. To this end, We present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by mis-alignment. We outperform other one-step methods by a large margin and achieve comparable performance to two-step methods on both CUHK-SYSU and PRW. Also, Our method is easy to train and resource-friendly, running at 12 fps on a single GPU.	[' Bernt Schiele', ' Jian Yang', ' Shanshan Zhang', 'Di Chen']	2020-06-01	null	null	null	cvpr-2020-6	['person-search']	['computer-vision']	[-4.89340611e-02 -5.53802848e-01 1.49668410e-01 -2.53560126e-01 -7.98705339e-01 -5.78476250e-01 4.25168902e-01 3.09515502e-02 -8.28754783e-01 6.02664590e-01 1.85584873e-01 1.97362602e-01 6.13671765e-02 -5.88400781e-01 -3.34983140e-01 -6.32582128e-01 3.61958623e-01 5.17600775e-01 1.38875488e-02 5.41139916e-02 -3.07886917e-02 3.64610970e-01 -1.67325175e+00 -2.88871199e-01 7.70846009e-01 7.69940972e-01 1.71983615e-01 5.71277380e-01 4.56479818e-01 1.73691854e-01 -6.05241716e-01 -8.49570751e-01 3.77932757e-01 -2.83479422e-01 -6.58280611e-01 1.21444859e-01 9.65873182e-01 -4.11181122e-01 -5.12209475e-01 1.30554020e+00 9.99871612e-01 3.33491355e-01 4.74939883e-01 -1.24241483e+00 -6.83324218e-01 -1.24084838e-02 -9.11787868e-01 2.59870410e-01 4.85930085e-01 2.37135276e-01 9.16812837e-01 -8.88419569e-01 2.37452269e-01 1.36572039e+00 7.99609065e-01 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8aa68209-70f7-478a-a593-60cb8121522f	universal-speaker-recognition-encoders-for	2210.16231	null	https://arxiv.org/abs/2210.16231v1	https://arxiv.org/pdf/2210.16231v1.pdf	Universal speaker recognition encoders for different speech segments duration	Creating universal speaker encoders which are robust for different acoustic and speech duration conditions is a big challenge today. According to our observations systems trained on short speech segments are optimal for short phrase speaker verification and systems trained on long segments are superior for long segments verification. A system trained simultaneously on pooled short and long speech segments does not give optimal verification results and usually degrades both for short and long segments. This paper addresses the problem of creating universal speaker encoders for different speech segments duration. We describe our simple recipe for training universal speaker encoder for any type of selected neural network architecture. According to our evaluation results of wav2vec-TDNN based systems obtained for NIST SRE and VoxCeleb1 benchmarks the proposed universal encoder provides speaker verification improvements in case of different enrollment and test speech segment duration. The key feature of the proposed encoder is that it has the same inference time as the selected neural network architecture.	['Galina Lavrentyeva', 'Vladimir Volokhov', 'Sergey Novoselov']	2022-10-28	null	null	null	null	['speaker-recognition', 'speaker-verification']	['speech', 'speech']	[ 2.09737107e-01 5.46671040e-02 -1.71952248e-01 -6.70834959e-01 -1.11404085e+00 -4.97936964e-01 3.71782392e-01 -5.79065941e-02 -4.62053478e-01 4.61388260e-01 3.23688209e-01 -4.89839733e-01 1.74411580e-01 -3.06578904e-01 -4.85858679e-01 -7.07317173e-01 1.56473786e-01 4.03747797e-01 5.94520569e-02 -3.05706203e-01 -9.03910100e-02 4.61356580e-01 -1.70683324e+00 9.54994485e-02 5.24346173e-01 8.69400263e-01 2.53538370e-01 1.25555241e+00 -4.57203649e-02 3.29786688e-01 -8.32288504e-01 -3.14039707e-01 1.62078962e-01 -5.14291584e-01 -8.07282150e-01 -3.90636325e-02 7.92917371e-01 -2.53801107e-01 -2.55233377e-01 9.32255387e-01 1.12971783e+00 1.05429478e-01 6.48887694e-01 -9.05138433e-01 -5.81724405e-01 1.26285076e+00 2.32264623e-02 5.33531368e-01 2.35746667e-01 6.71267137e-02 1.16655779e+00 -5.91987908e-01 2.16859862e-01 1.09874547e+00 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4094c24e-a4d1-4037-bc41-985c2e30f8ba	learning-fair-classifiers-via-min-max-f	2306.16552	null	https://arxiv.org/abs/2306.16552v1	https://arxiv.org/pdf/2306.16552v1.pdf	Learning Fair Classifiers via Min-Max F-divergence Regularization	As machine learning (ML) based systems are adopted in domains such as law enforcement, criminal justice, finance, hiring and admissions, ensuring the fairness of ML aided decision-making is becoming increasingly important. In this paper, we focus on the problem of fair classification, and introduce a novel min-max F-divergence regularization framework for learning fair classification models while preserving high accuracy. Our framework consists of two trainable networks, namely, a classifier network and a bias/fairness estimator network, where the fairness is measured using the statistical notion of F-divergence. We show that F-divergence measures possess convexity and differentiability properties, and their variational representation make them widely applicable in practical gradient based training methods. The proposed framework can be readily adapted to multiple sensitive attributes and for high dimensional datasets. We study the F-divergence based training paradigm for two types of group fairness constraints, namely, demographic parity and equalized odds. We present a comprehensive set of experiments for several real-world data sets arising in multiple domains (including COMPAS, Law Admissions, Adult Income, and CelebA datasets). To quantify the fairness-accuracy tradeoff, we introduce the notion of fairness-accuracy receiver operating characteristic (FA-ROC) and a corresponding \textit{low-bias} FA-ROC, which we argue is an appropriate measure to evaluate different classifiers. In comparison to several existing approaches for learning fair classifiers (including pre-processing, post-processing and other regularization methods), we show that the proposed F-divergence based framework achieves state-of-the-art performance with respect to the trade-off between accuracy and fairness.	['Ravi Tandon', 'Meiyu Zhong']	2023-06-28	null	null	null	null	['fairness', 'fairness', 'decision-making']	['computer-vision', 'miscellaneous', 'reasoning']	[ 1.01653337e-02 1.83834415e-02 -6.52628005e-01 -1.01766443e+00 -6.90818071e-01 -1.53760374e-01 2.54270971e-01 4.38468575e-01 -8.94048572e-01 1.19862592e+00 -1.77280471e-01 -5.07079244e-01 -5.28698921e-01 -8.12775970e-01 -2.28670210e-01 -6.29235506e-01 1.10240830e-02 3.24374169e-01 -4.33206320e-01 -1.09484918e-01 5.23652315e-01 4.89658833e-01 -1.60961545e+00 -1.08788967e-01 1.61677694e+00 1.30919647e+00 -6.12428904e-01 4.11914170e-01 7.09253773e-02 8.96786571e-01 -3.26388299e-01 -1.23453796e+00 3.69873106e-01 -4.85524535e-01 -8.36735070e-01 -4.10747886e-01 6.04104280e-01 -3.17592561e-01 7.80916512e-02 1.24260616e+00 6.13257647e-01 4.08966005e-01 1.15605307e+00 -1.60064173e+00 -6.95090115e-01 3.08676779e-01 -5.50581098e-01 1.10440172e-01 -1.79980677e-02 -1.70355216e-01 1.21596813e+00 -7.37124383e-01 1.22619353e-01 1.36240661e+00 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-1.22576499e+00 -6.39166772e-01 -1.12373435e+00 6.48423851e-01 3.56132597e-01 -7.47700393e-01 -5.26835501e-01 -4.12244380e-01]	[8.867706298828125, 5.22838830947876]
