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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7f59e3a0-c940-49e9-8868-b7092d8af1b2 | object-aware-multi-branch-relation-networks | 2008.06941 | null | https://arxiv.org/abs/2008.06941v2 | https://arxiv.org/pdf/2008.06941v2.pdf | Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding | Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of a queried object according to the given sentence. Currently, most existing grounding methods are restricted to well-aligned segment-sentence pairs. In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sent... | ['Zhijie Lin', 'Zhou Zhao', 'Zhu Zhang', 'Nicholas Jing Yuan', 'Baoxing Huai'] | 2020-08-16 | null | null | null | null | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [ 2.48397827e-01 2.98098265e-03 -6.54532313e-01 -3.88072491e-01
-8.87437105e-01 -4.34290409e-01 3.35646987e-01 4.36722308e-01
-1.43049255e-01 4.98281598e-01 3.23553145e-01 -7.57644400e-02
-4.68181968e-01 -7.71451414e-01 -8.26308608e-01 -4.91198301e-01
-3.91311832e-02 4.71029669e-01 8.72879446e-01 4.88170236... | [9.6812105178833, 0.6747910976409912] |
43c1b055-77f7-4770-a517-83a74d392d7e | learning-camera-aware-noise-models | 2008.09370 | null | https://arxiv.org/abs/2008.09370v1 | https://arxiv.org/pdf/2008.09370v1.pdf | Learning Camera-Aware Noise Models | Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a gen... | ['Hwann-Tzong Chen', 'Yu-Lin Chang', 'Chia-Ping Chen', 'Yu-Lun Liu', 'Hung-Jin Lin', 'Ke-Chi Chang', 'Ren Wang'] | 2020-08-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4617_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690341.pdf | eccv-2020-8 | ['noise-estimation'] | ['medical'] | [ 1.52385801e-01 -7.03221560e-01 3.59156728e-01 -3.27860653e-01
-8.30201864e-01 -5.52988172e-01 4.31334615e-01 -2.50371873e-01
-3.68792832e-01 2.35267103e-01 2.03868300e-01 6.94702193e-02
-2.15162426e-01 -6.58073604e-01 -6.03958428e-01 -9.29968834e-01
5.26646435e-01 6.31090105e-02 5.35782337e-01 1.66316628... | [11.387425422668457, -2.479482889175415] |
d723ca85-c86b-424d-ae97-f813eb5a0c5f | the-weltmodell-a-data-driven-commonsense | null | null | https://aclanthology.org/L14-1351 | https://aclanthology.org/L14-1351.pdf | The Weltmodell: A Data-Driven Commonsense Knowledge Base | We present the Weltmodell, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3.5 million English language books. We leverage the magnitude and diversity of this dataset to arrive at close to ten million distinct N-ary commonsense facts using tech... | ['Alan Akbik', 'Thilo Michael'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.92141578e-01 3.62122357e-01 -5.29021621e-01 -3.55010033e-01
-9.55071867e-01 -1.01751363e+00 8.34732592e-01 6.07372522e-01
-5.19758224e-01 1.29541183e+00 7.52943575e-01 -2.07564667e-01
-2.66169637e-01 -6.90558016e-01 -5.65117955e-01 4.90029827e-02
2.15667635e-01 6.61935031e-01 1.80655375e-01 -5.44323981... | [9.90664005279541, 8.15768814086914] |
13d4ed29-40be-4cd2-8e0b-cbd63a957126 | covid-19-ct-cxr-a-freely-accessible-and | 2006.06177 | null | https://arxiv.org/abs/2006.06177v2 | https://arxiv.org/pdf/2006.06177v2.pdf | COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature | The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedi... | ['Sung-Won Lee', 'Yu-Xing Tang', 'Yingying Zhu', 'Yifan Peng', 'Ronald M. Summers', 'Zhiyong Lu'] | 2020-06-11 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.62166387e-01 -3.30462515e-01 -1.05082415e-01 -1.62072986e-01
-1.04260767e+00 -6.65730476e-01 2.22722605e-01 6.55578494e-01
-5.65092564e-01 6.79902077e-01 1.75543740e-01 -8.24447334e-01
1.02318013e-02 -7.73848951e-01 -9.37883437e-01 -6.26019478e-01
-3.91660482e-01 9.08916354e-01 3.21903229e-02 3.83990794... | [15.487689018249512, -1.7840631008148193] |
c191a847-f468-4dd2-a01f-5c7863a2a8c1 | monet-unsupervised-scene-decomposition-and | 1901.11390 | null | http://arxiv.org/abs/1901.11390v1 | http://arxiv.org/pdf/1901.11390v1.pdf | MONet: Unsupervised Scene Decomposition and Representation | The ability to decompose scenes in terms of abstract building blocks is
crucial for general intelligence. Where those basic building blocks share
meaningful properties, interactions and other regularities across scenes, such
decompositions can simplify reasoning and facilitate imagination of novel
scenarios. In particu... | ['Alexander Lerchner', 'Irina Higgins', 'Nicholas Watters', 'Christopher P. Burgess', 'Rishabh Kabra', 'Matt Botvinick', 'Loic Matthey'] | 2019-01-22 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 2.57503808e-01 1.88774541e-01 2.44916752e-01 -4.58057463e-01
-3.49220157e-01 -5.39440393e-01 7.19282448e-01 2.18901873e-01
9.52448249e-02 4.05883789e-01 6.25970304e-01 -2.08658904e-01
-1.91714779e-01 -7.17322052e-01 -9.09388542e-01 -5.12345076e-01
-2.37975288e-02 4.54134673e-01 -1.19483434e-02 2.11835280... | [10.054214477539062, 1.0241361856460571] |
147a3d16-dc61-4131-aae6-c5258c7894f4 | cooperative-audio-source-separation-and | 1912.05038 | null | http://arxiv.org/abs/1912.05038v1 | http://arxiv.org/pdf/1912.05038v1.pdf | Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices | Augmented listening devices such as hearing aids often perform poorly in
noisy and reverberant environments with many competing sound sources. Large
distributed microphone arrays can improve performance, but data from remote
microphones often cannot be used for delay-constrained real-time processing. We
present a coope... | [] | 2019-12-10 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.86698914e-01 -6.67592347e-01 7.71737635e-01 -2.07644445e-03
-1.52699053e+00 -6.68465555e-01 -3.24457496e-01 1.42094493e-01
-2.78151840e-01 2.98004448e-01 8.26687813e-01 -3.64902824e-01
-5.92741743e-02 -2.58747995e-01 -8.50486532e-02 -6.00001216e-01
-5.24141788e-01 -3.73327911e-01 3.31828564e-01 -3.47244088... | [15.045682907104492, 5.8096842765808105] |
b95d889c-b01b-40b4-b22e-ce60b4d979f0 | hsr-diff-hyperspectral-image-super-resolution | 2306.12085 | null | https://arxiv.org/abs/2306.12085v1 | https://arxiv.org/pdf/2306.12085v1.pdf | HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models | Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HS... | ['Ying Li', 'Hanyu Mao', 'Dong Wang', 'Chanyue Wu'] | 2023-06-21 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 8.55163515e-01 -2.26811737e-01 4.25184309e-01 -5.72119839e-02
-1.26551545e+00 -2.88132995e-01 7.07490683e-01 -4.65788096e-01
-4.77590635e-02 9.15762067e-01 3.38584810e-01 1.60833433e-01
-4.32014823e-01 -1.13120973e+00 -3.89034927e-01 -1.36938131e+00
2.38476127e-01 -2.82439664e-02 1.89600453e-01 -1.28936216... | [10.227842330932617, -1.9520615339279175] |
1cedfe03-7490-43e6-9fa8-ed3190cc6827 | hierarchical-pretraining-for-biomedical-term | 2307.00266 | null | https://arxiv.org/abs/2307.00266v1 | https://arxiv.org/pdf/2307.00266v1.pdf | Hierarchical Pretraining for Biomedical Term Embeddings | Electronic health records (EHR) contain narrative notes that provide extensive details on the medical condition and management of patients. Natural language processing (NLP) of clinical notes can use observed frequencies of clinical terms as predictive features for downstream applications such as clinical decision maki... | ['Lu Tian', 'Doudou Zhou', 'Zheng Yuan', 'Yucong Lin', 'Sihang Zeng', 'Bryan Cai'] | 2023-07-01 | null | null | null | null | ['trajectory-prediction', 'knowledge-graphs', 'management', 'decision-making'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'reasoning'] | [ 6.89305887e-02 1.49804473e-01 -5.75675488e-01 -3.79048049e-01
-7.73239613e-01 -3.00269902e-01 2.28515029e-01 1.24853981e+00
-2.82665253e-01 5.84969699e-01 9.17609394e-01 -3.94474328e-01
-5.61732054e-01 -8.86509895e-01 -1.01409018e-01 -7.34704673e-01
-5.51361561e-01 5.26520908e-01 -3.50996107e-01 2.54773982... | [7.90706729888916, 6.853460311889648] |
c6661b00-0a1e-450b-bbeb-df72f97f56de | authorship-verification-in-the-absence-of | null | null | https://link.springer.com/chapter/10.1007/978-3-319-76941-7_34 | https://link.springer.com/chapter/10.1007/978-3-319-76941-7_34 | Authorship verification in the absence of explicit features and thresholds | Enhancing information retrieval systems with the ability to take the writing style of people into account opens the door for a number of applications. For example, one can link articles by authorships that can help identifying authors who generate hoaxes and deliberate misinformation in news stories, distributed across... | ['Inna Vogel', 'Lukas Graner', 'Oren Halvani'] | 2018-03-01 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 8.39071944e-02 -1.19299062e-01 -3.72270525e-01 -1.18451901e-01
-6.55887306e-01 -9.12036061e-01 1.27050078e+00 5.49758077e-01
-5.69905102e-01 5.48414290e-01 8.47856775e-02 -3.23426783e-01
-1.76444560e-01 -6.09122336e-01 -2.93567747e-01 -2.60035068e-01
3.90725076e-01 7.69484222e-01 2.54401416e-01 -1.93394825... | [9.556314468383789, 10.589239120483398] |
88bc4ebb-40f6-4341-93ed-e0a06188a5fe | unifying-identification-and-context-learning | 1806.03084 | null | http://arxiv.org/abs/1806.03084v1 | http://arxiv.org/pdf/1806.03084v1.pdf | Unifying Identification and Context Learning for Person Recognition | Despite the great success of face recognition techniques, recognizing persons
under unconstrained settings remains challenging. Issues like profile views,
unfavorable lighting, and occlusions can cause substantial difficulties.
