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e06f19b9-01ba-43a4-afc4-5a2aae54d928 | bridging-the-gap-entailment-fused-t5-for-open | 2212.09353 | null | https://arxiv.org/abs/2212.09353v1 | https://arxiv.org/pdf/2212.09353v1.pdf | Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversational interaction scenes. Machines are required to make a decision of "Yes/No/Inquire" or generate a follow-up question when the decision is "Inquire" based on retrieved rule texts, user scenario, user question, and dialogu... | ['Xian-Ling Mao', 'Zewen Chi', 'Heyan Huang', 'Xiao Zhang'] | 2022-12-19 | null | null | null | null | ['question-generation', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.13551342e-01 5.20190239e-01 2.51540780e-01 -6.69144690e-01
-1.12234020e+00 -5.65320253e-01 8.01938534e-01 2.54292011e-01
-3.26079279e-01 7.07428694e-01 6.96639359e-01 -7.68008411e-01
4.53743637e-02 -9.25547481e-01 -3.86516333e-01 -3.30942869e-02
5.02182782e-01 6.72110319e-01 3.21224958e-01 -6.76959097... | [11.858101844787598, 8.051702499389648] |
a2912325-1467-4067-a895-050edf1bd822 | atiss-autoregressive-transformers-for-indoor | 2110.03675 | null | https://arxiv.org/abs/2110.03675v1 | https://arxiv.org/pdf/2110.03675v1.pdf | ATISS: Autoregressive Transformers for Indoor Scene Synthesis | The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation. In this paper, we present ATISS, a novel autoregressive transformer architecture for creating diver... | ['Sanja Fidler', 'Andreas Geiger', 'Karsten Kreis', 'Maria Shugrina', 'Amlan Kar', 'Despoina Paschalidou'] | 2021-10-07 | null | http://proceedings.neurips.cc/paper/2021/hash/64986d86a17424eeac96b08a6d519059-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/64986d86a17424eeac96b08a6d519059-Paper.pdf | neurips-2021-12 | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 4.37651813e-01 2.63693541e-01 5.55705667e-01 -5.02517819e-01
-8.57992709e-01 -1.04338562e+00 9.19576108e-01 -1.89882547e-01
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-1.10545948e-01 -9.30200994e-01 -1.08427691e+00 -4.31829840e-01
1.10880665e-01 9.01686788e-01 -1.79141641e-01 -2.33298957... | [9.238838195800781, -3.019449234008789] |
67d1d5c9-9615-4797-89ea-b2a3f0968686 | identifying-the-style-by-a-qualified-reader | 2306.02771 | null | https://arxiv.org/abs/2306.02771v1 | https://arxiv.org/pdf/2306.02771v1.pdf | Identifying the style by a qualified reader on a short fragment of generated poetry | Style is an important concept in today's challenges in natural language generating. After the success in the field of image style transfer, the task of text style transfer became actual and attractive. Researchers are also interested in the tasks of style reproducing in generation of the poetic text. Evaluation of styl... | ['Boris Orekhov'] | 2023-06-05 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 3.45981658e-01 2.16200158e-01 2.55820960e-01 -2.16025233e-01
-5.83844304e-01 -6.52406871e-01 6.99315727e-01 -8.65308270e-02
-7.20810950e-01 9.97065246e-01 4.06389743e-01 -3.21295977e-01
2.81760097e-01 -1.04790831e+00 -5.46068966e-01 -3.40396196e-01
6.11710012e-01 7.88753867e-01 -2.88321853e-01 -5.95789611... | [11.512092590332031, 9.45094108581543] |
972ca1ee-f971-42fd-b86f-a1d8f409e919 | sem-k-is-my-knowledge-graph-embedding-model | 2301.05601 | null | https://arxiv.org/abs/2301.05601v1 | https://arxiv.org/pdf/2301.05601v1.pdf | Sem@$K$: Is my knowledge graph embedding model semantic-aware? | Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims... | ['Davy Monticolo', 'Armelle Brun', 'Pierre Monnin', 'Nicolas Hubert'] | 2023-01-13 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-3.22253257e-01 7.60738492e-01 -4.33023602e-01 -2.03391671e-01
-2.12699562e-01 -4.96313840e-01 7.37059772e-01 8.30821097e-01
-4.19745117e-01 6.83807373e-01 9.88999382e-02 -1.79356188e-01
-9.84342992e-01 -1.40864122e+00 -5.12109697e-01 -1.69153884e-01
-3.53793323e-01 5.89667022e-01 4.39659089e-01 -4.72157389... | [8.923632621765137, 7.908614635467529] |
213ec4c2-f901-420d-babc-9b651c0edbb1 | modality-aware-triplet-hard-mining-for-zero | 2112.07966 | null | https://arxiv.org/abs/2112.07966v2 | https://arxiv.org/pdf/2112.07966v2.pdf | Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval | This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. This task has two characteristics: 1) the zero-shot setting requires a metric space with good within-class compactness and the between-class discrepancy for recognizing the novel classes... | ['Nong Sang', 'Changxin Gao', 'Chuchu Han', 'Yifan Sun', 'Zongheng Huang'] | 2021-12-15 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 0.04774119 -0.5146642 -0.5376586 -0.24896 -1.6857822 -0.42075464
0.64971334 -0.17971563 -0.19161609 0.44103438 0.2174963 -0.03416561
-0.49028778 -0.67979133 -0.69212896 -0.70462924 0.09533126 0.3645792
0.18234713 -0.32361645 0.2941273 0.1950143 -1.573213 0.44211283
0.71178997 1.3489877 0.1... | [11.417348861694336, 0.8288123607635498] |
e8d35296-dbec-440d-aa1f-88d2aa1d3635 | skeleton-based-hand-gesture-recognition-by | 1905.07917 | null | https://arxiv.org/abs/1905.07917v1 | https://arxiv.org/pdf/1905.07917v1.pdf | Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks | In this paper, we propose a new hand gesture recognition method based on skeletal data by learning SPD matrices with neural networks. We model the hand skeleton as a graph and introduce a neural network for SPD matrix learning, taking as input the 3D coordinates of hand joints. The proposed network is based on two newl... | ['Sébastien Bougleux', 'Olivier Lezoray', 'Xuan Nguyen', 'Luc Brun'] | 2019-05-20 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 2.96791881e-01 -1.91431418e-01 -5.04443526e-01 -2.06027940e-01
-2.52372473e-01 -3.67308825e-01 7.09522247e-01 -5.54957271e-01
-6.81099951e-01 -2.76885685e-02 1.62578613e-01 -1.70049876e-01
-2.31934965e-01 -4.81333762e-01 -6.73874378e-01 -7.05028117e-01
-2.56653011e-01 9.47674990e-01 4.25135344e-01 -4.96326387... | [6.633845329284668, -0.4878823459148407] |
34ed8f80-d797-40bc-b5cb-92d45e999781 | zero-shot-approach-to-overcome-perturbation | 2305.15689 | null | https://arxiv.org/abs/2305.15689v2 | https://arxiv.org/pdf/2305.15689v2.pdf | Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. Specifically, these methods utilize few-shot learning settings to fine-tune the sentiment classification model using manua... | ['Qi Li', 'Adithya Kulkarni', 'Mohna Chakraborty'] | 2023-05-25 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 4.47784007e-01 -1.35273740e-01 -4.13621247e-01 -8.69768918e-01
-1.46142220e+00 -6.56437457e-01 6.85203850e-01 7.06334770e-01
-5.89489639e-01 6.23813629e-01 4.61155921e-01 -1.21621765e-01
4.03008051e-02 -5.25616646e-01 -2.55475372e-01 -4.22118396e-01
5.25058091e-01 1.45297393e-01 2.20711470e-01 -7.42723346... | [10.722515106201172, 7.761662483215332] |
3bd67ac4-2a6a-4918-b994-3f334b19612c | diffusion-models-for-memory-efficient | 2303.15288 | null | https://arxiv.org/abs/2303.15288v1 | https://arxiv.org/pdf/2303.15288v1.pdf | Diffusion Models for Memory-efficient Processing of 3D Medical Images | Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In t... | ['Philippe C. Cattin', 'Robin Sandkühler', 'Alicia Durrer', 'Julia Wolleb', 'Florentin Bieder'] | 2023-03-27 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.91074389e-01 3.17013085e-01 2.82476962e-01 -2.43044365e-02
-9.53194678e-01 -2.99483448e-01 6.43402696e-01 3.49450037e-02
-4.78951424e-01 5.16856790e-01 -1.23056002e-01 -4.49010313e-01
-8.30142498e-02 -9.73897576e-01 -5.41434526e-01 -9.21413958e-01
8.80212709e-02 7.61561275e-01 6.85263395e-01 -6.89377338... | [14.206286430358887, -2.3099169731140137] |
1c3894bd-633d-422d-8583-58d1eb51afc6 | a-mask-free-neural-network-for-monaural | 2306.04286 | null | https://arxiv.org/abs/2306.04286v1 | https://arxiv.org/pdf/2306.04286v1.pdf | A Mask Free Neural Network for Monaural Speech Enhancement | In speech enhancement, the lack of clear structural characteristics in the target speech phase requires the use of conservative and cumbersome network frameworks. It seems difficult to achieve competitive performance using direct methods and simple network architectures. However, we propose the MFNet, a direct and simp... | ['Shaowei Ding', 'Guangyong Wang', 'Wei Dai', 'Jinlong Ma', 'Haixin Guan', 'Liang Liu'] | 2023-06-07 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 1.55843705e-01 -9.34653357e-02 2.44543880e-01 -5.21411672e-02
-6.94320500e-01 -3.10364693e-01 5.28655469e-01 -5.83286822e-01
-4.04482991e-01 5.99197745e-01 7.22438216e-01 -6.82350814e-01
-2.23122790e-01 -3.74482512e-01 -3.64277393e-01 -6.37617230e-01
-7.33401775e-02 -2.78090954e-01 2.94468969e-01 -6.66818321... | [15.113147735595703, 5.863367557525635] |
44308373-482a-46e9-b504-f19dbbf8c4e3 | improving-video-generation-for-multi | 1711.11453 | null | http://arxiv.org/abs/1711.11453v2 | http://arxiv.org/pdf/1711.11453v2.pdf | Improving Video Generation for Multi-functional Applications | In this paper, we aim to improve the state-of-the-art video generative
adversarial networks (GANs) with a view towards multi-functional applications.
Our improved video GAN model does not separate foreground from background nor
dynamic from static patterns, but learns to generate the entire video clip
conjointly. Our m... | ['Luc van Gool', 'Bernhard Kratzwald', 'Zhiwu Huang', 'Danda Pani Paudel', 'Acharya Dinesh'] | 2017-11-30 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 3.30908448e-01 2.99304468e-03 4.40553650e-02 -3.82162854e-02
-8.55798423e-01 -5.48810780e-01 6.69461250e-01 -8.60107720e-01
2.57081701e-03 8.48879039e-01 7.90826455e-02 -9.89974439e-02
4.33316410e-01 -6.93196237e-01 -1.25075269e+00 -7.67979503e-01
1.59107044e-01 3.02462608e-01 5.91625683e-02 -1.89935580... | [10.933935165405273, -0.6299391984939575] |
4ae931b5-0dd3-4b25-9d57-6a05909a968e | object-scene-flow-for-autonomous-vehicles | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Menze_Object_Scene_Flow_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Menze_Object_Scene_Flow_2015_CVPR_paper.pdf | Object Scene Flow for Autonomous Vehicles | This paper proposes a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and each super... | ['Moritz Menze', 'Andreas Geiger'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.32137671e-01 -6.78146929e-02 -2.78489292e-01 -4.39487994e-01
-3.10295850e-01 -7.57643163e-01 7.70471454e-01 -3.39515358e-01
-3.70166242e-01 6.20490611e-01 1.99192967e-02 -1.86804429e-01
2.87754953e-01 -7.50769675e-01 -8.88722181e-01 -6.70615673e-01
-1.19171835e-01 9.01736259e-01 7.30397642e-01 -1.35313412... | [8.504509925842285, -1.943937063217163] |
12e4b6ce-bebd-42ee-ac32-520b5afa0700 | finite-time-distributed-optimization-with | 2211.10855 | null | https://arxiv.org/abs/2211.10855v1 | https://arxiv.org/pdf/2211.10855v1.pdf | Finite-Time Distributed Optimization with Quantized Gradient Descent | In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, and thus nodes can send information to their out-neighbors only. Additionally, the communication channels among the nodes have limited bandwidth, t... | ['Karl H. Johansson', 'Themistoklis Charalambous', 'Wei Jiang', 'Apostolos I. Rikos'] | 2022-11-20 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.45659888e-01 3.00462425e-01 -2.57831722e-01 -3.98222089e-01
-5.79859734e-01 -5.43140590e-01 -3.18545774e-02 3.20078969e-01
-5.21325827e-01 1.11369681e+00 -1.08993784e-01 -2.58094549e-01
-1.15795478e-01 -1.11145365e+00 -3.05950820e-01 -1.12732673e+00
-5.04125416e-01 1.32459715e-01 2.09843856e-03 -8.52574259... | [6.198610305786133, 4.942416191101074] |
1115a504-e610-417f-bbd7-8e88092b0125 | distributed-methods-with-absolute-compression | 2203.02383 | null | https://arxiv.org/abs/2203.02383v2 | https://arxiv.org/pdf/2203.02383v2.pdf | Distributed Methods with Absolute Compression and Error Compensation | Distributed optimization methods are often applied to solving huge-scale problems like training neural networks with millions and even billions of parameters. In such applications, communicating full vectors, e.g., (stochastic) gradients, iterates, is prohibitively expensive, especially when the number of workers is la... | ['Eduard Gorbunov', 'Marina Danilova'] | 2022-03-04 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 3.92255217e-01 -7.21652731e-02 8.74902382e-02 -1.28599048e-01
-8.93131673e-01 -2.42607400e-01 4.14245538e-02 3.95954937e-01
-8.37615848e-01 9.12336946e-01 1.94830522e-02 -4.43973780e-01
-3.03540379e-01 -6.72232091e-01 -1.27894735e+00 -1.09720945e+00
-1.69620112e-01 3.44672561e-01 -1.82179421e-01 -1.45598829... | [6.415709495544434, 4.7169880867004395] |