8d3048a1-6397-4208-838a-ee55afd9718d	point-cloud-pre-training-with-natural-3d	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Yamada_Point_Cloud_Pre-Training_With_Natural_3D_Structures_CVPR_2022_paper.pdf	Point Cloud Pre-Training With Natural 3D Structures	The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-driven supervised learning inspired by fractal geometry encountered in natural 3D structures. Our research is based on the hypothesis that we could learn representations from more real-world 3D patterns than conventional 3D datasets by learning fractal geometry. We show how the PC-FractalDB facilitates solving several recent dataset-related problems in 3D scene understanding, such as 3D model collection and labor-intensive annotation. The experimental section shows how we achieved the performance rate of up to 61.9% and 59.0% for the ScanNetV2 and SUN RGB-D datasets, respectively, over the current highest scores obtained with the PointContrast, contrastive scene contexts (CSC), and RandomRooms. Moreover, the PC-FractalDB pre-trained model is especially effective in training with limited data. For example, in 10% of training data on ScanNetV2, the PC-FractalDB pre-trained VoteNet performs at 38.3%, which is +14.8% higher accuracy than CSC. Of particular note, we found that the proposed method achieves the highest results for 3D object detection pre-training in limited point cloud data.	['Tetsuya OGATA', 'Yukiyasu Domae', 'Naoya Chiba', 'Hirokatsu Kataoka', 'Ryosuke Yamada']	2022-01-01	null	null	null	cvpr-2022-1	['point-cloud-pre-training']	['computer-vision']	[-1.70908794e-01 -5.20441309e-02 2.81803936e-01 -2.50489205e-01 -4.90061313e-01 -3.47608060e-01 6.81923449e-01 1.08552039e-01 -1.46813527e-01 2.53726214e-01 -4.31871533e-01 -3.04351509e-01 -2.72909701e-01 -1.06272995e+00 -8.07309330e-01 -3.67865413e-01 -2.55844802e-01 7.12079227e-01 6.09857023e-01 -2.51840025e-01 4.35070008e-01 1.18519688e+00 -2.04463935e+00 1.82175592e-01 8.74232769e-01 1.34589493e+00 5.52868783e-01 4.40517396e-01 -5.19681156e-01 4.90682244e-01 -4.06095177e-01 -2.77405232e-02 5.18008053e-01 3.46899390e-01 -5.53220570e-01 3.69822644e-02 5.94406366e-01 -3.87653708e-02 -3.77156772e-02 6.89377546e-01 5.71732461e-01 -1.25777856e-01 8.60797167e-01 -1.10675776e+00 -4.54065263e-01 -5.11108339e-03 -5.47330141e-01 1.26661837e-01 1.47381261e-01 7.96086416e-02 8.14010680e-01 -1.18991423e+00 4.73993093e-01 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84db6e91-9b82-4744-8515-08eeb95a6734	intriguing-property-of-gan-for-remote-sensing	2303.05240	null	https://arxiv.org/abs/2303.05240v2	https://arxiv.org/pdf/2303.05240v2.pdf	Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation	Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN.	['Jie Hu', 'Wenwen Qiang', 'Fuchun Sun', 'Changwen Zheng', 'Fengge Wu', 'Xingzhe Su']	2023-03-09	null	null	null	null	['counterfactual-explanation']	['miscellaneous']	[ 5.35207152e-01 3.05396557e-01 -1.95095703e-01 -1.51527658e-01 -3.29314411e-01 -4.41561043e-01 9.17379022e-01 -3.94572794e-01 4.49903645e-02 9.52546120e-01 4.30517107e-01 -1.88611478e-01 -4.12482694e-02 -1.25405383e+00 -9.61615682e-01 -1.13121235e+00 3.62796515e-01 -6.23743013e-02 -4.41942781e-01 -2.68100113e-01 -4.45267037e-02 2.77924538e-01 -1.48322177e+00 -1.14531301e-01 1.26726460e+00 9.27831709e-01 3.07214826e-01 3.57288182e-01 