paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
85f2804e-6632-4257-a4b4-c719fd07974e | preliminary-wildfire-detection-using-state-of | 2109.05083 | null | https://arxiv.org/abs/2109.05083v1 | https://arxiv.org/pdf/2109.05083v1.pdf | Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks | Wildfires are uncontrolled fires in the environment that can be caused by humans or nature. In 2020 alone, wildfires in California have burned 4.2 million acres, damaged 10,500 buildings or structures, and killed more than 31 people, exacerbated by climate change and a rise in average global temperatures. This also mea... | ['Samarth Shah'] | 2021-09-10 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.24261123e-01 -2.09652171e-01 -8.65715090e-03 1.27743021e-01
1.90804526e-02 -5.41425645e-01 6.15896285e-01 1.07729509e-01
-7.15528488e-01 1.03866506e+00 1.72957703e-01 -4.87559587e-01
-1.85854331e-01 -1.02355695e+00 -4.46431220e-01 -8.64886940e-01
1.01106912e-01 3.37567836e-01 1.87179089e-01 -2.37248868... | [9.178571701049805, -1.2437747716903687] |
1d96e9dc-d160-4525-bf1a-e95f3a318faa | mask-r-cnn-with-pyramid-attention-network-for | 1811.09058 | null | http://arxiv.org/abs/1811.09058v1 | http://arxiv.org/pdf/1811.09058v1.pdf | Mask R-CNN with Pyramid Attention Network for Scene Text Detection | In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as ... | ['Qiang Huo', 'Zhuoyao Zhong', 'Zhida Huang', 'Lei Sun'] | 2018-11-22 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 4.54877019e-01 -5.49841702e-01 1.82252675e-01 -1.78787068e-01
-7.57831216e-01 -3.44421536e-01 7.02750027e-01 -2.42753282e-01
-3.08342546e-01 -1.00022361e-01 2.08651036e-01 -2.49036938e-01
4.91800666e-01 -4.99162912e-01 -5.92869520e-01 -3.41000289e-01
7.03196108e-01 1.04388297e-01 7.04207838e-01 -1.96399376... | [12.056587219238281, 2.2627532482147217] |
c8a92b38-723d-4d79-b7ef-470373891cae | backdoor-attacks-for-remote-sensing-data-with | 2211.08044 | null | https://arxiv.org/abs/2211.08044v2 | https://arxiv.org/pdf/2211.08044v2.pdf | Backdoor Attacks for Remote Sensing Data with Wavelet Transform | Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical remote sensing tasks. In this paper, we provide a systematic analysis of backd... | ['Pedram Ghamisi', 'Yonghao Xu', 'Nikolaus Dräger'] | 2022-11-15 | null | null | null | null | ['data-poisoning', 'scene-classification'] | ['adversarial', 'computer-vision'] | [ 2.57861376e-01 -3.84265751e-01 2.04580262e-01 1.66720688e-01
-4.33325082e-01 -1.00324762e+00 5.94672680e-01 1.94696933e-02
-4.49518234e-01 1.50370583e-01 -2.01155484e-01 -8.52086842e-01
-1.98216811e-01 -1.29278564e+00 -6.22763455e-01 -1.32844090e+00
-3.38938892e-01 -4.15677726e-01 2.69639701e-01 -3.51573497... | [5.559612274169922, 7.852515697479248] |
a7ea859b-6a75-422a-afa1-3c8a1d6f762e | learning-from-heterogeneity-a-dynamic | 2307.03411 | null | https://arxiv.org/abs/2307.03411v1 | https://arxiv.org/pdf/2307.03411v1.pdf | Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs | Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the... | ['Jiong Jin', 'Jun Yin', 'Xiaowei Huang', 'Xin Chen', 'Xingjun Ma', 'Zhishu Shen', 'Yuze Liu', 'Tiehua Zhang'] | 2023-07-07 | null | null | null | null | ['node-classification', 'link-prediction', 'graph-learning'] | ['graphs', 'graphs', 'graphs'] | [-8.70536119e-02 4.33113128e-01 -5.17869771e-01 -9.89792645e-02
-9.79811773e-02 -2.55131453e-01 5.14031053e-01 4.02877808e-01
5.22171445e-02 6.50201321e-01 9.73727778e-02 -4.00005668e-01
-5.59546113e-01 -1.11053288e+00 -3.65946323e-01 -8.32857609e-01
-6.36346400e-01 5.16723692e-01 1.90623149e-01 -1.98409438... | [7.283670425415039, 6.257596969604492] |
4e7556aa-38a0-4fdd-8ab5-e6bec7b786ef | counting-and-locating-high-density-objects | 2102.04366 | null | https://arxiv.org/abs/2102.04366v1 | https://arxiv.org/pdf/2102.04366v1.pdf | Counting and Locating High-Density Objects Using Convolutional Neural Network | This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was ... | ['Wesley Nunes Gonçalves', 'Jonathan de Andrade Silva', 'Jonathan Li', 'Zhipeng Luo', 'Edson Takashi Matsubara', 'Ana Paula Marques Ramos', 'José Marcato Junior', 'Diogo Nunes Gonçalves', 'Plabiany Rodrigo Acosta', 'Lucas Prado Osco', 'Mauro dos Santos de Arruda'] | 2021-02-08 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-8.83221030e-02 -3.73984754e-01 1.55789316e-01 -1.90240905e-01
-4.92876917e-01 -1.80833057e-01 7.35311031e-01 4.33709830e-01
-1.10830235e+00 7.61736453e-01 -5.16583383e-01 -3.68272550e-02
-1.79510906e-01 -1.21219134e+00 -6.54855371e-01 -3.74635249e-01
-3.65308136e-01 1.77606776e-01 5.56692481e-01 2.73348123... | [8.635086059570312, -0.2503683567047119] |
251b1c8c-8a9f-4a49-bd1f-a1dc61b2c36a | label-assisted-autoencoder-for-anomaly | 2302.02896 | null | https://arxiv.org/abs/2302.02896v1 | https://arxiv.org/pdf/2302.02896v1.pdf | Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants | One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including... | ['Arnaud Nguembang Fadja', 'Franklin Tchakounte', 'Theophilus Ansah-Narh', 'Jecinta Mulongo', 'Sisipho Hamlomo', 'Rockefeller Rockefeller', 'Victor Osanyindoro', 'Marcellin Atemkeng'] | 2023-02-06 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-1.25641435e-01 3.22941411e-03 8.38978291e-02 -9.42930654e-02
1.59427188e-02 -4.58697528e-01 4.19541448e-01 4.01519060e-01
-3.16700727e-01 6.49165511e-01 -5.82433820e-01 -4.49787527e-01
-1.65062800e-01 -1.22829843e+00 -1.77686021e-01 -1.02770030e+00
1.39570683e-01 4.59257752e-01 1.32375360e-01 -1.34509951... | [6.609653949737549, 2.4061920642852783] |
7a8be63d-f61b-4370-a6b5-1c107c937ab3 | eden-a-high-performance-general-purpose | 2106.06752 | null | https://arxiv.org/abs/2106.06752v1 | https://arxiv.org/pdf/2106.06752v1.pdf | EDEN: A high-performance, general-purpose, NeuroML-based neural simulator | Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modelling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a sim... | ['Christos Strydis', 'Dimitrios Soudris', 'Mario Negrello', 'Harry Sidiropoulos', 'Sotirios Panagiotou'] | 2021-06-12 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-3.68369550e-01 -1.70176029e-01 5.05579293e-01 3.93111967e-02
-1.41095594e-02 -5.83012640e-01 6.61324918e-01 -4.22909036e-02
-6.93494201e-01 8.07577729e-01 -2.96603292e-01 -3.97925019e-01
-4.00437653e-01 -4.60294455e-01 -5.32034338e-01 -5.66571951e-01
-2.77285367e-01 4.11351472e-01 4.55444992e-01 -2.20334157... | [8.033407211303711, 2.61555552482605] |
d74912ae-f65d-44cb-bd8e-4c6373ce3008 | learning-to-compose-dynamic-tree-structures | 1812.01880 | null | http://arxiv.org/abs/1812.01880v1 | http://arxiv.org/pdf/1812.01880v1.pdf | Learning to Compose Dynamic Tree Structures for Visual Contexts | We propose to compose dynamic tree structures that place the objects in an
image into a visual context, helping visual reasoning tasks such as scene graph
generation and visual Q&A. Our visual context tree model, dubbed VCTree, has
two key advantages over existing structured object representations including
chains and ... | ['Wenhan Luo', 'Baoyuan Wu', 'Wei Liu', 'Hanwang Zhang', 'Kaihua Tang'] | 2018-12-05 | learning-to-compose-dynamic-tree-structures-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tang_Learning_to_Compose_Dynamic_Tree_Structures_for_Visual_Contexts_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning_to_Compose_Dynamic_Tree_Structures_for_Visual_Contexts_CVPR_2019_paper.pdf | cvpr-2019-6 | ['panoptic-scene-graph-generation'] | ['computer-vision'] | [ 4.04970914e-01 2.02396080e-01 -3.95767123e-01 -3.92395884e-01
-1.26513869e-01 -4.08670634e-01 5.50859809e-01 9.45658758e-02
6.32115528e-02 6.21849597e-01 2.68748522e-01 -4.19653922e-01
-7.01597631e-02 -7.80870497e-01 -9.60008025e-01 -6.50693655e-01
-1.16556972e-01 3.46065968e-01 3.31598431e-01 7.35258386... | [10.359137535095215, 1.6338856220245361] |
2503dac9-03c0-42cc-a09c-c0474272cf0a | opencl-based-fpga-accelerator-for-disparity | 1903.03509 | null | http://arxiv.org/abs/1903.03509v1 | http://arxiv.org/pdf/1903.03509v1.pdf | OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event cameras | Although event-based cameras are already commercially available. Vision
algorithms based on them are still not common. As a consequence, there are few
Hardware Accelerators for them. In this work we present some experiments to
create FPGA accelerators for a well-known vision algorithm using event-based
cameras. We pres... | ['David Castells-Rufas', 'Jordi Carrabina'] | 2019-03-08 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-4.09303699e-03 -6.38970792e-01 3.79676938e-01 -6.67513371e-01
2.09430650e-01 -1.70051083e-01 7.25661695e-01 2.19479546e-01
-6.83637142e-01 4.97774154e-01 -2.89790839e-01 -5.11368394e-01
4.13822234e-01 -1.03054881e+00 -6.80008769e-01 -2.95537204e-01
2.19754040e-01 1.89099163e-01 8.46120715e-01 -1.91375032... | [8.923805236816406, -1.9206863641738892] |
7f4b3bdd-7a5c-4d33-9ac5-81c33b5704f9 | learning-what-makes-a-difference-from | 2004.09034 | null | https://arxiv.org/abs/2004.09034v1 | https://arxiv.org/pdf/2004.09034v1.pdf | Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision | One of the primary challenges limiting the applicability of deep learning is its susceptibility to learning spurious correlations rather than the underlying mechanisms of the task of interest. The resulting failure to generalise cannot be addressed by simply using more data from the same distribution. We propose an aux... | ['Anton Van Den Hengel', 'Ehsan Abbasnedjad', 'Damien Teney'] | 2020-04-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1165_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550579.pdf | eccv-2020-8 | ['multi-label-image-classification'] | ['computer-vision'] | [ 7.28020549e-01 1.93485960e-01 -3.91451389e-01 -8.86793017e-01
-6.13151789e-01 -6.03689253e-01 1.17138314e+00 1.24567561e-01
-6.69182003e-01 1.05801415e+00 1.63730428e-01 -5.99594831e-01
-4.90780026e-01 -4.37661320e-01 -1.06908417e+00 -8.56800258e-01
1.61020365e-02 4.14556473e-01 -1.95973247e-01 5.87444305... | [8.627787590026855, 5.286247730255127] |
219ec872-17c6-4b8e-b95c-96baca511541 | joint-multi-person-body-detection-and | 2210.15586 | null | https://arxiv.org/abs/2210.15586v2 | https://arxiv.org/pdf/2210.15586v2.pdf | Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding | Human body orientation estimation (HBOE) is widely applied into various applications, including robotics, surveillance, pedestrian analysis and autonomous driving. Although many approaches have been addressing the HBOE problem from specific under-controlled scenes to challenging in-the-wild environments, they assume hu... | ['Hongtao Lu', 'Jiaxin Si', 'Fei Jiang', 'Huayi Zhou'] | 2022-10-27 | null | null | null | null | ['body-detection'] | ['computer-vision'] | [ 1.45758400e-02 1.95643932e-01 1.40247434e-01 -6.17330790e-01
-6.60601020e-01 -1.77286729e-01 2.05493107e-01 -1.42341942e-01
-6.48886740e-01 6.06603265e-01 1.55198455e-01 2.41729081e-01
1.43160790e-01 -6.34750783e-01 -9.15304005e-01 -6.59864426e-01
-9.64185745e-02 5.21288157e-01 5.84724128e-01 -3.71470988... | [7.365406036376953, -0.8004368543624878] |
89e0aff3-ef7b-40c9-a1c4-3ac3f2f7a66c | on-the-robustness-of-average-losses-for | 2106.06152 | null | https://arxiv.org/abs/2106.06152v2 | https://arxiv.org/pdf/2106.06152v2.pdf | On the Robustness of Average Losses for Partial-Label Learning | Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to select the (most likely) TL for training; average-based strategy (ABS) treats all candidate labels equall... | ['Ning Xu', 'Lei Feng', 'Biao Liu', 'Masashi Sugiyama', 'Xin Geng', 'Gang Niu', 'Bo An', 'Miao Xu', 'Jiaqi Lv'] | 2021-06-11 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.04489017e-01 2.63285041e-01 -5.14781296e-01 -2.28398040e-01
-1.02723825e+00 -5.56152582e-01 8.06976259e-02 4.38744605e-01
-3.36622834e-01 1.09386706e+00 -3.73167604e-01 -2.61951923e-01
-3.91913176e-01 -7.42490768e-01 -8.90673280e-01 -1.00132239e+00
1.19483456e-01 7.04015195e-01 4.76244032e-01 1.20946042... | [9.240002632141113, 4.082457065582275] |