5b43494e-134e-4d63-8f31-35f8b8f465c3 | self-supervised-3d-human-pose-estimation-from | 2304.02349 | null | https://arxiv.org/abs/2304.02349v1 | https://arxiv.org/pdf/2304.02349v1.pdf | Self-supervised 3D Human Pose Estimation from a Single Image | We propose a new self-supervised method for predicting 3D human body pose from a single image. The prediction network is trained from a dataset of unlabelled images depicting people in typical poses and a set of unpaired 2D poses. By minimising the need for annotated data, the method has the potential for rapid applica... | ['David Hogg', 'Jose Sosa'] | 2023-04-05 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 7.80843198e-02 7.15241134e-01 -2.27043718e-01 -5.12872756e-01
-7.56609440e-01 -3.71646315e-01 4.75076258e-01 -4.31703329e-01
-4.60949540e-01 6.03771508e-01 3.14499855e-01 3.13723356e-01
1.67719409e-01 -2.17137322e-01 -8.61499369e-01 -3.31169635e-01
-3.82655561e-01 1.50451255e+00 4.10917670e-01 -3.68261993... | [6.966066360473633, -0.9775527715682983] |
1bacf1cc-7917-43ba-9074-900144471fcd | bertopic-neural-topic-modeling-with-a-class | 2203.05794 | null | https://arxiv.org/abs/2203.05794v1 | https://arxiv.org/pdf/2203.05794v1.pdf | BERTopic: Neural topic modeling with a class-based TF-IDF procedure | Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process by extracting coherent topic representation through the development of a class-b... | ['Maarten Grootendorst'] | 2022-03-11 | null | null | null | null | ['document-embedding', 'topic-models'] | ['methodology', 'natural-language-processing'] | [-2.0575768e-01 3.0375588e-01 -5.1154280e-01 -4.5881003e-01
-1.0966551e+00 -4.5541230e-01 1.5011870e+00 4.2870498e-01
1.3822676e-01 2.3722360e-01 8.1390047e-01 -1.0151279e-01
-2.4811397e-01 -9.2455250e-01 -3.9836451e-01 -7.7265042e-01
-3.9708278e-01 1.0919925e+00 3.2808310e-01 7.9805955e-02
5.3896075e-01... | [10.405468940734863, 6.942245960235596] |
7b4ceea4-0538-4b2f-b0c3-700da48dcad7 | urban-spatiotemporal-data-synthesis-via | 2306.07292 | null | https://arxiv.org/abs/2306.07292v1 | https://arxiv.org/pdf/2306.07292v1.pdf | Urban Spatiotemporal Data Synthesis via Neural Disaggregation | The level of granularity of open data often conflicts the benefits it can provide. Less granular data can protect individual privacy, but to certain degrees, sabotage the promise of open data to promote transparency and assist research. Similar in the urban setting, aggregated urban data at high-level geographic units ... | ['Bill Howe', 'Bin Han'] | 2023-06-09 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [-3.45194966e-01 2.86903441e-01 -2.84879208e-01 -5.21789134e-01
-8.42202067e-01 -5.74078083e-01 8.59160900e-01 1.37363836e-01
-2.70551234e-01 1.23926997e+00 8.96487653e-01 -6.09895229e-01
-2.38009334e-01 -1.31049490e+00 -5.84521055e-01 -5.20087481e-01
-2.13640168e-01 7.50054792e-02 5.72788790e-02 -1.74680069... | [6.458557605743408, 2.2522192001342773] |
fbdc0392-bdd7-4a55-bb57-d2910780d3c8 | qatm-quality-aware-template-matching-for-deep | 1903.07254 | null | http://arxiv.org/abs/1903.07254v2 | http://arxiv.org/pdf/1903.07254v2.pdf | QATM: Quality-Aware Template Matching For Deep Learning | Finding a template in a search image is one of the core problems many
computer vision, such as semantic image semantic, image-to-GPS verification
\etc. We propose a novel quality-aware template matching method, QATM, which is
not only used as a standalone template matching algorithm, but also a trainable
layer that can... | ['Premkumar Natarajan', 'Wael Abd-Almageed', 'Jiaxin Cheng', 'Yue Wu'] | 2019-03-18 | qatm-quality-aware-template-matching-for-deep-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cheng_QATM_Quality-Aware_Template_Matching_for_Deep_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cheng_QATM_Quality-Aware_Template_Matching_for_Deep_Learning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-to-gps-verification'] | ['computer-vision'] | [ 4.40713882e-01 -2.96958536e-01 -2.09649950e-01 -6.67876840e-01
-1.19815779e+00 -5.35470605e-01 4.88650024e-01 -3.91160488e-01
-1.35240495e-01 2.03320310e-01 1.61102474e-01 -1.16000481e-01
-4.74356562e-01 -9.43813860e-01 -1.08002234e+00 -5.64992547e-01
4.19700027e-01 7.41584599e-01 3.55646908e-01 -4.23822850... | [8.359681129455566, -1.9121620655059814] |
061e64e9-95bb-4ebf-aae3-03dc58c77a97 | a-hidden-variables-approach-to-multilabel | 1912.01241 | null | https://arxiv.org/abs/1912.01241v1 | https://arxiv.org/pdf/1912.01241v1.pdf | A Hidden Variables Approach to Multilabel Logistic Regression | Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hid... | ['Hoda Shajari', 'Jaemoon Lee'] | 2019-12-03 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 1.87460780e-01 -1.33501425e-01 -9.12542045e-01 -7.45204866e-01
-8.69481206e-01 -4.32208091e-01 4.92688686e-01 2.67629057e-01
-4.09724653e-01 1.13215709e+00 -9.17904899e-02 -2.55779386e-01
-1.00696109e-01 -5.52981317e-01 -3.66114527e-01 -9.61349607e-01
8.35352987e-02 4.94752496e-01 1.21754356e-01 9.50211287... | [9.534170150756836, 4.186456680297852] |
92f49677-2ee7-4cf3-b8b5-1e767261d18d | rethinking-boundary-detection-in-deep | 2305.00678 | null | https://arxiv.org/abs/2305.00678v1 | https://arxiv.org/pdf/2305.00678v1.pdf | Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation | Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit... | ['Hao Chen', 'Kwang-Ting Cheng', 'Yufan Chen', 'Xiao Fang', 'Dong Zhang', 'Yi Lin'] | 2023-05-01 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 7.54873693e-01 3.89672071e-01 -1.52406126e-01 -3.71329129e-01
-6.50781929e-01 -6.97468296e-02 2.75866002e-01 -1.42499469e-02
-5.71723998e-01 2.26510853e-01 -7.27752224e-02 -3.75245571e-01
1.86892360e-01 -7.10269809e-01 -7.43986547e-01 -6.73801005e-01
2.08392233e-01 3.70158851e-01 6.10435188e-01 1.83358029... | [14.631047248840332, -2.550947904586792] |
6941628c-be3f-4e53-bf66-015a1f29171f | joint-optimization-of-maintenance-and | 2303.06174 | null | https://arxiv.org/abs/2303.06174v1 | https://arxiv.org/pdf/2303.06174v1.pdf | Joint Optimization of Maintenance and Production in Offshore Wind Farms: Balancing the Short- and Long-Term Needs of Wind Energy Operation | The rapid increase in scale and sophistication of offshore wind (OSW) farms poses a critical challenge related to the cost-effective operation and management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: power production (the primary driv... | ['Ahmed Aziz Ezzat', 'Murat Yildirim', 'Farnaz Fallahi', 'Petros Papadopoulos'] | 2023-03-10 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.46092281e-01 -8.00198317e-02 -7.95186833e-02 1.90949440e-01
-1.39711663e-01 -9.20965254e-01 4.77138668e-01 2.15679735e-01
-3.72279286e-02 1.11817265e+00 1.55226171e-01 -5.04883707e-01
-8.86471391e-01 -1.15765119e+00 -1.90257713e-01 -1.04337478e+00
-4.29370016e-01 -4.78109345e-02 -3.93504173e-01 -3.92704993... | [5.475894927978516, 2.632464647293091] |
8c904aa8-1068-4220-a1ff-e51f85388f26 | trainsim-a-railway-simulation-framework-for | 2302.14486 | null | https://arxiv.org/abs/2302.14486v1 | https://arxiv.org/pdf/2302.14486v1.pdf | TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation | The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders... | ['Gianluigi Lauro', 'Salvatore Sabina', 'Giorgio Buttazzo', 'Giulio Rossolini', 'Federico Nesti', 'Mauro Marinoni', "Gianluca D'Amico"] | 2023-02-28 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 1.19966693e-01 -1.93429366e-01 1.93955779e-01 -5.78695595e-01
-7.91566223e-02 -3.91381025e-01 4.62805212e-01 1.68462485e-01
-8.21508884e-01 8.11904132e-01 -4.30176139e-01 -1.86924592e-01
-3.12928408e-01 -1.06666350e+00 -9.16330755e-01 -6.30081534e-01
1.80359900e-01 7.58665085e-01 3.46167952e-01 -4.18000549... | [7.916357517242432, -1.5341994762420654] |
be85d6ed-adb6-4c57-9db6-b3ad0e183a37 | finnish-dialect-identification-the-effect-of-1 | 2111.03800 | null | https://arxiv.org/abs/2111.03800v1 | https://arxiv.org/pdf/2111.03800v1.pdf | Finnish Dialect Identification: The Effect of Audio and Text | Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recordi... | ['Jack Rueter', 'Niko Partanen', 'Khalid Alnajjar', 'Mika Hämäläinen'] | 2021-11-06 | finnish-dialect-identification-the-effect-of | https://aclanthology.org/2021.emnlp-main.692 | https://aclanthology.org/2021.emnlp-main.692.pdf | emnlp-2021-11 | ['dialect-identification'] | ['natural-language-processing'] | [-1.54948354e-01 -2.01004013e-01 1.04457103e-01 -4.15429443e-01
-1.08940470e+00 -1.21774173e+00 6.96549594e-01 6.65662363e-02
-3.34647268e-01 4.64822203e-01 5.65652728e-01 -2.73408890e-01
-2.19023805e-02 -5.46002746e-01 -1.72052473e-01 -2.86601573e-01
-1.42841609e-02 6.80138648e-01 1.31070390e-01 -5.73761523... | [14.19363784790039, 6.657234191894531] |
5038a6c7-c38a-4c75-913b-184721d8c10f | first-target-and-opinion-then-polarity | 2102.08549 | null | https://arxiv.org/abs/2102.08549v3 | https://arxiv.org/pdf/2102.08549v3.pdf | First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities. Existing methods are short on building correlation between target-opinion pairs, and neglect the mutual interference among ... | ['Houfeng Wang', 'Dawei Yin', 'Zhicong Cheng', 'Xiaodong Zhang', 'Tianyu Liu', 'Sujian Li', 'Peiyi Wang', 'Lianzhe Huang'] | 2021-02-17 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.69196367e-01 -1.69194490e-01 -2.95758188e-01 -6.54511929e-01
-5.68483949e-01 -9.27819371e-01 4.13565934e-01 2.93721795e-01
-5.11337779e-02 6.13957107e-01 7.59697616e-01 -2.01137707e-01
1.86496735e-01 -7.34641433e-01 -2.80600458e-01 -5.97966790e-01
3.96966010e-01 1.51773214e-01 5.74595947e-03 -3.78400594... | [11.491887092590332, 6.6357879638671875] |
54b60e72-c7d4-4d82-9fac-3b32b9ebfde6 | multi-channel-reverse-dictionary-model | 1912.08441 | null | https://arxiv.org/abs/1912.08441v2 | https://arxiv.org/pdf/1912.08441v2.pdf | Multi-channel Reverse Dictionary Model | A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word infer... | ['Qun Liu', 'Fanchao Qi', 'Maosong Sun', 'Lei Zhang', 'Zhiyuan Liu', 'Yasheng Wang'] | 2019-12-18 | null | null | null | null | ['reverse-dictionary'] | ['natural-language-processing'] | [ 9.47393775e-02 -4.27887857e-01 -6.06959701e-01 -4.72672582e-01
-1.12286425e+00 -7.54058719e-01 5.98532200e-01 2.59552449e-01
-6.84493303e-01 5.79758286e-01 5.46470225e-01 -1.54201344e-01
3.35794210e-01 -6.68296933e-01 -4.12073106e-01 -2.75801063e-01
4.12345380e-01 6.30090952e-01 9.80533659e-02 -4.49732363... | [11.208540916442871, 8.982477188110352] |
1f339b04-9a68-48b5-bd7d-7fd6120ab0ee | ltu-attacker-for-membership-inference | 2202.02278 | null | https://arxiv.org/abs/2202.02278v1 | https://arxiv.org/pdf/2202.02278v1.pdf | LTU Attacker for Membership Inference | We address the problem of defending predictive models, such as machine learning classifiers (Defender models), against membership inference attacks, in both the black-box and white-box setting, when the trainer and the trained model are publicly released. The Defender aims at optimizing a dual objective: utility and pr... | ['Isabelle Guyon', 'Wei-Wei Tu', 'Haozhe Sun', 'Jiangnan Huang', 'Rafael Muñoz-Gómez', 'Joseph Pedersen'] | 2022-02-04 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.87806064e-01 2.68595397e-01 -1.75824255e-01 -1.47941992e-01
-7.32516289e-01 -1.38323498e+00 2.44113341e-01 2.18029603e-01
-6.04106545e-01 6.72233164e-01 -5.08244038e-01 -7.22306669e-01
-2.16775388e-01 -9.15870488e-01 -1.01446354e+00 -1.21603835e+00
-1.09858572e-01 3.39979470e-01 -2.14205161e-01 1.28586501... | [5.886078834533691, 7.114137172698975] |
aaf8dafe-c3f1-4637-b57b-2f007cd5509f | towards-high-accuracy-named-entity | null | null | https://aclanthology.org/W19-6142 | https://aclanthology.org/W19-6142.pdf | Towards High Accuracy Named Entity Recognition for Icelandic | We report on work in progress which consists of annotating an Icelandic corpus for named entities (NEs) and using it for training a named entity recognizer based on a Bidirectional Long Short-Term Memory model. Currently, we have annotated 7,538 NEs appearing in the first 200,000 tokens of a 1 million token corpus, MIM... | ['Hrafn Loftsson', 'Sigurjón Þorsteinsson', 'Svanhvít Lilja Ingólfsdóttir'] | null | null | null | null | ws-nodalida-2019-9 | ['miscellaneous'] | ['miscellaneous'] | [-5.96726120e-01 4.00247395e-01 -1.58551902e-01 -3.85187954e-01
-8.06243718e-01 -6.96615934e-01 6.41541779e-01 5.33717632e-01
-1.24571860e+00 9.99458492e-01 7.81322837e-01 -2.36229166e-01
4.55660671e-01 -8.13991368e-01 -2.78849661e-01 -2.00393453e-01
-3.20478797e-01 7.73406088e-01 1.19467802e-01 -2.28994247... | [9.74045467376709, 9.634249687194824] |
c94b208e-aa32-4638-983f-6a2b68320a50 | unsupervised-shape-and-pose-disentanglement | 2007.11341 | null | https://arxiv.org/abs/2007.11341v1 | https://arxiv.org/pdf/2007.11341v1.pdf | Unsupervised Shape and Pose Disentanglement for 3D Meshes | Parametric models of humans, faces, hands and animals have been widely used for a range of tasks such as image-based reconstruction, shape correspondence estimation, and animation. Their key strength is the ability to factor surface variations into shape and pose dependent components. Learning such models requires lots... | ['Gerard Pons-Moll', 'Bharat Lal Bhatnagar', 'Keyang Zhou'] | 2020-07-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4065_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670341.pdf | eccv-2020-8 | ['pose-transfer'] | ['computer-vision'] | [-2.43148468e-02 -6.47570938e-03 2.25635711e-02 -6.29215837e-01
