paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 qualit... | ['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 -6.38164282... | [4.468939304351807, 0.9431220889091492] |
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 be... | ['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 -1.57845870... | [14.667240142822266, 0.8360029458999634] |
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 ca... | ['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 -7.47154236... | [5.539113521575928, 4.864256381988525] |
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... | ['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 -3.09440017... | [8.400506019592285, 4.988071918487549] |
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 architect... | ['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 2.85849392... | [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. esti... | ['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 3.15004587... | [10.384113311767578, -2.5260169506073] |
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 parap... | ['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 -4.49474081... | [10.498797416687012, 9.135146141052246] |
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 cons... | ['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 -3.85564715... | [9.498271942138672, 5.572089195251465] |
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)... | ['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 -4.26215410... | [14.507233619689941, 7.088164806365967] |
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 o... | ['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 1.05102971... | [14.413640975952148, -1.6568487882614136] |
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 m... | ['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 -6.47228122... | [14.389411926269531, 7.178722858428955] |
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... | ['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 -8.59221756... | [7.36046028137207, -2.2100226879119873] |
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, g... | ['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 -1.03679597... | [11.413976669311523, -0.12364558130502701] |
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 reliabili... | ['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 -1.98057905... | [9.726247787475586, 4.901041030883789] |
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 speci... | ['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 -6.01346254... | [8.28336238861084, 10.116193771362305] |
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 ex... | ['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 -4.16732989... | [10.829612731933594, -2.182199716567993] |
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... | ['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 -2.58086234... | [10.171027183532715, 2.2741992473602295] |
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-supervise... | ['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 -1.03482753... | [8.297816276550293, -1.6490445137023926] |
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 multi... | ['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
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-7.57150710e-01 3.22524846e-01 -1.20783858e-01 -2.84923375... | [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 hi... | ['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
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2.83258647e-01 2.24863008e-01 2.89410144e-01 -1.83264866... | [7.177722454071045, 2.0170300006866455] |
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 i... | ['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
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-1.08651944e-01 -1.45800292e-01 -7.58122325e-01 -3.56330574e-01
-8.43113065e-02 -8.02492723e-02 1.10910511e+00 -2.58050621... | [10.003968238830566, 0.5926032066345215] |
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... | ['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
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-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 -1.38900369... | [10.2373046875, 1.8724908828735352] |
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 p... | ['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
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2.17885941e-01 9.50054228e-01 7.99313188e-01 -1.76041648... | [8.050771713256836, -3.2028560638427734] |
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 ... | ['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
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-6.26394153e-01 2.79853940e-01 1.94534034e-01 3.33423793... | [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 dev... | ['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 3.99514474... | [14.483951568603516, -2.7113656997680664] |
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
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.389515399932861, 3.699814796447754] |
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... | ['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 -1.72220796... | [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 ... | ['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 1.18575275... | [8.002387046813965, 6.150883197784424] |
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... | ['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 -8.19326222... | [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 th... | ['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 -2.62747675... | [9.745726585388184, -0.4420795440673828] |
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 ... | ['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 -3.84805620... | [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 ... | ['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 -2.33328253... | [11.398086547851562, -1.8090394735336304] |
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 the... | ['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 -3.79065663... | [4.214636325836182, 1.4006661176681519] |
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. Recen... | ['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 -2.45026313... | [11.955790519714355, 2.379462480545044] |
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 predi... | ['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... | [8.222749710083008, 10.119956970214844] |
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 cap... | ['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 -2.73535736... | [8.744477272033691, 5.472020149230957] |
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... | ['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 -6.86077103... | [9.635581970214844, -1.3974529504776] |
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 p... | ['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 -1.62926987... | [9.831342697143555, 7.5254435539245605] |
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 confidenc... | ['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 5.34323752... | [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 af... | ['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 -2.72589196... | [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 bec... | ['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 -7.76428938... | [12.123639106750488, 9.055880546569824] |
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 de... | ['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 2.85613835... | [8.711668968200684, -1.5289638042449951] |
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 m... | ['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 -5.77282608... | [15.217195510864258, 6.224547386169434] |
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, m... | ['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 3.11278045... | [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 ... | ['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 -2.61709839... | [10.919229507446289, 9.76730728149414] |
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 proble... | ['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 -6.47763014... | [10.973995208740234, 8.553013801574707] |
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 i... | ['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 -2.39383742... | [6.941654682159424, 3.8801681995391846] |
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 a... | ['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 -1.30269870... | [8.954564094543457, 5.427595138549805] |