cec45503-725a-44b7-a519-58d0d7f25ff3 | grouped-variable-selection-with-discrete | 2104.07084 | null | https://arxiv.org/abs/2104.07084v2 | https://arxiv.org/pdf/2104.07084v2.pdf | Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives | We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on optimal solutions for the $\ell_0$-regularized formulation, a problem that is relati... | ['Peter Radchenko', 'Rahul Mazumder', 'Hussein Hazimeh'] | 2021-04-14 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.70478091e-01 1.46611556e-01 -5.58221340e-01 -4.87280309e-01
-1.53420007e+00 -2.18264535e-01 -1.53385684e-01 1.57790691e-01
-2.69952625e-01 1.26798904e+00 -1.36779904e-01 -1.82977468e-01
-7.47541487e-01 -7.25424647e-01 -9.76513565e-01 -9.90285814e-01
-4.40728724e-01 6.91286862e-01 -5.13360679e-01 2.63401084... | [6.781520843505859, 4.483091354370117] |
6cdeb286-7faf-4570-92db-e32dadbc4add | alem-at-case-2021-task-1-multilingual-text | null | null | https://aclanthology.org/2021.case-1.19 | https://aclanthology.org/2021.case-1.19.pdf | ALEM at CASE 2021 Task 1: Multilingual Text Classification on News Articles | We participated CASE shared task in ACL-IJCNLP 2021. This paper is a summary of our experiments and ideas about this shared task. For each subtask we shared our approach, successful and failed methods and our thoughts about them. We submit our results once for every subtask, except for subtask3, in task submission syst... | ['Emre Emin', 'Alaeddin Gürel'] | null | null | null | null | acl-case-2021-8 | ['multilingual-text-classification'] | ['miscellaneous'] | [-6.94354177e-02 1.96153775e-01 2.60543302e-02 -7.70275652e-01
-1.66746390e+00 -6.38605535e-01 1.01317966e+00 -6.70149848e-02
-1.05751896e+00 1.59399021e+00 8.22114050e-01 -3.33717287e-01
-9.80070606e-02 7.24360868e-02 -7.64707983e-01 -1.54924199e-01
-2.47839332e-01 9.97246861e-01 4.68196392e-01 -3.13516766... | [10.89016056060791, 10.103487014770508] |
558c2bae-9e7c-45c4-a02f-124925509ebd | explaining-agent-s-decision-making-in-a | 2212.06967 | null | https://arxiv.org/abs/2212.06967v1 | https://arxiv.org/pdf/2212.06967v1.pdf | Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario | Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of th... | ['Francisco Cruz', 'Bruno Fernandes', 'Angel Ayala', 'Ernesto Portugal', 'Hugo Muñoz'] | 2022-12-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 4.35540192e-02 4.27114338e-01 1.78682163e-01 -2.79232800e-01
2.09715828e-01 -7.95011818e-02 5.12571752e-01 5.04268110e-01
-4.38901514e-01 1.17829323e+00 -4.40103740e-01 -2.81961828e-01
-3.92947316e-01 -9.95307922e-01 -7.52722979e-01 -4.92565453e-01
-3.12598377e-01 8.53910804e-01 4.14288193e-01 -4.79865193... | [4.216502666473389, 1.5676673650741577] |
639bb615-ebe6-4074-98f2-2927f24bf9d2 | d-calm-a-dynamic-clustering-based-active | 2305.17013 | null | https://arxiv.org/abs/2305.17013v1 | https://arxiv.org/pdf/2305.17013v1.pdf | D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias | Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the annotation process. Escalated integration of these models in our lives calls for methods to mitigate bias without overbearing annotation cos... | ['Malihe Alikhani', 'Sabit Hassan'] | 2023-05-26 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.56479079e-01 4.74311352e-01 -7.03147233e-01 -8.36131394e-01
-8.34686995e-01 -6.34360611e-01 5.80195129e-01 6.22919440e-01
-7.30361938e-01 6.72659695e-01 3.55259627e-01 -9.19154212e-02
1.13425449e-01 -4.30011034e-01 -1.76157132e-01 -7.12986648e-01
3.34694773e-01 6.77290857e-01 1.11235961e-01 -2.98564155... | [9.647675514221191, 4.441342353820801] |
75eb922d-5165-4bd0-b654-5f06a77632cf | learning-to-move-with-affordance-maps-1 | 2001.02364 | null | https://arxiv.org/abs/2001.02364v2 | https://arxiv.org/pdf/2001.02364v2.pdf | Learning to Move with Affordance Maps | The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model ... | ['William Qi', 'Deva Ramanan', 'Ravi Teja Mullapudi', 'Saurabh Gupta'] | 2020-01-08 | null | https://openreview.net/forum?id=BJgMFxrYPB | https://openreview.net/pdf?id=BJgMFxrYPB | iclr-2020-1 | ['pointgoal-navigation'] | ['robots'] | [-2.76293196e-02 4.11832064e-01 1.31326960e-03 -2.78387487e-01
-3.71136039e-01 -9.21564162e-01 7.53037214e-01 2.29675785e-01
-4.96102095e-01 7.97399402e-01 2.53121555e-01 -5.39063632e-01
-2.59589732e-01 -9.44002867e-01 -8.88529599e-01 -2.55967498e-01
-6.78930998e-01 8.49506676e-01 4.40334499e-01 -7.24235713... | [4.589200019836426, 0.7190520763397217] |
42223d67-c78f-4eb4-95f1-5cb6ab1d928e | headlinecause-a-dataset-of-news-headlines-for | 2108.12626 | null | https://arxiv.org/abs/2108.12626v2 | https://arxiv.org/pdf/2108.12626v2.pdf | HeadlineCause: A Dataset of News Headlines for Detecting Causalities | Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news h... | ['Alexey Tikhonov', 'Ilya Gusev'] | 2021-08-28 | null | https://aclanthology.org/2022.lrec-1.662 | https://aclanthology.org/2022.lrec-1.662.pdf | lrec-2022-6 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-3.88151146e-02 2.78811574e-01 -6.28903091e-01 -5.94428718e-01
-6.03443444e-01 -6.47674739e-01 1.17332888e+00 7.84371257e-01
-1.36879325e-01 1.45796645e+00 1.16848314e+00 -3.30407441e-01
-3.73112500e-01 -6.60404980e-01 -8.73641253e-01 -3.04092526e-01
-1.12200059e-01 6.76202595e-01 2.18772739e-01 -6.82091594... | [9.455690383911133, 8.55898380279541] |
652b5831-0c41-4ed4-b723-3457ff6f47fd | few-shot-object-detection-with-refined | 2211.13495 | null | https://arxiv.org/abs/2211.13495v1 | https://arxiv.org/pdf/2211.13495v1.pdf | Few-shot Object Detection with Refined Contrastive Learning | Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice t... | ['Xingqun Jiang', 'Tong Liu', 'Lian Huai', 'Zeyu Shangguan'] | 2022-11-24 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.94176668e-01 -2.77737468e-01 1.30674735e-01 -2.97779888e-01
-4.83339667e-01 -2.14904815e-01 7.89465189e-01 2.66463667e-01
-4.12834227e-01 5.50806046e-01 -2.62127489e-01 2.12528661e-01
-2.92374671e-01 -7.39580095e-01 -4.41047966e-01 -9.14056838e-01
2.33541861e-01 1.78525835e-01 1.01821566e+00 -7.12966472... | [9.390582084655762, 1.5343618392944336] |
0b1255e2-b572-40e6-9514-503062f31276 | graphmr-graph-neural-network-for-mathematical | null | null | https://aclanthology.org/2021.emnlp-main.273 | https://aclanthology.org/2021.emnlp-main.273.pdf | GraphMR: Graph Neural Network for Mathematical Reasoning | Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or synt... | ['Yun Xu', 'Qilong Zheng', 'Dongpeng Xu', 'Binbin Liu', 'Weijie Feng'] | null | null | null | null | emnlp-2021-11 | ['graph-to-sequence', 'mathematical-reasoning'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.91726464e-01 2.55249619e-01 -2.12040022e-01 -6.48117840e-01
-2.62051821e-01 -5.60259879e-01 1.71611652e-01 1.76531643e-01
1.20367482e-01 6.24957621e-01 4.35447097e-01 -6.74817681e-01
-4.01585072e-01 -1.27791905e+00 -7.48230159e-01 -1.71348348e-01
-2.88812667e-01 3.14168662e-01 -1.06813177e-01 -4.33862090... | [9.642804145812988, 7.481727600097656] |
bdc20196-fa89-4d12-bac3-4d87dc2edbf1 | classification-of-remote-sensing-images-using | 1806.06985 | null | http://arxiv.org/abs/1806.06985v1 | http://arxiv.org/pdf/1806.06985v1.pdf | Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study | The motivation of this paper is to conduct a comparative study on remote
sensing image classification using the morphological attribute profiles (APs)
and feature profiles (FPs) generated from different types of tree structures.
Over the past few years, APs have been among the most effective methods to
model the image'... | ['Sébastien Lefèvre', 'Minh-Tan Pham', 'Erchan Aptoula'] | 2018-06-18 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.56939566e-01 -3.42210740e-01 1.40324041e-01 -3.60893279e-01
-8.67697150e-02 -3.56597245e-01 5.85959256e-01 3.22669894e-01
-9.92711261e-03 8.57606411e-01 -2.90899366e-01 -5.57457387e-01
-7.76741982e-01 -1.37624180e+00 1.32481620e-01 -9.22624707e-01
-2.77883798e-01 2.52140075e-01 2.73633063e-01 -6.98385164... | [9.69385051727295, -1.7540429830551147] |
5f6fe37e-0487-4f62-9d19-bf868ea17d08 | k-meansnet-when-k-means-meets-differentiable | 1808.07292 | null | https://arxiv.org/abs/1808.07292v3 | https://arxiv.org/pdf/1808.07292v3.pdf | XAI Beyond Classification: Interpretable Neural Clustering | In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a black-box model. The second is implementing discrete $k$-means with a differentiable neural net... | ['Joey Tianyi Zhou', 'Jiancheng Lv', 'Ivor W. Tsang', 'Yunnan Li', 'Xi Peng', 'Hongyuan Zhu'] | 2018-08-22 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 1.05329074e-01 4.45415497e-01 -1.73031449e-01 -7.36285985e-01
-4.36351031e-01 -2.92344332e-01 2.63619840e-01 -1.64797947e-01
8.36588517e-02 3.88223469e-01 -7.10930154e-02 -5.26470840e-01
-9.00429785e-01 -5.45465887e-01 -7.39444911e-01 -7.49366999e-01
-2.76105136e-01 8.57068837e-01 -5.53007007e-01 3.63499373... | [9.046817779541016, 3.328251361846924] |
5ca7f82d-222d-42b1-b723-8a7d99f5b649 | bridging-resolution-making-sense-of-the-state | null | null | https://aclanthology.org/2021.naacl-main.131 | https://aclanthology.org/2021.naacl-main.131.pdf | Bridging Resolution: Making Sense of the State of the Art | While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complement... | ['Vincent Ng', 'Hideo Kobayashi'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-8.27674419e-02 4.44688678e-01 -6.20981216e-01 -1.35344371e-01
-7.15271890e-01 -3.32366884e-01 5.82533419e-01 3.00243944e-01
-5.45545518e-01 1.01782155e+00 5.84362149e-01 -7.68366992e-01
-7.25673199e-01 -5.78332782e-01 -4.74163502e-01 -6.10017069e-02
6.65171146e-02 7.16594338e-01 4.24170524e-01 -5.05098164... | [9.931915283203125, 8.724617004394531] |
7a146518-5c0b-4e35-8189-589370d99ec0 | efficient-text-based-reinforcement-learning | null | null | https://aclanthology.org/2021.acl-short.91 | https://aclanthology.org/2021.acl-short.91.pdf | Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations | Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficientl... | ['Murray Campbell', 'Mrinmaya Sachan', 'Kartik Talamadupula', 'Pavan Kapanipathi', 'Mattia Atzeni', 'Keerthiram Murugesan'] | 2021-08-01 | null | null | null | acl-2021-5 | ['text-based-games'] | ['playing-games'] | [ 1.33964822e-01 5.92555523e-01 -1.53210819e-01 7.75637403e-02
-5.94550490e-01 -5.59732437e-01 8.32587123e-01 4.39295918e-01
-5.57737231e-01 6.91115558e-01 7.07871437e-01 -3.99244517e-01
-1.41274020e-01 -1.35676801e+00 -6.64458930e-01 -9.63248983e-02
-4.70527969e-02 7.65445828e-01 2.36509129e-01 -8.77511501... | [3.806570529937744, 1.2678804397583008] |
4d869554-188b-40e4-9f87-453213b0266c | from-random-search-to-bandit-learning-in | 2305.11509 | null | https://arxiv.org/abs/2305.11509v3 | https://arxiv.org/pdf/2305.11509v3.pdf | From Random Search to Bandit Learning in Metric Measure Spaces | Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, little non-heuristic theory has been developed to describe the underlying working mechanism. This paper gives a theoretical accounting of Rand... | ['Tianyu Wang', 'Yasong Feng', 'Chuying Han'] | 2023-05-19 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 1.18518375e-01 3.13010782e-01 -8.17216188e-02 6.63816258e-02
-9.77719545e-01 -7.70421743e-01 6.74896687e-02 -6.96331868e-03
-7.61055350e-01 1.15198123e+00 -5.12318075e-01 -5.07078886e-01
-8.73540342e-01 -1.08418000e+00 -1.01727712e+00 -1.28751910e+00
-6.99401975e-01 4.58871216e-01 2.95775048e-02 -1.59142971... | [6.3250532150268555, 4.4708757400512695] |
4f567732-8f39-434b-8222-c50978e9eb43 | learning-variational-neighbor-labels-for-test | 2307.04033 | null | https://arxiv.org/abs/2307.04033v1 | https://arxiv.org/pdf/2307.04033v1.pdf | Learning Variational Neighbor Labels for Test-Time Domain Generalization | This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed at unseen target domains. We follow the strict separation of source training and target testing but exploit the value of the unlabeled target data itself during inference. We make three contributio... | ['Cees G. M. Snoek', 'XianTong Zhen', 'Jiayi Shen', 'Zehao Xiao', 'Sameer Ambekar'] | 2023-07-08 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 3.50715280e-01 3.69942099e-01 -6.00826085e-01 -7.29037285e-01
-1.29126036e+00 -9.52533424e-01 7.60258913e-01 -4.68674332e-01
-2.64238231e-02 1.24189520e+00 -1.68935791e-01 -6.58498183e-02