-5.16097665e-01 -6.39864564e-01 6.08356714e-01 -2.29783118e-01
-1.57024905e-01 5.80307364e-01 -5.96862622e-02 2.77076513e-01
-1.65074557e-01 -5.79477012e-01 -8.12404990e-01 -6.69175267e-01
-3.15693319e-02 8.99266124e-01 4.63924222e-02 -1.14815310... | [7.050345420837402, -1.3548616170883179] |
8ac8d84d-00df-4116-89ad-0f2bf067e884 | real-time-single-channel-dereverberation-and | null | null | https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2290.pdf | https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2290.pdf | Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network | We investigate the recently proposed Time-domain Audio Sep-aration Network (TasNet) in the task of real-time single-channel speech dereverberation. Unlike systems that take time-frequency representation of the audio as input, TasNet learns anadaptive front-end in replacement of the time-frequency rep-res... | ['Nima Mesgarani', 'Yi Luo'] | 2018-09-02 | null | null | null | isca-interspeech-2018-9 | ['speech-dereverberation'] | ['speech'] | [-7.41763189e-02 -5.07667959e-02 4.35236603e-01 -3.03555161e-01
-1.26661074e+00 -4.55624729e-01 2.39621058e-01 -6.42024457e-01
-1.90192625e-01 7.03099906e-01 8.97361398e-01 -3.52941930e-01
-4.71943766e-02 -4.13667381e-01 -8.88959169e-01 -9.50054944e-01
-3.63090426e-01 -1.37414038e-01 -2.84151256e-01 -1.74528942... | [15.123488426208496, 5.962754726409912] |
a204458b-2cef-4231-9433-41fc6a4894f9 | landmarkboost-efficient-visual-context | 1807.04702 | null | http://arxiv.org/abs/1807.04702v2 | http://arxiv.org/pdf/1807.04702v2.pdf | LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization | The growing popularity of autonomous systems creates a need for reliable and
efficient metric pose retrieval algorithms. Currently used approaches tend to
rely on nearest neighbor search of binary descriptors to perform the 2D-3D
matching and guarantee realtime capabilities on mobile platforms. These methods
struggle, ... | ['Simon Lynen', 'Juan Nieto', 'Roland Siegwart', 'Igor Gilitschenski', 'Bernhard Zeisl', 'Marcin Dymczyk'] | 2018-07-12 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [-1.32442281e-01 -6.86782360e-01 -5.13342202e-01 -3.93073469e-01
-9.80117619e-01 -7.84200549e-01 1.10433722e+00 4.60878193e-01
-4.98874813e-01 3.19178343e-01 1.29599366e-02 -1.53866764e-02
-4.52651083e-01 -6.61183596e-01 -5.24427593e-01 -4.26111549e-01
-1.93651706e-01 6.96156681e-01 5.85571945e-01 -3.24379534... | [7.6947126388549805, -2.1235320568084717] |
3b075b99-f3f2-4e37-ba37-9f7bcd785554 | reusable-slotwise-mechanisms | 2302.10503 | null | https://arxiv.org/abs/2302.10503v1 | https://arxiv.org/pdf/2302.10503v1.pdf | Reusable Slotwise Mechanisms | Agents that can understand and reason over the dynamics of objects can have a better capability to act robustly and generalize to novel scenarios. Such an ability, however, requires a suitable representation of the scene as well as an understanding of the mechanisms that govern the interactions of different subsets of ... | ['Yoshua Bengio', 'Dianbo Liu', 'Kartik Ahuja', 'Khuong Nguyen', 'Kanika Madan', 'Amin Mansouri', 'Trang Nguyen'] | 2023-02-21 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.14471802e-01 2.31480622e-03 -8.35651681e-02 -2.26436853e-01
-1.22482460e-02 -7.24747777e-01 9.17732000e-01 2.00839147e-01
-3.56857330e-01 6.40834033e-01 1.49173811e-01 -3.18318844e-01
-6.34736478e-01 -1.02237391e+00 -1.05413425e+00 -5.55798471e-01
-7.10218430e-01 1.04146051e+00 5.48620939e-01 -7.29008555... | [4.865527629852295, 0.6498308181762695] |
f765dce2-fdb4-46e3-b648-27ace5df31a6 | multi-view-information-bottleneck-without | 2204.10530 | null | https://arxiv.org/abs/2204.10530v1 | https://arxiv.org/pdf/2204.10530v1.pdf | Multi-view Information Bottleneck Without Variational Approximation | By "intelligently" fusing the complementary information across different views, multi-view learning is able to improve the performance of classification tasks. In this work, we extend the information bottleneck principle to a supervised multi-view learning scenario and use the recently proposed matrix-based R{\'e}nyi's... | ['Badong Chen', 'Jingmin Xin', 'Shujian Yu', 'Qi Zhang'] | 2022-04-22 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.82158607e-02 1.08648486e-01 -3.58382612e-01 -4.38457102e-01
-1.15458274e+00 -5.54406166e-01 3.77205312e-01 -6.66983500e-02
-2.82205582e-01 8.33133698e-01 2.06124425e-01 -1.45912588e-01
-3.38290960e-01 -4.09524769e-01 -5.64262390e-01 -7.80451298e-01
4.85925712e-02 1.40918300e-01 -4.08842653e-01 -2.90487595... | [8.476968765258789, 4.448549270629883] |
59948f24-7d75-4ae6-8bbb-af17a8f2dbce | sat2density-faithful-density-learning-from | 2303.14672 | null | https://arxiv.org/abs/2303.14672v1 | https://arxiv.org/pdf/2303.14672v1.pdf | Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs | This paper aims to develop an accurate 3D geometry representation of satellite images using satellite-ground image pairs. Our focus is on the challenging problem of generating ground-view panoramas from satellite images. We draw inspiration from the density field representation used in volumetric neural rendering and p... | ['Nan Xue', 'Gui-Song Xia', 'Jincheng Xiong', 'Ming Qian'] | 2023-03-26 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 1.70083568e-01 1.28584698e-01 -2.26020172e-01 -3.67125690e-01
-8.19793940e-01 -4.91838187e-01 8.55869472e-01 -4.69620556e-01
2.21151754e-01 6.19513452e-01 1.56112000e-01 -3.77678216e-01
1.89124256e-01 -1.65462875e+00 -1.02375114e+00 -6.22524202e-01
-5.26623093e-02 7.86034822e-01 1.21204354e-01 -3.67613077... | [9.212709426879883, -3.2269275188446045] |
1fe54056-8339-4582-8161-c85749dcb190 | rosenthal-type-inequalities-for-linear | 2303.05838 | null | https://arxiv.org/abs/2303.05838v2 | https://arxiv.org/pdf/2303.05838v2.pdf | Rosenthal-type inequalities for linear statistics of Markov chains | In this paper, we establish novel deviation bounds for additive functionals of geometrically ergodic Markov chains similar to Rosenthal and Bernstein inequalities for sums of independent random variables. We pay special attention to the dependence of our bounds on the mixing time of the corresponding chain. More precis... | ['Marina Sheshukova', 'Sergey Samsonov', 'Alexey Naumov', 'Eric Moulines', 'Alain Durmus'] | 2023-03-10 | null | null | null | null | ['type'] | ['speech'] | [ 4.55935225e-02 2.88260411e-02 5.78892902e-02 8.98118615e-02
-5.44670522e-01 -7.32222736e-01 3.00180346e-01 1.75948530e-01
-3.59245539e-01 9.60889578e-01 1.77839085e-01 -3.94703537e-01
-3.67686868e-01 -5.84840059e-01 -6.04144633e-01 -1.13854992e+00
-1.55560464e-01 7.23044455e-01 7.19541535e-02 -5.94243035... | [6.932143211364746, 4.2244367599487305] |
d54e108d-21c4-428a-af26-fb272d8b50a8 | diffusum-generation-enhanced-extractive | 2305.01735 | null | https://arxiv.org/abs/2305.01735v2 | https://arxiv.org/pdf/2305.01735v2.pdf | DiffuSum: Generation Enhanced Extractive Summarization with Diffusion | Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by making individual sentence label predictions. This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generatin... | ['Jiawei Zhang', 'Xiao Liu', 'Haopeng Zhang'] | 2023-05-02 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.57389385e-01 3.87014568e-01 -3.77916247e-01 -4.14289117e-01
-1.23431480e+00 -5.03796041e-01 6.18892550e-01 2.31098697e-01
-3.76286924e-01 9.94762421e-01 9.42553878e-01 -7.96372592e-02
2.63766199e-01 -6.82310343e-01 -6.35655046e-01 -4.09179389e-01
1.41201645e-01 2.87045270e-01 -1.15986899e-01 -2.98648208... | [12.439910888671875, 9.427226066589355] |
184bd96c-5c51-4fa8-967c-f02bb126e811 | 1cademy-at-semeval-2022-task-1-investigating | 2206.03702 | null | https://arxiv.org/abs/2206.03702v1 | https://arxiv.org/pdf/2206.03702v1.pdf | 1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task | This paper describes our system for the SemEval2022 task of matching dictionary glosses to word embeddings. We focus on the Reverse Dictionary Track of the competition, which maps multilingual glosses to reconstructed vector representations. More specifically, models convert the input of sentences to three types of emb... | ['Nineli Lashkarashvili', 'Ge Zhang', 'Zhiyong Wang'] | 2022-06-08 | null | https://aclanthology.org/2022.semeval-1.2 | https://aclanthology.org/2022.semeval-1.2.pdf | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-4.65108305e-01 -1.57312900e-01 -4.55816060e-01 -3.36034238e-01
-1.04037404e+00 -7.12721229e-01 7.88548768e-01 -1.66210249e-01
-8.47574353e-01 6.84123099e-01 6.39190495e-01 -6.76333547e-01
2.83938646e-01 -4.90107208e-01 -4.09627527e-01 -4.05293196e-01
2.26219580e-01 8.27724814e-01 -1.30921751e-01 -7.37719953... | [10.93530559539795, 9.890972137451172] |
95d5dfb7-2c02-4c47-b66d-aebdb308a52e | near-field-mimo-isar-millimeter-wave-imaging | 2305.02030 | null | https://arxiv.org/abs/2305.02030v1 | https://arxiv.org/pdf/2305.02030v1.pdf | Near-Field MIMO-ISAR Millimeter-Wave Imaging | Multiple-input-multiple-output (MIMO) millimeter-wave (mmWave) sensors for synthetic aperture radar (SAR) and inverse SAR (ISAR) address the fundamental challenges of cost-effectiveness and scalability inherent to near-field imaging. In this paper, near-field MIMO-ISAR mmWave imaging systems are discussed and developed... | ['Murat Torlak', 'Muhammet Emin Yanik', 'Josiah W. Smith'] | 2023-05-03 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.44310176e-01 -2.30621606e-01 7.74396420e-01 -6.02593601e-01
-7.13156104e-01 -6.08654976e-01 5.60115397e-01 -1.01799607e+00
-4.80803460e-01 4.14344102e-01 -2.17421889e-01 -5.50526500e-01
-9.95751560e-01 -8.65181386e-01 -3.21511090e-01 -6.11863732e-01
-2.32226346e-02 9.82894301e-01 -1.38573706e-01 -3.15235645... | [6.776394844055176, 0.9193317890167236] |
3e8eaf5e-c8da-4028-ab29-c435bb19c3c6 | learning-context-aware-embedding-for-person | 2111.14316 | null | https://arxiv.org/abs/2111.14316v1 | https://arxiv.org/pdf/2111.14316v1.pdf | Learning Context-Aware Embedding for Person Search | Person Search is a relevant task that aims to jointly solve Person Detection and Person Re-identification(re-ID). Though most previous methods focus on learning robust individual features for retrieval, it's still hard to distinguish confusing persons because of illumination, large pose variance, and occlusion. Context... | ['Boxun Li', 'Yueqing Zhuang', 'Shihui Chen'] | 2021-11-29 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-8.53167288e-03 -6.34862185e-01 2.52760295e-02 -4.20387775e-01
-7.02397048e-01 -2.62567043e-01 6.36414707e-01 8.87994021e-02
-8.62698555e-01 6.12179399e-01 4.76117432e-01 2.51802325e-01
-2.12407172e-01 -6.13877237e-01 -3.32533717e-01 -4.26306188e-01
1.56185314e-01 4.79306370e-01 1.74456567e-01 4.85440791... | [14.773531913757324, 0.7852233052253723] |
3a90da02-5bcb-44ba-b4dd-fa0499e1c82c | g-2-uardfl-safeguarding-federated-learning | 2306.04984 | null | https://arxiv.org/abs/2306.04984v1 | https://arxiv.org/pdf/2306.04984v1.pdf | G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering | As a collaborative paradigm, Federated Learning (FL) empowers clients to engage in collective model training without exchanging their respective local data. Nevertheless, FL remains vulnerable to backdoor attacks in which an attacker compromises malicious clients, and injects poisoned model weights into the aggregation... | ['Ming Ding', 'Zhe Liu', 'Xinwang Liu', 'Meng Liu', 'Chuan Ma', 'Hao Yu'] | 2023-06-08 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.41586334e-02 1.62532583e-01 -3.37983072e-01 1.93177551e-01
-5.56620359e-01 -1.14359856e+00 6.27674282e-01 1.23709537e-01
2.85774358e-02 2.57523179e-01 -3.49073768e-01 -9.04674232e-01
-2.12773010e-02 -1.02818930e+00 -5.89385211e-01 -9.19349313e-01
-3.86021167e-01 3.71858388e-01 3.78816575e-01 -7.82472417... | [5.699354648590088, 7.2299885749816895] |
9f1ce698-16b3-49b5-acdf-1309e714bd6a | gammatonegram-representation-for-end-to-end | 2307.03296 | null | https://arxiv.org/abs/2307.03296v1 | https://arxiv.org/pdf/2307.03296v1.pdf | Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment | Dysarthria is a disability that causes a disturbance in the human speech system and reduces the quality and intelligibility of a person's speech. Because of this effect, the normal speech processing systems can not work properly on impaired speech. This disability is usually associated with physical disabilities. There... | ['Hadi Veisi', 'Aref Farhadipour'] | 2023-07-06 | null | null | null | null | ['transfer-learning', 'speech-recognition', 'speaker-identification'] | ['miscellaneous', 'speech', 'speech'] | [-1.82357822e-02 -1.66797400e-01 3.61062855e-01 -2.67895967e-01