Previous works have attempted to tackle this problem by exploiting the context,
e.g. clothes... | ['Yu Xiong', 'Qingqiu Huang', 'Dahua Lin'] | 2018-06-08 | unifying-identification-and-context-learning-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Unifying_Identification_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Unifying_Identification_and_CVPR_2018_paper.pdf | cvpr-2018-6 | ['person-recognition'] | ['computer-vision'] | [ 1.86896235e-01 -3.83537829e-01 -1.38540208e-01 -6.40008271e-01
-3.28597963e-01 -3.59119564e-01 8.50967884e-01 -2.72752881e-01
-3.76951784e-01 6.15362704e-01 3.57142031e-01 7.23309293e-02
-1.34378701e-01 -4.98482853e-01 -5.40348232e-01 -6.10232174e-01
2.24015176e-01 1.99948594e-01 7.22544044e-02 -7.37009645... | [14.581668853759766, 0.9546076059341431] |
d7e49f4f-44cc-427e-8b13-3c46b459ca0d | diffclip-leveraging-stable-diffusion-for | 2305.15957 | null | https://arxiv.org/abs/2305.15957v2 | https://arxiv.org/pdf/2305.15957v2.pdf | DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification | Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation. However, the model's performance on 3D point cloud processing tasks is limited due to the... | ['Xinxiao wu', 'Harry Zhang', 'Linqian Fan', 'Zilin Zhu', 'Sitian Shen'] | 2023-05-25 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-3.59332298e-05 7.63817653e-02 -2.27044687e-01 -3.89516532e-01
-8.46160829e-01 -2.86832213e-01 4.14218426e-01 -2.33142093e-01
-3.12084824e-01 -3.58520299e-02 -4.53841388e-01 -1.84886396e-01
2.90088564e-01 -8.17798197e-01 -6.94133222e-01 -3.16921622e-01
4.07643944e-01 7.11408496e-01 1.01129961e+00 -8.79357830... | [8.115486145019531, -3.056816339492798] |
f943e9db-da78-4c3e-a2ff-ac2fbeff40b1 | graph-structure-learning-with-variational | 2112.08903 | null | https://arxiv.org/abs/2112.08903v1 | https://arxiv.org/pdf/2112.08903v1.pdf | Graph Structure Learning with Variational Information Bottleneck | Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts the accurate and complete relations between nodes. However, graphs in the real world are inevitably n... | ['Philip S. Yu', 'Cheng Ji', 'Xingcheng Fu', 'Jia Wu', 'Hao Peng', 'JianXin Li', 'Qingyun Sun'] | 2021-12-16 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 0.25698164 0.6176557 -0.423129 -0.06739556 -0.26276866 -0.24198711
0.2512225 0.23558508 0.2022323 0.621987 0.29472816 -0.48560205
-0.7837894 -1.0096892 -0.85222685 -0.8486445 -0.2867816 0.39566886
-0.1289176 -0.14327145 -0.21983816 0.24850719 -0.9650507 -0.08355199
0.88315886 0.8659638 0.... | [7.1199493408203125, 6.221828460693359] |
0441cb95-3d0d-4c42-9e48-474230563fa7 | rgb-d-salient-object-detection-with | 2109.03425 | null | https://arxiv.org/abs/2109.03425v1 | https://arxiv.org/pdf/2109.03425v1.pdf | RGB-D Salient Object Detection with Ubiquitous Target Awareness | Conventional RGB-D salient object detection methods aim to leverage depth as complementary information to find the salient regions in both modalities. However, the salient object detection results heavily rely on the quality of captured depth data which sometimes are unavailable. In this work, we make the first attempt... | ['Xiaowu Chen', 'Jia Li', 'Jiawei Zhao', 'Yifan Zhao'] | 2021-09-08 | null | null | null | null | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.78270775e-01 7.58379623e-02 -2.37529948e-01 -2.72470325e-01
-9.17073190e-01 -6.93840832e-02 1.26255915e-01 1.03875510e-01
-3.72387081e-01 3.41174871e-01 2.79555678e-01 2.74566002e-02
5.27952723e-02 -7.20646262e-01 -7.82436848e-01 -7.55105138e-01
1.03971161e-01 -2.57993072e-01 1.09290540e+00 -3.98859650... | [9.731304168701172, -0.7387523055076599] |
43a16598-08e1-4024-a22d-68a9a4da03af | a-lightweight-neural-network-for-monocular | 2007.12577 | null | https://arxiv.org/abs/2007.12577v1 | https://arxiv.org/pdf/2007.12577v1.pdf | A Lightweight Neural Network for Monocular View Generation with Occlusion Handling | In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, w... | ['Simon Evain', 'Christine Guillemot'] | 2020-07-24 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 5.53446472e-01 3.31622720e-01 2.81242043e-01 -4.81652945e-01
-7.29439855e-01 -4.09807473e-01 5.88101566e-01 -8.20811167e-02
-4.51857656e-01 8.00615191e-01 -1.49888858e-01 -2.31270209e-01
2.38762513e-01 -9.53508437e-01 -1.04300368e+00 -6.14060283e-01
1.53759435e-01 6.53761744e-01 4.37615305e-01 3.20087783... | [8.891796112060547, -2.582298517227173] |
60a01982-36cf-49cf-b84d-298905cb34aa | a-control-centric-benchmark-for-video | 2304.13723 | null | https://arxiv.org/abs/2304.13723v1 | https://arxiv.org/pdf/2304.13723v1.pdf | A Control-Centric Benchmark for Video Prediction | Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it rema... | ['Jiajun Wu', 'Chelsea Finn', 'Stephen Tian'] | 2023-04-26 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 5.43266758e-02 -4.76556178e-03 -2.59809673e-01 -2.64486492e-01
-5.18752277e-01 -5.91804504e-01 7.78732657e-01 1.79582089e-02
-5.68303287e-01 5.36489248e-01 5.93191504e-01 -2.14133888e-01
-6.34307861e-02 -4.37070668e-01 -9.61308658e-01 -1.86210856e-01
-6.38018310e-01 4.50069696e-01 3.24429125e-01 -1.73299789... | [4.529105186462402, 0.7856602668762207] |
9e8bde04-f746-42f6-a207-c8d1eb1f671e | a-gold-anaphora-annotation-layer-on-an-eye | null | null | https://aclanthology.org/L18-1555 | https://aclanthology.org/L18-1555.pdf | A Gold Anaphora Annotation Layer on an Eye Movement Corpus | null | ['Pascal Amsili', 'Olga Seminck'] | 2018-05-01 | a-gold-anaphora-annotation-layer-on-an-eye-1 | https://aclanthology.org/L18-1555 | https://aclanthology.org/L18-1555.pdf | lrec-2018-5 | ['abstract-anaphora-resolution'] | ['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... | [-7.439014434814453, 3.6454989910125732] |
a4148790-8d84-440b-b963-f0ecc1baa98a | semantic-enhanced-image-clustering | 2208.09849 | null | https://arxiv.org/abs/2208.09849v2 | https://arxiv.org/pdf/2208.09849v2.pdf | Semantic-Enhanced Image Clustering | Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. ... | ['Longteng Chen', 'Qin Zhang', 'Xiaojun Chen', 'Liping Qiu', 'Shaotian Cai'] | 2022-08-21 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.09890658e-01 -9.86817107e-02 -1.80831820e-01 -5.19167721e-01
-6.04508162e-01 -4.09055978e-01 2.52628148e-01 1.91325963e-01
-5.01263499e-01 1.05432764e-01 -2.31856525e-01 -1.46702483e-01
-3.84683549e-01 -6.31546557e-01 -7.52933681e-01 -8.50007296e-01
3.48789155e-01 3.95046264e-01 2.17627838e-01 2.95674324... | [9.42833423614502, 2.9814491271972656] |
793ae31d-305d-4b67-bb93-c64c6a7dd61c | devolutionary-genetic-algorithms-with | 2004.10048 | null | https://arxiv.org/abs/2004.10048v1 | https://arxiv.org/pdf/2004.10048v1.pdf | Devolutionary genetic algorithms with application to the minimum labeling Steiner tree problem | This paper characterizes and discusses devolutionary genetic algorithms and evaluates their performances in solving the minimum labeling Steiner tree (MLST) problem. We define devolutionary algorithms as the process of reaching a feasible solution by devolving a population of super-optimal unfeasible solutions over tim... | ['Nassim Dehouche'] | 2020-04-18 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 6.67530358e-01 3.39922398e-01 -7.70753771e-02 -1.98988989e-01
-1.43552363e-01 -5.27801037e-01 1.19899269e-02 4.91383404e-01
-5.19317150e-01 1.11423886e+00 -4.75433499e-01 -2.77662545e-01
-1.00894094e+00 -1.09718549e+00 -3.83647859e-01 -8.61999631e-01
-1.90944657e-01 9.93362248e-01 2.41673633e-01 -3.98344815... | [5.746589183807373, 3.6707987785339355] |
5df6ede4-bfb4-4cbf-8478-eaab69c31305 | location-free-human-pose-estimation | 2205.12619 | null | https://arxiv.org/abs/2205.12619v1 | https://arxiv.org/pdf/2205.12619v1.pdf | Location-free Human Pose Estimation | Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate this issue, we revisit HPE and propose a location-free framework without supervision of keypoint locati... | ['Qi Zou', 'Xue Lin', 'Ke Yan', 'Yingguo Gao', 'Xixia Xu'] | 2022-05-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_Location-Free_Human_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_Location-Free_Human_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 5.96055463e-02 -2.14298274e-02 -2.80066520e-01 -4.07063872e-01
-8.77554119e-01 -2.96463609e-01 3.20287645e-01 -5.35384566e-02
-4.56678867e-01 4.57252413e-01 3.79581749e-01 3.74761641e-01
-3.47666293e-02 -6.19465292e-01 -9.14090753e-01 -4.52494323e-01
2.83613540e-02 4.74492580e-01 5.59930444e-01 -3.89468700... | [7.232062339782715, -0.7080006003379822] |
cb08b3fe-6bfb-4e79-84d0-848090be76d1 | inferring-fluid-dynamics-via-inverse | 2304.04446 | null | https://arxiv.org/abs/2304.04446v1 | https://arxiv.org/pdf/2304.04446v1.pdf | Inferring Fluid Dynamics via Inverse Rendering | Humans have a strong intuitive understanding of physical processes such as fluid falling by just a glimpse of such a scene picture, i.e., quickly derived from our immersive visual experiences in memory. This work achieves such a photo-to-fluid-dynamics reconstruction functionality learned from unannotated videos, witho... | ['Zhenbo Yu', 'Jiyao Mao', 'Bingbing Ni', 'Ye Chen', 'Jinxian Liu'] | 2023-04-10 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [-1.60483688e-01 1.87493354e-01 7.90285110e-01 -2.21387461e-01
-2.23184884e-01 -3.12375516e-01 6.70640349e-01 -1.67892277e-01
-2.41798654e-01 7.12083817e-01 1.40992299e-01 8.95856172e-02
3.33281964e-01 -1.07515216e+00 -1.12082052e+00 -4.33271259e-01
-3.43084872e-01 5.91274679e-01 -6.16921186e-02 -2.83297330... | [9.092000961303711, -3.068535327911377] |
7693d80f-d344-4193-a0b4-2a7d39ae4d77 | mudpt-multi-modal-deep-symphysis-prompt | 2306.11400 | null | https://arxiv.org/abs/2306.11400v1 | https://arxiv.org/pdf/2306.11400v1.pdf | MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language Models | Prompt tuning, like CoOp, has recently shown promising vision recognizing and transfer learning ability on various downstream tasks with the emergence of large pre-trained vision-language models like CLIP. However, we identify that existing uni-modal prompt tuning approaches may result in sub-optimal performance since ... | ['Ting Wang', 'Jintao Tang', 'Shasha Li', 'Yongzhu Miao'] | 2023-06-20 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 1.11566484e-01 -9.90238786e-02 -1.70979053e-01 -2.56204516e-01
-1.09510231e+00 -7.85329461e-01 1.03611898e+00 -1.79062083e-01
-4.83447462e-01 1.41726211e-01 4.42908883e-01 -2.94119835e-01
-1.14866480e-01 -4.45524991e-01 -8.09102118e-01 -5.55852830e-01
4.75608945e-01 3.70410889e-01 3.98659378e-01 -3.67905289... | [10.306297302246094, 1.923471212387085] |
061f0519-268b-4743-83f1-441c5e39dcc7 | generalizable-person-re-identification-by | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.pdf | Generalizable Person Re-Identification by Domain-Invariant Mapping Network | We aim to learn a domain generalizable person re-identification (ReID) model. When such a model is trained on a set of source domains (ReID datasets collected from different camera networks), it can be directly applied to any new unseen dataset for effective ReID without any model updating. Despite its practical value ... | [' Timothy M. Hospedales', ' Tao Xiang', ' Yi-Zhe Song', ' Yongxin Yang', 'Jifei Song'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 1.88699067e-01 -3.33923548e-01 -2.51412541e-01 -6.85929954e-01