7.58044794e-02 -8.42583954e-01 -9.20360744e-01 -7.38695621e-01
2.90703714e-01 8.16323221e-01 1.98233888e-01 2.62693256... | [10.288786888122559, 3.128920555114746] |
6f6f8a7c-f959-400c-9fd8-c533f0bd2d5b | a-probabilistic-framework-for-imitating-human | 2001.08255 | null | https://arxiv.org/abs/2001.08255v2 | https://arxiv.org/pdf/2001.08255v2.pdf | A Probabilistic Framework for Imitating Human Race Driver Behavior | Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilist... | ['Stefan Löckel', 'Jan Peters', 'Peter van Vliet'] | 2020-01-22 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.36286193e-01 -6.98729977e-02 -5.99776506e-01 -4.21199828e-01
-3.14183146e-01 -3.84962797e-01 7.57458925e-01 -5.29115021e-01
-4.01193202e-01 4.80327576e-01 -1.62533224e-01 -5.39073110e-01
-2.75574148e-01 -6.00864530e-01 -5.36925435e-01 -7.51029193e-01
3.35021883e-01 4.00050461e-01 5.26537359e-01 -3.93120646... | [5.593777179718018, 1.0381505489349365] |
d066c218-1a61-47f3-8a40-2c4306e42f4f | effects-of-lead-position-cardiac-rhythm | 1912.04672 | null | https://arxiv.org/abs/1912.04672v2 | https://arxiv.org/pdf/1912.04672v2.pdf | Effects of lead position, cardiac rhythm variation and drug-induced QT prolongation on performance of machine learning methods for ECG processing | Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue ... | ['Konstantin Ushenin', 'Aygul Fabarisova', 'Marat Bogdanov', 'Salim Baigildin', 'Olga Solovyova'] | 2019-12-10 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 5.57552695e-01 -4.18844879e-01 2.24213645e-01 -3.65064144e-01
-4.64742631e-01 -4.56271857e-01 -2.10600138e-01 6.34223580e-01
-5.29105961e-01 8.76692057e-01 -2.91706353e-01 -4.78135586e-01
-3.62057954e-01 -5.20635247e-01 -2.86763489e-01 -8.98510695e-01
-4.44410443e-01 4.15267706e-01 -3.28337729e-01 -5.88868652... | [14.161890983581543, 3.16575288772583] |
b1d9cb65-88b3-4083-8f19-e66e8b0f0f5e | 190601054 | 1906.01054 | null | https://arxiv.org/abs/1906.01054v1 | https://arxiv.org/pdf/1906.01054v1.pdf | Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis | Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to... | ['Pallavi Asthana', 'Anil Kumar', 'Sumita Mishra', 'Naresh Kumar Chaudhary'] | 2019-05-04 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-2.24440739e-01 5.53125814e-02 -1.24333188e-01 -3.52673918e-01
-7.12799251e-01 -1.36393651e-01 4.35294211e-01 2.52639711e-01
-6.21041000e-01 2.64434159e-01 8.95016640e-02 -8.39946508e-01
-4.97350812e-01 -8.34066331e-01 -2.80050904e-01 -6.83029115e-01
-4.67834592e-01 8.30516458e-01 3.53097737e-01 2.58700579... | [15.382953643798828, -2.139153003692627] |
7ff86bd5-23d8-4f89-b8ca-caa5df6fa1cd | nnsvs-a-neural-network-based-singing-voice | 2210.15987 | null | https://arxiv.org/abs/2210.15987v2 | https://arxiv.org/pdf/2210.15987v2.pdf | NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit | This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency mod... | ['Tomoki Toda', 'Reo Yoneyama', 'Ryuichi Yamamoto'] | 2022-10-28 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-4.80171829e-01 -3.00821096e-01 -3.50538641e-01 8.37236643e-02
-5.24006069e-01 -5.15937269e-01 1.20464891e-01 -9.49322939e-01
1.37762174e-01 3.16961080e-01 5.90431750e-01 -3.09578747e-01
4.44821358e-01 -3.62482935e-01 -2.57072151e-01 -5.30574083e-01
1.55903563e-01 1.46005244e-03 -8.02509561e-02 -4.45627689... | [15.517742156982422, 6.149023532867432] |
03f177c0-bb51-43f3-a624-69c1a4865804 | deep-learning-framework-with-multi-head | 2306.11137 | null | https://arxiv.org/abs/2306.11137v1 | https://arxiv.org/pdf/2306.11137v1.pdf | Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging | T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components for cervical cancer diagnosis. However, combining these channels for training deep learning models are challenging due to misalignment of images. Here, we propose a novel multi-head framework that uses dilated con... | ['Dow-Mu Koh', 'Matthew D Blackledge', 'Christina Messiou', 'Gigin Lin', 'Jessica M Winfield', 'Sebastian Curcean', 'Reza Kalantar'] | 2023-06-19 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.93504947e-01 -4.24369276e-02 -1.48754299e-01 -3.13563645e-01
-1.11077201e+00 -6.87182188e-01 4.19890791e-01 3.41013104e-01
-7.83775508e-01 5.73337853e-01 2.16438830e-01 -7.23815084e-01
-3.25142473e-01 -5.36492467e-01 -5.95481992e-01 -9.92230415e-01
-2.78168887e-01 1.16937257e-01 3.21547598e-01 2.59371936... | [14.373346328735352, -2.3651065826416016] |
9bdaa40d-9023-4226-8fd3-b4f198e31dae | htlm-hyper-text-pre-training-and-prompting-of | 2107.06955 | null | https://arxiv.org/abs/2107.06955v1 | https://arxiv.org/pdf/2107.06955v1.pdf | HTLM: Hyper-Text Pre-Training and Prompting of Language Models | We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provides rich document-level and end-task-adjacent supervision (e.g. class and id attributes often encode document category information), and (3) ... | ['Luke Zettlemoyer', 'Gargi Ghosh', 'Hu Xu', 'Mandar Joshi', 'Mike Lewis', 'Dmytro Okhonko', 'Armen Aghajanyan'] | 2021-07-14 | htlm-hyper-text-pre-training-and-prompting-of-1 | https://openreview.net/forum?id=P-pPW1nxf1r | https://openreview.net/pdf?id=P-pPW1nxf1r | iclr-2022-4 | ['table-to-text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.56549442e-01 3.68939042e-01 -3.56180906e-01 -3.09749007e-01
-1.48670268e+00 -5.86555779e-01 7.28207707e-01 5.74801385e-01
-5.18634498e-01 4.32803780e-01 8.35806966e-01 -2.91434795e-01
6.67359307e-02 -6.57218874e-01 -9.18493509e-01 -2.73034424e-01
2.14744750e-02 7.32193530e-01 3.35657179e-01 -2.72016972... | [11.518660545349121, 8.821904182434082] |
3c4d1221-b4e9-4031-9482-f0044d8fe7c9 | haav-hierarchical-aggregation-of-augmented-1 | 2305.16295 | null | https://arxiv.org/abs/2305.16295v1 | https://arxiv.org/pdf/2305.16295v1.pdf | HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning | A great deal of progress has been made in image captioning, driven by research into how to encode the image using pre-trained models. This includes visual encodings (e.g. image grid features or detected objects) and more recently textual encodings (e.g. image tags or text descriptions of image regions). As more advance... | ['Zsolt Kira', 'Chia-Wen Kuo'] | 2023-05-25 | haav-hierarchical-aggregation-of-augmented | http://openaccess.thecvf.com//content/CVPR2023/html/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-captioning'] | ['computer-vision'] | [ 5.62185228e-01 2.81063616e-01 -3.11031908e-01 -4.91951942e-01
-9.67032135e-01 -6.79127753e-01 7.11821556e-01 -3.28595527e-02
-2.69274443e-01 4.89414006e-01 6.68020904e-01 -1.08059064e-01
3.07061106e-01 -6.49267972e-01 -1.02482951e+00 -5.33897340e-01
1.57211453e-01 9.83486846e-02 6.13036193e-02 -4.52038161... | [10.950536727905273, 1.0243130922317505] |
1afc0e49-2f06-4272-8e46-446868b13184 | riemannian-low-rank-model-compression-for | 2306.02433 | null | https://arxiv.org/abs/2306.02433v1 | https://arxiv.org/pdf/2306.02433v1.pdf | Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation | Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing the low-rank constraint of the model weights using a regularized nuclear norm penalty, which requires an appropriate hyperparameter that can b... | ['Vincent Lau', 'Ye Xue'] | 2023-06-04 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.31436825e-01 -1.40948385e-01 -2.05951244e-01 -4.54833210e-01
-8.82866204e-01 -2.94580877e-01 3.51430893e-01 -1.39707059e-01
-1.90987170e-01 2.88544774e-01 6.13959059e-02 -3.13473254e-01
-6.46069527e-01 -5.71592391e-01 -7.64064193e-01 -7.64037490e-01
4.01957668e-02 4.44967955e-01 -3.14591646e-01 1.26569390... | [7.379437446594238, 4.4061126708984375] |
9b502e0d-8f7e-4a08-a9dd-a810549e9963 | challenging-on-car-racing-problem-from-openai | 1911.04868 | null | https://arxiv.org/abs/1911.04868v1 | https://arxiv.org/pdf/1911.04868v1.pdf | Challenging On Car Racing Problem from OpenAI gym | This project challenges the car racing problem from OpenAI gym environment. The problem is very challenging since it requires computer to finish the continuous control task by learning from pixels. To tackle this challenging problem, we explored two approaches including evolutionary algorithm based genetic multi-layer ... | ['Changmao Li'] | 2019-11-02 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.20737919e-01 -2.45546445e-01 -6.07365780e-02 -7.78193250e-02
-2.63388634e-01 -9.93313417e-02 -4.70601082e-01 7.96092674e-03
-8.09280097e-01 1.25225365e+00 -4.41288531e-01 -2.35358894e-01
-3.38561893e-01 -9.16058421e-01 -9.66122568e-01 -7.91505635e-01
4.31250455e-03 4.78510350e-01 3.42209280e-01 -7.45158195... | [3.566983222961426, 1.5522562265396118] |
9158745b-8020-4759-a5b5-e5ab720c6054 | leaf-cultivar-identification-via-prototype | 2305.03351 | null | https://arxiv.org/abs/2305.03351v1 | https://arxiv.org/pdf/2305.03351v1.pdf | Leaf Cultivar Identification via Prototype-enhanced Learning | Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years. Due to the high similarity among different varieties, leaf cultivar recognition is also considered to be an ultra-fine-grained visual classification (UFGVC) task, whic... | ['Xiaogang Xu', 'Xianzhong Feng', 'Jun Wang', 'Nannan Li', 'Cuiling Wu', 'Ying Zheng', 'Zhiwen Ying', 'Yiyi Zhang'] | 2023-05-05 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.61110371e-01 -4.51592833e-01 -3.49461108e-01 -5.43150604e-01
-2.99494833e-01 -9.30935979e-01 3.44874412e-01 4.35601622e-01
1.10282011e-01 3.49233150e-01 -6.94767833e-02 -2.17537582e-01
-1.41493961e-01 -7.72834361e-01 -4.74107742e-01 -1.02234840e+00
2.10216120e-01 1.39551371e-01 3.39566469e-01 8.23167115... | [9.680999755859375, 2.193845748901367] |
bd21bc84-75c8-40cc-9c70-7e52a874daf2 | on-frequency-wise-normalizations-for-better | 2306.11764 | null | https://arxiv.org/abs/2306.11764v1 | https://arxiv.org/pdf/2306.11764v1.pdf | On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers | Varying conditions between the data seen at training and at application time remain a major challenge for machine learning. We study this problem in the context of Acoustic Scene Classification (ASC) with mismatching recording devices. Previous works successfully employed frequency-wise normalization of inputs and hidd... | ['Gerhard Widmer', 'Paul Primus and'] | 2023-06-20 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification'] | ['audio', 'computer-vision'] | [ 6.63569450e-01 -3.00120085e-01 2.11446524e-01 -2.58233249e-01
-5.05716443e-01 -4.84692395e-01 2.17687581e-02 1.71740353e-01
-4.25638765e-01 -6.30880594e-02 2.53810704e-01 -3.74729306e-01
-4.52545062e-02 -4.42076355e-01 -8.48493040e-01 -7.20311940e-01
1.39197826e-01 -3.89550209e-01 -4.21492942e-02 -3.38783383... | [15.144376754760742, 5.345508575439453] |
d088796a-b101-4270-a1e1-c8b35182fde1 | achieving-rgb-d-level-segmentation | 2306.17636 | null | https://arxiv.org/abs/2306.17636v1 | https://arxiv.org/pdf/2306.17636v1.pdf | Achieving RGB-D level Segmentation Performance from a Single ToF Camera | Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to obtain the same level of accuracy as RGB-D cameras on a semantic segmentation task using infrared (IR) and depth images from a single Time-of... | ['Juergen Seiler', 'Didier Stricker', 'Bruno Mirbach', 'Jason Rambach', 'Jigyasa Singh Katrolia', 'Pranav Sharma'] | 2023-06-30 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 5.47342300e-01 -9.29794535e-02 7.07725659e-02 -4.87206250e-01
-9.94672179e-01 -6.77078128e-01 3.77505541e-01 -1.56201124e-01
-8.54602277e-01 1.86591685e-01 -4.98060137e-01 -4.48106170e-01
1.32726192e-01 -7.66231656e-01 -8.73556376e-01 -5.01426697e-01
6.73809350e-01 4.35079724e-01 6.80465221e-01 -9.14108232... | [8.592755317687988, -2.5619871616363525] |
95b30ebf-17e0-40c1-b3b3-f42c45b5787f | age-and-gender-classification-from-ear-images | 1806.05742 | null | http://arxiv.org/abs/1806.05742v1 | http://arxiv.org/pdf/1806.05742v1.pdf | Age and Gender Classification From Ear Images | In this paper, we present a detailed analysis on extracting soft biometric
traits, age and gender, from ear images. Although there have been a few
previous work on gender classification using ear images, to the best of our
knowledge, this study is the first work on age classification from ear images.