-3.21168780e-01 -3.86126228e-02 1.76957503e-01 -5.68585336e-01
-4.25538242e-01 3.79113406e-01 4.22187716e-01 -3.59364241e-01
4.97995615e-02 -9.15473163e-01 -1.54132545e-01 -6.87711895e-01
5.60273290e-01 1.19507030e-01 4.51029837e-02 -4.98363703... | [14.480653762817383, 5.9855756759643555] |
69ebad60-d88a-4ae6-ba25-38b4b293fbb0 | inferem-inferring-the-speaker-s-intention-for | 2212.06373 | null | https://arxiv.org/abs/2212.06373v6 | https://arxiv.org/pdf/2212.06373v6.pdf | InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation | Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance ... | ['Zhigang Zeng', 'XiaoPing Wang', 'Jiang Li', 'Guoqing Lv'] | 2022-12-13 | null | null | null | null | ['response-generation', 'empathetic-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-7.06642270e-02 8.12811315e-01 -1.48962975e-01 -6.66467249e-01
-8.25860679e-01 -8.90925452e-02 6.59440756e-01 -1.14623398e-01
-4.09559071e-01 7.86092758e-01 1.10019290e+00 1.36639535e-01
5.67644656e-01 -6.11425102e-01 -1.14632100e-01 -6.19025469e-01
6.73407495e-01 3.78257781e-01 -5.42040050e-01 -7.77033627... | [13.05813217163086, 7.695221900939941] |
be0766c4-153c-4455-a0bc-65a9b3dd1a63 | multiscale-flow-for-robust-and-optimal | 2306.04689 | null | https://arxiv.org/abs/2306.04689v1 | https://arxiv.org/pdf/2306.04689v1.pdf | Multiscale Flow for Robust and Optimal Cosmological Analysis | We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing. Multiscale Flow uses hierarchical decomposition of cosmological fields via a wavelet basis, and then models different wavelet components separat... | ['Uros Seljak', 'Biwei Dai'] | 2023-06-07 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-3.16352814e-01 -1.47916704e-01 4.46326286e-01 -2.90309161e-01
-7.63219476e-01 -8.20555806e-01 1.20330989e+00 -2.00485379e-01
-1.28383085e-01 5.91753006e-01 4.55128342e-01 -1.27478749e-01
-2.03188181e-01 -1.23340404e+00 -7.82982647e-01 -1.21430504e+00
-3.73845279e-01 7.05942035e-01 5.93053162e-01 1.46094393... | [7.0142621994018555, 3.70684814453125] |
32ccfc7d-1227-4d39-a129-5961aea75ef6 | a-survey-on-out-of-distribution-evaluation-of | 2306.15261 | null | https://arxiv.org/abs/2306.15261v1 | https://arxiv.org/pdf/2306.15261v1.pdf | A Survey on Out-of-Distribution Evaluation of Neural NLP Models | Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. In this survey, we 1) compare the... | ['Wray Buntine', 'Shang Gao', 'Ming Liu', 'Xinzhe Li'] | 2023-06-27 | null | null | null | null | ['adversarial-robustness', 'domain-generalization'] | ['adversarial', 'methodology'] | [ 2.71954864e-01 2.24664018e-01 -7.80840039e-01 -4.46536243e-01
-9.20744538e-01 -1.40216982e+00 7.11089551e-01 7.75585398e-02
-5.15963078e-01 1.07061267e+00 1.10823065e-01 -3.40067565e-01
-2.54148424e-01 -4.50601906e-01 -8.38855684e-01 -4.94471133e-01
-5.01452154e-03 3.73587042e-01 -4.78651002e-02 -1.05010919... | [9.978617668151855, 3.223422050476074] |
e901849d-0996-4409-9500-dc204d68f398 | ku-nlp-lt-edi-eacl2021-a-multilingual-hope | null | null | https://aclanthology.org/2021.ltedi-1.10 | https://aclanthology.org/2021.ltedi-1.10.pdf | KU_NLP@LT-EDI-EACL2021: A Multilingual Hope Speech Detection for Equality, Diversity, and Inclusion using Context Aware Embeddings | Hope speech detection is a new task for finding and highlighting positive comments or supporting content from user-generated social media comments. For this task, we have used a Shared Task multilingual dataset on Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) for three languages English, code-s... | ['Ajees A P', 'Junaida M K'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.88948677e-02 2.43731990e-01 -2.34308094e-01 -9.67362523e-02
-1.23133564e+00 -4.24803436e-01 1.04453278e+00 6.45679772e-01
-6.65923774e-01 7.11986363e-01 1.28264558e+00 -3.96595001e-01
2.37650678e-01 -1.57618448e-01 -5.57749532e-02 -4.14028168e-01
-5.06402180e-02 2.84404516e-01 -2.62575150e-02 -5.10821104... | [9.252873420715332, 10.714736938476562] |
0487b001-9aee-49f0-b3f8-4253af2cb5ee | preventing-gradient-explosions-in-gated | null | null | http://papers.nips.cc/paper/6647-preventing-gradient-explosions-in-gated-recurrent-units | http://papers.nips.cc/paper/6647-preventing-gradient-explosions-in-gated-recurrent-units.pdf | Preventing Gradient Explosions in Gated Recurrent Units | A gated recurrent unit (GRU) is a successful recurrent neural network architecture for time-series data. The GRU is typically trained using a gradient-based method, which is subject to the exploding gradient problem in which the gradient increases significantly. This problem is caused by an abrupt change in the dynamic... | ['Yasuhiro Fujiwara', 'Sekitoshi Kanai', 'Sotetsu Iwamura'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['music-modeling'] | ['music'] | [-4.59660962e-03 -2.81846881e-01 -7.24917203e-02 7.54768699e-02
1.07962983e-02 -2.77909935e-01 2.81318694e-01 -3.90398026e-01
-2.99784809e-01 5.74329138e-01 -4.19420227e-02 -4.25283939e-01
2.31283605e-01 -6.99174106e-01 -8.73400509e-01 -7.77512252e-01
-1.67682931e-01 -2.42134690e-01 4.10851806e-01 -5.79953969... | [7.218855857849121, 3.4347870349884033] |
25f036bb-8a60-41bb-9659-f56888538e83 | model-and-evaluation-towards-fairness-in | 2303.15697 | null | https://arxiv.org/abs/2303.15697v1 | https://arxiv.org/pdf/2303.15697v1.pdf | Model and Evaluation: Towards Fairness in Multilingual Text Classification | Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classification is still very limited. In this paper, we focus on the task of ... | ['Aimin Yang', 'Dong Zhou', 'Zhenghang Tang', 'Junheng He', 'Nankai Lin'] | 2023-03-28 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-4.84403133e-01 -1.39579371e-01 -8.23182106e-01 -6.56975448e-01
-6.62351072e-01 -4.07144070e-01 8.62990797e-01 4.82604921e-01
-7.99528003e-01 9.32898283e-01 2.72057921e-01 -5.27362227e-01
1.80342138e-01 -6.35219276e-01 -2.04665978e-02 -6.25778437e-01
7.01383531e-01 6.04109526e-01 -1.93981946e-01 -4.74482447... | [10.25900650024414, 10.089192390441895] |
432bf262-ad57-48f8-9d8b-1be955d7895f | physics-assisted-deep-learning-for-fmcw-radar | 2307.02119 | null | https://arxiv.org/abs/2307.02119v1 | https://arxiv.org/pdf/2307.02119v1.pdf | Physics-assisted Deep Learning for FMCW Radar Quantitative Imaging of Two-dimension Target | Radar imaging is crucial in remote sensing and has many applications in detection and autonomous driving. However, the received radar signal for imaging is enormous and redundant, which degrades the speed of real-time radar quantitative imaging and leads to obstacles in the downlink applications. In this paper, we prop... | ['Feng Xu', 'Huilin Xu', 'Zhuoyang Liu'] | 2023-07-05 | null | null | null | null | ['denoising', 'autonomous-driving'] | ['computer-vision', 'computer-vision'] | [ 5.46197951e-01 -6.25968516e-01 5.22275925e-01 -6.57987952e-01
-7.29783595e-01 2.09096044e-01 1.55552417e-01 -7.51405358e-01
-5.19776225e-01 6.56687975e-01 2.81620063e-02 -1.00221574e-01
-7.27994263e-01 -7.16718495e-01 -4.15972799e-01 -1.12473238e+00
-2.52243340e-01 7.43906498e-02 -1.27514020e-01 -1.86530784... | [10.488348007202148, -2.21437406539917] |
7b4c5e89-e5ca-4948-bb8b-c10b245cdd3c | spatial-temporal-transformer-for-video | 2209.01578 | null | https://arxiv.org/abs/2209.01578v2 | https://arxiv.org/pdf/2209.01578v2.pdf | Spatial-Temporal Transformer for Video Snapshot Compressive Imaging | Video snapshot compressive imaging (SCI) captures multiple sequential video frames by a single measurement using the idea of computational imaging. The underlying principle is to modulate high-speed frames through different masks and these modulated frames are summed to a single measurement captured by a low-speed 2D s... | ['Xin Yuan', 'Yong Zhong', 'Miao Cao', 'Lishun Wang'] | 2022-09-04 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.01950884e-01 -2.68260658e-01 1.49634868e-01 1.65955853e-02
-5.61008453e-01 -3.84956419e-01 4.47386116e-01 -7.56642342e-01
-1.58345729e-01 5.11622965e-01 3.04050237e-01 -8.92480984e-02
6.29639253e-02 -4.16654974e-01 -7.70000577e-01 -9.76370573e-01
-3.11192293e-02 -4.64168698e-01 3.04933995e-01 3.71544003... | [11.030563354492188, -2.1526641845703125] |
555eb9ca-af27-4139-94ca-d784db2df99f | triple-cooperative-video-shadow-detection | 2103.06533 | null | https://arxiv.org/abs/2103.06533v1 | https://arxiv.org/pdf/2103.06533v1.pdf | Triple-cooperative Video Shadow Detection | Shadow detection in a single image has received significant research interest in recent years. However, much fewer works have been explored in shadow detection over dynamic scenes. The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection. In this work, we collect... | ['Jing Qin', 'Wennan Liu', 'Huazhu Fu', 'Jia Shen', 'Lei Zhu', 'Liang Wan', 'Zhihao Chen'] | 2021-03-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Triple-Cooperative_Video_Shadow_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Triple-Cooperative_Video_Shadow_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['shadow-detection'] | ['computer-vision'] | [ 6.98545873e-01 -2.07255304e-01 -4.63225424e-01 -3.31066251e-01
-4.33541030e-01 -1.01350114e-01 1.34789273e-01 -5.66861749e-01
-2.68871635e-01 6.84181631e-01 1.64528325e-01 -2.12285683e-01
5.80079734e-01 -1.79753155e-01 -9.76989985e-01 -8.70208979e-01
8.42419714e-02 -1.17818207e-01 1.44548917e+00 7.02481866... | [10.838957786560059, -4.1045331954956055] |
c6a40cf0-1c7c-4939-a1cc-0afe65090d98 | investigating-the-use-of-one-class-support | 2202.12074 | null | https://arxiv.org/abs/2202.12074v1 | https://arxiv.org/pdf/2202.12074v1.pdf | Investigating the Use of One-Class Support Vector Machine for Software Defect Prediction | Early software defect identification is considered an important step towards software quality assurance. Software defect prediction aims at identifying software components that are likely to cause faults before a software is made available to the end-user. To date, this task has been modeled as a two-class classificati... | ['Federica Sarro', 'Danielle Azar', 'Rebecca Moussa'] | 2022-02-24 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.35045648e-01 4.94594313e-02 -4.29138720e-01 -5.18693030e-01
-4.79541093e-01 -9.31322277e-02 1.93328470e-01 5.72021365e-01
1.23289473e-01 3.49102765e-01 -3.76719803e-01 -6.74743116e-01
-3.03789049e-01 -6.70457840e-01 -3.64292681e-01 -3.91119033e-01
5.78057170e-02 2.72439122e-01 1.52877718e-01 -7.55578578... | [7.587364196777344, 7.688073635101318] |
2427e1fd-d0f6-4b1f-a1ed-8b297c13a2fb | learning-to-detect-scene-landmarks-for-camera | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Do_Learning_To_Detect_Scene_Landmarks_for_Camera_Localization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Do_Learning_To_Detect_Scene_Landmarks_for_Camera_Localization_CVPR_2022_paper.pdf | Learning To Detect Scene Landmarks for Camera Localization | Modern camera localization methods that use image retrieval, feature matching, and 3D structure-based pose estimation require long-term storage of numerous scene images or a vast amount of image features. This can make them unsuitable for resource constrained VR/AR devices and also raises serious privacy concerns. ... | ['Sudipta N. Sinha', 'Hyun Soo Park', 'Joseph DeGol', 'Ondrej Miksik', 'Tien Do'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['camera-localization'] | ['computer-vision'] | [-5.10092787e-02 -2.65085131e-01 -3.06316435e-01 -3.78998280e-01
-1.03773093e+00 -8.90532076e-01 3.75444055e-01 1.82920635e-01
-5.07039666e-01 1.44748241e-01 -8.41506422e-02 -5.38056977e-02
1.71185985e-01 -5.25895834e-01 -1.21661103e+00 -2.02370197e-01
-2.51346547e-02 3.84989887e-01 3.36663693e-01 1.82401001... | [7.647117614746094, -2.2774338722229004] |
93735b83-cd14-4421-b0ec-9930fc7e6078 | semantic-human-parsing-via-scalable-semantic | 2304.04140 | null | https://arxiv.org/abs/2304.04140v1 | https://arxiv.org/pdf/2304.04140v1.pdf | Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains | This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i.e. various levels of label granularity) to train a powerful human parsing network. In practice, two common application scenarios are addressed, term... | ['Ruimao Zhang', 'Junle Wang', 'Zhen Li', 'Chaoqun Wang', 'Jie Yang'] | 2023-04-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Semantic_Human_Parsing_via_Scalable_Semantic_Transfer_Over_Multiple_Label_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Semantic_Human_Parsing_via_Scalable_Semantic_Transfer_Over_Multiple_Label_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-parsing'] | ['computer-vision'] | [ 3.56373787e-01 5.87872744e-01 -3.97608399e-01 -5.71657240e-01
-7.76407182e-01 -5.87365150e-01 3.38677108e-01 -1.11150332e-01
-3.87918949e-01 7.64197350e-01 4.52978630e-03 -6.30593896e-02