-2.40972862e-01 -6.63525641e-01 7.63989747e-01 2.98945364e-02
-5.37226915e-01 7.70308137e-01 -1.85593925e-02 1.99616402e-01
1.20470226e-02 -8.18260193e-01 -7.69957602e-01 -4.10576820e-01
2.43777528e-01 8.21834266e-01 2.40886196e-01 -2.00266510... | [14.7474946975708, 1.0804717540740967] |
5178dfe1-2c4d-47d9-991f-a1e93fc6bd13 | balpa-a-balanced-primal-dual-algorithm-for | 2212.02835 | null | https://arxiv.org/abs/2212.02835v1 | https://arxiv.org/pdf/2212.02835v1.pdf | BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization | In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a nonsmooth term composed with a linear mapping. In BALPA, the dual update is designed as a proximal p... | ['Shaofu Yang', 'Xinli Shi', 'Jinde Cao', 'Luyao Guo'] | 2022-12-06 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.29386616e-01 -2.01365709e-01 1.34711564e-01 6.90281158e-04
-9.57668483e-01 -2.82641172e-01 3.09735853e-02 8.90214071e-02
-2.82799125e-01 1.07992744e+00 5.70030995e-02 -1.23276472e-01
-5.53672016e-01 -6.91308558e-01 -8.40757251e-01 -1.20061493e+00
1.28702059e-01 2.55644977e-01 6.64432943e-02 -4.51429754... | [6.424151420593262, 4.81345796585083] |
df2b65d7-118e-476f-b3f2-ee29ef49b9b4 | brain-tumor-segmentation-using-enhanced-u-net | 2210.13336 | null | https://arxiv.org/abs/2210.13336v2 | https://arxiv.org/pdf/2210.13336v2.pdf | Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis | Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net netw... | ['Faisal Muhammad Shah', 'Md. Mahim Anjum Haque', 'Md Aminul Haque Palash', 'Maksuda Islam', 'Abdullah Al Munem', 'MD Abdullah Al Nasim'] | 2022-10-24 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 5.30369096e-02 3.32904786e-01 -1.59048080e-01 -2.06302568e-01
-4.54098344e-01 -2.58677274e-01 4.32087958e-01 3.90828013e-01
-3.92558277e-01 8.09340000e-01 2.51887292e-01 -5.84159791e-01
1.43904418e-01 -9.58946347e-01 -4.09759462e-01 -7.54315138e-01
-4.20693517e-01 1.03468381e-01 3.12679797e-01 3.66823003... | [14.590232849121094, -2.453660726547241] |
ba0e38f0-cbb7-463a-8e14-cc408ff82e6f | an-efficient-network-design-for-face-video | 2109.13626 | null | https://arxiv.org/abs/2109.13626v1 | https://arxiv.org/pdf/2109.13626v1.pdf | An Efficient Network Design for Face Video Super-resolution | Face video super-resolution algorithm aims to reconstruct realistic face details through continuous input video sequences. However, existing video processing algorithms usually contain redundant parameters to guarantee different super-resolution scenes. In this work, we focus on super-resolution of face areas in origin... | ['Yongming Tang', 'Sige Bian', 'He Li', 'Feng Yu'] | 2021-09-28 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.01385522e-01 -3.89803290e-01 -1.53505981e-01 -3.50416601e-01
-5.57108402e-01 -1.65234078e-02 -6.27704263e-02 -9.73978102e-01
-4.08940703e-01 8.55945587e-01 1.13608994e-01 1.39099389e-01
3.80058140e-02 -7.36001313e-01 -7.57135570e-01 -7.09819019e-01
-1.08626612e-01 5.40578663e-02 3.67446840e-01 -2.06011668... | [11.066776275634766, -1.891029715538025] |
319262ad-f0fb-452f-bb7a-36c650105e53 | modeling-dialogue-in-conversational-cognitive | null | null | https://aclanthology.org/2020.lrec-1.147 | https://aclanthology.org/2020.lrec-1.147.pdf | Modeling Dialogue in Conversational Cognitive Health Screening Interviews | Automating straightforward clinical tasks can reduce workload for healthcare professionals, increase accessibility for geographically-isolated patients, and alleviate some of the economic burdens associated with healthcare. A variety of preliminary screening procedures are potentially suitable for automation, and one s... | ['Natalie Parde', 'Mina Valizadeh', 'Shahla Farzana'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 6.57864928e-01 1.11123288e+00 5.60856014e-02 -8.33303690e-01
-8.78432095e-01 -6.16272628e-01 4.68329132e-01 5.89155972e-01
-4.95254755e-01 8.21412981e-01 5.95019519e-01 -6.43015444e-01
-8.13390091e-02 -4.05772567e-01 1.21523649e-01 -5.26159585e-01
1.81319565e-01 1.33676100e+00 4.72679548e-02 -4.55203801... | [12.53559398651123, 8.314465522766113] |
86b828ef-f4fa-4b30-8678-3fd0b25238f3 | jointly-learning-convolutional | null | null | https://drive.google.com/file/d/1i2jl5M0ddr-STAma0a2Bsr5rOMtcCSyB/view | https://drive.google.com/file/d/1i2jl5M0ddr-STAma0a2Bsr5rOMtcCSyB/view | Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain | Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by us- ing interpolation techniques before feeding them to deep learning models that are ImageNet compliant and accept only low-reso... | ['Soumava Paul', 'Ramanathan Sethuraman', 'Ekagra Ranjan', 'Siddharth Kapoor', 'Debdoot Sheet', 'Aupendu Kar'] | 2018-12-18 | null | null | null | icvgip-2018-2018-12 | ['thoracic-disease-classification', 'pneumonia-detection'] | ['computer-vision', 'medical'] | [ 4.05862540e-01 3.95095825e-01 -1.00583375e-01 -4.68873888e-01
-8.61053586e-01 2.11940140e-01 3.74927878e-01 1.35112911e-01
-7.26784706e-01 7.99269080e-01 2.19878972e-01 -1.89087972e-01
5.61567508e-02 -1.02268243e+00 -9.31110740e-01 -6.12727880e-01
-2.51050174e-01 5.24379313e-01 1.55295908e-01 -7.25432038... | [14.91383171081543, -2.373023748397827] |
a7b535fc-99a9-4089-a57a-3f4facb2d6b7 | immune-defense-a-novel-adversarial-defense | 2303.04502 | null | https://arxiv.org/abs/2303.04502v1 | https://arxiv.org/pdf/2303.04502v1.pdf | Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examples | The vulnerability of Deep Neural Networks (DNNs) to adversarial examples has been confirmed. Existing adversarial defenses primarily aim at preventing adversarial examples from attacking DNNs successfully, rather than preventing their generation. If the generation of adversarial examples is unregulated, images within r... | ['Bin Ma', 'Xiangyang Luo', 'Jiawei Zhang', 'Haihua Wang', 'Hao Wu', 'Jinwei Wang'] | 2023-03-08 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.88771236e-01 -8.37159976e-02 3.34259450e-01 -1.75322905e-01
-5.34347057e-01 -9.16945636e-01 6.30117536e-01 -3.71369749e-01
-5.12821615e-01 5.69831014e-01 -8.95746425e-02 -5.63637853e-01
3.13659400e-01 -8.52137029e-01 -9.40535307e-01 -1.06840873e+00
-2.36512674e-03 -5.46060443e-01 2.64570117e-01 -3.61665964... | [5.531124591827393, 7.918909549713135] |
7fd34156-61e6-4b3d-82f7-b772603869e1 | ra-v-net-deep-learning-network-for-automated | 2112.08232 | null | https://arxiv.org/abs/2112.08232v2 | https://arxiv.org/pdf/2112.08232v2.pdf | RA V-Net: Deep learning network for automated liver segmentation | Accurate segmentation of the liver is a prerequisite for the diagnosis of disease. Automated segmentation is an important application of computer-aided detection and diagnosis of liver disease. In recent years, automated processing of medical images has gained breakthroughs. However, the low contrast of abdominal scan ... | ['Ziwei Xie', 'Chongchong Fan', 'Sumin Qi', 'Zhiqi Lee'] | 2021-12-15 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.56433682e-03 -2.39976943e-01 -9.72552747e-02 -1.02125831e-01
-2.42469206e-01 -1.07823744e-01 1.99199036e-01 1.74601182e-01
-6.94852471e-01 5.26633084e-01 6.76839240e-03 -2.57612199e-01
6.40462562e-02 -8.59769285e-01 -3.74339521e-01 -1.00399601e+00
-3.36487114e-01 -2.44552046e-01 1.58616662e-01 5.59643991... | [14.536794662475586, -2.612531900405884] |
d1074c89-e8ca-4748-a3e3-c2e3461d3792 | fully-automated-pancreas-segmentation-with | 1906.01795 | null | https://arxiv.org/abs/1906.01795v2 | https://arxiv.org/pdf/1906.01795v2.pdf | Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks | Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (... | ['Ningning Zhao', 'Dan Ruan', 'Nuo Tong', 'Ke Sheng'] | 2019-06-05 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [-6.63024262e-02 1.61391363e-01 -5.64756542e-02 -4.84795421e-01
-6.11455083e-01 -5.08648038e-01 2.42851570e-01 5.07412553e-01
-3.63806605e-01 5.97849548e-01 1.16518037e-02 -3.16908479e-01
-3.51390727e-02 -7.31763959e-01 -7.51379490e-01 -6.70029223e-01
-4.23769474e-01 7.61764288e-01 1.82465658e-01 3.74053031... | [14.476716995239258, -2.6888554096221924] |
86a29ae1-8613-46b4-a6b9-bb07c1517065 | a-comparative-study-of-different-machine | 2104.07469 | null | https://arxiv.org/abs/2104.07469v1 | https://arxiv.org/pdf/2104.07469v1.pdf | A comparative study of Different Machine Learning Regressors For Stock Market Prediction | For the development of successful share trading strategies, forecasting the course of action of the stock market index is important. Effective prediction of closing stock prices could guarantee investors attractive benefits. Machine learning algorithms have the ability to process and forecast almost reliable closing pr... | ['Muhammad Ilyas', 'Zubair Nawaz', 'Nazish Ashfaq'] | 2021-04-14 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-6.85200334e-01 -2.98035920e-01 -1.79354027e-01 -2.99811065e-01
-4.60354716e-01 -7.44490027e-01 7.73535252e-01 -1.86937213e-01
-4.47637349e-01 1.18027329e+00 -9.05236900e-02 -6.14985526e-01
-2.97343612e-01 -9.72549081e-01 -2.23669205e-02 -6.25267386e-01
-2.08296731e-01 4.64840680e-01 4.07378599e-02 -3.44277769... | [4.547225475311279, 4.192534446716309] |
501f21e7-4b11-4c39-b3c1-eecd3299980f | word-level-fine-grained-story-visualization | 2208.02341 | null | https://arxiv.org/abs/2208.02341v3 | https://arxiv.org/pdf/2208.02341v3.pdf | Word-Level Fine-Grained Story Visualization | Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story with a global consistency across dynamic scenes and characters. Current works still struggle with output images' quality and consistency, and rely on additional semantic information or auxiliary captioning netwo... | ['Thomas Lukasiewicz', 'Bowen Li'] | 2022-08-03 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 4.39168245e-01 -1.33969948e-01 1.36231869e-01 -3.67073357e-01
-4.93443847e-01 -3.41795117e-01 7.80706942e-01 -1.44306734e-01
-8.96329656e-02 8.99491310e-01 5.65861404e-01 1.30373865e-01
2.20375612e-01 -6.29118443e-01 -6.23564005e-01 -5.37493229e-01
5.75071812e-01 -1.00425370e-01 5.59479535e-01 -3.82911891... | [11.129158020019531, 0.5939027070999146] |
15b89722-2550-4146-b098-c741e0f32f96 | perception-test-a-diagnostic-benchmark-for-1 | 2305.13786 | null | https://arxiv.org/abs/2305.13786v1 | https://arxiv.org/pdf/2305.13786v1.pdf | Perception Test: A Diagnostic Benchmark for Multimodal Video Models | We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, BEiT-3, or GPT-4). Compared to existing benchmarks that focus on computational tasks (e.g. classification, detection or tracking), the Perception Test fo... | ['João Carreira', 'Andrew Zisserman', 'Dima Damen', 'Simon Osindero', 'Yusuf Aytar', 'Stephanie Winkler', 'Junlin Zhang', 'Raphael Koster', 'Hanna Klimczak', 'Alex Frechette', 'Antoine Miech', 'Yury Sulsky', 'Tatiana Matejovicova', 'Carl Doersch', 'Yi Yang', 'Mateusz Malinowski', 'Joseph Heyward', 'Skanda Koppula', 'Dy... | 2023-05-23 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 1.13780543e-01 -5.01448587e-02 -2.43827164e-01 -1.60441145e-01