In the study, we h... | ['Hazim Kemal Ekenel', 'Nurdan Sezgin', 'Fevziye Irem Eyiokur', 'Dogucan Yaman'] | 2018-06-14 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-3.45466882e-01 9.95874628e-02 1.25350982e-01 -4.67079580e-01
-3.42284173e-01 5.94989806e-02 1.58646613e-01 1.36150122e-01
-6.05682254e-01 5.99530101e-01 -8.32462311e-02 1.01879366e-01
-1.02422036e-01 -7.19136834e-01 -1.46125987e-01 -7.37744093e-01
-3.13409269e-01 3.72745246e-01 -2.55004227e-01 -1.38225526... | [13.54067325592041, 0.9655308127403259] |
d8eb255f-f7d0-4d15-8b3c-95f3dd0660c5 | bayesian-optimistic-optimisation-with | 2105.04332 | null | https://arxiv.org/abs/2105.04332v1 | https://arxiv.org/pdf/2105.04332v1.pdf | Bayesian Optimistic Optimisation with Exponentially Decaying Regret | Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper exp... | ['Svetha Venkatesh', 'Santu Rana', 'Sunil Gupta', 'Hung Tran-The'] | 2021-05-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.10445836e-01 2.58219630e-01 7.81044513e-02 -3.32332820e-01
-1.15310144e+00 -5.75608194e-01 -3.59387253e-03 3.26382965e-01
-9.90228653e-01 1.03639209e+00 -2.56256044e-01 -7.24219799e-01
-7.52788007e-01 -7.22539127e-01 -7.92249620e-01 -1.01480138e+00
-6.26561821e-01 7.40036011e-01 2.71181390e-02 -8.34869742... | [4.799559593200684, 3.472414016723633] |
63fb2cc7-37a5-4be5-8be2-67f7c934934c | lighten-learning-interactions-with-graph-and | 2012.09402 | null | https://arxiv.org/abs/2012.09402v1 | https://arxiv.org/pdf/2012.09402v1.pdf | LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos | Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video. It can be thought of as a specialized version of Visual Relationship Detection, wherein one of the objects must be a human. While traditional methods formu... | ['Ganesh Ramakrishnan', 'Rishabh Dabral', 'Sai Praneeth Reddy Sunkesula'] | 2020-12-17 | null | null | null | null | ['video-visual-relation-detection', 'visual-relationship-detection'] | ['computer-vision', 'computer-vision'] | [-3.18999812e-02 -2.40244582e-01 -1.18168741e-01 -3.18534225e-01
-8.06939825e-02 -5.00154138e-01 4.63885188e-01 2.02107996e-01
-3.50961328e-01 1.74300432e-01 1.83407947e-01 9.01074558e-02
-1.80695027e-01 -5.26103497e-01 -7.74444938e-01 -4.27908748e-01
-4.45976168e-01 5.15802264e-01 4.72107410e-01 -1.11912131... | [8.333759307861328, 0.45581361651420593] |
66ff8a21-e2bd-4058-b8bd-30bbe7a512f6 | multi-layer-trajectory-clustering-a-network | 2005.14472 | null | https://arxiv.org/abs/2005.14472v2 | https://arxiv.org/pdf/2005.14472v2.pdf | Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping | Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work present a ... | ['Sanjukta Krishnagopal'] | 2020-05-29 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-1.57510057e-01 -2.59292901e-01 -6.44561470e-01 -1.95798814e-01
-2.57777512e-01 -6.77360892e-01 4.40379739e-01 3.23126465e-01
-1.63520142e-01 7.98870265e-01 5.60231149e-01 -2.01811835e-01
-8.99133682e-01 -7.33817101e-01 2.05194235e-01 -9.51460004e-01
-7.04558492e-01 9.36418474e-01 2.13008970e-01 -7.61858448... | [7.182051658630371, 5.503049373626709] |
ab6c0eb0-2d23-4bc9-8fb7-417e516c8ceb | unitedqa-a-hybrid-approach-for-open-domain | 2101.00178 | null | https://arxiv.org/abs/2101.00178v2 | https://arxiv.org/pdf/2101.00178v2.pdf | UnitedQA: A Hybrid Approach for Open Domain Question Answering | To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. We apply novel techniques to enhance both extractive and generative readers built upo... | ['Jianfeng Gao', 'Weizhu Chen', 'Pengcheng He', 'Xiaodong Liu', 'Yelong Shen', 'Hao Cheng'] | 2021-01-01 | null | https://aclanthology.org/2021.acl-long.240 | https://aclanthology.org/2021.acl-long.240.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [ 1.10978253e-01 4.16751742e-01 -4.12601940e-02 -2.17293993e-01
-2.04645729e+00 -9.79001999e-01 9.44275081e-01 -1.39162272e-01
-3.76473248e-01 1.04322541e+00 5.16250253e-01 -4.61492181e-01
-3.05260032e-01 -9.67812300e-01 -7.62862802e-01 -3.71645629e-01
6.73890531e-01 1.27927852e+00 6.28434479e-01 -7.75966167... | [11.279519081115723, 8.001813888549805] |
44b8d4dd-ac07-456f-8ec5-e23e3b99d5f8 | generating-soap-notes-from-doctor-patient | 2005.01795 | null | https://arxiv.org/abs/2005.01795v3 | https://arxiv.org/pdf/2005.01795v3.pdf | Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques | Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization ... | ['Jeffrey P. Bigham', 'Kundan Krishna', 'Zachary C. Lipton', 'Sopan Khosla'] | 2020-05-04 | null | https://aclanthology.org/2021.acl-long.384 | https://aclanthology.org/2021.acl-long.384.pdf | acl-2021-5 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.51416337e-01 8.30062151e-01 -1.22840859e-01 -4.22410399e-01
-1.62135768e+00 -7.66833186e-01 3.43734503e-01 1.03367186e+00
-8.92032981e-02 8.81303549e-01 1.38178980e+00 -3.50262612e-01
-1.87207177e-01 -6.80785626e-02 -2.11915851e-01 -1.77381054e-01
1.40557766e-01 7.17661977e-01 -4.35531765e-01 8.68151635... | [12.217475891113281, 9.344918251037598] |
3b9c58cb-4aba-465f-8d94-a555c32e94c7 | on-the-soundness-of-xai-in-prognostics-and | 2303.05517 | null | https://arxiv.org/abs/2303.05517v1 | https://arxiv.org/pdf/2303.05517v1.pdf | On the Soundness of XAI in Prognostics and Health Management (PHM) | The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails,... | ['Joaquín Borrego-Díaz', 'Juan Galán-Páez', 'David Solís-Martín'] | 2023-03-09 | null | null | null | null | ['time-series-classification', 'time-series-regression'] | ['time-series', 'time-series'] | [ 2.93205798e-01 -2.44945660e-01 -4.98790815e-02 -1.09763578e-01
3.73087898e-02 -1.12414159e-01 3.54594976e-01 2.29301676e-01
1.78051189e-01 7.90962040e-01 -4.80915278e-01 -7.15177953e-01
-9.98815060e-01 -6.75145447e-01 -5.11837363e-01 -8.42009008e-01
-3.08706403e-01 3.11732918e-01 -1.91527784e-01 -3.05448174... | [6.753669738769531, 2.4753739833831787] |
477405a5-9c6d-4ac5-8e5b-e19a9c78e94b | optimal-estimation-and-computational-limit-of | 2201.09040 | null | https://arxiv.org/abs/2201.09040v1 | https://arxiv.org/pdf/2201.09040v1.pdf | Optimal Estimation and Computational Limit of Low-rank Gaussian Mixtures | Structural matrix-variate observations routinely arise in diverse fields such as multi-layer network analysis and brain image clustering. While data of this type have been extensively investigated with fruitful outcomes being delivered, the fundamental questions like its statistical optimality and computational limit a... | ['Dong Xia', 'Zhongyuan Lyu'] | 2022-01-22 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.85374755e-01 1.36890218e-01 -1.79626927e-01 6.39450876e-03
-7.36145496e-01 -5.42994559e-01 2.31339514e-01 -1.31460931e-02
-3.61003041e-01 5.81097901e-01 -1.42429918e-01 -3.33302885e-01
-7.70316541e-01 -3.69303107e-01 -9.24357474e-01 -1.01562274e+00
-5.29747546e-01 1.03060506e-01 -1.68339580e-01 2.15048239... | [7.0406494140625, 4.619889736175537] |
bc77466f-7d3d-4e4b-a565-eba1b7010f5d | hyperspectral-unmixing-based-on-nonnegative | 2205.09933 | null | https://arxiv.org/abs/2205.09933v1 | https://arxiv.org/pdf/2205.09933v1.pdf | Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review | Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the ... | ['Antonio Plaza', 'Xiuping Jia', 'Qian Du', 'Rui Wang', 'Heng-Chao Li', 'Xin-Ru Feng'] | 2022-05-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 8.28781962e-01 -7.69734442e-01 -3.08455557e-01 -6.75172210e-02
-4.22491044e-01 -7.42984593e-01 3.61531824e-01 -4.29408044e-01
1.18408334e-02 7.51201153e-01 1.70507163e-01 -3.24762344e-01
-3.91827077e-01 -5.15780866e-01 -2.43419260e-01 -1.20247531e+00
-2.14534607e-02 1.41421705e-01 -8.87910485e-01 -7.96595663... | [10.057945251464844, -2.0357937812805176] |
d94d4cbf-19a3-4b51-aee2-b1aa1fd54168 | nero-neural-geometry-and-brdf-reconstruction | 2305.17398 | null | https://arxiv.org/abs/2305.17398v1 | https://arxiv.org/pdf/2305.17398v1.pdf | NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images | We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment. Multiview reconstruction of reflective objects is extremely challenging because specular reflections are view-dependent and thus violate the... | ['Wenping Wang', 'Taku Komura', 'Lingjie Liu', 'Jiepeng Wang', 'Xiaoxiao Long', 'Cheng Lin', 'Peng Wang', 'YuAn Liu'] | 2023-05-27 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 3.13866466e-01 -2.36414075e-01 6.18105948e-01 -2.95224309e-01
-3.38378668e-01 -4.83875185e-01 3.76458198e-01 -3.87010038e-01
-1.54832087e-03 4.99340177e-01 -2.20806271e-01 6.94121495e-02
1.34962186e-01 -1.05869246e+00 -9.01218355e-01 -1.01925850e+00
5.12752593e-01 3.80020231e-01 1.93808839e-01 -1.49676241... | [9.760740280151367, -3.065560817718506] |
e0ad5570-5366-4adf-a4c3-d7b3a20c5311 | bounding-boxes-segmentations-and-object | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.pdf | Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? | Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estim... | ['Aseem Behl', 'Omid Hosseini Jafari', 'Carsten Rother', 'Siva Karthik Mustikovela', 'Hassan Abu Alhaija', 'Andreas Geiger'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['scene-flow-estimation'] | ['computer-vision'] | [ 4.05088782e-01 -1.28723100e-01 -1.68970913e-01 -9.11889076e-02
-8.32392812e-01 -7.90292144e-01 8.52929711e-01 -6.28531054e-02
-3.89479458e-01 5.09857595e-01 4.74160701e-01 -3.90692025e-01
5.60701936e-02 -5.83864868e-01 -8.67450833e-01 -4.42125946e-01
1.17900468e-01 4.11025047e-01 5.08290827e-01 -7.44747743... | [8.57087230682373, -1.988318681716919] |
7080203c-8a96-4f85-9067-323bf471e10a | appearance-harmonization-for-single-image | 1603.06398 | null | http://arxiv.org/abs/1603.06398v1 | http://arxiv.org/pdf/1603.06398v1.pdf | Appearance Harmonization for Single Image Shadow Removal | Shadows often create unwanted artifacts in photographs, and removing them can
be very challenging. Previous shadow removal methods often produce de-shadowed
regions that are visually inconsistent with the rest of the image. In this work
we propose a fully automatic shadow region harmonization approach that improves
the... | ['Shi-Min Hu', 'Kalyan Sunkavalli', 'Liqian Ma', 'Jue Wang', 'Eli Shechtman'] | 2016-03-21 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 8.38459730e-01 1.52075976e-01 4.92896020e-01 -2.22911865e-01
-1.82840139e-01 -3.38866144e-01 4.43661481e-01 -2.95820594e-01
2.76662201e-01 8.83942425e-01 2.51497000e-01 -2.01897889e-01
4.22541410e-01 -6.03003263e-01 -6.53469324e-01 -9.19034779e-01
5.06836057e-01 2.02610373e-01 1.11879480e+00 -3.05984885... | [10.815296173095703, -4.047720432281494] |
0290bc7e-c1c0-46c0-a6df-cbc02ae97231 | aweu-net-an-attention-aware-weight-excitation | 2110.05144 | null | https://arxiv.org/abs/2110.05144v1 | https://arxiv.org/pdf/2110.05144v1.pdf | AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation | Lung cancer is deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage. Most of the existing systems are semi-automated and need to manually select... | ['Hatem A. Raswan', 'Domenec Puig', 'Mohamed Abdel-Nasser', 'Md. Mostafa Kamal Sarker', 'Syeda Furruka Banu'] | 2021-10-11 | null | null | null | null | ['lung-nodule-detection', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [-2.14584712e-02 1.12589933e-01 -3.92223120e-01 -8.12988281e-02
-1.02166021e+00 -2.28940040e-01 1.94827497e-01 -3.00463915e-01
-4.81108546e-01 4.49639201e-01 1.09347813e-01 -3.60796720e-01
1.26848161e-01 -7.17903674e-01 -3.93411487e-01 -8.13729107e-01
1.60246640e-01 5.90613961e-01 8.00667107e-01 4.44416612... | [15.384818077087402, -2.1207480430603027] |