1.79049164e-01 -9.39343274e-01 -8.10101032e-01 -7.04611957e-01
3.87764841e-01 6.61697328e-01 4.83720839e-01 -2.04910785... | [8.975884437561035, 0.3252985179424286] |
fdce9102-1b72-44a8-a28f-58e7dcbdf4b8 | pointer-networks | 1506.03134 | null | http://arxiv.org/abs/1506.03134v2 | http://arxiv.org/pdf/1506.03134v2.pdf | Pointer Networks | We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because th... | ['Navdeep Jaitly', 'Oriol Vinyals', 'Meire Fortunato'] | 2015-06-09 | pointer-networks-1 | http://papers.nips.cc/paper/5866-pointer-networks | http://papers.nips.cc/paper/5866-pointer-networks.pdf | neurips-2015-12 | ['point-cloud-completion'] | ['computer-vision'] | [ 5.54259837e-01 2.26306185e-01 -1.99652448e-01 -2.68743068e-01
-7.69706547e-01 -8.66005361e-01 2.55185038e-01 2.59403020e-01
-6.52330577e-01 7.69579232e-01 5.26897190e-03 -6.80903673e-01
3.73300940e-01 -1.15437722e+00 -1.45309222e+00 -6.58545256e-01
-3.00567061e-01 1.00571597e+00 -3.58509608e-02 -1.61965877... | [10.499699592590332, 7.494303226470947] |
02220314-d5e1-4c9f-baee-4d09dacfe31c | fast-color-constancy-with-patch-wise-bright | 1911.07177 | null | https://arxiv.org/abs/1911.07177v1 | https://arxiv.org/pdf/1911.07177v1.pdf | Fast Color Constancy with Patch-wise Bright Pixels | In this paper, a learning-free color constancy algorithm called the Patch-wise Bright Pixels (PBP) is proposed. In this algorithm, an input image is first downsampled and then cut equally into a few patches. After that, according to the modified brightness of each patch, a proper fraction of brightest pixels in the pat... | ['xiangyang xue', 'Yiyao Shi', 'Jian Wang'] | 2019-11-17 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.13777828e-01 -4.62893069e-01 -6.48186682e-03 -2.49901935e-01
-6.06030345e-01 -2.67715573e-01 -5.99101409e-02 -2.55044520e-01
-4.43383425e-01 7.24120021e-01 -6.37982845e-01 -2.17230201e-01
3.74339312e-01 -9.41838801e-01 -7.51577854e-01 -9.29232061e-01
1.83911458e-01 -3.33755642e-01 4.22259688e-01 -1.89723913... | [10.518280029296875, -2.549161672592163] |
2f8641d2-7b80-454d-8e4e-97a7a584016d | segmentation-by-detection-a-cascade-network | 1711.00139 | null | http://arxiv.org/abs/1711.00139v1 | http://arxiv.org/pdf/1711.00139v1.pdf | Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation | We propose an attention mechanism for 3D medical image segmentation. The
method, named segmentation-by-detection, is a cascade of a detection module
followed by a segmentation module. The detection module enables a region of
interest to come to attention and produces a set of object region candidates
which are further ... | ['Zichen Zhang', 'Min Tang', 'Jacob L. Jaremko', 'Martin Jagersand', 'Dana Cobzas'] | 2017-10-31 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 1.57093778e-01 6.40539110e-01 9.72990766e-02 -2.28695452e-01
-6.25383794e-01 1.21356763e-01 5.08368090e-02 5.30315697e-01
-5.70603132e-01 3.69125962e-01 4.14052084e-02 -8.48836824e-02
3.93579185e-01 -8.21488440e-01 -5.76398015e-01 -7.32119262e-01
-2.37038866e-01 4.07847106e-01 7.49610841e-01 7.45328665... | [14.595184326171875, -2.450671434402466] |
c56e4d9e-a537-43ac-a84d-7d633f527cf0 | collaborative-filtering-via-sparse-markov | 1602.02842 | null | http://arxiv.org/abs/1602.02842v1 | http://arxiv.org/pdf/1602.02842v1.pdf | Collaborative filtering via sparse Markov random fields | Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov rand... | ['Truyen Tran', 'Dinh Phung', 'Svetha Venkatesh'] | 2016-02-09 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.74454048e-01 -1.42104745e-01 -7.20731914e-01 -7.93853581e-01
-1.90438658e-01 -4.16071117e-01 4.54153627e-01 3.60085033e-02
-1.95286959e-01 4.56182390e-01 6.16186142e-01 -1.83831573e-01
-8.97238910e-01 -7.81906486e-01 -3.14006925e-01 -2.95033365e-01
-4.17876452e-01 5.29845953e-01 1.06316172e-01 -1.01666346... | [9.926579475402832, 5.627002239227295] |
a53ec407-95e3-4766-a94f-6a8b0550e395 | d-crypto-deep-learning-based-analysis-of | 2210.06538 | null | https://arxiv.org/abs/2210.06538v1 | https://arxiv.org/pdf/2210.06538v1.pdf | D-CryptO: Deep learning-based analysis of colon organoid morphology from brightfield images | Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While ... | ['Boyang Zhang', 'Hamidreza Mahyar', 'Nicky Anvari', 'Shravanthi Rajasekar', 'Jerry Gao', 'Abbas Chaudary', 'Alexander Sotra', 'Jocelyn Xu', 'Lyan Abdul'] | 2022-10-12 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-2.10623056e-01 -3.29517871e-01 -7.76799023e-02 4.76171583e-01
-5.45055509e-01 -1.08247530e+00 3.10176641e-01 1.12221217e+00
-3.22678566e-01 3.10001701e-01 2.65420884e-01 -4.02430177e-01
4.28484708e-01 -7.85818994e-01 -8.04933250e-01 -8.45714629e-01
-2.29002178e-01 1.47854179e-01 1.88469574e-01 -1.59617648... | [14.82856273651123, -3.1274001598358154] |
52518123-ec44-4573-b5cc-10ccb17e5044 | hstformer-hierarchical-spatial-temporal | 2301.07322 | null | https://arxiv.org/abs/2301.07322v1 | https://arxiv.org/pdf/2301.07322v1.pdf | HSTFormer: Hierarchical Spatial-Temporal Transformers for 3D Human Pose Estimation | Transformer-based approaches have been successfully proposed for 3D human pose estimation (HPE) from 2D pose sequence and achieved state-of-the-art (SOTA) performance. However, current SOTAs have difficulties in modeling spatial-temporal correlations of joints at different levels simultaneously. This is due to the pose... | ['Ruei-Sung Lin', 'Ming-Chun Huang', 'Jing Xiao', 'Mei Han', 'Ning Zhang', 'YouBao Tang', 'Xiaoye Qian'] | 2023-01-18 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-4.52549011e-01 -2.72185296e-01 -7.89927244e-02 -8.47086161e-02
-8.52262139e-01 -2.08457068e-01 1.88017309e-01 -4.94290084e-01
-2.80239642e-01 4.31693614e-01 2.70093322e-01 1.63484335e-01
-1.43913105e-01 -4.61712956e-01 -8.12506616e-01 -4.70508426e-01
-3.47657233e-01 7.86709726e-01 4.55354989e-01 -5.37827194... | [7.158674240112305, -0.6412318348884583] |
2bb7489b-f48b-46e6-85bf-d6c2411ddfea | densefusion-6d-object-pose-estimation-by | 1901.04780 | null | http://arxiv.org/abs/1901.04780v1 | http://arxiv.org/pdf/1901.04780v1.pdf | DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion | A key technical challenge in performing 6D object pose estimation from RGB-D
image is to fully leverage the two complementary data sources. Prior works
either extract information from the RGB image and depth separately or use
costly post-processing steps, limiting their performances in highly cluttered
scenes and real-... | ['Li Fei-Fei', 'Roberto Martín-Martín', 'Danfei Xu', 'Silvio Savarese', 'Cewu Lu', 'Yuke Zhu', 'Chen Wang'] | 2019-01-15 | densefusion-6d-object-pose-estimation-by-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_DenseFusion_6D_Object_Pose_Estimation_by_Iterative_Dense_Fusion_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_DenseFusion_6D_Object_Pose_Estimation_by_Iterative_Dense_Fusion_CVPR_2019_paper.pdf | cvpr-2019-6 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 7.40099177e-02 -1.47561550e-01 2.80475859e-02 -5.15482903e-01
-7.76367188e-01 -4.71111178e-01 1.61416307e-01 -4.42684628e-02
-4.70652997e-01 2.17677474e-01 -1.58095896e-01 1.89653918e-01
-4.04604189e-02 -6.34854078e-01 -9.02913809e-01 -5.55054188e-01
4.51569967e-02 8.14434767e-01 3.54608893e-01 1.07923321... | [7.31171989440918, -2.511659860610962] |
1f807d01-fdec-4b0c-92d8-b77d070d83b3 | pwc-net-cnns-for-optical-flow-using-pyramid | 1709.02371 | null | http://arxiv.org/abs/1709.02371v3 | http://arxiv.org/pdf/1709.02371v3.pdf | PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume | We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow
estimate to warp the CNN... | ['Ming-Yu Liu', 'Deqing Sun', 'Xiaodong Yang', 'Jan Kautz'] | 2017-09-07 | pwc-net-cnns-for-optical-flow-using-pyramid-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Sun_PWC-Net_CNNs_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Sun_PWC-Net_CNNs_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [-3.20773393e-01 -4.00379241e-01 1.45234391e-01 -1.18778348e-01
-4.41959128e-02 -4.68382478e-01 5.20071507e-01 -3.47032338e-01
-6.34131491e-01 8.98473024e-01 3.38611901e-01 -2.33827710e-01
7.93538466e-02 -8.26527238e-01 -5.42469621e-01 -5.23214996e-01
-4.17958438e-01 9.96719077e-02 4.03507143e-01 -3.90743166... | [8.809690475463867, -1.7994017601013184] |
c52da71e-8b0a-47c0-8ef2-7ca6ebe49bc6 | semantic-enrichment-towards-efficient-speech | 2307.01323 | null | https://arxiv.org/abs/2307.01323v1 | https://arxiv.org/pdf/2307.01323v1.pdf | Semantic enrichment towards efficient speech representations | Over the past few years, self-supervised learned speech representations have emerged as fruitful replacements for conventional surface representations when solving Spoken Language Understanding (SLU) tasks. Simultaneously, multilingual models trained on massive textual data were introduced to encode language agnostic s... | ['Yannick Estève', 'Bassam Jabaian', 'Sahar Ghannay', 'Ha Nguyen', 'Gaëlle Laperrière'] | 2023-07-03 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.35675052e-01 7.31944084e-01 -2.28861272e-01 -6.54871345e-01
-9.80341554e-01 -6.27968371e-01 8.32181990e-01 3.34797442e-01
-5.70247650e-01 8.63096952e-01 7.26862967e-01 -1.85754403e-01
-2.21998803e-02 -5.97795188e-01 -8.03535581e-01 -6.06062263e-02
2.11294755e-01 8.45525980e-01 -5.62890433e-02 -6.17924571... | [13.988100051879883, 7.043916702270508] |
f65191e8-88f3-458a-a256-5958be5c61ec | unbiased-teacher-v2-semi-supervised-object-1 | 2206.09500 | null | https://arxiv.org/abs/2206.09500v1 | https://arxiv.org/pdf/2206.09500v1.pdf | Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors | With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are still two challenges that are not addressed: (1) there is no prior SS-OD work on anchor-free detectors, and (2) p... | ['Zsolt Kira', 'Chih-Yao Ma', 'Yen-Cheng Liu'] | 2022-06-19 | unbiased-teacher-v2-semi-supervised-object | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Unbiased_Teacher_v2_Semi-Supervised_Object_Detection_for_Anchor-Free_and_Anchor-Based_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Unbiased_Teacher_v2_Semi-Supervised_Object_Detection_for_Anchor-Free_and_Anchor-Based_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-2.25429665e-02 1.71480432e-01 -4.10190850e-01 -5.73323548e-01
-1.33539498e+00 -4.56370801e-01 4.02499199e-01 2.42584199e-02
-5.05238831e-01 5.80335796e-01 -2.76514888e-01 -9.28935111e-02
1.44133136e-01 -3.59698474e-01 -8.44721496e-01 -7.91618168e-01
1.74461603e-01 5.14599562e-01 9.14559424e-01 -8.97808224... | [9.171382904052734, 1.21976637840271] |
ec1e609d-76d8-4db8-a41c-4fb0cdd76d82 | joint-learning-of-neural-transfer-and | 2103.16889 | null | https://arxiv.org/abs/2103.16889v1 | https://arxiv.org/pdf/2103.16889v1.pdf | Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition | Current state-of-the-art visual recognition systems usually rely on the following pipeline: (a) pretraining a neural network on a large-scale dataset (e.g., ImageNet) and (b) finetuning the network weights on a smaller, task-specific dataset. Such a pipeline assumes the sole weight adaptation is able to transfer the ne... | ['Jiqi Zhang', 'Guangcong Wang', 'Rongcong Chen', 'Liang Lin', 'Guangrun Wang'] | 2021-03-31 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 5.16009033e-01 1.19433767e-04 -2.43354633e-01 -5.15749335e-01
1.85364306e-01 -5.98156154e-01 5.45295417e-01 -5.36375195e-02
-6.88538134e-01 4.60588694e-01 -2.91465987e-02 -1.25989944e-01
-3.31670612e-01 -8.39745164e-01 -6.26938641e-01 -7.03862607e-01
1.89995885e-01 7.83550620e-01 3.04903686e-01 -2.13673543... | [9.148594856262207, 3.0207297801971436] |
82f01026-4023-4369-b5a6-c17222070f3b | tableye-seeing-small-tables-through-the-lens | 2307.02491 | null | https://arxiv.org/abs/2307.02491v1 | https://arxiv.org/pdf/2307.02491v1.pdf | TablEye: Seeing small Tables through the Lens of Images | The exploration of few-shot tabular learning becomes imperative. Tabular data is a versatile representation that captures diverse information, yet it is not exempt from limitations, property of data and model size. Labeling extensive tabular data can be challenging, and it may not be feasible to capture every important... | ['Sang-Chul Lee', 'Seung-eon Lee'] | 2023-07-04 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 2.28851900e-01 2.06009313e-01 -4.89675939e-01 -1.16967350e-01
-1.01429451e+00 -6.88329160e-01 7.17325211e-01 1.46921590e-01
-2.22224385e-01 9.48827982e-01 8.97317678e-02 9.03850645e-02
-5.19567847e-01 -7.18325496e-01 -6.55582786e-01 -8.40885997e-01
1.81821406e-01 6.30978763e-01 1.71386320e-02 -1.74416214... | [9.993090629577637, 2.840644598007202] |