-1.21857417e+00 -9.48870897e-01 7.78125346e-01 1.00114331e-01
-4.01212782e-01 4.31767642e-01 5.62291384e-01 -2.11521447e-01
1.05909906e-01 -4.28574145e-01 -1.02256095e+00 -4.20154721e-01
-1.20067939e-01 4.01020944e-01 2.08204851e-01 -2.00484842... | [10.448195457458496, 1.051189661026001] |
671e0a82-06a3-4fc6-909c-4b9d22aa34b5 | satellite-image-classification-methods-and | 1308.1801 | null | http://arxiv.org/abs/1308.1801v1 | http://arxiv.org/pdf/1308.1801v1.pdf | Satellite image classification methods and Landsat 5TM bands | This paper attempts to find the most accurate classification method among
parallelepiped, minimum distance and chain methods. Moreover, this study also
challenges to find the suitable combination of bands, which can lead to better
results in case combinations of bands occur. After comparing these three
methods, the cha... | ['Nasser Lotfi', 'Jamshid Tamouk', 'Mina Farmanbar'] | 2013-08-08 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 5.72887994e-02 -4.78939235e-01 -2.81990826e-01 -3.49481893e-03
-1.66964695e-01 -3.25952560e-01 9.99282300e-02 2.54977882e-01
-3.14016104e-01 6.91394031e-01 -1.14483982e-01 -5.07548630e-01
-6.76446438e-01 -1.26380706e+00 -1.63169846e-01 -9.63555634e-01
-9.60045606e-02 4.02068421e-02 2.39377558e-01 -3.98420155... | [9.595450401306152, -1.7379859685897827] |
7be6ca3a-07cc-4f5d-9c66-7415d6605f38 | neural-speech-synthesis-in-german | null | null | https://www.iaria.org/conferences2021/filesCENTRIC21/30009_centric.pdf | https://www.thinkmind.org/articles/centric_2021_2_30_30009.pdf | Neural Speech Synthesis in German | While many speech synthesis systems based on deep neural networks are thoroughly evaluated and released for free use in English, models for languages with far less active speakers like German are scarcely trained and most often not published for common use. This work covers specific challenges in training text to speec... | ['René Peinl', 'Pascal Puchtler', 'Johannes Wirth'] | 2021-10-03 | null | null | null | 14th-international-conference-on-advances-in | ['text-to-speech-synthesis'] | ['speech'] | [-3.23947333e-02 2.00895965e-01 -3.26399982e-01 -2.75125444e-01
-8.26670408e-01 -6.10693455e-01 6.64688587e-01 -2.71261573e-01
-2.47973979e-01 5.89196920e-01 4.19032156e-01 -6.16196036e-01
2.16120422e-01 -4.85071063e-01 -3.25022459e-01 -6.13651156e-01
3.30175608e-01 2.74593383e-01 -4.87911463e-01 -6.36849761... | [14.794325828552246, 6.660373210906982] |
77025c89-9ad7-469b-9fb5-4bf5a1fd3347 | a-proposal-project-for-a-blind-image-quality | 1512.04354 | null | http://arxiv.org/abs/1512.04354v1 | http://arxiv.org/pdf/1512.04354v1.pdf | A proposal project for a blind image quality assessment by learning distortions from the full reference image quality assessments | This short paper presents a perspective plan to build a null reference image
quality assessment. Its main goal is to deliver both the objective score and
the distortion map for a given distorted image without the knowledge of its
reference image. | ['Stéfane Paris'] | 2015-11-04 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 5.97138882e-01 8.27038381e-03 4.98362303e-01 -5.06957948e-01
-5.96932948e-01 -2.76110321e-01 6.76090419e-01 -3.23564857e-01
-1.50732517e-01 5.18211603e-01 3.71281296e-01 9.30728987e-02
-3.29896152e-01 -6.15089357e-01 -1.00004092e-01 -7.28417814e-01
-9.80460942e-02 -2.90370196e-01 1.23155646e-01 -1.53399572... | [11.737582206726074, -1.983135461807251] |
574b4c9f-6ecd-4ce3-9e02-bfe920d2b1c9 | bridging-textual-and-tabular-data-for-cross | 2012.12627 | null | https://arxiv.org/abs/2012.12627v2 | https://arxiv.org/pdf/2012.12627v2.pdf | Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing | We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the questio... | ['Caiming Xiong', 'Richard Socher', 'Xi Victoria Lin'] | 2020-12-23 | null | https://aclanthology.org/2020.findings-emnlp.438 | https://aclanthology.org/2020.findings-emnlp.438.pdf | findings-of-the-association-for-computational | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-1.02998704e-01 5.18009424e-01 -2.78546989e-01 -7.05227077e-01
-1.38907003e+00 -7.90078461e-01 3.91782939e-01 5.12536228e-01
-3.18802804e-01 6.30666375e-01 2.38760069e-01 -5.32960176e-01
-2.47595191e-01 -9.46771979e-01 -1.18824828e+00 5.80253303e-02
6.59684688e-02 1.01970410e+00 4.95274842e-01 -4.84025180... | [9.880845069885254, 7.855257511138916] |
1d6eaa8c-11ec-40d6-a201-030ad48bc420 | ground-roll-suppression-using-convolutional | 2010.15209 | null | https://arxiv.org/abs/2010.15209v1 | https://arxiv.org/pdf/2010.15209v1.pdf | Ground Roll Suppression using Convolutional Neural Networks | Seismic data processing plays a major role in seismic exploration as it conditions much of the seismic interpretation performance. In this context, generating reliable post-stack seismic data depends also on disposing of an efficient pre-stack noise attenuation tool. Here we tackle ground roll noise, one of the most ch... | ['Semen Zaytsev', 'Daniil Semin', 'Dario Augusto Borges Oliveira'] | 2020-10-28 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 2.24243507e-01 2.01777369e-02 5.64333975e-01 -6.69495314e-02
-1.00711036e+00 -4.96276170e-01 5.46381831e-01 4.24580544e-01
-6.14173651e-01 6.47801697e-01 5.02872169e-01 -1.01034351e-01
-3.56579840e-01 -1.19202471e+00 -7.81941831e-01 -9.14463162e-01
-4.13824528e-01 1.87589571e-01 6.83149397e-01 -6.76084042... | [6.949085235595703, 2.491682529449463] |
d7ffcb24-9865-45cf-a9a4-679957af177b | scrabblegan-semi-supervised-varying-length | 2003.10557 | null | https://arxiv.org/abs/2003.10557v1 | https://arxiv.org/pdf/2003.10557v1.pdf | ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation | Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design. That said, deep learning based HTR is limited... | ['Roee Litman', 'Hadar Averbuch-Elor', 'Sharon Fogel', 'Shai Mazor', 'Sarel Cohen'] | 2020-03-23 | scrabblegan-semi-supervised-varying-length-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Fogel_ScrabbleGAN_Semi-Supervised_Varying_Length_Handwritten_Text_Generation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fogel_ScrabbleGAN_Semi-Supervised_Varying_Length_Handwritten_Text_Generation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['handwriting-generation'] | ['computer-vision'] | [ 4.77794617e-01 -1.25984356e-01 6.94405884e-02 -1.47510618e-01
-3.89524728e-01 -1.05520749e+00 5.80373466e-01 -2.09486604e-01
-4.46614355e-01 7.93297231e-01 -2.33852684e-01 -2.74208993e-01
1.52814209e-01 -8.86894166e-01 -8.36434722e-01 -7.46993005e-01
7.06516802e-01 7.65045643e-01 8.95417929e-02 -2.13240668... | [11.778715133666992, 2.339946985244751] |
3f83ef04-1a87-4baf-a565-062ca3c8ebea | dual-path-style-learning-for-end-to-end-noise | 2203.14838 | null | https://arxiv.org/abs/2203.14838v3 | https://arxiv.org/pdf/2203.14838v3.pdf | Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition | Automatic speech recognition (ASR) systems degrade significantly under noisy conditions. Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but it also suppresses some important speech information, i.e., over-suppression. To alleviate this, we propose a dual-path style learning approa... | ['Eng Siong Chng', 'Chen Chen', 'Nana Hou', 'Yuchen Hu'] | 2022-03-28 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 4.61122692e-01 -3.59021053e-02 3.25753391e-01 -4.31804836e-01
-1.18605328e+00 -2.78156042e-01 3.07638526e-01 -1.55104294e-01
-4.56127793e-01 3.46901685e-01 7.90926278e-01 -3.45911384e-01
1.89191505e-01 -2.81836718e-01 -6.45908713e-01 -9.13247645e-01
4.84656930e-01 -4.34545189e-01 -5.25809228e-02 -3.29776794... | [14.797005653381348, 6.136204719543457] |
0e2d4644-d714-41d7-8498-0164a7606fc3 | harmonic-networks-deep-translation-and | 1612.04642 | null | http://arxiv.org/abs/1612.04642v2 | http://arxiv.org/pdf/1612.04642v2.pdf | Harmonic Networks: Deep Translation and Rotation Equivariance | Translating or rotating an input image should not affect the results of many
computer vision tasks. Convolutional neural networks (CNNs) are already
translation equivariant: input image translations produce proportionate feature
map translations. This is not the case for rotations. Global rotation
equivariance is typic... | ['Gabriel J. Brostow', 'Stephan J. Garbin', 'Daniyar Turmukhambetov', 'Daniel E. Worrall'] | 2016-12-14 | harmonic-networks-deep-translation-and-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Worrall_Harmonic_Networks_Deep_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Worrall_Harmonic_Networks_Deep_CVPR_2017_paper.pdf | cvpr-2017-7 | ['rotated-mnist'] | ['computer-vision'] | [ 7.58039504e-02 1.24834433e-01 -1.30834132e-01 -4.22394007e-01
-8.68361741e-02 -6.96627438e-01 8.60911369e-01 -4.23498124e-01
-6.05345428e-01 9.33673456e-02 4.02125031e-01 -7.18551725e-02
1.39957175e-01 -5.78405261e-01 -1.16032541e+00 -7.74203539e-01
-1.15672825e-02 2.43583158e-01 5.89788426e-03 -6.43896520... | [8.909558296203613, 2.32755970954895] |
f5480e98-965a-4958-b5d5-a89c65338713 | cocatt-a-cognitive-conditioned-driver | 2111.10014 | null | https://arxiv.org/abs/2111.10014v2 | https://arxiv.org/pdf/2111.10014v2.pdf | CoCAtt: A Cognitive-Conditioned Driver Attention Dataset | The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention predictio... | ['Katie Driggs-Campbell', 'Peter Du', 'Aamir Hasan', 'Pranav Sriram', 'Niviru Wijayaratne', 'Yuan Shen'] | 2021-11-19 | null | null | null | null | ['driver-attention-monitoring'] | ['computer-vision'] | [-2.97187239e-01 7.60718249e-04 -4.48613793e-01 -2.22384974e-01
-1.09686181e-01 -9.49289128e-02 5.03513217e-01 5.83265536e-02
-4.82323676e-01 2.56749123e-01 2.14798361e-01 -5.28230667e-01
-1.14665702e-01 -2.26118803e-01 -3.09801728e-01 -3.50660354e-01
6.38205588e-01 5.08825528e-03 5.22359431e-01 -4.71270502... | [7.5804338455200195, -0.061216700822114944] |
494002ed-80ce-4a2f-9940-f5a2409e9101 | license-plate-recognition-system-based-on | 1506.03128 | null | http://arxiv.org/abs/1506.03128v1 | http://arxiv.org/pdf/1506.03128v1.pdf | License Plate Recognition System Based on Color Coding Of License Plates | License Plate Recognition Systems are used to determine the license plate
number of a vehicle. The current system mainly uses Optical Character
Recognition to recognize the number plate. There are several problems to this
system. Some of them include interchanging of several letters or numbers
(letter O with digit 0), ... | ['Jani Biju Babjan'] | 2015-06-08 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-1.56677678e-01 -9.85540926e-01 -6.13775700e-02 -1.26588970e-01
5.65863699e-02 -1.17434156e+00 2.20336869e-01 -6.48505926e-01
-2.35233143e-01 5.83109081e-01 -4.17941540e-01 -6.42123282e-01
3.18145484e-01 -6.89314663e-01 -2.07753062e-01 -4.74248230e-01
6.74105406e-01 4.81000155e-01 9.20714438e-01 -2.29593277... | [9.797786712646484, -4.995074272155762] |
ea2a5173-cff4-449c-9641-530e6bb3b493 | self-knowledge-distillation-adversarial | null | null | https://openreview.net/forum?id=rJejta4KDS | https://openreview.net/pdf?id=rJejta4KDS | SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK | Neural networks show great vulnerability under the threat of adversarial examples.