3be9b6eb-90ec-4f10-bf59-7820f76d5e27 | graph2vid-flow-graph-to-video-grounding | 2210.04996 | null | https://arxiv.org/abs/2210.04996v2 | https://arxiv.org/pdf/2210.04996v2.pdf | Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization | In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute a procedure successfully, by following the set of steps in slightly va... | ['Allan D. Jepson', 'Afsaneh Fazly', 'Brais Martinez', 'Dhaivat Bhatt', 'Hai Pham', 'Isma Hadji', 'Nikita Dvornik'] | 2022-10-10 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.56027281e-01 -3.41346145e-01 -3.41257364e-01 -3.46265316e-01
-7.54300356e-01 -1.09041560e+00 4.24288690e-01 4.71939087e-01
-4.61879522e-01 4.85716969e-01 2.47112010e-02 -4.83646810e-01
-1.92970008e-01 -6.39744818e-01 -1.09956384e+00 -3.83338958e-01
1.85080573e-01 3.67936671e-01 4.16060984e-01 1.65671408... | [8.707191467285156, 0.6205253005027771] |
b862fd6f-fc19-4e0d-9668-f7ef57ba46c7 | investigation-of-japanese-png-bert-language | 2212.08321 | null | https://arxiv.org/abs/2212.08321v1 | https://arxiv.org/pdf/2212.08321v1.pdf | Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language | End-to-end text-to-speech synthesis (TTS) can generate highly natural synthetic speech from raw text. However, rendering the correct pitch accents is still a challenging problem for end-to-end TTS. To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised... | ['Tomoki Toda', 'Yusuke Yasuda'] | 2022-12-16 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 1.06064074e-01 3.06580275e-01 -5.43765612e-02 -5.62498748e-01
-1.06817937e+00 -7.53563702e-01 1.29937813e-01 -6.68606758e-01
-6.80190697e-02 6.26196921e-01 6.73203826e-01 -5.66890657e-01
3.53489429e-01 -5.86392403e-01 -7.94925451e-01 -5.19627750e-01
3.07405263e-01 6.57893419e-01 8.40881392e-02 -6.98467553... | [14.90963077545166, 6.651187419891357] |
f295170c-9456-46a0-a738-6cdcc6d94ce9 | 3d-object-aided-self-supervised-monocular | 2212.01768 | null | https://arxiv.org/abs/2212.01768v1 | https://arxiv.org/pdf/2212.01768v1.pdf | 3D Object Aided Self-Supervised Monocular Depth Estimation | Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynam... | ['Lining Sun', 'Zhenhua Wang', 'Wenzheng Chi', 'Guodong Chen', 'Songlin Wei'] | 2022-12-04 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.17531791e-01 -1.64870307e-01 -1.83440551e-01 -3.08100224e-01
-1.46013200e-01 -5.98026395e-01 4.21947569e-01 -4.09147292e-01
-4.75856543e-01 2.46675238e-01 -4.79259878e-01 -5.57138724e-03
4.27635133e-01 -6.20241523e-01 -8.75274360e-01 -7.79841065e-01
4.14811879e-01 6.03578448e-01 7.41227448e-01 2.16624588... | [8.082791328430176, -2.333843946456909] |
7f01a71c-a26f-4bc2-9c84-5d0df84a4a9e | octnet-learning-deep-3d-representations-at | 1611.05009 | null | http://arxiv.org/abs/1611.05009v4 | http://arxiv.org/pdf/1611.05009v4.pdf | OctNet: Learning Deep 3D Representations at High Resolutions | We present OctNet, a representation for deep learning with sparse 3D data. In
contrast to existing models, our representation enables 3D convolutional
networks which are both deep and high resolution. Towards this goal, we exploit
the sparsity in the input data to hierarchically partition the space using a
set of unbal... | ['Ali Osman Ulusoy', 'Gernot Riegler', 'Andreas Geiger'] | 2016-11-15 | octnet-learning-deep-3d-representations-at-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-classification'] | ['computer-vision'] | [-4.16502059e-01 3.09559375e-01 -1.48821309e-01 -3.92032683e-01
-3.03440481e-01 -5.68042517e-01 5.55718720e-01 2.92229235e-01
-9.31898952e-02 3.68030965e-01 2.94062018e-01 -2.39896774e-01
-1.25973642e-01 -1.09053636e+00 -8.45683217e-01 -2.51251459e-02
-4.66481715e-01 6.59605920e-01 4.46661830e-01 1.72928318... | [8.113251686096191, -3.69313907623291] |
dde17d8c-66b6-4d56-a2da-081e6e886278 | prototex-explaining-model-decisions-with | 2204.05426 | null | https://arxiv.org/abs/2204.05426v2 | https://arxiv.org/pdf/2204.05426v2.pdf | ProtoTEx: Explaining Model Decisions with Prototype Tensors | We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks. ProtoTEx faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples. At inference time, classification decisions are based on the distances between the input text and ... | ['Junyi Jessy Li', 'Matthew Lease', 'Venelin Kovatchev', 'Chitrank Gupta', 'Anubrata Das'] | 2022-04-11 | null | https://aclanthology.org/2022.acl-long.213 | https://aclanthology.org/2022.acl-long.213.pdf | acl-2022-5 | ['propaganda-detection'] | ['natural-language-processing'] | [-1.59388974e-01 5.78230023e-01 -7.35489547e-01 -4.44919437e-01
-3.11045766e-01 -6.24961674e-01 1.15646148e+00 5.00505090e-01
1.59185901e-01 1.88619882e-01 7.17658401e-01 -7.26534367e-01
-6.76250994e-01 -5.83603323e-01 -5.47276855e-01 -2.81444430e-01
-2.36101180e-01 9.50735450e-01 -1.54005662e-01 -2.01541200... | [9.488327980041504, 6.760933876037598] |
2b7d5786-c00d-443e-a98e-61bda59fe78b | attacut-a-fast-and-accurate-neural-thai-word | 1911.07056 | null | https://arxiv.org/abs/1911.07056v1 | https://arxiv.org/pdf/1911.07056v1.pdf | AttaCut: A Fast and Accurate Neural Thai Word Segmenter | Word segmentation is a fundamental pre-processing step for Thai Natural Language Processing. The current off-the-shelf solutions are not benchmarked consistently, so it is difficult to compare their trade-offs. We conducted a speed and accuracy comparison of the popular systems on three different domains and found that... | ['Pattarawat Chormai', 'Attapol Rutherford', 'Ponrawee Prasertsom'] | 2019-11-16 | null | null | null | null | ['thai-word-tokenization'] | ['natural-language-processing'] | [-1.31607249e-01 -1.76094338e-01 -2.34310940e-01 -3.51283789e-01
-5.14713824e-01 -5.94761491e-01 1.91971973e-01 -3.08859888e-02
-9.09076452e-01 5.34750998e-01 1.50038078e-01 -7.47390389e-01
8.42379630e-01 -7.82117128e-01 -5.79351604e-01 -5.44068933e-01
1.30506083e-01 8.62992465e-01 5.41381061e-01 -1.69771880... | [10.150810241699219, 10.164302825927734] |
956eef06-ec31-4cf6-a2e5-7c31db6e78e8 | differentially-private-video-activity | 2306.15742 | null | https://arxiv.org/abs/2306.15742v1 | https://arxiv.org/pdf/2306.15742v1.pdf | Differentially Private Video Activity Recognition | In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored. This paper addresses the challenges of applying differential privacy to video activity recognition, which primarily stem from: (1) a discrepancy... | ['Animashree Anandkumar', 'Li Fei-Fei', 'Chaowei Xiao', 'Zhiding Yu', 'De-An Huang', 'Zane Durante', 'Yijin Yang', 'Yuliang Zou', 'Zelun Luo'] | 2023-06-27 | null | null | null | null | ['activity-recognition', 'classification-1', 'transfer-learning'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 4.19890314e-01 -4.83393997e-01 -4.49185133e-01 -2.89081961e-01
-1.11947000e+00 -5.53306222e-01 1.02386944e-01 -1.93317652e-01
-6.08101070e-01 6.38173461e-01 9.40694809e-02 -2.90369183e-01
1.93355843e-01 -3.43611836e-01 -8.50939512e-01 -7.33653367e-01
-5.08750319e-01 -2.75791615e-01 -2.98367329e-02 4.35597807... | [5.851177215576172, 6.744853496551514] |
07ec02cf-f804-430a-a000-8b78cca2f765 | robust-superpixel-guided-attentional | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.pdf | Robust Superpixel-Guided Attentional Adversarial Attack | Deep Neural Networks are vulnerable to adversarial samples, which can fool classifiers by adding small perturbations onto the original image. Since the pioneering optimization-based adversarial attack method, many following methods have been proposed in the past several years. However most of these methods add perturba... | [' Nenghai Yu', ' Weiming Zhang', ' Xiaogang Wang', ' Hongsheng Li', ' Zehua Ma', ' Huanyu Bian', ' Jiayang Liu', ' Dongdong Chen', ' Jiangfan Han', 'Xiaoyi Dong'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['steganalysis'] | ['computer-vision'] | [ 6.22748971e-01 1.13130165e-02 1.66334748e-01 6.71691746e-02
-1.91704527e-01 -6.28652096e-01 5.66877961e-01 -2.92434692e-01
-4.42678809e-01 6.44819498e-01 -9.55840871e-02 -2.02814892e-01
2.19025895e-01 -9.00458336e-01 -8.08320463e-01 -9.48295891e-01
9.05327573e-02 -4.74394917e-01 7.37793863e-01 -5.68733692... | [5.484187602996826, 7.94251012802124] |
a148de49-6ea5-4d4d-ab61-79165acdadad | aria-digital-twin-a-new-benchmark-dataset-for | 2306.06362 | null | https://arxiv.org/abs/2306.06362v2 | https://arxiv.org/pdf/2306.06362v2.pdf | Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception | We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth. This ADT release contains 200 sequences of real-world activities conducted by Aria wearers in two real indoor scenes with 398 object instances (324 stationary an... | ['Carl Yuheng Ren', 'Richard Newcombe', 'Omkar Parkhi', 'Chen Kong', 'Thomas Whelan', 'Scott Peters', 'Yongqian Yang', 'Nicholas Charron', 'Xiaqing Pan'] | 2023-06-10 | null | null | null | null | ['pose-prediction', '3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.86651194e-01 -2.84122285e-02 1.95305631e-01 -2.75213510e-01
-4.59860086e-01 -5.40609300e-01 3.44044149e-01 -6.43282592e-01
-1.74781084e-01 2.96452463e-01 1.53547116e-02 5.56842163e-02
-1.54918507e-01 -2.73544252e-01 -1.05112362e+00 -2.20602363e-01
-1.05546072e-01 9.41028893e-01 3.37402016e-01 -1.56566471... | [7.0450663566589355, -1.762964129447937] |
af3193d9-9809-4783-911f-080fd89b362c | learning-personalized-end-to-end-goal | 1811.04604 | null | http://arxiv.org/abs/1811.04604v1 | http://arxiv.org/pdf/1811.04604v1.pdf | Learning Personalized End-to-End Goal-Oriented Dialog | Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first ... | ['Xu sun', 'Qi Zeng', 'Liangchen Luo', 'Zaiqing Nie', 'Wenhao Huang'] | 2018-11-12 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-3.49701911e-01 3.00894529e-01 -2.05459088e-01 -6.02100670e-01
-2.80101478e-01 -6.82807505e-01 8.50796998e-01 1.60640327e-03
-7.66662717e-01 7.91230738e-01 9.15713012e-01 -6.37459904e-02
-1.00350156e-01 -4.71388489e-01 1.78352520e-01 -3.56197923e-01
1.15402453e-01 1.01006269e+00 3.92858326e-01 -7.93312788... | [12.78563117980957, 7.9291253089904785] |
f545df17-5fe6-4bbc-87f6-e97b1a816d29 | unsupervised-representation-learning-in | 2303.07437 | null | https://arxiv.org/abs/2303.07437v1 | https://arxiv.org/pdf/2303.07437v1.pdf | Unsupervised Representation Learning in Partially Observable Atari Games | State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation learning research. Although some researchers realize the connections between masked image modeling and contrastive representation learning, th... | ['Paal Engelstad', 'Anis Yazidi', 'Morten Goodwin', 'Li Meng'] | 2023-03-13 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 3.03811491e-01 -7.65034929e-03 -4.85366851e-01 -2.45516986e-01
-7.57695377e-01 -2.64029235e-01 1.17511797e+00 -3.06097120e-01
-5.77605307e-01 6.54919147e-01 5.34682631e-01 -1.61851734e-01
1.94731474e-01 -3.40977937e-01 -6.64346576e-01 -8.16296697e-01
-2.92394638e-01 4.40823108e-01 3.38424116e-01 -3.09872895... | [4.425314903259277, 1.2820872068405151] |
49b25997-c432-4d6f-a8a0-373ab1dc5bef | text-to-audio-generation-using-instruction | 2304.13731 | null | https://arxiv.org/abs/2304.13731v2 | https://arxiv.org/pdf/2304.13731v2.pdf | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model | The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instr... | ['Soujanya Poria', 'Ambuj Mehrish', 'Navonil Majumder', 'Deepanway Ghosal'] | 2023-04-24 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.12520434e-01 2.58913606e-01 -2.67962217e-01 -1.14726290e-01