26f94538-ee1a-4deb-9193-5c33edccb692 | discretely-constrained-deep-network-for | 1908.05770 | null | https://arxiv.org/abs/1908.05770v1 | https://arxiv.org/pdf/1908.05770v1.pdf | Discretely-constrained deep network for weakly supervised segmentation | An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), however this has so far been done within a continuous optimization framework... | ['Christian Desrosiers', 'Marco Pedersoli', 'Hoel Kervadec', 'Jose Dolz', 'Jizong Peng', 'Ismail Ben Ayed'] | 2019-08-15 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 6.44960284e-01 5.70586145e-01 -3.23921561e-01 -7.79055297e-01
-4.11078781e-01 -1.43202513e-01 2.37577289e-01 -5.72027750e-02
-8.01797211e-01 6.57082260e-01 -1.10263303e-01 -3.09870213e-01
1.40703976e-01 -3.40094864e-01 -5.71253240e-01 -6.09865069e-01
2.15313733e-01 3.96983325e-01 2.33628616e-01 1.04415871... | [14.59535026550293, -2.146289110183716] |
3aae0865-91cf-4925-b6d2-25ba1ce9c0d0 | image-quality-assessment-using-contrastive | 2110.13266 | null | https://arxiv.org/abs/2110.13266v1 | https://arxiv.org/pdf/2110.13266v1.pdf | Image Quality Assessment using Contrastive Learning | We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions. We then train a deep Convolutional Neural Ne... | ['Alan C. Bovik', 'Balu Adsumilli', 'Yilin Wang', 'Neil Birkbeck', 'Pavan C. Madhusudana'] | 2021-10-25 | null | null | null | null | ['image-quality-estimation', 'blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.88240254e-01 -7.66464397e-02 -2.87784748e-02 -7.50502050e-01
-1.27184165e+00 -5.25946617e-01 4.68400657e-01 -1.15354203e-01
-3.85110319e-01 5.70989609e-01 1.26687065e-01 -3.67647433e-03
-9.97167155e-02 -6.31095171e-01 -9.45191443e-01 -5.55561721e-01
2.60863956e-02 1.49056703e-01 -3.57248574e-01 -1.60166904... | [11.84081745147705, -1.7831263542175293] |
2421f1bc-a272-4947-95f7-44b6e4afcddf | self-supervised-auxiliary-loss-for-metric | 2304.07449 | null | https://arxiv.org/abs/2304.07449v1 | https://arxiv.org/pdf/2304.07449v1.pdf | Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging | In the realm of music information retrieval, similarity-based retrieval and auto-tagging serve as essential components. Given the limitations and non-scalability of human supervision signals, it becomes crucial for models to learn from alternative sources to enhance their performance. Self-supervised learning, which ex... | ['Natalia Polouliakh', 'Yasushi Miyajima', 'Katsuhiro Takematsu', 'Hiroaki Kitano', 'Taketo Akama'] | 2023-04-15 | null | null | null | null | ['metric-learning', 'metric-learning', 'music-information-retrieval'] | ['computer-vision', 'methodology', 'music'] | [ 4.38199013e-01 -3.05790808e-02 -3.00853133e-01 -3.99023205e-01
-1.27615249e+00 -7.27800727e-01 6.01090968e-01 3.84522885e-01
-6.37193143e-01 5.99916637e-01 2.58481145e-01 3.33761483e-01
-6.11533105e-01 -5.07215917e-01 -5.37679315e-01 -4.93988276e-01
-2.64630973e-01 4.41859752e-01 1.99697509e-01 -2.51752108... | [15.637609481811523, 5.204324722290039] |
275a6b5c-7489-4ce9-8d60-b72ab8581fbb | tldr9-a-large-scale-resource-for-extreme | 2110.01159 | null | https://arxiv.org/abs/2110.01159v2 | https://arxiv.org/pdf/2110.01159v2.pdf | TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts | Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data. While most existing summarization corpora contain data in the order of thousands to one million, generation of large-scale summarization datasets in order ... | ['Hanieh Deilamsalehy', 'Nazli Goharian', 'Franck Dernoncourt', 'Sajad Sotudeh'] | 2021-10-04 | null | https://aclanthology.org/2021.newsum-1.15 | https://aclanthology.org/2021.newsum-1.15.pdf | emnlp-newsum-2021-11 | ['extreme-summarization'] | ['natural-language-processing'] | [ 2.60812044e-01 3.32633048e-01 -3.76635730e-01 -1.43672541e-01
-1.31731296e+00 -6.40463114e-01 5.94265461e-01 5.89986145e-01
-2.85548031e-01 1.20557463e+00 9.72350717e-01 -1.04680404e-01
3.64812091e-02 -6.10925734e-01 -6.04323268e-01 -3.04773569e-01
2.32859299e-01 7.03290999e-01 -1.54891098e-02 -5.11173964... | [12.467202186584473, 9.433855056762695] |
93c212f6-51b8-4e09-a311-5d721bb65590 | test-an-end-to-end-network-traffic | 1908.10271 | null | https://arxiv.org/abs/1908.10271v1 | https://arxiv.org/pdf/1908.10271v1.pdf | TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction | With more encrypted network traffic gets involved in the Internet, how to effectively identify network traffic has become a top priority in the field. Accurate identification of the network traffic is the footstone of basic network services, say QoE, bandwidth allocation, and IDS. Previous identification methods either... | ['Wen-Cheng Chen', 'Yi Zeng', 'Zihao Qi', 'Xingxin Zheng', 'Yanzhe Huang', 'Han Qiu'] | 2019-08-26 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-5.51125556e-02 -7.11367786e-01 -2.72069395e-01 -4.59207147e-01
-3.41708004e-01 -3.51259351e-01 3.46009940e-01 -2.57619411e-01
-4.79537934e-01 6.30728245e-01 -7.50677884e-01 -9.04979050e-01
-3.69017929e-01 -9.55160558e-01 -1.02567077e-01 -4.35545117e-01
-1.72297597e-01 4.32908714e-01 1.74079373e-01 1.63642503... | [5.097450256347656, 7.237783908843994] |
25ed15dd-268f-4850-acd0-2098d09eb21a | music-generation-using-deep-learning | 2105.09046 | null | https://arxiv.org/abs/2105.09046v3 | https://arxiv.org/pdf/2105.09046v3.pdf | Music Generation using Three-layered LSTM | This paper explores the idea of utilising Long Short-Term Memory neural networks (LSTMNN) for the generation of musical sequences in ABC notation. The proposed approach takes ABC notations from the Nottingham dataset and encodes it to be fed as input for the neural networks. The primary objective is to input the neural... | ['Krishan Kumar', 'Mohit Gupta', 'Divit Adlakha', 'Anush Mohan', 'Vaishali Ingale'] | 2021-05-19 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.04530013e-01 2.13017032e-01 3.08722615e-01 -8.07103217e-02
-1.39482036e-01 -5.86404979e-01 3.76201332e-01 -3.27541769e-01
-3.34108174e-01 7.43786216e-01 1.48591563e-01 -1.85725287e-01
-3.12728584e-01 -7.37658322e-01 -5.08545518e-01 -5.41086376e-01
1.04450062e-01 3.56542051e-01 -2.47426346e-01 -3.73364896... | [16.033172607421875, 5.522214412689209] |
e30467a5-0664-4b5c-adfe-69bcfc1e47ff | pv-raft-point-voxel-correlation-fields-for | 2012.00987 | null | https://arxiv.org/abs/2012.00987v2 | https://arxiv.org/pdf/2012.00987v2.pdf | PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds | In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important rol... | ['Jie zhou', 'Jiwen Lu', 'Yongming Rao', 'Ziyi Wang', 'Yi Wei'] | 2020-12-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wei_PV-RAFT_Point-Voxel_Correlation_Fields_for_Scene_Flow_Estimation_of_Point_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.64706761e-01 -8.30052376e-01 -7.77126774e-02 -3.12919468e-01
-3.08330774e-01 -6.85332894e-01 5.98810256e-01 1.82870254e-01
-2.14576617e-01 4.67702150e-01 6.88872710e-02 6.85339049e-02
-4.04771060e-01 -1.14164150e+00 -7.20282435e-01 -4.59024906e-01
-2.92654604e-01 5.57248235e-01 8.42349112e-01 -3.25409591... | [8.549003601074219, -2.0636913776397705] |
699bcba3-f57b-4fee-96de-f5ac898177f9 | milli-rio-ego-motion-estimation-with | 1909.05774 | null | https://arxiv.org/abs/1909.05774v1 | https://arxiv.org/pdf/1909.05774v1.pdf | Milli-RIO: Ego-Motion Estimation with Millimetre-Wave Radar and Inertial Measurement Unit Sensor | With the fast-growing demand of location-based services in various indoor environments, robust indoor ego-motion estimation has attracted significant interest in the last decades. Single-chip millimeter-wave (MMWave) radar as an emerging technology provides an alternative and complementary solution for robust ego-motio... | ['Andrew Markham', 'Niki Trigoni', 'Mehmet Turan', 'Yasin Almalioglu', 'Chris Xiaoxuan Lu'] | 2019-09-12 | null | null | null | null | ['rf-based-pose-estimation'] | ['computer-vision'] | [-1.22275777e-01 -2.76971281e-01 8.24119747e-02 -5.18425286e-01
-7.01379359e-01 -4.37794685e-01 5.17566323e-01 -3.92886192e-01
-4.84103590e-01 1.00304174e+00 1.36954933e-01 -2.47637823e-01
-6.05624318e-01 -8.65506172e-01 -1.35241136e-01 -6.82675600e-01
-1.13253020e-01 2.35923588e-01 5.21211140e-02 -6.89730272... | [6.357464790344238, 1.0114624500274658] |
9fec56e1-fa22-4cc0-9381-108a0bf0bdfb | learning-with-noisy-labels-via-self | 2302.06805 | null | https://arxiv.org/abs/2302.06805v2 | https://arxiv.org/pdf/2302.06805v2.pdf | Learning with Noisy labels via Self-supervised Adversarial Noisy Masking | Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the sta... | ['Cai Rong Zhao', 'Chengjie Wang', 'Yabiao Wang', 'Jiangning Zhang', 'Jian Li', 'Liang Liu', 'Yuxi Li', 'Boshen Zhang', 'Yuanpeng Tu'] | 2023-02-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tu_Learning_With_Noisy_Labels_via_Self-Supervised_Adversarial_Noisy_Masking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tu_Learning_With_Noisy_Labels_via_Self-Supervised_Adversarial_Noisy_Masking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 5.22221565e-01 -2.45320741e-02 1.84541285e-01 -5.25025725e-01
-8.07598591e-01 -5.50986528e-01 4.80480880e-01 8.58076513e-02
-4.89513755e-01 9.47118104e-01 1.24870002e-01 1.16848111e-01
-3.25463526e-02 -6.32264495e-01 -7.29900837e-01 -1.13317835e+00
2.62852997e-01 -1.78363714e-02 -1.20777696e-01 5.30622201... | [9.359107971191406, 3.8581902980804443] |
a93e1842-ea49-465c-8953-8a73c42cb8e4 | closed-ecosystems-extract-energy-through-self | 2305.19102 | null | https://arxiv.org/abs/2305.19102v1 | https://arxiv.org/pdf/2305.19102v1.pdf | Closed ecosystems extract energy through self-organized nutrient cycles | Our planet is roughly closed to matter, but open to energy input from the sun. However, to harness this energy, organisms must transform matter from one chemical (redox) state to another. For example, photosynthetic organisms can capture light energy by carrying out a pair of electron donor and acceptor transformations... | ['Arvind Murugan', 'Alexander P. Petroff', 'Avi I. Flamholz', 'Akshit Goyal'] | 2023-05-30 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 2.82396376e-01 -1.15157820e-01 -1.86187670e-01 4.14605528e-01
7.10073531e-01 -1.24173629e+00 7.42408872e-01 1.62034929e-01
-1.57460883e-01 1.14119935e+00 9.47204307e-02 -4.24081475e-01
2.10996523e-01 -1.18679214e+00 -5.46271443e-01 -1.03968680e+00
-2.73858458e-01 3.38625126e-02 1.85670793e-01 -3.06893528... | [5.628694534301758, 4.149716854095459] |
243165b1-973c-4450-b43c-7befc869f484 | 3d-mpa-multi-proposal-aggregation-for-3d-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Engelmann_3D-MPA_Multi-Proposal_Aggregation_for_3D_Semantic_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Engelmann_3D-MPA_Multi-Proposal_Aggregation_for_3D_Semantic_Instance_Segmentation_CVPR_2020_paper.pdf | 3D-MPA: Multi-Proposal Aggregation for 3D Semantic Instance Segmentation | We present 3D-MPA, a method for instance segmentation on 3D point clouds. Given an input point cloud, we propose an object-centric approach where each point votes for its object center. We sample object proposals from the predicted object centers. Then, we learn proposal features from grouped point features that voted ... | [' Matthias Niessner', ' Bastian Leibe', ' Alireza Fathi', ' Martin Bokeloh', 'Francis Engelmann'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['3d-semantic-instance-segmentation'] | ['computer-vision'] | [ 1.88857272e-01 4.07940894e-01 -2.60827363e-01 -7.25606322e-01
-7.75549412e-01 -4.26545978e-01 5.97003222e-01 6.68813407e-01
-2.37756789e-01 -7.85462931e-03 -4.63886738e-01 -8.02155361e-02
4.68856618e-02 -8.94256115e-01 -1.11287546e+00 -4.95372295e-01
-3.11474085e-01 1.22415757e+00 1.27179015e+00 3.69877875... | [7.925767421722412, -3.1210451126098633] |
e5247671-e675-42ac-9090-1366438de20d | location-sensitive-visual-recognition-with | 2104.04899 | null | https://arxiv.org/abs/2104.04899v1 | https://arxiv.org/pdf/2104.04899v1.pdf | Location-Sensitive Visual Recognition with Cross-IOU Loss | Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper summarizes these tasks as location-sensitive visual recognition and proposes a unified solution named location-sensitive network (LSNet). Ba... | ['Qi Tian', 'Qingming Huang', 'Song Bai', 'Honggang Qi', 'Lingxi Xie', 'Kaiwen Duan'] | 2021-04-11 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-3.34880911e-02 -3.82087484e-04 -3.07842165e-01 -5.57619035e-01
-1.19778073e+00 -4.80855912e-01 3.26539695e-01 -3.91813032e-02
-5.64157486e-01 3.68582785e-01 -9.53410938e-02 3.69822860e-01
2.08768889e-01 -3.99078548e-01 -1.00140178e+00 -5.00611484e-01
-1.29784644e-01 5.91887116e-01 6.31867826e-01 5.06213047... | [8.21475887298584, -0.33457687497138977] |
a6a0f61d-12e2-4bff-8dd2-241d4412676f | estimation-of-acoustic-impedance-from-seismic | 1906.02684 | null | https://arxiv.org/abs/1906.02684v1 | https://arxiv.org/pdf/1906.02684v1.pdf | Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network | In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data. Knowledge of physical properties can prove helpful in identifying key structures in the subsurface for hydrocarbon exploration. In this work, we propose a workflow for predicting acou... | ['Ahmad Mustafa', 'Ghassan AlRegib', 'Motaz Alfarraj'] | 2019-06-06 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 2.83850610e-01 4.59092110e-02 5.86658180e-01 -2.80934155e-01