By adding small perturbation to a clean image, neural networks with high classification accuracy can be completely fooled.
One intriguing property of the adversarial examples is transferability. This property allows adversarial ex... | ['Duan Zhenhua', 'Dong Zeqian', 'Tian Cong', 'Wang Renzhi[1]', 'Ma Xiaoxiong[1]'] | 2019-09-25 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 3.87925774e-01 5.06468356e-01 1.04879461e-01 5.07708006e-02
-6.73424542e-01 -1.13510215e+00 5.15429974e-01 -4.89297420e-01
-3.66360426e-01 1.14787722e+00 -3.42885852e-01 -5.38433909e-01
-3.55532691e-02 -1.36207557e+00 -1.22506642e+00 -9.24067438e-01
-6.41059056e-02 -6.17845505e-02 2.39043623e-01 -5.46991229... | [5.676266193389893, 7.86157751083374] |
274a8dd5-3804-4c84-8d9a-775480512c57 | bi-directional-loop-closure-for-visual-slam | 2204.01524 | null | https://arxiv.org/abs/2204.01524v1 | https://arxiv.org/pdf/2204.01524v1.pdf | Bi-directional Loop Closure for Visual SLAM | A key functional block of visual navigation system for intelligent autonomous vehicles is Loop Closure detection and subsequent relocalisation. State-of-the-Art methods still approach the problem as uni-directional along the direction of the previous motion. As a result, most of the methods fail in the absence of a sig... | ['Atanas Gotchev', 'Sari Peltonen', 'Ihtisham Ali'] | 2022-04-01 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-7.95276240e-02 -8.88552964e-02 -2.27806360e-01 -5.47163785e-01
-1.80535883e-01 -8.02183330e-01 9.09000635e-01 -7.93811586e-03
-6.99076355e-01 6.89196527e-01 -1.66071624e-01 -7.56181121e-01
-8.88222083e-02 -7.02947438e-01 -9.44726825e-01 -4.37796324e-01
-2.29233071e-01 5.33091605e-01 5.85707366e-01 -6.54471517... | [7.51947021484375, -1.9667763710021973] |
23b4b6aa-0646-4fc8-8ed6-df65f40cd1d0 | non-autoregressive-neural-dialogue-generation | 2002.04250 | null | https://arxiv.org/abs/2002.04250v2 | https://arxiv.org/pdf/2002.04250v2.pdf | Non-Autoregressive Neural Dialogue Generation | Maximum Mutual information (MMI), which models the bidirectional dependency between responses ($y$) and contexts ($x$), i.e., the forward probability $\log p(y|x)$ and the backward probability $\log p(x|y)$, has been widely used as the objective in the \sts model to address the dull-response issue in open-domain dialog... | ['Qinghong Han', 'Yuxian Meng', 'Jiwei Li', 'Fei Wu'] | 2020-02-11 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [ 2.29365602e-01 7.27458745e-02 -3.72866690e-02 -5.12430966e-01
-1.27017832e+00 -5.59689999e-01 1.69442907e-01 -1.89918727e-02
-6.10586286e-01 1.07266212e+00 2.46597156e-01 -4.17490929e-01
-7.60509819e-02 -7.48967171e-01 -4.63577628e-01 -6.15939558e-01
1.06079973e-01 5.22714078e-01 4.06638198e-02 -3.42539370... | [12.48780345916748, 8.38443660736084] |
5c1614dd-87cc-4dac-896b-b08f7cc3af9c | flow-guided-video-inpainting-with-scene | 2108.12845 | null | https://arxiv.org/abs/2108.12845v1 | https://arxiv.org/pdf/2108.12845v1.pdf | Flow-Guided Video Inpainting with Scene Templates | We consider the problem of filling in missing spatio-temporal regions of a video. We provide a novel flow-based solution by introducing a generative model of images in relation to the scene (without missing regions) and mappings from the scene to images. We use the model to jointly infer the scene template, a 2D repres... | ['Ganesh Sundaramoorthi', 'Peter Wonka', 'Peihao Zhu', 'Dong Lao'] | 2021-08-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lao_Flow-Guided_Video_Inpainting_With_Scene_Templates_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lao_Flow-Guided_Video_Inpainting_With_Scene_Templates_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-inpainting'] | ['computer-vision'] | [ 3.29646379e-01 4.55546007e-02 1.82777405e-01 -1.43376738e-01
-3.71940941e-01 -7.63574183e-01 6.87360048e-01 -4.53273356e-01
-4.09071669e-02 8.35632324e-01 5.68633080e-01 1.54826835e-01
4.69863042e-02 -7.16242373e-01 -9.21881139e-01 -3.27240497e-01
7.88277313e-02 1.93470955e-01 3.20131510e-01 -5.00777252... | [10.739469528198242, -1.3715078830718994] |
582f5d42-e9b0-49b4-9e3c-c775391d9fa8 | pca-event-based-otical-flow-for-visual | 2105.03760 | null | https://arxiv.org/abs/2105.03760v2 | https://arxiv.org/pdf/2105.03760v2.pdf | PCA Event-Based Optical Flow for Visual Odometry | With the advent of neuromorphic vision sensors such as event-based cameras, a paradigm shift is required for most computer vision algorithms. Among these algorithms, optical flow estimation is a prime candidate for this process considering that it is linked to a neuromorphic vision approach. Usage of optical flow is wi... | ['Samia Bouchafa', 'David Roussel', 'Fabien Bonardi', 'Mahmoud Z. Khairallah'] | 2021-05-08 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 1.54337183e-01 -3.62264693e-01 2.08206370e-01 5.62888496e-02
2.48657942e-01 -3.11298519e-01 8.14016402e-01 -7.09459707e-02
-9.67714548e-01 8.56275380e-01 1.10987142e-01 2.61906922e-01
-1.79160953e-01 -5.59068024e-01 -5.71665883e-01 -5.80748260e-01
1.10582314e-01 4.76256832e-02 4.96437132e-01 3.19307186... | [8.650249481201172, -1.3145411014556885] |
bebb5f95-90a4-4bed-b94c-dfcd63651275 | on-residual-cnn-in-text-dependent-speaker | 1705.10134 | null | http://arxiv.org/abs/1705.10134v2 | http://arxiv.org/pdf/1705.10134v2.pdf | On Residual CNN in text-dependent speaker verification task | Deep learning approaches are still not very common in the speaker
verification field. We investigate the possibility of using deep residual
convolutional neural network with spectrograms as an input features in the
text-dependent speaker verification task. Despite the fact that we were not
able to surpass the baseline ... | ['Egor Malykh', 'Oleg Kudashev', 'Sergey Novoselov'] | 2017-05-29 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [-2.28996705e-02 1.00603141e-01 5.31642318e-01 -5.96709430e-01
-1.12552083e+00 -3.53753835e-01 8.90412390e-01 -2.89340347e-01
-7.46958315e-01 6.93018734e-01 3.93515080e-01 -3.64856511e-01
1.47157282e-01 -2.25308850e-01 -6.31259024e-01 -8.99392903e-01
6.97671697e-02 1.59213141e-01 -8.01150948e-02 -5.25738537... | [14.32711410522461, 6.077618598937988] |
7cb2ab81-88d2-4b48-9d22-96656af60f7e | monogrnet-a-geometric-reasoning-network-for | 1811.10247 | null | https://arxiv.org/abs/1811.10247v2 | https://arxiv.org/pdf/1811.10247v2.pdf | MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization | Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular RGB image via geom... | ['Yan Lu', 'Zengyi Qin', 'Jinglu Wang'] | 2018-11-26 | null | null | null | null | ['monocular-3d-object-localization'] | ['computer-vision'] | [ 1.66315690e-01 2.00539261e-01 -1.07207581e-01 -3.55261266e-01
-6.41961455e-01 -4.45685893e-01 4.53096032e-01 -2.35818848e-01
-3.83392483e-01 1.20224312e-01 7.92365372e-02 -2.90826082e-01
1.52187526e-01 -6.01701081e-01 -9.85823512e-01 -6.96851254e-01
2.56528050e-01 4.79102284e-01 3.77819180e-01 2.08396420... | [7.794857978820801, -2.632065773010254] |
01e94139-6cd2-4ae2-a26c-44e4d4ea0383 | spatial-attentive-single-image-deraining-with | 1904.01538 | null | https://arxiv.org/abs/1904.01538v2 | https://arxiv.org/pdf/1904.01538v2.pdf | Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset | Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. ... | ['Xin Yang', 'Tianyu Wang', 'Rynson Lau', 'Qiang Zhang', 'Ke Xu', 'Shaozhe Chen'] | 2019-04-02 | spatial-attentive-single-image-deraining-with-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Spatial_Attentive_Single-Image_Deraining_With_a_High_Quality_Real_Rain_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Spatial_Attentive_Single-Image_Deraining_With_a_High_Quality_Real_Rain_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [ 1.55533701e-01 -5.94939768e-01 4.29775983e-01 -7.22586513e-01
-6.07142389e-01 -3.53788167e-01 1.48354068e-01 -5.61444700e-01
-3.45598400e-01 1.08044040e+00 -1.14854842e-01 -1.55145049e-01
2.35232621e-01 -8.57747853e-01 -6.95015430e-01 -1.07937717e+00
-2.02969611e-02 -1.31690189e-01 3.23399782e-01 -5.73311865... | [10.921175003051758, -3.251901865005493] |
b43c23f8-8d66-43f6-af0b-22b35d2a589b | cross-lingual-question-answering-over | 2302.13241 | null | https://arxiv.org/abs/2302.13241v1 | https://arxiv.org/pdf/2302.13241v1.pdf | Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension | Although many large-scale knowledge bases (KBs) claim to contain multilingual information, their support for many non-English languages is often incomplete. This incompleteness gives birth to the task of cross-lingual question answering over knowledge base (xKBQA), which aims to answer questions in languages different ... | ['Dongyan Zhao', 'Haowei Du', 'Xingyu Shen', 'Yansong Feng', 'Yuxuan Lai', 'Chen Zhang'] | 2023-02-26 | null | null | null | null | ['cross-lingual-question-answering', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.60817906e-01 9.06257108e-02 -3.79912019e-01 -3.36498111e-01
-1.50524473e+00 -7.83543646e-01 3.62020224e-01 3.28056812e-01
-4.47180271e-01 1.04688919e+00 5.46832979e-01 -6.96010530e-01
-1.10299341e-01 -9.39254224e-01 -9.62657034e-01 -9.35077369e-02
6.16024673e-01 6.33807302e-01 3.78135473e-01 -9.92340326... | [10.878190994262695, 8.357419967651367] |
6430de06-d563-432b-aa63-ed1c7dd78cc0 | learning-occupancy-function-from-point-clouds | 2010.11378 | null | https://arxiv.org/abs/2010.11378v1 | https://arxiv.org/pdf/2010.11378v1.pdf | Learning Occupancy Function from Point Clouds for Surface Reconstruction | Implicit function based surface reconstruction has been studied for a long time to recover 3D shapes from point clouds sampled from surfaces. Recently, Signed Distance Functions (SDFs) and Occupany Functions are adopted in learning-based shape reconstruction methods as implicit 3D shape representation. This paper propo... | ['Matthew Kyan', 'Meng Jia'] | 2020-10-22 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-8.15840662e-02 -1.99850965e-02 -4.58328985e-02 -4.59245533e-01
-7.16964185e-01 -2.77706116e-01 5.42524695e-01 -2.21807718e-01
-5.75138479e-02 4.58191216e-01 -1.47325769e-01 -2.50264019e-01
-1.32474318e-01 -1.24407721e+00 -1.27659571e+00 -5.71962953e-01
1.12728961e-02 1.09184432e+00 2.61597097e-01 2.11194460... | [8.311874389648438, -3.6449122428894043] |