-1.24217319e+00 -1.42577633e-01 9.99446273e-01 8.22035596e-02
-4.16664332e-01 4.59520489e-01 8.03889751e-01 -3.66681695e-01
1.27591088e-01 -5.74358404e-01 -9.81093168e-01 -7.84508407e-01
-2.83963308e-02 5.90750039e-01 4.67935443e-01 -3.11631083... | [15.299288749694824, 5.171802997589111] |
bae6d03b-76c2-4cff-a807-e8f7e9e2bdb1 | improved-inference-via-deep-input-transfer | 1904.02307 | null | https://arxiv.org/abs/1904.02307v4 | https://arxiv.org/pdf/1904.02307v4.pdf | Improved Inference via Deep Input Transfer | Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation perf... | ['Saied Asgari Taghanaki', 'Kumar Abhishek', 'Ghassan Hamarneh'] | 2019-04-04 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.60881853e-01 7.93268085e-02 -3.01447153e-01 -4.83882129e-01
-7.57872641e-01 -4.74167138e-01 1.33336753e-01 3.44764590e-01
-7.59380102e-01 6.02725148e-01 -8.05475190e-02 -3.56545627e-01
-4.08805832e-02 -6.01570368e-01 -5.66374838e-01 -8.45515132e-01
8.14078078e-02 1.92656860e-01 3.03743064e-01 3.57975364... | [14.672194480895996, -2.558436155319214] |
4411e911-9691-425e-b69c-46476a61cb7d | dunhuang-murals-contour-generation-network | 2212.00935 | null | https://arxiv.org/abs/2212.00935v2 | https://arxiv.org/pdf/2212.00935v2.pdf | Dunhuang murals contour generation network based on convolution and self-attention fusion | Dunhuang murals are a collection of Chinese style and national style, forming a self-contained Chinese-style Buddhist art. It has very high historical and cultural value and research significance. Among them, the lines of Dunhuang murals are highly general and expressive. It reflects the character's distinctive charact... | ['Jianhua Wang', 'Kaiwu Zhang', 'Shiqiang Du', 'Fengjie He', 'Baokai Liu'] | 2022-12-02 | null | null | null | null | ['edge-detection', 'culture'] | ['computer-vision', 'speech'] | [-1.10517658e-01 -1.99505106e-01 2.80855179e-01 3.83690465e-03
5.17046452e-02 -2.42434382e-01 5.97369552e-01 -2.99578011e-01
-3.40949565e-01 5.65478563e-01 2.62250960e-01 1.54971182e-01
1.29530519e-01 -1.34498000e+00 -3.65678757e-01 -6.83626175e-01
7.03326166e-02 -1.18118480e-01 2.21149087e-01 -7.87090242... | [11.333905220031738, -1.099946141242981] |
1775ebe3-56a9-4aeb-9a2d-6733bb4d9672 | reweighted-low-rank-tensor-decomposition | 1611.05963 | null | http://arxiv.org/abs/1611.05963v4 | http://arxiv.org/pdf/1611.05963v4.pdf | Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising | The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes
low rank multi-linear signal corrupted by gross errors into low multi-rank and
sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm.
But if the multi-rank of the signal is considerably large and/or large amount
of no... | ['Sudhish N. George', 'M. Baburaj'] | 2016-11-18 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-1.30798981e-01 -6.22863591e-01 1.13110080e-01 2.64513433e-01
-7.25655317e-01 -4.95080769e-01 4.89977859e-02 -6.53317213e-01
1.98456831e-02 4.26282257e-01 6.95215225e-01 1.02752179e-01
-5.34651458e-01 -1.07797727e-01 -3.71806234e-01 -1.05620503e+00
-4.09361571e-01 -9.81156975e-02 -1.13105878e-01 -1.46043465... | [7.435389041900635, 4.450991630554199] |
a47e34aa-630b-4b85-aab8-3b76dc917ca8 | bayesian-calibration-of-mems-accelerometers | 2306.06144 | null | https://arxiv.org/abs/2306.06144v1 | https://arxiv.org/pdf/2306.06144v1.pdf | Bayesian Calibration of MEMS Accelerometers | This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of ... | ['Zong-Xian Yin', 'Po-Yu Fan', 'Oliver Dürr'] | 2023-06-09 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.11105168e-01 -3.49320531e-01 -2.21671656e-01 -8.08149695e-01
-6.79760337e-01 -3.26925427e-01 4.69643146e-01 2.71834731e-01
-6.86306179e-01 9.25269306e-01 -1.12865925e-01 -2.28636876e-01
-2.95063436e-01 -8.61115038e-01 -7.72218764e-01 -7.87313521e-01
3.82182002e-01 6.86814308e-01 2.10288003e-01 3.05860043... | [6.468790054321289, 3.633730888366699] |
9c5f5767-a41e-4075-bc72-11432e2edce3 | large-language-models-are-versatile | 2301.13808 | null | https://arxiv.org/abs/2301.13808v3 | https://arxiv.org/pdf/2301.13808v3.pdf | Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning | Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on hug... | ['Yongbin Li', 'Fei Huang', 'Binhua Li', 'Min Yang', 'Binyuan Hui', 'Yunhu Ye'] | 2023-01-31 | null | null | null | null | ['semantic-parsing', 'table-based-fact-verification'] | ['natural-language-processing', 'natural-language-processing'] | [-2.20868275e-01 6.09471440e-01 -1.71685487e-01 -2.41096035e-01
-1.32283080e+00 -8.57106507e-01 3.85490388e-01 5.41373074e-01
-1.67534739e-01 8.83513331e-01 7.32180953e-01 -8.49973559e-01
-1.72238067e-01 -1.22590351e+00 -9.39328194e-01 -4.72489633e-02
4.71630782e-01 7.68869638e-01 2.82218158e-01 -4.19268399... | [10.331624984741211, 7.736525058746338] |
fb49c258-4635-4126-a393-676e27c059aa | resource-evaluation-for-usable-speech | null | null | https://aclanthology.org/L12-1583 | https://aclanthology.org/L12-1583.pdf | Resource Evaluation for Usable Speech Interfaces: Utilizing Human-Human Dialogue | Human-human spoken dialogues are considered an important tool for effective speech interface design and are often used for stochastic model training in speech based applications. However, the less restricted nature of human-human interaction compared to human-system interaction may undermine the usefulness of such corp... | ['Dimitris Spiliotopoulos', 'Georgios Kouroupetroglou', 'Pepi Stavropoulou'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 6.92909881e-02 3.94374073e-01 1.72223687e-01 -5.48696935e-01
-5.06318212e-01 -5.59030950e-01 8.83666992e-01 2.38668904e-01
-7.94797003e-01 8.14494610e-01 4.02332187e-01 -6.77849054e-01
-9.39625949e-02 -3.35796773e-01 2.55973786e-01 -3.35083365e-01
2.91910648e-01 8.05037498e-01 1.97597966e-01 -6.55361712... | [13.124581336975098, 7.822612285614014] |
9b95959c-65af-42e4-89b8-2a549b3c2282 | homonym-normalisation-by-word-sense | null | null | https://aclanthology.org/2020.coling-main.295 | https://aclanthology.org/2020.coling-main.295.pdf | Homonym normalisation by word sense clustering: a case in Japanese | This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to ... | ['Kevin Heffernan', 'Yo Sato'] | 2020-12-01 | null | null | null | coling-2020-8 | ['transliteration'] | ['natural-language-processing'] | [ 9.28027555e-03 -5.46193160e-02 -1.33679971e-01 -3.51678103e-01
4.56447387e-03 -8.73454273e-01 7.25159049e-01 3.35141122e-01
-9.30784941e-01 6.39359593e-01 8.69832277e-01 -4.36727136e-01
1.18615098e-01 -8.15111995e-01 1.77414138e-02 -5.22499740e-01
3.60911489e-01 4.82140392e-01 3.22102100e-01 -7.00160563... | [10.32515811920166, 9.113131523132324] |
3669ca63-6327-4ebc-9e25-24a4fcb3140e | sound-demixing-challenge-2023-music-demixing | 2306.09382 | null | https://arxiv.org/abs/2306.09382v2 | https://arxiv.org/pdf/2306.09382v2.pdf | Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3 | In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a time-efficient music source separation model that achieves state-of-the-art results on the MUSDB benchmark. We then give full details regarding our solutions for eac... | ['Soonyoung Jung', 'Jun Hyung Lee', 'Minseok Kim'] | 2023-06-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 5.15518673e-02 -6.77085876e-01 -1.07697986e-01 7.99297094e-02
-1.64822125e+00 -7.34226525e-01 3.71375233e-02 -2.70791501e-01
-1.59812458e-02 3.74408990e-01 6.47364914e-01 -1.02399342e-01
-5.15422285e-01 1.32305160e-01 -7.03329980e-01 -5.07389128e-01
-3.64935219e-01 2.12755948e-01 -1.43980831e-01 -7.19607398... | [15.456121444702148, 5.535373210906982] |
c48c4058-41d0-459d-ae50-51a88da74caa | powarematch-a-quality-aware-deep-learning | 2109.07321 | null | https://arxiv.org/abs/2109.07321v1 | https://arxiv.org/pdf/2109.07321v1.pdf | PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching | Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web and data mining for many years, the main challenge remains the ability to generate quality matches among data concepts (e.g., database attributes). In this work, we examine a novel angle on th... | ['Avigdor Gal', 'Roee Shraga'] | 2021-09-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 7.66232833e-02 1.57671839e-01 -2.65660465e-01 -6.49432182e-01
-6.41122103e-01 -5.78523934e-01 7.72780061e-01 8.62962604e-01
-6.53771043e-01 3.99523407e-01 2.23639473e-01 -8.05074349e-02
-4.69552577e-01 -1.22313845e+00 -8.65705132e-01 -9.30600986e-02
1.52248383e-01 9.14429426e-01 2.02144504e-01 -5.36538124... | [9.481382369995117, 8.442062377929688] |
aaa534ee-2e7d-46b8-bdeb-d1facf353407 | semi-supervised-models-via-data | 2004.10972 | null | https://arxiv.org/abs/2004.10972v1 | https://arxiv.org/pdf/2004.10972v1.pdf | Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses | We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sentence and employs back translation techniques to paraphrase given sentences as augmented data. For la... | ['Yuwei Wu', 'Jiaao Chen', 'Diyi Yang'] | 2020-04-23 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 7.81562567e-01 9.02268827e-01 -3.52087766e-01 -1.06565523e+00
-7.84211099e-01 -4.81463462e-01 6.56010807e-01 2.52660275e-01
-3.37412268e-01 1.14690030e+00 5.08831441e-01 -9.50263515e-02
7.42699265e-01 -5.75491190e-01 -6.81930006e-01 -2.12168708e-01
1.53504461e-01 6.83373868e-01 -3.93207252e-01 -2.02946037... | [10.91670036315918, 8.235418319702148] |
fd25141e-35b1-4f4b-868e-68454c70663d | combining-deep-and-depth-deep-learning-and | 1812.05831 | null | http://arxiv.org/abs/1812.05831v1 | http://arxiv.org/pdf/1812.05831v1.pdf | Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring | Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we investigate the combination of
deep learning based methods and depth maps as input images to tackle the
problem of driver attention monitoring. Moreover, we assume the concept of
attention as Head ... | ['Guido Borghi'] | 2018-12-14 | null | null | null | null | ['head-pose-estimation', 'driver-attention-monitoring'] | ['computer-vision', 'computer-vision'] | [-1.83609858e-01 3.07486296e-01 -3.01717043e-01 -4.48978126e-01
-6.55181885e-01 -1.37651160e-01 8.18456411e-01 -1.36830330e-01
-9.29688990e-01 4.07842606e-01 -9.12791342e-02 -1.28110915e-01
1.24595398e-02 -5.55019319e-01 -4.58650678e-01 -5.53343594e-01
3.99823844e-01 4.76205945e-01 5.57060480e-01 -2.24336728... | [13.703062057495117, 0.270916223526001] |
4b2b2a3b-1c65-4df3-aab4-c571ecdc8141 | action-tubelet-detector-for-spatio-temporal | 1705.01861 | null | http://arxiv.org/abs/1705.01861v3 | http://arxiv.org/pdf/1705.01861v3.pdf | Action Tubelet Detector for Spatio-Temporal Action Localization | Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that ... | ['Vittorio Ferrari', 'Cordelia Schmid', 'Vicky Kalogeiton', 'Philippe Weinzaepfel'] | 2017-05-04 | action-tubelet-detector-for-spatio-temporal-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Kalogeiton_Action_Tubelet_Detector_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kalogeiton_Action_Tubelet_Detector_ICCV_2017_paper.pdf | iccv-2017-10 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 6.26028553e-02 -4.01668727e-01 -3.08435231e-01 -1.59791604e-01
-7.41923869e-01 -5.56418777e-01 6.71829462e-01 1.78033635e-01
-6.95543349e-01 3.19137126e-01 4.00612533e-01 2.91643113e-01
-2.16108691e-02 -4.83943909e-01 -8.81171823e-01 -5.24031341e-01
-3.95803392e-01 -3.31973322e-02 1.23928964e+00 1.44265359... | [8.371387481689453, 0.3580344617366791] |