-6.96859002e-01 -1.71863601e-01 1.92145333e-01 -8.95720348e-02
-5.19721627e-01 4.85731125e-01 2.81923920e-01 -7.93952465e-01
-3.85222673e-01 -9.69641805e-01 -7.32267857e-01 -8.25636625e-01
-8.51305902e-01 4.12219912e-02 3.19229037e-01 -2.97080755... | [6.787278652191162, 2.5650100708007812] |
3d03b436-1130-4af2-8af1-c4ab7881295f | selecting-learnable-training-samples-is-all | 2305.10801 | null | https://arxiv.org/abs/2305.10801v1 | https://arxiv.org/pdf/2305.10801v1.pdf | Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection | DEtection TRansformer (DETR) and its variants (DETRs) achieved impressive performance in general object detection. However, in crowded pedestrian detection, the performance of DETRs is still unsatisfactory due to the inappropriate sample selection method which results in more false positives. To settle the issue, we pr... | ['Xinbo Gao', 'Gan Ji', 'Jiaxu Leng', 'Feng Gao'] | 2023-05-18 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 5.67191206e-02 -2.30565295e-01 -4.61251996e-02 -5.01792252e-01
-6.60442650e-01 -1.64940685e-01 3.15142006e-01 1.03771705e-02
-8.16591680e-01 9.94425595e-01 -1.06940754e-01 -7.87131935e-02
2.75438994e-01 -8.11295271e-01 -5.59159517e-01 -9.51531112e-01
9.94585603e-02 2.20913574e-01 9.43483710e-01 1.06457040... | [8.16435718536377, -0.5138264894485474] |
772594d5-2711-45ed-a868-78face95720d | zero-shot-action-recognition-with-error | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Qin_Zero-Shot_Action_Recognition_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Qin_Zero-Shot_Action_Recognition_CVPR_2017_paper.pdf | Zero-Shot Action Recognition With Error-Correcting Output Codes | Recently, zero-shot action recognition (ZSAR) has emerged with the explosive growth of action categories. In this paper, we explore ZSAR from a novel perspective by adopting the Error-Correcting Output Codes (dubbed ZSECOC). Our ZSECOC equips the conventional ECOC with the additional capability of ZSAR, by addressing t... | ['Fumin Shen', 'Ling Shao', 'Bingbing Ni', 'Jie Qin', 'Yunhong Wang', 'Li Liu', 'Jiaxin Chen'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.31207961e-01 -1.57277688e-01 -1.03289373e-01 -3.95875067e-01
-5.78581333e-01 -3.40146691e-01 6.37051821e-01 2.31046811e-01
-4.86655235e-01 5.81958055e-01 5.45253456e-01 5.54658413e-01
-3.75072896e-01 -6.88119233e-01 -5.81636369e-01 -7.23203719e-01
4.05433506e-01 2.56902486e-01 5.35430253e-01 -4.08781409... | [8.548142433166504, 0.871467649936676] |
8585f0dc-7c53-4537-b74a-235687da1c3b | robot-bed-making-deep-transfer-learning-using | 1809.09810 | null | https://arxiv.org/abs/1809.09810v3 | https://arxiv.org/pdf/1809.09810v3.pdf | Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making | A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. We present a supervised deep transfer learning approach to locate pick points using depth images for invariance to color and texture. We cons... | ['Soshi Iba', 'Michael Laskey', 'Ajay Kumar Tanwani', 'Daniel Seita', 'Prakash Baskaran', 'Ron Berenstein', 'Nawid Jamali', 'Ken Goldberg', 'John Canny'] | 2018-09-26 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 3.67908150e-01 3.34793717e-01 1.26147598e-01 -3.49903435e-01
-5.50049961e-01 -7.77052879e-01 -3.03531766e-01 -2.42990032e-01
-6.17022030e-02 8.01596940e-01 -3.43959689e-01 3.66043448e-02
-2.42760599e-01 -5.81611335e-01 -1.57549763e+00 -6.24931574e-01
-3.19480687e-01 9.38484490e-01 -9.56025794e-02 -5.22411704... | [5.74861478805542, -0.8182904720306396] |
0c9ee165-d8d9-4e13-b657-6d3eec704fc1 | an-improved-topic-masking-technique-for | 2005.06605 | null | https://arxiv.org/abs/2005.06605v2 | https://arxiv.org/pdf/2005.06605v2.pdf | POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis | Authorship verification (AV) is a fundamental research task in digital text forensics, which addresses the problem of whether two texts were written by the same person. In recent years, a variety of AV methods have been proposed that focus on this problem and can be divided into two categories: The first category refer... | ['Oren Halvani', 'Lukas Graner'] | 2020-05-02 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 3.75150800e-01 -1.14498930e-02 -7.99131021e-02 -1.66706160e-01
-4.24709529e-01 -5.24646044e-01 1.07069767e+00 5.15555501e-01
-5.46770036e-01 6.03168428e-01 5.69486208e-02 -2.87841529e-01
6.43668845e-02 -8.13169241e-01 -2.62089670e-01 -7.83174753e-01
4.97684151e-01 2.07048744e-01 4.50114399e-01 6.47856966... | [9.526641845703125, 10.601717948913574] |
2e3cd82a-8493-4f9a-bb66-4b36c17a8893 | large-language-models-scientific-knowledge | 2305.17819 | null | https://arxiv.org/abs/2305.17819v1 | https://arxiv.org/pdf/2305.17819v1.pdf | Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery | Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing medical evidence. This work examines the potential of LLMs for dialoguing with biomedical ba... | ['Andre Freitas', 'Vincent Mutel', 'Maxime Delmas', 'Oskar Wysocki', 'Magdalena Wysocka'] | 2023-05-28 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.51973385e-01 8.97913873e-01 -4.71431136e-01 -4.07372788e-02
-8.62889647e-01 -7.46575058e-01 8.78546715e-01 8.20640802e-01
-4.28810865e-01 1.23439598e+00 4.96818990e-01 -6.75695896e-01
-4.98092353e-01 -5.75830340e-01 -6.79165900e-01 -3.07396650e-01
8.74831229e-02 8.74361932e-01 -1.35534003e-01 -3.11419964... | [8.51856803894043, 8.701921463012695] |
3d72afe9-1845-4557-bd20-b96797a327db | voxelmorph-a-learning-framework-for | 1809.05231 | null | https://arxiv.org/abs/1809.05231v3 | https://arxiv.org/pdf/1809.05231v3.pdf | VoxelMorph: A Learning Framework for Deformable Medical Image Registration | We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on re... | ['Adrian V. Dalca', 'Amy Zhao', 'Mert R. Sabuncu', 'Guha Balakrishnan', 'John Guttag'] | 2018-09-14 | null | null | null | null | ['deformable-medical-image-registration', 'diffeomorphic-medical-image-registration'] | ['medical', 'medical'] | [ 4.32531565e-01 1.59608632e-01 -1.66867852e-01 -6.48922324e-01
-1.21661544e+00 -6.21038318e-01 3.12744588e-01 3.76060992e-01
-7.82793760e-01 1.85663044e-01 9.59793180e-02 -7.07392246e-02
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-1.86418191e-01 8.54469001e-01 2.17327505e-01 -2.84341313... | [14.028436660766602, -2.561042070388794] |
c2536854-5f26-4bd7-9daa-7c39b1142037 | som-vae-interpretable-discrete-representation | 1806.02199 | null | http://arxiv.org/abs/1806.02199v7 | http://arxiv.org/pdf/1806.02199v7.pdf | SOM-VAE: Interpretable Discrete Representation Learning on Time Series | High-dimensional time series are common in many domains. Since human
cognition is not optimized to work well in high-dimensional spaces, these areas
could benefit from interpretable low-dimensional representations. However, most
representation learning algorithms for time series data are difficult to
interpret. This is... | ['Gunnar Rätsch', 'Matthias Hüser', 'Heiko Strathmann', 'Francesco Locatello', 'Vincent Fortuin'] | 2018-06-06 | som-vae-interpretable-discrete-representation-1 | https://openreview.net/forum?id=rygjcsR9Y7 | https://openreview.net/pdf?id=rygjcsR9Y7 | iclr-2019-5 | ['time-series-clustering'] | ['time-series'] | [-5.76721653e-02 -1.37636364e-02 5.08348495e-02 -4.85581249e-01
-5.06832659e-01 -6.38178110e-01 8.21146607e-01 3.11657131e-01
-7.61736035e-02 3.94912899e-01 5.78680694e-01 -2.32807055e-01
-7.99795568e-01 -6.43640578e-01 -3.26370537e-01 -8.56413007e-01
-5.15157163e-01 6.63701653e-01 -3.32314938e-01 -1.34809032... | [7.392240047454834, 3.2549102306365967] |
186f5607-d7bf-4c16-aa1e-4f8fbf45a9fd | towards-a-theoretical-understanding-of-word | 2202.00486 | null | https://arxiv.org/abs/2202.00486v1 | https://arxiv.org/pdf/2202.00486v1.pdf | Towards a Theoretical Understanding of Word and Relation Representation | Representing words by vectors, or embeddings, enables computational reasoning and is foundational to automating natural language tasks. For example, if word embeddings of similar words contain similar values, word similarity can be readily assessed, whereas judging that from their spelling is often impossible (e.g. cat... | ['Carl Allen'] | 2022-02-01 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [-1.99472941e-02 3.09773386e-01 -4.19130802e-01 -3.33441257e-01
2.90819049e-01 -8.64792705e-01 9.05467927e-01 8.09631169e-01
-5.26898324e-01 3.93878609e-01 6.65541828e-01 -4.93001997e-01
-3.98246020e-01 -1.28214896e+00 -5.08825302e-01 -4.43110049e-01
-1.06467225e-01 5.42840958e-01 -6.75187856e-02 -3.18887413... | [10.23066520690918, 8.780757904052734] |
8a6391ea-3efb-49fb-8184-5f6fee58a40a | m3d-rpn-monocular-3d-region-proposal-network | 1907.06038 | null | https://arxiv.org/abs/1907.06038v2 | https://arxiv.org/pdf/1907.06038v2.pdf | M3D-RPN: Monocular 3D Region Proposal Network for Object Detection | Understanding the world in 3D is a critical component of urban autonomous driving. Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been paramount for successful 3D object detection algorithms, whereas monocular image-only methods experience drastically reduced performance. We propose to... | ['Xiaoming Liu', 'Garrick Brazil'] | 2019-07-13 | m3d-rpn-monocular-3d-region-proposal-network-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Brazil_M3D-RPN_Monocular_3D_Region_Proposal_Network_for_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Brazil_M3D-RPN_Monocular_3D_Region_Proposal_Network_for_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-detection-from-monocular-images', 'vehicle-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.16662286e-01 -1.63825713e-02 -5.33357076e-02 -4.40438628e-01
-4.05732125e-01 -6.70006037e-01 7.12732613e-01 -2.24914700e-01
-6.33335292e-01 1.40865013e-01 -3.20701092e-01 -6.97713614e-01
2.26103127e-01 -7.94334710e-01 -7.53239930e-01 -2.15879917e-01
2.93862969e-01 5.12545645e-01 7.67448604e-01 -3.47490638... | [7.805055141448975, -2.5617940425872803] |
b0eee211-5b17-45fe-9464-324295a104f0 | an-ensemble-cnn-method-for-biomedical-entity | null | null | https://aclanthology.org/D19-5721 | https://aclanthology.org/D19-5721.pdf | An ensemble CNN method for biomedical entity normalization | Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related ... | ['Liang Xu', 'Mengyao Huang', 'Haipeng Chen', 'Pan Deng', 'Xiaowen Ruan'] | 2019-11-01 | null | null | null | ws-2019-11 | ['medical-concept-normalization'] | ['medical'] | [ 2.66320348e-01 8.20357352e-02 -1.12998916e-03 -3.40526044e-01
-2.29666397e-01 -4.83476043e-01 7.29762018e-01 1.24806893e+00
-9.50388610e-01 1.29725730e+00 3.32617551e-01 -3.25611711e-01
3.21739651e-02 -9.11096931e-01 -8.90992999e-01 -6.09744370e-01
-4.01348062e-02 7.60660529e-01 -4.76106286e-01 -3.01569402... | [8.512980461120605, 8.75177001953125] |
de66634b-6473-4f22-8a3a-2dc29b51863e | emotion-cause-pair-extraction-as-question | 2301.01982 | null | https://arxiv.org/abs/2301.01982v2 | https://arxiv.org/pdf/2301.01982v2.pdf | Emotion-Cause Pair Extraction as Question Answering | The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential emotion-cause pairs of a document without any annotation of emotion or cause clauses. Previous approaches on ECPE have tried to improve conventional two-step processing schemes by using complex architectures for modeling emotion-cause intera... | ['Minh-Tien Nguyen', 'Huu-Hiep Nguyen'] | 2023-01-05 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.32570416e-01 5.99535108e-01 3.06445360e-01 -8.24363172e-01
-1.43415773e+00 -5.89272618e-01 4.95127261e-01 3.79175335e-01
-1.24055788e-01 5.75971961e-01 2.43417367e-01 -2.50328600e-01
5.60870022e-02 -5.17701447e-01 -3.10130537e-01 -3.10231864e-01
7.00246841e-02 5.45214951e-01 3.67149375e-02 -4.11994964... | [12.669833183288574, 6.231616973876953] |
90ca6b25-9149-41fa-bdf3-efbf03c03f63 | deep-embedded-clustering-algorithm-for | 2206.12417 | null | https://arxiv.org/abs/2206.12417v1 | https://arxiv.org/pdf/2206.12417v1.pdf | Deep embedded clustering algorithm for clustering PACS repositories | Creating large datasets of medical radiology images from several sources can be challenging because of the differences in the acquisition and storage standards. One possible way of controlling and/or assessing the image selection process is through medical image clustering. This, however, requires an efficient method f... | ['Ivan Štajduhar', 'Matija Milanič', 'Teo Manojlović'] | 2022-06-24 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.27629855e-01 6.58328310e-02 1.36162579e-01 -3.05178434e-01
-8.94265532e-01 -4.20226306e-01 6.11375391e-01 7.03716516e-01
-8.51263762e-01 3.27093244e-01 3.81505519e-01 -3.58796939e-02
-7.63456702e-01 -5.23493886e-01 -3.15432638e-01 -1.23092234e+00