51f62dfd-b750-4ce0-b72d-d68d8a4f25ae | search-time-efficient-device-constraints | 2307.04443 | null | https://arxiv.org/abs/2307.04443v1 | https://arxiv.org/pdf/2307.04443v1.pdf | Search-time Efficient Device Constraints-Aware Neural Architecture Search | Edge computing aims to enable edge devices, such as IoT devices, to process data locally instead of relying on the cloud. However, deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. Creating manual architectures specialized for each devic... | ['Sumeet Agarwal', 'Tanu Kanvar', 'Oshin Dutta'] | 2023-07-10 | null | null | null | null | ['architecture-search', 'edge-computing'] | ['methodology', 'time-series'] | [-3.3621567e-01 -4.9310288e-01 -2.7194011e-01 -2.5797215e-01
-5.1505083e-01 -3.9958113e-01 2.4086151e-01 -1.8698423e-01
-7.0611203e-01 3.0511463e-01 -3.1825933e-01 -8.8480639e-01
-5.9800144e-02 -7.6232725e-01 -8.2983422e-01 -2.1978311e-01
-2.6538840e-01 4.5426238e-01 1.3593054e-01 2.1655242e-01
2.9519282e-02... | [8.516139030456543, 2.918426513671875] |
fcd455eb-8328-4db9-a7f8-e518ea2c439c | nuwa-xl-diffusion-over-diffusion-for | 2303.12346 | null | https://arxiv.org/abs/2303.12346v1 | https://arxiv.org/pdf/2303.12346v1.pdf | NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation | In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inef... | ['Nan Duan', 'Houqiang Li', 'Zicheng Liu', 'Lijuan Wang', 'Gong Ming', 'Jianlong Fu', 'Fan Yang', 'Shuguang Liu', 'Linjie Li', 'Zhengyuan Yang', 'Minheng Ni', 'Xiaodong Wang', 'JianFeng Wang', 'Huan Yang', 'Chenfei Wu', 'Shengming Yin'] | 2023-03-22 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [-3.92236449e-02 -3.44988704e-02 -2.21100092e-01 6.07790388e-02
-1.00384450e+00 -5.34549296e-01 4.87861246e-01 -4.24724162e-01
-3.09199274e-01 9.59085822e-01 2.27406591e-01 -4.08480406e-01
2.83936799e-01 -9.57357526e-01 -8.62416744e-01 -7.10403562e-01
-2.48369891e-02 2.60350943e-01 5.77952325e-01 1.06411651... | [10.94238567352295, -0.6056921482086182] |
f4d6a5b6-c7f7-409d-8967-84c51ac9ce24 | deep-speech-scaling-up-end-to-end-speech | 1412.5567 | null | http://arxiv.org/abs/1412.5567v2 | http://arxiv.org/pdf/1412.5567v2.pdf | Deep Speech: Scaling up end-to-end speech recognition | We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. I... | ['Sanjeev Satheesh', 'Jared Casper', 'Greg Diamos', 'Awni Hannun', 'Andrew Y. Ng', 'Adam Coates', 'Erich Elsen', 'Carl Case', 'Shubho Sengupta', 'Ryan Prenger', 'Bryan Catanzaro'] | 2014-12-17 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [-1.48924351e-01 -3.48420322e-01 4.81565177e-01 -4.61473018e-01
-1.05664301e+00 -5.17505944e-01 5.74315786e-01 -1.73046008e-01
-5.18927693e-01 4.00856078e-01 2.86556065e-01 -7.51350403e-01
4.18993473e-01 -4.45772618e-01 -6.57282293e-01 -6.95152998e-01
-3.19540575e-02 5.23452401e-01 2.77600199e-01 -7.21154809... | [14.466208457946777, 6.442663192749023] |
06a9eb27-15ca-4a9b-a4dd-c532e9be9921 | enabling-automatic-repair-of-source-code | 2202.03055 | null | https://arxiv.org/abs/2202.03055v1 | https://arxiv.org/pdf/2202.03055v1.pdf | Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods | Users around the world rely on software-intensive systems in their day-to-day activities. These systems regularly contain bugs and security vulnerabilities. To facilitate bug fixing, data-driven models of automatic program repair use pairs of buggy and fixed code to learn transformations that fix errors in code. Howeve... | ['Anastasiia Grishina'] | 2022-02-07 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.65274948e-01 2.97882140e-01 -3.17821383e-01 -3.48344058e-01
-7.27917254e-01 -8.02243412e-01 -4.93820496e-02 6.05481088e-01
4.63119209e-01 1.13659211e-01 1.57179490e-01 -8.76338720e-01
1.68649212e-01 -1.03682005e+00 -9.03126121e-01 1.86867341e-01
-1.48025334e-01 -5.58339596e-01 2.06971884e-01 -4.92754638... | [7.520407676696777, 7.7369065284729] |
a5c6b02c-1663-46ad-99a6-e67917067d8e | optic-net-a-novel-convolutional-neural | 1910.05672 | null | https://arxiv.org/abs/1910.05672v1 | https://arxiv.org/pdf/1910.05672v1.pdf | Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images | Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning algorithms have been used for early detection and diagnosis of retinal diseases. Unfortunately, these... | ['Ali Shihab Sabbir', 'Sharif Amit Kamran', 'Alireza Tavakkoli', 'Sourajit Saha'] | 2019-10-13 | null | null | null | null | ['retinal-oct-disease-classification'] | ['computer-vision'] | [ 1.25759661e-01 -1.77007571e-01 3.64835054e-01 -2.71602273e-01
-3.87580663e-01 -2.95693308e-01 -5.77366240e-02 7.63750216e-03
-4.63786572e-01 8.97935212e-01 -9.74946246e-02 -6.43756449e-01
-3.88653517e-01 -5.36249518e-01 -1.00074276e-01 -6.00647688e-01
-2.98276190e-02 3.24595064e-01 2.70559937e-01 3.91829699... | [15.82298755645752, -3.994412422180176] |
b0053006-f6f5-483e-b0d8-3eebeaba30d9 | from-time-series-to-networks-in-r-with-the | 2208.09660 | null | https://arxiv.org/abs/2208.09660v1 | https://arxiv.org/pdf/2208.09660v1.pdf | From Time Series to Networks in R with the ts2net Package | Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between ... | ['Leonardo N. Ferreira'] | 2022-08-20 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-7.36498907e-02 -2.54701644e-01 -2.17805281e-01 -1.50788084e-01
2.92696685e-01 -8.94470215e-01 5.33305824e-01 6.15130246e-01
7.70522431e-02 2.48370647e-01 -2.02885509e-01 -8.46286595e-01
-7.95095742e-01 -1.35951817e+00 8.91438946e-02 -3.44375044e-01
-9.93991613e-01 3.96851331e-01 3.08843613e-01 -3.73563230... | [7.267972946166992, 3.436136245727539] |
03fb0353-04b9-4b5e-8a06-22eb0aeb6286 | combining-particle-and-tensor-network-methods | 2305.17884 | null | https://arxiv.org/abs/2305.17884v2 | https://arxiv.org/pdf/2305.17884v2.pdf | Combining Particle and Tensor-network Methods for Partial Differential Equations via Sketching | In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks. Our approach offers a comprehensive solution methodology, wherein we employ a combination of particle simulations to update the solution and re-estimations of the new solution as a tensor-netw... | ['Yuehaw Khoo', 'Yian Chen'] | 2023-05-29 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 9.26619917e-02 7.54374862e-02 3.54866832e-01 1.72413155e-01
-1.59118250e-01 -6.21062458e-01 9.96071815e-01 -4.46441084e-01
-6.43774092e-01 9.76553440e-01 -8.36058185e-02 -4.35966164e-01
-4.80865359e-01 -8.32460523e-01 -4.21968371e-01 -9.73455727e-01
-2.67619669e-01 7.37858474e-01 1.50718600e-01 -7.03544021... | [5.66885232925415, 4.945534706115723] |
a7433bf4-1983-4f9e-8f4a-e613c45a6c8e | open-set-object-detection-using | 2211.11530 | null | https://arxiv.org/abs/2211.11530v1 | https://arxiv.org/pdf/2211.11530v1.pdf | Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning with Appendix | Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this paper, we presen... | ['Rong Xiong', 'Yue Wang', 'Yifei Yang', 'Zhongxiang Zhou'] | 2022-11-21 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.62156105e-01 1.92325726e-01 -1.20522141e-01 -2.71767706e-01
-5.37273109e-01 -7.18274236e-01 3.23531151e-01 -6.59655407e-02
-2.59309530e-01 3.76146287e-01 -2.81838685e-01 1.38852715e-01
-8.20335373e-02 -5.14006913e-01 -1.03117406e+00 -7.30997801e-01
-1.59941539e-01 6.08147860e-01 6.63053095e-01 -1.22336864... | [6.010989665985107, -1.0403622388839722] |
2d1af57d-c310-4db7-b8b0-eb2dcde3750d | joint-routing-and-resource-allocation-for | 1809.07470 | null | http://arxiv.org/abs/1809.07470v1 | http://arxiv.org/pdf/1809.07470v1.pdf | Joint Routing and Resource Allocation for Millimeter Wave Picocellular Backhaul | Picocellular architectures are essential for providing the spatial reuse
required to satisfy the ever-increasing demand for mobile data. A key
deployment challenge is to provide backhaul connections with sufficiently high
data rate. Providing wired support (e.g., using optical fiber) to pico base
stations deployed oppo... | [] | 2018-09-20 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 3.63221206e-03 5.02001882e-01 -3.45309854e-01 1.56037793e-01
-5.76793179e-02 -7.91192889e-01 1.23723652e-02 -4.64423627e-01
-2.51039267e-01 1.50746441e+00 -8.97087529e-02 -7.07262337e-01
-7.50314236e-01 -1.06668413e+00 7.88395330e-02 -1.03150320e+00
-5.04039586e-01 2.43872583e-01 -3.91410828e-01 3.02283224... | [6.20794153213501, 1.3018405437469482] |
9d911968-04ba-40ca-8409-b16a9548fa86 | technical-report-disentangled-action-parsing | 2111.03225 | null | https://arxiv.org/abs/2111.03225v1 | https://arxiv.org/pdf/2111.03225v1.pdf | Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing | Part-level Action Parsing aims at part state parsing for boosting action recognition in videos. Despite of dramatic progresses in the area of video classification research, a severe problem faced by the community is that the detailed understanding of human actions is ignored. Our motivation is that parsing human action... | ['Jingkuan Song', 'Lechao Chen', 'Lianli Gao', 'Xiaojia Chen', 'Xuanhan Wang'] | 2021-11-05 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 7.39233792e-01 4.12778527e-01 -4.43499744e-01 -3.11442584e-01
-1.07594085e+00 -4.01065618e-01 6.64273083e-01 -2.51234502e-01
-4.60701317e-01 4.66590494e-01 6.66409016e-01 1.03324495e-01
4.29773808e-01 -5.81309557e-01 -6.91316605e-01 -7.12547839e-01
-2.00452536e-01 3.13370228e-01 3.85848343e-01 1.73770398... | [8.33236312866211, 0.47201403975486755] |
2314a065-bc25-416f-aa78-a628c54e8de1 | document-level-relation-extraction-with-2 | 2201.04826 | null | https://arxiv.org/abs/2201.04826v1 | https://arxiv.org/pdf/2201.04826v1.pdf | Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning | Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contrib... | ['Jianhua Dai', 'Lu Xu', 'Daojian Zeng', 'Chao Zhao'] | 2022-01-13 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.60333980e-02 5.19819796e-01 -3.64306450e-01 -3.73796433e-01
-8.84410501e-01 -6.41304135e-01 5.47190964e-01 7.63654411e-01
-3.18306714e-01 7.63847768e-01 5.19981921e-01 -4.04977173e-01
-1.97996929e-01 -8.92350852e-01 -3.65471244e-01 -3.06288987e-01
4.67498899e-02 3.69118154e-01 5.31412959e-01 -4.37181920... | [9.281805038452148, 8.653569221496582] |
2babdd23-1f57-4884-b48b-d878ba7651d5 | robust-and-accurate-object-detection-via-self | 2111.07239 | null | https://arxiv.org/abs/2111.07239v1 | https://arxiv.org/pdf/2111.07239v1.pdf | Robust and Accurate Object Detection via Self-Knowledge Distillation | Object detection has achieved promising performance on clean datasets, but how to achieve better tradeoff between the adversarial robustness and clean precision is still under-explored. Adversarial training is the mainstream method to improve robustness, but most of the works will sacrifice clean precision to gain robu... | ['Hongcheng Huang', 'Xiongziyan Xiao', 'Renhao Xie', 'Pengzhi Chu', 'Weipeng Xu'] | 2021-11-14 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-1.49117127e-01 -1.60362750e-01 1.16309769e-01 -1.21793061e-01