2.64527230e-03 8.75945330e-01 -5.52560627e-01 -2.84627050e-01 5.76992154e-01 -9.85701919e-01 -4.42545146e-01 2.09437340e-01 -1.45622611e-01 -2.64029413e-01 -3.83806437e-01 1.23853159e+00 4.30720061e-01 9.12110507e-02 8.49794209e-01 -1.26993585e+00 -1.13138187e+00 6.98620617e-01 -6.99269354e-01 -8.84062052e-02 -4.14763913e-02 3.62455010e-01 7.89780378e-01 -5.15452623e-01 5.17939806e-01 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b2d45f45-85a3-40bb-a5d9-cfe1be6d9f3c	out-of-boundary-view-synthesis-towards-full	2108.09041	null	https://arxiv.org/abs/2108.09041v1	https://arxiv.org/pdf/2108.09041v1.pdf	Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization	Warping-based video stabilizers smooth camera trajectory by constraining each pixel's displacement and warp stabilized frames from unstable ones accordingly. However, since the view outside the boundary is not available during warping, the resulting holes around the boundary of the stabilized frame must be discarded (i.e., cropping) to maintain visual consistency, and thus does leads to a tradeoff between stability and cropping ratio. In this paper, we make a first attempt to address this issue by proposing a new Out-of-boundary View Synthesis (OVS) method. By the nature of spatial coherence between adjacent frames and within each frame, OVS extrapolates the out-of-boundary view by aligning adjacent frames to each reference one. Technically, it first calculates the optical flow and propagates it to the outer boundary region according to the affinity, and then warps pixels accordingly. OVS can be integrated into existing warping-based stabilizers as a plug-and-play module to significantly improve the cropping ratio of the stabilized results. In addition, stability is improved because the jitter amplification effect caused by cropping and resizing is reduced. Experimental results on the NUS benchmark show that OVS can improve the performance of five representative state-of-the-art methods in terms of objective metrics and subjective visual quality. The code is publicly available at https://github.com/Annbless/OVS_Stabilization.	['DaCheng Tao', 'Jing Zhang', 'Yufei Xu']	2021-08-20	null	http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Out-of-Boundary_View_Synthesis_Towards_Full-Frame_Video_Stabilization_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Out-of-Boundary_View_Synthesis_Towards_Full-Frame_Video_Stabilization_ICCV_2021_paper.pdf	iccv-2021-1	['video-stabilization']	['computer-vision']	[ 2.12156642e-02 -1.40972421e-01 -1.11641295e-01 1.06642298e-01 -9.01624188e-02 -5.93683064e-01 1.56831667e-01 -1.49876788e-01 -1.26929820e-01 5.72267890e-01 1.54362202e-01 -1.32640377e-01 3.15321714e-01 -6.74716055e-01 -5.21936536e-01 -8.67309749e-01 2.00036407e-01 -6.24281406e-01 9.11961377e-01 -1.56267986e-01 3.03707272e-01 3.97998303e-01 -1.14783752e+00 -8.00190214e-03 9.31135654e-01 7.98434973e-01 2.71385729e-01 3.46744627e-01 1.43752649e-01 5.36115944e-01 -3.12746435e-01 -1.17299557e-01 4.46292907e-01 -6.75613701e-01 -3.62594932e-01 2.17785865e-01 2.71229029e-01 -5.44600189e-01 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925b78b0-820b-46b9-b64d-36e3629dcd85	incorporating-polar-field-data-for-improved	2212.01730	null	https://arxiv.org/abs/2212.01730v1	https://arxiv.org/pdf/2212.01730v1.pdf	Incorporating Polar Field Data for Improved Solar Flare Prediction	In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle's intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.	