1be77f2c-2718-4ffc-aef3-da2a1e6535e8 | happy-or-grumpy-a-machine-learning-approach | 2209.14363 | null | https://arxiv.org/abs/2209.14363v1 | https://arxiv.org/pdf/2209.14363v1.pdf | Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets | As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This stu... | ['Yi Gao', 'Shengyang Wu'] | 2022-09-28 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-3.63481976e-02 -4.02426898e-01 -5.09005964e-01 -5.44891000e-01
-6.19616747e-01 -7.39141226e-01 5.32965660e-01 9.19350326e-01
-5.47376037e-01 4.86730903e-01 3.30227882e-01 -4.32437003e-01
2.45610476e-01 -1.13761723e+00 -7.70955235e-02 -4.17642325e-01
-9.76711162e-04 1.95918325e-02 2.66747344e-02 -7.69455552... | [10.832446098327637, 6.931869029998779] |
e8339194-e886-439d-ae7f-a1e136c9c866 | active-fire-detection-in-landsat-8-imagery-a | 2101.03409 | null | https://arxiv.org/abs/2101.03409v2 | https://arxiv.org/pdf/2101.03409v2.pdf | Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study | Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement. This is a well established field, with many techniques being proposed over the years, usually based on pixel or region-level comparisons involvin... | ['Rodrigo Minetto', 'Bogdan Tomoyuki Nassu', 'André Minoro Fusioka', 'Gabriel Henrique de Almeida Pereira'] | 2021-01-09 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 5.83723545e-01 -2.02536210e-01 -2.68140972e-01 -2.38166451e-01
-5.37423193e-01 -6.83512211e-01 7.10249603e-01 1.78447813e-01
-8.60763133e-01 8.67418170e-01 1.48949936e-01 -3.40988755e-01
-3.38267714e-01 -1.40757263e+00 -4.54357624e-01 -9.87692475e-01
-6.45192444e-01 2.68660009e-01 3.29791397e-01 -3.69761735... | [9.45893669128418, -1.4810445308685303] |
99d57b9e-793b-4003-9694-ce27c012d0f9 | medical-image-retrieval-via-nearest-neighbor | 2210.02401 | null | https://arxiv.org/abs/2210.02401v1 | https://arxiv.org/pdf/2210.02401v1.pdf | Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features | Nearest neighbor search (NNS) aims to locate the points in high-dimensional space that is closest to the query point. The brute-force approach for finding the nearest neighbor becomes computationally infeasible when the number of points is large. The NNS has multiple applications in medicine, such as searching large me... | ['Dina Demner-Fushman', 'Soumya Gayen', 'Russell Loane', 'Deepak Gupta'] | 2022-10-05 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 2.22296566e-01 -3.76760900e-01 -4.20670182e-01 -2.93033600e-01
-1.37548208e+00 -3.97593200e-01 3.74847919e-01 7.78444409e-01
-5.25653362e-01 3.08003724e-01 4.14913505e-01 -2.83472955e-01
-8.88993561e-01 -8.81251395e-01 -4.50506210e-01 -7.02548742e-01
-1.48616701e-01 8.10264945e-01 5.17282784e-01 -9.15993229... | [14.367053985595703, -1.551896572113037] |
645c23f7-f21c-42c9-b59c-fd96c526f264 | explanatory-machine-learning-for-sequential | 2205.10250 | null | https://arxiv.org/abs/2205.10250v2 | https://arxiv.org/pdf/2205.10250v2.pdf | Explanatory machine learning for sequential human teaching | The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descript... | ['Ute Schmid', 'Stephen H. Muggleton', 'Johannes Langer', 'Lun Ai'] | 2022-05-20 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 4.22459632e-01 4.77209419e-01 -1.39645755e-01 -4.10413146e-01
-1.35871381e-01 -5.96093118e-01 4.26704943e-01 8.74628246e-01
-4.65874523e-01 6.11733496e-01 1.45570472e-01 -1.04007483e+00
-8.53915632e-01 -1.00603759e+00 -7.66596973e-01 -1.39127597e-01
-2.01778382e-01 3.85999322e-01 2.81460583e-01 -4.16286051... | [10.086076736450195, 7.870797157287598] |
7a2e25c5-4335-442b-9630-def60a8566c1 | learning-latent-graph-dynamics-for-deformable | 2104.12149 | null | https://arxiv.org/abs/2104.12149v2 | https://arxiv.org/pdf/2104.12149v2.pdf | Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects | Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception. The key difficulty is a suitable representation, rich enough to capture the object shape, dynamics for m... | ['Wee Sun Lee', 'David Hsu', 'Xiao Ma'] | 2021-04-25 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-9.47762206e-02 2.05855310e-01 -1.54402167e-01 1.15432441e-01
-1.42307696e-03 -5.45409918e-01 3.81103307e-01 -1.37098908e-01
1.75485894e-01 2.97746599e-01 1.01067126e-01 4.26468074e-01
-3.93004417e-01 -5.16316593e-01 -1.11447835e+00 -5.44926405e-01
-5.70188344e-01 1.05810416e+00 3.71337086e-01 -5.54863691... | [4.926650047302246, 0.3387676775455475] |
6e59c653-d3e6-4df4-8301-10f8a7b4d744 | multi-view-harmonized-bilinear-network-for-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.pdf | Multi-View Harmonized Bilinear Network for 3D Object Recognition | View-based methods have achieved considerable success in $3$D object recognition tasks. Different from existing view-based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilin... | ['Tan Yu', 'Jingjing Meng', 'Junsong Yuan'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-object-recognition'] | ['computer-vision'] | [-8.51140171e-02 -4.97046679e-01 1.36764133e-02 -7.09732771e-01
-9.54192340e-01 -4.95861351e-01 5.11772692e-01 -1.00125022e-01
-1.06987320e-01 1.12923399e-01 2.51809180e-01 2.91579187e-01
-2.55006820e-01 -9.14168537e-01 -8.30505788e-01 -8.00270140e-01
3.40364352e-02 -3.47113401e-01 1.91999108e-01 2.43162550... | [8.168828964233398, -3.8320140838623047] |
5fbd55ac-34f8-485a-bb5a-1177ca626142 | native-language-identification-a-simple-n | null | null | https://aclanthology.org/W13-1729 | https://aclanthology.org/W13-1729.pdf | Native Language Identification: a Simple n-gram Based Approach | null | ['Binod Gyawali', 'Thamar Solorio', 'Gabriela Ramirez'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.223201274871826, 3.536831855773926] |
05d5baca-fbca-4849-a063-790a074834b8 | deep-hyperspectral-unmixing-using-transformer | 2203.17076 | null | https://arxiv.org/abs/2203.17076v1 | https://arxiv.org/pdf/2203.17076v1.pdf | Deep Hyperspectral Unmixing using Transformer Network | Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising re... | ['Paul Scheunders', 'Behnood Rasti', 'Bikram Koirala', 'Swalpa Kumar Roy', 'Preetam Ghosh'] | 2022-03-31 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.64996010e-01 -4.64496434e-01 2.10804403e-01 -2.01039404e-01
-6.38186693e-01 -4.34406132e-01 5.17986655e-01 -9.34449658e-02
-2.81578481e-01 5.05018115e-01 2.22397074e-01 -2.21311107e-01
-2.56287545e-01 -8.92139554e-01 -6.55600131e-01 -1.11200523e+00
6.64496273e-02 -2.94261631e-02 -4.97509152e-01 -1.10315382... | [10.096344947814941, -2.0047054290771484] |
cb914df9-6dad-4a35-a976-9946c99bc6db | relational-symmetry-based-knowledge-graph | 2211.10738 | null | https://arxiv.org/abs/2211.10738v4 | https://arxiv.org/pdf/2211.10738v4.pdf | Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure | Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely leveraged in graph learning as an effective mechanism to enhance the discriminative capacity of the learned representations. However, the com... | ['Xiangjun Dong', 'Xihong Yang', 'Yi Wen', 'Wenxuan Tu', 'Xinwang Liu', 'Sihang Zhou', 'Yue Liu', 'Ke Liang'] | 2022-11-19 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.00983912e-01 3.32961082e-01 -6.21382296e-01 -2.41907910e-01
-8.40143412e-02 -3.19484174e-01 5.59530735e-01 3.75553489e-01
-9.36742797e-02 4.87189323e-01 5.31694405e-02 -2.98639685e-01
-4.20507044e-01 -1.30186045e+00 -8.53805721e-01 -5.45905530e-01
-2.84120679e-01 2.29320914e-01 2.45907485e-01 -4.85587806... | [8.614913940429688, 7.797213554382324] |
c76f7182-f8b4-4aa4-95d5-d8ef082dd35b | automatic-portrait-video-matting-via-context | 2109.04598 | null | https://arxiv.org/abs/2109.04598v2 | https://arxiv.org/pdf/2109.04598v2.pdf | Automatic Portrait Video Matting via Context Motion Network | Automatic portrait video matting is an under-constrained problem. Most state-of-the-art methods only exploit the semantic information and process each frame individually. Their performance is compromised due to the lack of temporal information between the frames. To solve this problem, we propose the context motion net... | ['Charlie Wang', 'Qiqi Hou'] | 2021-09-10 | automatic-portrait-video-matting-via-context-1 | https://openreview.net/forum?id=zNlkpFBT9aD | https://openreview.net/pdf?id=zNlkpFBT9aD | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 2.70411402e-01 -4.74707633e-01 -5.78920543e-01 -3.52862656e-01
-1.93303391e-01 -3.36509943e-01 4.19605583e-01 -4.45987642e-01
-2.38707319e-01 6.20972157e-01 3.69850963e-01 2.45600771e-02
1.80422306e-01 -5.88064611e-01 -7.18615830e-01 -5.13333499e-01
2.43219092e-01 -8.98937732e-02 4.10744429e-01 -1.88813150... | [10.615875244140625, -0.8995346426963806] |
69929153-9e4b-43e0-b117-509f5f8bcc5d | nerd-neural-field-based-demosaicking | 2304.06566 | null | https://arxiv.org/abs/2304.06566v1 | https://arxiv.org/pdf/2304.06566v1.pdf | NeRD: Neural field-based Demosaicking | We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates ... | ['Jan Flusser', 'Adam Novozamsky', 'Filip Sroubek', 'Tomas Kerepecky'] | 2023-04-13 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 1.56751692e-01 -1.28747001e-01 3.01589876e-01 -2.45683149e-01
-5.96085906e-01 -4.41181451e-01 5.84127843e-01 -7.84783959e-01
-1.27943456e-01 5.12682736e-01 2.90753871e-01 -3.35594118e-01
4.46327776e-01 -1.08480299e+00 -1.04155207e+00 -2.93419898e-01
1.40598625e-01 -1.48204803e-01 1.43586129e-01 -4.75557685... | [9.674814224243164, -2.8506360054016113] |
94941563-eca6-4cf7-91ff-231e98f3eb43 | benchmarking-graph-neural-networks | 2003.00982 | null | https://arxiv.org/abs/2003.00982v5 | https://arxiv.org/pdf/2003.00982v5.pdf | Benchmarking Graph Neural Networks | In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for... | ['Xavier Bresson', 'Yoshua Bengio', 'Thomas Laurent', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi', 'Chaitanya K. Joshi'] | 2020-03-02 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.33684853e-01 -3.64792608e-02 -2.98604310e-01 1.15997583e-01
-7.93090835e-02 -6.56914234e-01 7.10119843e-01 6.03110731e-01
-2.60179996e-01 9.49647546e-01 -2.08038643e-01 -7.25484073e-01
-4.30836290e-01 -1.06839609e+00 -7.43317187e-01 -6.47823393e-01
-6.79064631e-01 4.39265698e-01 2.67667770e-01 -5.37681878... | [6.130181312561035, 5.799367427825928] |
105765d8-85f2-4aff-b511-baad03789126 | self-supervised-learning-via-multi | 2102.10378 | null | https://arxiv.org/abs/2102.10378v1 | https://arxiv.org/pdf/2102.10378v1.pdf | Self-Supervised Learning via multi-Transformation Classification for Action Recognition | Self-supervised tasks have been utilized to build useful representations that can be used in downstream tasks when the annotation is unavailable. In this paper, we introduce a self-supervised video representation learning method based on the multi-transformation classification to efficiently classify human actions. Sel... | ['Jia-Ching Wang', 'Ngan T. H. Le', 'Duc Quang Vu'] | 2021-02-20 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.93348897e-01 -2.86669672e-01 -3.36007416e-01 -5.34041822e-01
-3.21967006e-01 -4.47186172e-01 6.92991495e-01 -3.94017935e-01
-5.03544867e-01 7.44845450e-01 6.13916934e-01 1.20896399e-02
2.28058130e-01 -5.47347426e-01 -7.46956527e-01 -6.33418441e-01
-1.38617590e-01 2.29611769e-01 5.98046720e-01 -5.33213504... | [8.45139217376709, 0.7804473042488098] |