-1.11950910e+00 -8.64039183e-01 6.54992402e-01 -3.08191389e-01
-4.30128098e-01 5.40791750e-01 -3.21037434e-02 -1.70116857e-01
3.36370170e-01 -8.74931395e-01 -9.85738337e-01 -8.89031351e-01
2.52341270e-01 -8.77061412e-02 5.99216163e-01 -4.01361853... | [5.5436835289001465, 7.932278156280518] |
e267d5d9-a27e-403e-ae78-a509027d6d68 | classification-of-lung-pathologies-in | 2302.07157 | null | https://arxiv.org/abs/2302.07157v2 | https://arxiv.org/pdf/2302.07157v2.pdf | Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform | Annually 8500 neonatal deaths are reported in the US due to respiratory failure. Recently, Lung Ultrasound (LUS), due to its radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for lung conditions. However, lack of highly trained medical professionals has limited its... | ['Karthikeyan Umapathy', 'Naimul Khan', 'Lei Gao', 'Randy Tan', 'Ryan Tan', 'Adel Mohamed', 'Sagarjit Aujla'] | 2023-02-14 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 3.22391599e-01 -3.52046460e-01 1.06341958e-01 -1.35283740e-02
-5.14109135e-01 -4.57481831e-01 6.53407350e-02 1.96195468e-01
-1.40538618e-01 4.76695120e-01 -9.48348548e-03 -5.00002861e-01
-5.53827882e-01 -6.69872701e-01 -3.67954105e-01 -1.07368946e+00
-2.82564074e-01 3.42037350e-01 7.18882859e-01 6.31247461... | [15.383807182312012, -1.970115065574646] |
7615def0-5b46-4447-8fc7-eba9a7f58c77 | choosing-to-rank | 1809.05139 | null | http://arxiv.org/abs/1809.05139v2 | http://arxiv.org/pdf/1809.05139v2.pdf | Choosing to Rank | Ranking data arises in a wide variety of application areas but remains
difficult to model, learn from, and predict. Datasets often exhibit
multimodality, intransitivity, or incomplete rankings---particularly when
generated by humans---yet popular probabilistic models are often too rigid to
capture such complexities. In... | ['Stephen Ragain', 'Johan Ugander'] | 2018-09-13 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 4.72751632e-02 -1.28351256e-01 -6.57302082e-01 -7.57359087e-01
-1.23829961e+00 -8.10609221e-01 1.08604741e+00 1.93222195e-01
-5.15174627e-01 8.77981067e-01 4.43688214e-01 -4.21167076e-01
-9.59213436e-01 -5.58557153e-01 -5.67861497e-01 -3.88185441e-01
-1.90088987e-01 1.01232946e+00 -1.83408245e-01 -5.00111163... | [9.295289039611816, 5.470141410827637] |
7a18a03d-a12f-4ae1-b3bc-84108d6d8f1f | geovln-learning-geometry-enhanced-visual-1 | 2305.17102 | null | https://arxiv.org/abs/2305.17102v1 | https://arxiv.org/pdf/2305.17102v1.pdf | GeoVLN: Learning Geometry-Enhanced Visual Representation with Slot Attention for Vision-and-Language Navigation | Most existing works solving Room-to-Room VLN problem only utilize RGB images and do not consider local context around candidate views, which lack sufficient visual cues about surrounding environment. Moreover, natural language contains complex semantic information thus its correlations with visual inputs are hard to mo... | ['Yanwei Fu', 'Haitao Lin', 'Boyan Jiang', 'Qiang Sun', 'Jingyang Huo'] | 2023-05-26 | geovln-learning-geometry-enhanced-visual | http://openaccess.thecvf.com//content/CVPR2023/html/Huo_GeoVLN_Learning_Geometry-Enhanced_Visual_Representation_With_Slot_Attention_for_Vision-and-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huo_GeoVLN_Learning_Geometry-Enhanced_Visual_Representation_With_Slot_Attention_for_Vision-and-Language_CVPR_2023_paper.pdf | cvpr-2023-1 | ['vision-and-language-navigation'] | ['robots'] | [-6.93474859e-02 1.93643454e-03 -1.04517587e-01 -5.59371889e-01
-5.74674428e-01 -4.57563400e-01 4.32425946e-01 -3.70466746e-02
-2.40339860e-01 3.62268120e-01 5.59293330e-01 -4.10945296e-01
1.51446849e-01 -8.39687645e-01 -1.05346048e+00 -4.02183384e-01
6.57870948e-01 -7.14224502e-02 2.97728688e-01 -6.23056710... | [4.463918685913086, 0.4149438440799713] |
9b5c3e67-6d22-4042-8793-73cdb0fc6414 | joint-speech-recognition-and-audio-captioning | 2202.01405 | null | https://arxiv.org/abs/2202.01405v1 | https://arxiv.org/pdf/2202.01405v1.pdf | Joint Speech Recognition and Audio Captioning | Speech samples recorded in both indoor and outdoor environments are often contaminated with secondary audio sources. Most end-to-end monaural speech recognition systems either remove these background sounds using speech enhancement or train noise-robust models. For better model interpretability and holistic understandi... | ['Shinji Watanabe', 'Michael Hentschel', 'Yosuke Kashiwagi', 'Xuankai Chang', 'Emiru Tsunoo', 'Chaitanya Narisetty'] | 2022-02-03 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 3.70233715e-01 -3.49737316e-01 6.24224603e-01 -4.45521861e-01
-1.80343771e+00 -5.45787454e-01 5.97266018e-01 3.36655267e-02
-1.41069770e-01 7.12750196e-01 8.52894306e-01 -2.44590297e-01
2.75786728e-01 -2.04007495e-02 -7.10734725e-01 -4.24162775e-01
2.48252109e-01 2.10514933e-01 9.37910676e-02 -1.26308039... | [14.871525764465332, 6.014614582061768] |
ab8dd45b-31e2-4240-81c3-9d2323bf7441 | emc2-net-joint-equalization-and-modulation | 2303.10934 | null | https://arxiv.org/abs/2303.10934v1 | https://arxiv.org/pdf/2303.10934v1.pdf | EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Network | Modulation classification (MC) is the first step performed at the receiver side unless the modulation type is explicitly indicated by the transmitter. Machine learning techniques have been widely used for MC recently. In this paper, we propose a novel MC technique dubbed as Joint Equalization and Modulation Classificat... | ['Junil Choi', 'Hyun Ryu'] | 2023-03-20 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 6.63712680e-01 -8.21878165e-02 -3.12378019e-01 -2.86396652e-01
-6.20206952e-01 -8.66122171e-02 6.49980664e-01 -2.87858814e-01
-4.42451268e-01 9.45085645e-01 -3.58359963e-01 -9.77200031e-01
-1.96270451e-01 -4.25407231e-01 -6.01931751e-01 -9.82357502e-01
-6.48877561e-01 -2.18067273e-01 -2.24288732e-01 -2.15483069... | [6.426156997680664, 1.459825873374939] |
272b5366-1ac1-43a6-8d3d-4b82b0d3e8ee | weakly-supervised-temporal-action-5 | 2203.02925 | null | https://arxiv.org/abs/2203.02925v5 | https://arxiv.org/pdf/2203.02925v5.pdf | Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation | Weakly supervised temporal action localization aims to localize temporal boundaries of actions and simultaneously identify their categories with only video-level category labels. Many existing methods seek to generate pseudo labels for bridging the discrepancy between classification and localization, but usually only m... | ['Hongsheng Li', 'Liang Wang', 'Linjiang Huang'] | 2022-03-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Weakly_Supervised_Temporal_Action_Localization_via_Representative_Snippet_Knowledge_Propagation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Weakly_Supervised_Temporal_Action_Localization_via_Representative_Snippet_Knowledge_Propagation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 6.28245294e-01 -7.28565082e-02 -8.82935584e-01 -4.32784498e-01
-9.02128339e-01 -4.31042880e-01 5.10423362e-01 2.31717259e-01
-4.62514013e-01 8.75054359e-01 4.70957041e-01 3.64968032e-01
1.59464836e-01 -2.82882899e-01 -7.04743564e-01 -8.03874016e-01
-2.46916100e-01 3.20583321e-02 7.44197667e-01 2.48959526... | [8.493468284606934, 0.6134655475616455] |
47d5a06a-6b5c-4bdf-9f68-bcacb9b9d479 | self-point-flow-self-supervised-scene-flow | 2105.08248 | null | https://arxiv.org/abs/2105.08248v1 | https://arxiv.org/pdf/2105.08248v1.pdf | Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk | Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences... | ['Lihua Xie', 'Guosheng Lin', 'Ruibo Li'] | 2021-05-18 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Self-Point-Flow_Self-Supervised_Scene_Flow_Estimation_From_Point_Clouds_With_Optimal_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.86499149e-01 -2.88966328e-01 -4.72692847e-01 -2.76019514e-01
-4.87358540e-01 -5.13099611e-01 5.30580103e-01 1.80533469e-01
-2.66831458e-01 5.96753061e-01 -9.40370336e-02 -8.69792923e-02
-1.80818602e-01 -9.29863572e-01 -6.14006042e-01 -6.08989596e-01
2.64493264e-02 5.77632606e-01 5.83511770e-01 -1.55588880... | [8.487618446350098, -2.1079494953155518] |
8bf1b646-7f1f-4942-973c-3a2a550a1544 | reflect-summarizing-robot-experiences-for | 2306.15724 | null | https://arxiv.org/abs/2306.15724v1 | https://arxiv.org/pdf/2306.15724v1.pdf | REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction | The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework R... | ['Shuran Song', 'Arpit Bahety', 'Zeyi Liu'] | 2023-06-27 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 1.95509955e-01 7.92062044e-01 -3.46026331e-01 -4.21078533e-01
-6.91997945e-01 -5.06513834e-01 7.08783567e-01 2.56971061e-01
2.15148628e-01 5.67445040e-01 5.01564205e-01 -5.89414179e-01
-2.03597292e-01 -4.62592572e-01 -9.89714622e-01 3.59087676e-01
-7.17908442e-02 6.43280208e-01 7.91686475e-02 -1.28040552... | [4.418815612792969, 0.9061102271080017] |
7d747612-c5f9-4d08-af5d-9ed28964fa99 | from-pretraining-data-to-language-models-to | 2305.08283 | null | https://arxiv.org/abs/2305.08283v3 | https://arxiv.org/pdf/2305.08283v3.pdf | From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models | Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Ou... | ['Yulia Tsvetkov', 'YuHan Liu', 'Chan Young Park', 'Shangbin Feng'] | 2023-05-15 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [-2.54139483e-01 7.79691100e-01 -5.21964610e-01 -4.54551965e-01
-3.51190150e-01 -7.62058914e-01 1.25504601e+00 4.05112714e-01
-6.50883377e-01 7.09844410e-01 1.15611899e+00 -6.10764325e-01
2.87306339e-01 -6.27828360e-01 -5.16970277e-01 -2.87840545e-01
4.85388756e-01 2.06769690e-01 -3.86027128e-01 -2.42558554... | [8.89455509185791, 10.280733108520508] |
64f25a0f-204e-4782-a9fa-9bb5f95e8ebb | zero-shot-query-contextualization-for | 2204.10613 | null | https://arxiv.org/abs/2204.10613v1 | https://arxiv.org/pdf/2204.10613v1.pdf | Zero-shot Query Contextualization for Conversational Search | Current conversational passage retrieval systems cast conversational search into ad-hoc search by using an intermediate query resolution step that places the user's question in context of the conversation. While the proposed methods have proven effective, they still assume the availability of large-scale question resol... | ['Evangelos Kanoulas', 'Andrew Yates', 'Antonios Minas Krasakis'] | 2022-04-22 | null | null | null | null | ['passage-retrieval', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.58273488e-01 1.25272870e-01 -2.04244271e-01 -3.55753243e-01
-1.37461925e+00 -6.87115192e-01 1.06309974e+00 3.77661526e-01