['Alfred Hero', 'Yang Chen', 'Ward B. Manchester', 'Tamas Gombosi', 'Monica Bobra', 'Zeyu Sun', 'Mehmet Aktukmak']	2022-12-04	null	null	null	null	['solar-flare-prediction']	['time-series']	[ 2.38845259e-01 -6.75423816e-02 -5.29994071e-01 -4.56367344e-01 -6.16228640e-01 -3.80231857e-01 6.85204744e-01 -3.43123943e-01 1.23451836e-01 9.88498449e-01 2.74089485e-01 -5.11498690e-01 -3.62786770e-01 -8.66889358e-01 -3.97647917e-01 -7.93284416e-01 2.13099033e-01 4.87947613e-01 -1.22081570e-01 -6.61407351e-01 3.37130100e-01 5.94902933e-01 -2.02866149e+00 5.73917367e-02 1.58528924e+00 1.19995236e+00 2.25684643e-01 5.97874999e-01 1.43615827e-01 1.37283528e+00 -6.17877126e-01 4.79645073e-01 8.70097578e-02 -4.60652649e-01 -2.30210453e-01 -3.93835127e-01 4.84651059e-01 9.57232267e-02 -3.36112343e-02 7.39834547e-01 5.68585992e-01 6.66537806e-02 6.62754297e-01 -7.53070235e-01 -1.04506657e-01 5.25175095e-01 -5.16126394e-01 6.69001997e-01 2.52591342e-01 -8.21217299e-02 8.67756248e-01 -4.17888850e-01 4.25762594e-01 5.88475704e-01 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10af9eeb-68e0-4138-bceb-708e549692fb	an-intelligent-modular-real-time-vision-based	2303.16710	null	https://arxiv.org/abs/2303.16710v1	https://arxiv.org/pdf/2303.16710v1.pdf	An intelligent modular real-time vision-based system for environment perception	A significant portion of driving hazards is caused by human error and disregard for local driving regulations; Consequently, an intelligent assistance system can be beneficial. This paper proposes a novel vision-based modular package to ensure drivers' safety by perceiving the environment. Each module is designed based on accuracy and inference time to deliver real-time performance. As a result, the proposed system can be implemented on a wide range of vehicles with minimum hardware requirements. Our modular package comprises four main sections: lane detection, object detection, segmentation, and monocular depth estimation. Each section is accompanied by novel techniques to improve the accuracy of others along with the entire system. Furthermore, a GUI is developed to display perceived information to the driver. In addition to using public datasets, like BDD100K, we have also collected and annotated a local dataset that we utilize to fine-tune and evaluate our system. We show that the accuracy of our system is above 80% in all the sections. Our code and data are available at https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-Perception	['Saeed Ebadollahi', 'Abbas Omidi', 'Aida Mohammadshahi', 'Milad Soltany', 'Amirhossein Heydarian', 'Amirhossein Kazerouni']	2023-03-29	null	null	null	null	['lane-detection']	['computer-vision']	[-3.59182030e-01 7.74648470e-06 -3.97896916e-02 -8.35147560e-01 -4.54078108e-01 -6.80520773e-01 3.92668247e-01 -1.72972366e-01 -2.94893056e-01 2.38577619e-01 -3.80393147e-01 -7.64470875e-01 4.59613234e-01 -7.18856990e-01 -5.71552753e-01 -5.62045574e-01 4.14506823e-01 -2.75614895e-02 7.95883715e-01 -3.66214782e-01 4.69102323e-01 7.03527808e-01 -2.13396049e+00 1.28559954e-03 8.91002893e-01 1.04496300e+00 6.01779759e-01 7.20479667e-01 2.93575853e-01 5.94887197e-01 -3.54544044e-01 -2.52905607e-01 4.93428081e-01 1.09683499e-01 -9.20548066e-02 6.63322955e-02 5.84002018e-01 -7.20075548e-01 -5.37451386e-01 9.42346334e-01 5.53009450e-01 -7.38678575e-02 2.06129223e-01 -1.68052292e+00 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