f661ed10-a7b4-492c-ba37-452913b5a5f4 | h3wb-human3-6m-3d-wholebody-dataset-and | 2211.15692 | null | https://arxiv.org/abs/2211.15692v1 | https://arxiv.org/pdf/2211.15692v1.pdf | H3WB: Human3.6M 3D WholeBody Dataset and Benchmark | 3D human whole-body pose estimation aims to localize precise 3D keypoints on the entire human body, including the face, hands, body, and feet. Due to the lack of a large-scale fully annotated 3D whole-body dataset, a common approach has been to train several deep networks separately on datasets dedicated to specific bo... | ['David Picard', 'Nermin Samet', 'Yue Zhu'] | 2022-11-28 | null | null | null | null | ['3d-hand-pose-estimation', '3d-human-pose-estimation', '3d-facial-landmark-localization', '3d-hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [-4.04216886e-01 1.20263584e-01 -1.23691231e-01 -2.84344912e-01
-8.45128417e-01 -4.72696185e-01 2.60006517e-01 -4.56653327e-01
-4.02193755e-01 5.06450474e-01 4.27655697e-01 4.82808679e-01
2.14001179e-01 -3.37080747e-01 -9.09751236e-01 -4.19365168e-01
1.12415642e-01 1.06859803e+00 1.77229524e-01 -3.23941976... | [7.022427082061768, -0.9626047015190125] |
27dcf798-4548-423a-8c5f-668fa56322a8 | image-color-correction-enhancement-and | 2107.13117 | null | https://arxiv.org/abs/2107.13117v1 | https://arxiv.org/pdf/2107.13117v1.pdf | Image color correction, enhancement, and editing | This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipula... | ['Mahmoud Afifi'] | 2021-07-28 | null | null | null | null | ['color-manipulation'] | ['computer-vision'] | [ 7.72606909e-01 -6.67720199e-01 5.16556561e-01 -1.45718306e-01
-1.99303478e-01 -7.76785672e-01 8.36111158e-02 -1.64995402e-01
-4.10927087e-01 3.80546212e-01 -3.17604274e-01 -5.46617568e-01
2.40867063e-01 -6.47631228e-01 -6.14614904e-01 -5.34683645e-01
5.97601175e-01 -4.63443398e-01 3.31712723e-01 -1.78907737... | [10.60930061340332, -2.506688117980957] |
6e5619d4-5b56-4d2c-975e-8c611c8241dd | multi-scale-patch-aggregation-mpa-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Multi-Scale_Patch_Aggregation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Multi-Scale_Patch_Aggregation_CVPR_2016_paper.pdf | Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation | Aiming at simultaneous detection and segmentation (SDS), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method be... | ['Xiaojuan Qi', 'Shu Liu', 'Jiaya Jia', 'Hong Zhang', 'Jianping Shi'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 2.83313274e-01 1.91489339e-01 -6.24981001e-02 -3.45237672e-01
-1.44925642e+00 -5.51024795e-01 3.04565996e-01 -6.04240485e-02
-4.87492174e-01 4.77180839e-01 -4.79546517e-01 6.77996799e-02
3.79847348e-01 -5.83350480e-01 -1.09538841e+00 -5.15720010e-01
1.51839539e-01 4.37625885e-01 6.95720255e-01 3.41005474... | [9.455669403076172, 0.3663317859172821] |
7b97e850-a5f4-4eeb-9f58-fe35e34d60ab | forecasting-significant-stock-price-changes | 1912.08791 | null | https://arxiv.org/abs/1912.08791v1 | https://arxiv.org/pdf/1912.08791v1.pdf | Forecasting significant stock price changes using neural networks | Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either th... | ['Firuz Kamalov'] | 2019-11-21 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.93845078e-01 -4.24326599e-01 -4.73889589e-01 -4.37717170e-01
-3.36727887e-01 -3.14908981e-01 6.97123468e-01 -5.97035959e-02
-4.65105295e-01 8.65585268e-01 7.30372667e-02 -5.49873471e-01
-2.51380634e-02 -1.21114683e+00 -6.43996179e-01 -3.88247609e-01
-3.45228851e-01 9.33069810e-02 1.25419602e-01 -4.08816099... | [4.427573204040527, 4.247844696044922] |
8038a9cb-ab41-4f85-969f-ef0bb1b537a9 | investigating-the-effect-of-hard-negative | 2305.10563 | null | https://arxiv.org/abs/2305.10563v1 | https://arxiv.org/pdf/2305.10563v1.pdf | Investigating the Effect of Hard Negative Sample Distribution on Contrastive Knowledge Graph Embedding | The success of the knowledge graph completion task heavily depends on the quality of the knowledge graph embeddings (KGEs), which relies on self-supervised learning and augmenting the dataset with negative triples. There is a gap in literature between the theoretical analysis of negative samples on contrastive loss and... | ['June Zhang', 'Honggen Zhang'] | 2023-05-17 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-completion', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'knowledge-base', 'methodology'] | [ 1.22512132e-02 6.91037595e-01 -4.83148158e-01 -4.79545206e-01
-6.41503930e-01 -5.86884975e-01 3.75991762e-01 2.56692737e-01
-4.77451742e-01 1.00034022e+00 2.21572537e-02 -2.16894001e-01
-4.62934852e-01 -1.28276420e+00 -1.35187316e+00 -5.55093706e-01
-1.73512518e-01 6.00809753e-01 2.34770894e-01 -2.25983992... | [8.794549942016602, 7.865927696228027] |
9e61e98b-e007-4949-8b3a-a33ae52aaae0 | deep-steiner-learning-to-solve-the-euclidean | 2209.09983 | null | https://arxiv.org/abs/2209.09983v1 | https://arxiv.org/pdf/2209.09983v1.pdf | Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem | The Euclidean Steiner tree problem seeks the min-cost network to connect a collection of target locations, and it underlies many applications of wireless networks. In this paper, we present a study on solving the Euclidean Steiner tree problem using reinforcement learning enhanced by graph representation learning. Diff... | ['Guangmo Tong', 'Yifan Wang', 'Siqi Wang'] | 2022-09-20 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 1.97072327e-01 4.81573880e-01 -8.45734835e-01 -2.83583820e-01
-5.52121043e-01 -6.53490543e-01 -9.47054029e-02 1.36335105e-01
-1.17966756e-01 1.11165869e+00 -5.12878418e-01 -9.25209224e-01
-1.10944784e+00 -9.48682547e-01 -7.01933324e-01 -6.53788388e-01
-9.83794630e-01 5.15186191e-01 6.43089265e-02 -1.85000092... | [5.2257609367370605, 2.828925371170044] |
f6b36da1-7773-4ce3-9a13-c990fe1b23f5 | tractable-fully-bayesian-inference-via-convex | 1509.08582 | null | http://arxiv.org/abs/1509.08582v1 | http://arxiv.org/pdf/1509.08582v1.pdf | Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory | We consider the problem of transforming samples from one continuous source
distribution into samples from another target distribution. We demonstrate with
optimal transport theory that when the source distribution can be easily
sampled from and the target distribution is log-concave, this can be tractably
solved with c... | ['Sanggyun Kim', 'Todd P. Coleman', 'Rui Ma', 'Diego Mesa'] | 2015-09-29 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.97830492e-01 3.80477220e-01 -2.08872303e-01 -3.52230877e-01
-1.06701446e+00 -4.50358719e-01 2.33616382e-01 -4.44502896e-03
-7.28728592e-01 1.47588313e+00 1.47414327e-01 -5.33239126e-01
-4.56963331e-01 -4.56441313e-01 -9.33076680e-01 -8.82233381e-01
-4.38744366e-01 5.97416699e-01 -1.36558831e-01 4.53473955... | [6.920881748199463, 3.9635226726531982] |
910ce506-65c3-46bf-8778-4c7763e3321e | universal-mini-batch-consistency-for-set | 2208.12401 | null | https://arxiv.org/abs/2208.12401v5 | https://arxiv.org/pdf/2208.12401v5.pdf | Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation | Recent work on mini-batch consistency (MBC) for set functions has brought attention to the need for sequentially processing and aggregating chunks of a partitioned set while guaranteeing the same output for all partitions. However, existing constraints on MBC architectures lead to models with limited expressive power. ... | ['Sung Ju Hwang', 'Juho Lee', 'Kenji Kawaguchi', 'Bruno Andreis', 'Seanie Lee', 'Jeffrey Willette'] | 2022-08-26 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.63204834e-01 -2.87661195e-01 -1.00711226e-01 -7.89439261e-01
-9.43591356e-01 -7.01841056e-01 1.83307320e-01 2.28019506e-01
-3.42629313e-01 5.66064954e-01 -1.83389232e-01 -4.50854957e-01
-3.15375328e-01 -6.44556522e-01 -8.62370491e-01 -6.18001759e-01
-2.92879343e-01 6.10027254e-01 2.86143422e-01 6.67789057... | [8.534027099609375, 3.880798101425171] |
71ca4d53-3999-4c83-8a3d-1974b9a1c0aa | mmnet-a-model-based-multimodal-network-for | null | null | https://ieeexplore.ieee.org/abstract/document/9782511 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9782511 | MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos | Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth
sensors. Currently, unimodal approaches (e.g., skeleton-based and RGB video-based) have realized substantial improvements with
increasingly larger datasets. However, multimodal methods specifically with m... | ['Keith C.C. Chan', 'Sheng-hua Zhong', 'Xiang Zhang', 'Yan Liu', 'Bruce X.B. Yu'] | 2022-05-26 | null | null | null | ieee-transactions-on-pattern-analysis-and-21 | ['action-classification', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 1.75368816e-01 -5.37485540e-01 -1.16943784e-01 -2.39971891e-01
-7.93683052e-01 -4.33789939e-02 4.23756570e-01 -4.54245478e-01
-4.81407821e-01 5.11398792e-01 4.04767573e-01 9.66812968e-02
3.82902622e-02 -5.75843632e-01 -6.48227572e-01 -7.78894484e-01
1.37053072e-01 -1.34792343e-01 1.83222279e-01 -1.91176787... | [7.829336166381836, 0.5204086303710938] |
acaebb9a-7663-4096-8df1-a85872616cf4 | qdwi-morph-motion-compensated-quantitative | 2208.09836 | null | https://arxiv.org/abs/2208.09836v1 | https://arxiv.org/pdf/2208.09836v1.pdf | qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment | Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical util... | ['Moti Freiman', 'Simon Warfield', 'Sila Kurugol', 'Onur Afacan', 'Yael Zaffrani-Reznikov'] | 2022-08-21 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [-2.41458900e-02 1.18045941e-01 -1.96082696e-01 -4.19608712e-01
-1.03774810e+00 -4.06724215e-01 2.85315037e-01 -3.33578698e-02
-4.18729246e-01 3.51612240e-01 2.82020628e-01 -2.84611791e-01
-4.46915418e-01 -6.69142365e-01 -6.82458878e-01 -8.26541603e-01
-6.30374551e-01 7.32802689e-01 3.49687994e-01 4.68313545... | [14.012492179870605, -2.392021417617798] |
1e086924-930b-47c4-8926-9eedd4a7094a | review-on-6d-object-pose-estimation-with-the | 2212.01920 | null | https://arxiv.org/abs/2212.01920v1 | https://arxiv.org/pdf/2212.01920v1.pdf | Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding | 6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning, many breakthroughs have been made; however, approaches continue to s... | ['Pooya Fayyazsanavi', 'Negar Nejatishahidin'] | 2022-12-04 | null | null | null | null | ['6d-pose-estimation', 'robot-manipulation'] | ['computer-vision', 'robots'] | [ 2.47869909e-01 -1.84493616e-01 -1.97634235e-01 -4.38152283e-01
-1.68162376e-01 -4.69058812e-01 6.41494453e-01 1.50367498e-01
-1.31135240e-01 3.35336417e-01 -2.37350445e-02 -9.89960060e-02
-1.07287884e-01 -4.95723009e-01 -5.91316283e-01 -2.94716269e-01
-1.75181050e-02 5.62986910e-01 4.18004125e-01 -1.11144997... | [7.418238639831543, -2.299752950668335] |
c6f527f9-e691-4a1a-83a8-1241c69ebd28 | characterizing-the-load-profile-in-power | 2304.07832 | null | https://arxiv.org/abs/2304.07832v1 | https://arxiv.org/pdf/2304.07832v1.pdf | Characterizing the load profile in power grids by Koopman mode decomposition of interconnected dynamics | Electricity load forecasting is crucial for effectively managing and optimizing power grids. Over the past few decades, various statistical and deep learning approaches have been used to develop load forecasting models. This paper presents an interpretable machine learning approach that identifies load dynamics using d... | ['Heman Shakeri', 'Behnaz MoradiJamei', 'Ali Tavasoli'] | 2023-04-16 | null | null | null | null | ['interpretable-machine-learning', 'load-forecasting'] | ['methodology', 'miscellaneous'] | [-5.62939584e-01 -5.81471384e-01 2.68707369e-02 -1.06093191e-01
-1.62865266e-01 -7.98618257e-01 7.54182041e-01 2.94695288e-01
1.25368342e-01 6.71563864e-01 2.56472558e-01 -1.13339536e-01
-7.29096472e-01 -8.75841081e-01 -3.42907101e-01 -1.10698462e+00
-1.12846839e+00 4.61875468e-01 -3.83313924e-01 -3.51565450... | [6.133538246154785, 2.766176462173462] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.