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 ... | ['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 -3.84180427... | [10.948593139648438, -3.2672126293182373] |
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 co... | ['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 -4.12002921... | [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 attem... | ['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 -5.47323450... | [8.810785293579102, 7.0472846031188965] |
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 -6.93831444... | [-7.402324199676514, 3.6291873455047607] |
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... | ['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 5.02003990... | [10.823396682739258, 1.4497658014297485] |
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 t... | ['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 -3.82681072... | [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 a... | ['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 5.93654439... | [8.770310401916504, -2.4469316005706787] |
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 mo... | ['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 -1.45694360... | [10.025826454162598, -2.5037896633148193] |
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... | ['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 1.80654645... | [8.301504135131836, 2.7436130046844482] |
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... | ['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 0.2... | [5.672532558441162, -0.13808807730674744] |
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 c... | ['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 -5.79158291... | [13.679800033569336, 5.7433905601501465] |
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 rep... | ['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 -2.52538294... | [11.01791763305664, 10.662545204162598] |
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 fa... | ['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 -5.78850091... | [3.877685070037842, 2.2879645824432373] |
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 s... | ['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 -5.13647854... | [4.036456108093262, 1.487844467163086] |
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 ... | ['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 -1.54514089... | [8.820781707763672, 7.024808883666992] |
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 aggreg... | ['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 -3.77067834... | [8.860998153686523, -2.2529613971710205] |
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 y... | ['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 -4.79159147... | [11.375322341918945, 6.720127582550049] |
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... | ['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 -4.15136337... | [9.390539169311523, 6.268008708953857] |
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 ... | ['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 -2.71122694... | [9.53528118133545, 1.6143254041671753] |
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{Al... | ['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 -1.02827072... | [3.5461528301239014, 1.4459657669067383] |
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 sys... | ['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 -7.44165361... | [11.776174545288086, 8.977653503417969] |
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 ... | ['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 -5.70401773... | [8.310708999633789, 6.284003734588623] |
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 ... | ['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 -5.37868083... | [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 prim... | ['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 1.13985039... | [8.319868087768555, 3.019435405731201] |
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... | ['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 -4.64255549... | [7.352735996246338, 6.496857166290283] |
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 (nonterm... | ['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 -2.71540638... | [10.354519844055176, 9.59737777709961] |
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. Natural... | ['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 -5.04159518... | [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 va... | ['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 -3.20065796... | [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 ... | ['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 -4.57370609... | [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 vid... | ['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 -1.39501125... | [6.382866859436035, -0.9703443646430969] |
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 super... | ['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 1.94441736... | [9.852204322814941, -1.458770990371704] |
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 progres... | ['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 -4.48753119... | [10.544690132141113, 8.69667911529541] |
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 complex... | ['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 -3.40579361... | [11.18663215637207, 8.804267883300781] |
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... | ['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 1.55827224... | [8.493003845214844, 0.2293909192085266] |
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-vie... | ['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 9.06931460... | [8.774288177490234, -2.6740283966064453] |
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... | ['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 -4.58378255... | [11.707294464111328, 3.0470407009124756] |
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, des... | ['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 -3.16409588... | [9.81676959991455, -4.980132102966309] |
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 di... | ['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 -7.92179778... | [14.002528190612793, -2.0025761127471924] |
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 Transformer... | ['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 -1.84076801... | [12.253747940063477, 0.9323450326919556] |
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 ... | ['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 -1.50779307... | [11.901354789733887, 9.352021217346191] |
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... | ['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 -8.59678566... | [7.062717914581299, 4.749725341796875] |
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 effective... | ['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 -3.62847626... | [9.987919807434082, 6.2917866706848145] |
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 ... | ['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 -1.86885715... | [7.760719299316406, 9.781737327575684] |
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... | ['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 -5.08905888... | [7.795003414154053, 4.403250217437744] |
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 goal... | [' 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 5.41139916... | [14.775660514831543, 0.8737597465515137] |
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 segment... | ['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 -3.05706203... | [14.32343864440918, 6.1098952293396] |
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-di... | ['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 -1.09484918... | [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 ... | ['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 -2.51840025... | [7.926459789276123, -3.3433079719543457] |
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 natura... | ['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 -2.68100113... | [11.671830177307129, -0.3301287591457367] |
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... | ['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 -1.56267986... | [10.66526985168457, -1.4666223526000977] |
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 g... | ['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 -6.61407351... | [6.6051201820373535, 2.7911295890808105] |
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... | ['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 -3.66214782... | [7.882137775421143, -1.1757999658584595] |
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