-4.50838387e-01 7.28792787e-01 6.00893684e-02 4.58310783... | [14.719902038574219, -2.3896591663360596] |
d475a97b-54e6-48dd-af81-63dab862837a | scalable-lipid-droplet-microarray-fabrication | 2210.07377 | null | https://arxiv.org/abs/2210.07377v1 | https://arxiv.org/pdf/2210.07377v1.pdf | Scalable lipid droplet microarray fabrication, validation, and screening | High throughput screening of small molecules and natural products is costly, requiring significant amounts of time, reagents, and operating space. Although microarrays have proven effective in the miniaturization of screening for certain biochemical assays, such as nucleic acid hybridization or antibody binding, they a... | ['Steven Lenhert', 'M. Singh', 'Hongyuan Cao', 'David Van Winkle', 'F. Zhu', 'L. Zhu', 'Pengfei Lyu', 'Aubrey E. Kusi-Appiaha', 'Tracey N. Bell'] | 2022-10-13 | null | null | null | null | ['culture'] | ['speech'] | [ 3.49054515e-01 -2.35486671e-01 1.18765078e-01 2.36892954e-01
-7.29080796e-01 -1.09708321e+00 2.10724279e-01 1.05322576e+00
-5.37813067e-01 8.72063696e-01 -9.99062061e-02 -3.05990368e-01
4.58866119e-01 -8.90075505e-01 -6.11972868e-01 -7.26026714e-01
2.77626645e-02 5.77478349e-01 6.54328406e-01 1.34246901... | [13.760943412780762, -3.091541051864624] |
da7f8eae-a7f5-4c09-8581-c178c5f31130 | few-shot-table-to-text-generation-with-prompt-1 | 2302.12468 | null | https://arxiv.org/abs/2302.12468v1 | https://arxiv.org/pdf/2302.12468v1.pdf | Few-Shot Table-to-Text Generation with Prompt-based Adapter | Pre-trained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the topological gap between tabular data and text and the lack of domain-specific knowledge make it difficult for PLMs to produce faithful text, especially in real-world applications with limited resources. In t... | ['Xinbing Wang', 'Guanjie Zheng', 'Zhouhan Lin', 'Ziwei He', 'Jianping Zhou', 'Jiexing Qi', 'Minyxuan Yan', 'Zhixin Guo'] | 2023-02-24 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.53205627e-01 3.39904636e-01 -2.80109823e-01 -9.10655111e-02
-9.50439990e-01 -6.33960187e-01 1.04110610e+00 1.38085946e-01
-6.83269575e-02 9.66818988e-01 5.32832742e-01 -2.04774663e-01
2.54431784e-01 -1.05489230e+00 -6.19179547e-01 -2.05782115e-01
5.65360963e-01 8.20575178e-01 2.11253554e-01 -6.00065351... | [11.534930229187012, 8.678091049194336] |
552dee25-d3dd-417b-9b9e-c248ecded2f8 | could-giant-pretrained-image-models-extract | 2211.02043 | null | https://arxiv.org/abs/2211.02043v1 | https://arxiv.org/pdf/2211.02043v1.pdf | Could Giant Pretrained Image Models Extract Universal Representations? | Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output ... | ['Yue Cao', 'Stephen Lin', 'Nanning Zheng', 'Han Hu', 'Zheng Zhang', 'Ze Liu', 'Yutong Lin'] | 2022-11-03 | null | null | null | null | ['action-recognition-in-videos-2'] | ['computer-vision'] | [ 5.51591873e-01 -9.59807560e-02 -2.77363688e-01 -3.53234708e-01
-8.37456167e-01 -6.06028020e-01 3.26026231e-01 -5.26361406e-01
-9.07747269e-01 5.63929379e-01 -1.39825404e-01 -3.59105051e-01
3.70786898e-02 -4.17261332e-01 -1.01209080e+00 -7.23102331e-01
-1.28890797e-01 5.56764424e-01 6.65557742e-01 -3.00503522... | [9.599674224853516, 1.7949899435043335] |
3f0eb565-1ef5-42f5-a6a7-fce590ff908d | grounded-video-description | 1812.06587 | null | https://arxiv.org/abs/1812.06587v2 | https://arxiv.org/pdf/1812.06587v2.pdf | Grounded Video Description | Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in t... | ['Jason J. Corso', 'Yannis Kalantidis', 'Luowei Zhou', 'Xinlei Chen', 'Marcus Rohrbach'] | 2018-12-17 | grounded-video-description-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhou_Grounded_Video_Description_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhou_Grounded_Video_Description_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-description'] | ['computer-vision'] | [ 2.40058720e-01 2.59084910e-01 -4.03806895e-01 -5.42663276e-01
-1.02207279e+00 -6.70697987e-01 8.95412207e-01 -1.31699488e-01
-2.86901146e-01 8.35251927e-01 7.76146054e-01 2.44467050e-01
2.52786517e-01 -3.41890574e-01 -1.34436190e+00 -5.16145051e-01
1.40735656e-01 4.09185231e-01 2.03567535e-01 1.48806363... | [10.454045295715332, 0.726747453212738] |
4acd19ae-275e-4337-87d0-f24c520d4372 | relighting-images-in-the-wild-with-a-self | 2012.06444 | null | https://arxiv.org/abs/2012.06444v2 | https://arxiv.org/pdf/2012.06444v2.pdf | Relighting Images in the Wild with a Self-Supervised Siamese Auto-Encoder | We propose a self-supervised method for image relighting of single view images in the wild. The method is based on an auto-encoder which deconstructs an image into two separate encodings, relating to the scene illumination and content, respectively. In order to disentangle this embedding information without supervision... | ['Eric Sommerlade', 'Sunando Sengupta', 'Alexandros Neophytou', 'Yang Liu'] | 2020-12-11 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.88462305e-01 8.52707326e-02 2.32707351e-01 -5.01370788e-01
-3.81252825e-01 -7.19314754e-01 6.18470132e-01 -3.71533543e-01
-3.34279031e-01 5.43755651e-01 2.62927681e-01 -5.32003716e-02
5.62132239e-01 -6.79258704e-01 -1.09814060e+00 -7.46746063e-01
6.34316444e-01 5.51602279e-05 -7.01925019e-03 1.49979191... | [9.727934837341309, -2.9140594005584717] |
65eb8983-2e0a-4d59-9d86-0e49278c4443 | a-shading-guided-generative-implicit-model | 2110.15678 | null | https://arxiv.org/abs/2110.15678v3 | https://arxiv.org/pdf/2110.15678v3.pdf | A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis | The advancement of generative radiance fields has pushed the boundary of 3D-aware image synthesis. Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images. Despite the... | ['Bo Dai', 'Christian Theobalt', 'Chen Change Loy', 'Xudong Xu', 'Xingang Pan'] | 2021-10-29 | null | http://proceedings.neurips.cc/paper/2021/hash/a64c94baaf368e1840a1324e839230de-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a64c94baaf368e1840a1324e839230de-Paper.pdf | neurips-2021-12 | ['image-relighting', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.18811947e-01 3.39729944e-03 1.84617013e-01 -3.95496696e-01
-7.28051007e-01 -6.74760044e-01 7.03254461e-01 -4.70675021e-01
1.90665424e-01 5.48479021e-01 1.02049857e-01 -3.33231866e-01
2.98335761e-01 -9.58511889e-01 -7.99079835e-01 -8.50434065e-01
4.92976218e-01 2.43882805e-01 -5.01929522e-02 -1.71445891... | [9.353537559509277, -3.132920265197754] |
afc419a8-af2e-4906-8133-2d0b183b7b93 | towards-interpretable-and-robust-hand | 2001.04163 | null | https://arxiv.org/abs/2001.04163v1 | https://arxiv.org/pdf/2001.04163v1.pdf | Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction | The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions. In this paper, we propose a novel neural network model, which introduces interpretability into hand detection for the first time. The main improvements include: (1) Detect h... | ['Lili Tao', 'Tiejian Luo', 'Dan Liu', 'Yanjun Wu', 'Libo Zhang'] | 2020-01-13 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-4.82591018e-02 -5.99798821e-02 -3.34460765e-01 -1.28524944e-01
-6.75911978e-02 -4.95543063e-01 2.89148986e-01 -4.89052504e-01
-4.45642561e-01 6.13962114e-01 2.95881897e-01 -2.66791523e-01
2.83182591e-01 -3.69821161e-01 -7.35690057e-01 -7.01668561e-01
7.64851943e-02 2.36003190e-01 5.17701089e-01 7.85126444... | [6.590041160583496, -0.6570202112197876] |
b674e595-7fe7-451b-b797-78957c282baa | dual-camera-super-resolution-with-aligned | 2109.01349 | null | https://arxiv.org/abs/2109.01349v2 | https://arxiv.org/pdf/2109.01349v2.pdf | Dual-Camera Super-Resolution with Aligned Attention Modules | We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We furth... | ['Qifeng Chen', 'Qiong Yan', 'Wenxiu Sun', 'Jiaxin Xie', 'Tengfei Wang'] | 2021-09-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Dual-Camera_Super-Resolution_With_Aligned_Attention_Modules_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Dual-Camera_Super-Resolution_With_Aligned_Attention_Modules_ICCV_2021_paper.pdf | iccv-2021-1 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 5.97289085e-01 -4.99993950e-01 -4.31814715e-02 -3.39565873e-01
-1.45326376e+00 -3.73237759e-01 6.06806576e-01 -7.37530589e-01
-1.34122297e-01 8.95460367e-01 3.11430186e-01 3.27557623e-01
-1.03185095e-01 -7.33734310e-01 -6.97028220e-01 -4.05562788e-01
5.49301267e-01 1.71641812e-01 6.24329209e-01 -5.51878750... | [10.863409996032715, -2.1127865314483643] |
56dcd91e-6a36-4819-8c4a-094ea7c7627a | we-can-see-you-via-wi-fi-wifi-action | 1608.05461 | null | http://arxiv.org/abs/1608.05461v2 | http://arxiv.org/pdf/1608.05461v2.pdf | We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods | Recently, Wi-Fi has caught tremendous attention for its ubiquity, and,
motivated by Wi-Fi's low cost and privacy preservation, researchers have been
putting lots of investigation into its potential on action recognition and even
person identification. In this paper, we offer an comprehensive overview on
these two topic... | ['Kate Ching-Ju Lin', 'Yu-Lin Wei', 'Kuan-Ying Lee', 'Jen-Yin Chang', 'Winston Hsu'] | 2016-08-19 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 4.56918895e-01 -1.89021230e-01 -1.39320567e-01 -2.16278866e-01
-3.15942168e-01 -2.98081279e-01 2.73817748e-01 -5.35776615e-01
-1.10556751e-01 7.82694876e-01 3.32733631e-01 -8.64391327e-02
-3.14618289e-01 -7.25590944e-01 -2.38935366e-01 -8.37950349e-01
-4.35362935e-01 -5.98841488e-01 -1.05490927e-02 3.44362035... | [6.698882579803467, 0.7118332386016846] |
d3916364-be72-4f92-84d3-bed6b0ee01c5 | lossless-compression-of-structured | 2007.06567 | null | https://arxiv.org/abs/2007.06567v2 | https://arxiv.org/pdf/2007.06567v2.pdf | Lossless Compression of Structured Convolutional Models via Lifting | Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuously expanding from grid-like tensor data into structured representations, such as various attributed graphs and relational databases. To addr... | ['Ondrej Kuzelka', 'Gustav Sourek', 'Filip Zelezny'] | 2020-07-13 | null | https://openreview.net/forum?id=oxnp2q-PGL4 | https://openreview.net/pdf?id=oxnp2q-PGL4 | iclr-2021-1 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.52321541e-01 2.69078434e-01 -3.30321044e-01 -2.79576749e-01
-1.61469504e-02 -6.46812677e-01 3.06221634e-01 4.88526821e-01
1.34590834e-01 5.93963444e-01 3.19633037e-01 -6.66744232e-01
-2.65701562e-01 -1.07714951e+00 -1.04442370e+00 -5.62842369e-01
-6.60009444e-01 3.41341317e-01 2.81930566e-02 -2.64411151... | [6.855368137359619, 6.1887383460998535] |
7ac9a01c-bd94-4e72-83af-49ba67a52476 | lighthouse-predicting-lighting-volumes-for | 2003.08367 | null | https://arxiv.org/abs/2003.08367v2 | https://arxiv.org/pdf/2003.08367v2.pdf | Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination | We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair. Previous approaches for predicting global illumination from images either predict just a single illumination for the entire scene, or separately estimate the il... | ['Noah Snavely', 'Richard Tucker', 'Jonathan T. Barron', 'Pratul P. Srinivasan', 'Ben Mildenhall', 'Matthew Tancik'] | 2020-03-18 | lighthouse-predicting-lighting-volumes-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Srinivasan_Lighthouse_Predicting_Lighting_Volumes_for_Spatially-Coherent_Illumination_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Srinivasan_Lighthouse_Predicting_Lighting_Volumes_for_Spatially-Coherent_Illumination_CVPR_2020_paper.pdf | cvpr-2020-6 | ['lighting-estimation'] | ['computer-vision'] | [ 4.41543043e-01 1.46765247e-01 6.18943930e-01 -7.32356608e-01
-8.82150412e-01 -8.37312758e-01 4.98829246e-01 -3.46993595e-01
-1.20536820e-03 4.63068515e-01 1.55242741e-01 -2.37235039e-01
4.79526162e-01 -7.67470062e-01 -1.21355379e+00 -5.74049473e-01
2.61635214e-01 6.04568362e-01 8.02610740e-02 1.93267688... | [9.628180503845215, -3.041076183319092] |
c2373ba0-ba01-4040-a9d5-acf557398e7f | codeps-online-continual-learning-for-depth | 2303.10147 | null | https://arxiv.org/abs/2303.10147v2 | https://arxiv.org/pdf/2303.10147v2.pdf | CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation | Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments. Optimally, the robot is able to adapt by itself to new conditions without human supervision, e.g., automatically adjusting its perception system to changing lighting conditions. In this work, we addre... | ['Abhinav Valada', 'Wolfram Burgard', 'Kürsat Petek', 'Niclas Vödisch'] | 2023-03-17 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.42259189e-01 1.77834891e-02 2.35002443e-01 -3.41501772e-01
-5.29165089e-01 -7.06121266e-01 2.99010664e-01 1.17278588e-03
-6.30551755e-01 6.90333188e-01 -4.03990477e-01 -3.78981642e-02
1.00963891e-01 -6.90475166e-01 -1.13853621e+00 -6.60194993e-01
-1.75047815e-01 5.66626966e-01 5.17128944e-01 3.59733067... | [4.857233047485352, 0.5086372494697571] |
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