-6.68036044e-01 5.77477694e-01 8.31438661e-01 -3.14476222e-01
-4.05904531e-01 -7.41669357e-01 -1.89381316e-01 -4.25200343e-01
1.43596977e-01 6.79697752e-01 5.08820951e-01 -5.58880508... | [12.013164520263672, 7.810952186584473] |
2c62eaa0-43ca-4f1f-ba96-4a501f1ef4ad | scaling-vision-transformers-to-22-billion | 2302.05442 | null | https://arxiv.org/abs/2302.05442v1 | https://arxiv.org/pdf/2302.05442v1.pdf | Scaling Vision Transformers to 22 Billion Parameters | The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to ... | ['Neil Houlsby', 'Jeremiah Harmsen', 'Daniel Keysers', 'Xiaohua Zhai', 'Mario Lučić', 'Thomas Kipf', 'Dustin Tran', 'Filip Pavetić', 'Alexander Kolesnikov', 'Thomas Mensink', 'Yi Tay', 'Cristina Vasconcelos', 'Vighnesh Birodkar', 'Alexey Gritsenko', 'Mark Patrick Collier', 'Jasmijn Bastings', 'Fantine Huot', 'Avital Ol... | 2023-02-10 | null | null | null | null | ['zero-shot-transfer-image-classification', 'action-classification'] | ['computer-vision', 'computer-vision'] | [-2.48390622e-03 1.26773044e-01 6.96615726e-02 -4.88701344e-01
-7.74124384e-01 -5.13424695e-01 1.08913219e+00 -4.12952125e-01
-5.83380461e-01 1.53470069e-01 3.48255396e-01 -4.24844205e-01
3.59452307e-01 -1.17809027e-01 -6.79346800e-01 -4.67740506e-01
8.90553221e-02 4.71540630e-01 3.95008564e-01 -2.78608829... | [9.978906631469727, 1.4301166534423828] |
9f3e63fb-cc75-43d2-99ac-99b575be06c2 | self-supervised-audio-teacher-student | 2306.04186 | null | https://arxiv.org/abs/2306.04186v1 | https://arxiv.org/pdf/2306.04186v1.pdf | Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks | In recent years, self-supervised learning (SSL) has emerged as a popular approach for learning audio representations. The ultimate goal of audio self-supervised pre-training is to transfer knowledge to downstream audio tasks, generally including clip-level and frame-level tasks. Clip-level tasks classify the scene or s... | ['Xiaofei Li', 'Nian Shao', 'Xian Li'] | 2023-06-07 | null | null | null | null | ['audio-tagging', 'sound-event-detection', 'instrument-recognition', 'speaker-diarization'] | ['audio', 'audio', 'audio', 'speech'] | [ 4.35707927e-01 -3.33720148e-01 -2.04258099e-01 -4.55258042e-01
-1.66340411e+00 -4.16852325e-01 3.87360930e-01 5.62621653e-01
-1.84079319e-01 4.78268683e-01 3.25793296e-01 5.59474342e-02
-1.08198933e-02 -5.11566281e-01 -7.58253932e-01 -6.70502007e-01
-2.31661424e-01 -6.69643059e-02 5.09255588e-01 1.34550408... | [15.213295936584473, 5.1764607429504395] |
4252bf79-5eb6-42ac-9a00-c64939968b63 | gym-gazebo2-a-toolkit-for-reinforcement | 1903.06278 | null | http://arxiv.org/abs/1903.06278v2 | http://arxiv.org/pdf/1903.06278v2.pdf | gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo | This paper presents an upgraded, real world application oriented version of
gym-gazebo, the Robot Operating System (ROS) and Gazebo based Reinforcement
Learning (RL) toolkit, which complies with OpenAI Gym. The content discusses
the new ROS 2 based software architecture and summarizes the results obtained
using Proxima... | ['Víctor Mayoral Vilches', 'Lander Usategui San Juan', 'Elias Barba Moral', 'Nestor Gonzalez Lopez', 'Alejandro Solano Rueda', 'Yue Leire Erro Nuin', 'Risto Kojcev'] | 2019-03-14 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-6.09777093e-01 4.58678305e-01 -9.79375020e-02 9.48432162e-02
-1.28399491e-01 -5.44044554e-01 3.42166334e-01 -3.44454914e-01
-4.15853679e-01 9.61165905e-01 -2.01170132e-01 -2.35744402e-01
-8.33601892e-01 -2.92859375e-01 -5.66413939e-01 -8.54475260e-01
-6.45803571e-01 3.97752881e-01 3.46572250e-01 -1.13545024... | [4.3522748947143555, 1.2371909618377686] |
cb5e8403-bcce-4212-a846-763c62d29b82 | 190910094 | 1909.10094 | null | https://arxiv.org/abs/1909.10094v2 | https://arxiv.org/pdf/1909.10094v2.pdf | Deep Structured Neural Network for Event Temporal Relation Extraction | We propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, and 2) a structured support vector machine (SSVM) to make joint predictions. The neural network automatically learns... | ['Ralph Weischedel', 'I-Hung Hsu', 'Nanyun Peng', 'Aram Galstyan', 'Rujun Han', 'Mu Yang'] | 2019-09-22 | deep-structured-neural-network-for-event | https://aclanthology.org/K19-1062 | https://aclanthology.org/K19-1062.pdf | conll-2019-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 2.71839261e-01 4.29287553e-01 -5.56609631e-01 -7.20296621e-01
-9.35225129e-01 -2.03788225e-02 8.14939320e-01 4.90975618e-01
-3.92445177e-01 7.19739914e-01 5.73314786e-01 -1.86218500e-01
-3.86518538e-01 -8.07007790e-01 -5.66037059e-01 -3.39400113e-01
-5.24217725e-01 4.79814559e-01 4.57443774e-01 -2.62849152... | [9.081171035766602, 9.084399223327637] |
f3b3491e-5540-4552-bfb8-95ab3fc526fe | variational-em-based-deep-learning-for-noise | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Nan_Variational-EM-Based_Deep_Learning_for_Noise-Blind_Image_Deblurring_CVPR_2020_paper.pdf | Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring | Non-blind deblurring is an important problem encountered in many image restoration tasks. The focus of non-blind deblurring is on how to suppress noise magnification during deblurring. In practice, it often happens that the noise level of input image is unknown and varies among different images. This paper aims at deve... | [' Hui Ji', ' Yuhui Quan', 'Yuesong Nan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['blind-image-deblurring'] | ['computer-vision'] | [ 4.44920182e-01 -5.97331107e-01 2.51438022e-01 1.46564394e-01
-6.28458261e-01 -2.56516665e-01 5.65532684e-01 -6.54126883e-01
-3.26432347e-01 7.95170248e-01 6.23625696e-01 -2.94374138e-01
-1.01013280e-01 -3.48512650e-01 -5.70767939e-01 -1.18227804e+00
2.38061085e-01 8.31703003e-03 -1.80898264e-01 8.31163302... | [11.623723983764648, -2.7111918926239014] |
060bbed3-82a6-4fa3-9a50-c2bd38605328 | multilogue-net-a-context-aware-rnn-for-multi | 2002.08267 | null | https://arxiv.org/abs/2002.08267v3 | https://arxiv.org/pdf/2002.08267v3.pdf | Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation | Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use spec... | ['Ashish Sardana', 'Aman Shenoy'] | 2020-02-19 | multilogue-net-a-context-aware-rnn-for-multi-1 | https://128.84.21.199/abs/2002.08267 | https://128.84.21.199/pdf/2002.08267.pdf | arxiv-preprint-2020-2 | ['multimodal-emotion-recognition', 'emotion-recognition-in-conversation', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 1.67824954e-01 -1.59780204e-01 -1.65259182e-01 -5.05241156e-01
-6.49981260e-01 -5.61007977e-01 6.78050876e-01 1.38563558e-01
-4.46597457e-01 3.68977815e-01 7.09443390e-01 1.89040631e-01
5.94870262e-02 -4.21491176e-01 1.04575483e-02 -3.07793468e-01
1.00408144e-01 1.00981392e-01 -2.32698888e-01 -5.43973744... | [13.332121849060059, 5.436816215515137] |
8c7982cc-5d87-4e58-b64d-f04889da0e8c | epic-graph-augmentation-with-edit-path | 2306.01310 | null | https://arxiv.org/abs/2306.01310v1 | https://arxiv.org/pdf/2306.01310v1.pdf | EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost | Graph-based models have become increasingly important in various domains, but the limited size and diversity of existing graph datasets often limit their performance. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. Ou... | ['Dongwoo Kim', 'Sungsoo Ahn', 'Seungbeom Lee', 'Jaeseung Heo'] | 2023-06-02 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 5.06551147e-01 4.55706298e-01 -3.91819179e-01 -4.01148081e-01
-3.74884874e-01 -8.47663701e-01 5.16092300e-01 6.06877685e-01
-7.96418488e-02 7.74322212e-01 4.15386558e-02 -3.19141805e-01
-1.15171514e-01 -1.15886199e+00 -8.79222691e-01 -2.14908928e-01
-3.16175312e-01 4.19012755e-01 1.49924248e-01 -2.26541981... | [7.162132740020752, 6.259637355804443] |
15a21c7d-4439-4506-a2f4-09a3405f06f7 | 360o-surface-regression-with-a-hyper-sphere | 1909.07043 | null | https://arxiv.org/abs/1909.07043v1 | https://arxiv.org/pdf/1909.07043v1.pdf | $360^o$ Surface Regression with a Hyper-Sphere Loss | Omnidirectional vision is becoming increasingly relevant as more efficient $360^o$ image acquisition is now possible. However, the lack of annotated $360^o$ datasets has hindered the application of deep learning techniques on spherical content. This is further exaggerated on tasks where ground truth acquisition is diff... | ['Stamatis Samaras', 'Dimitrios Zarpalas', 'Dimitrios Ataloglou', 'Petros Daras', 'Antonis Karakottas', 'Vasileios Gkitsas', 'Nikolaos Zioulis'] | 2019-09-16 | null | null | null | null | ['surface-normals-estimation'] | ['computer-vision'] | [ 2.54483879e-01 2.05456868e-01 4.96341318e-01 -4.39715028e-01
-6.35331750e-01 -5.26451170e-01 5.80263674e-01 -2.67434061e-01
-6.22781038e-01 7.27449656e-01 1.35740649e-03 -3.38628262e-01
5.36132284e-05 -7.59873688e-01 -1.06558645e+00 -5.68128586e-01
5.67605942e-02 2.07694829e-01 -2.15405464e-01 -3.10882092... | [8.492130279541016, -2.6757993698120117] |
5caed062-3452-4d84-a3ff-04aee02f35aa | edge-based-video-analytics-a-survey | 2303.14329 | null | https://arxiv.org/abs/2303.14329v1 | https://arxiv.org/pdf/2303.14329v1.pdf | Edge-Based Video Analytics: A Survey | Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge comput... | ['Di wu', 'Yipeng Zhou', 'Young Choon Lee', 'Amirmohammad Pasdar', 'Zhenxiao Luo', 'Miao Hu'] | 2023-03-25 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.01879460e-01 -4.03991878e-01 -2.09373876e-01 -6.35749176e-02
2.05724849e-03 -5.25979996e-01 2.72686742e-02 7.61829615e-02
-2.94666082e-01 3.70929629e-01 1.01484852e-02 -5.55583179e-01
1.69412732e-01 -7.57156312e-01 -1.34955168e-01 -4.86570060e-01
-2.87835598e-01 2.46917337e-01 5.56188881e-01 -2.11892705... | [8.401601791381836, -0.4851190149784088] |
23d4d5b5-fc60-49a7-902b-c616cfdffdff | using-human-guided-causal-knowledge-for-more | 2110.04664 | null | https://arxiv.org/abs/2110.04664v1 | https://arxiv.org/pdf/2110.04664v1.pdf | Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning | A major challenge in research involving artificial intelligence (AI) is the development of algorithms that can find solutions to problems that can generalize to different environments and tasks. Unlike AI, humans are adept at finding solutions that can transfer. We hypothesize this is because their solutions are inform... | ['Steven Sloman', 'R. Iris Bahar', 'Emily Sheetz', 'Yanqi Liu', 'Semir Tatlidil'] | 2021-10-09 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 1.63058308e-03 7.44989038e-01 5.82562573e-03 -5.53658545e-01
-2.38104492e-01 -4.04609621e-01 6.09720051e-01 2.66441882e-01
-2.09134698e-01 1.01431155e+00 5.64101040e-01 -5.09118676e-01
-5.00700355e-01 -7.11788654e-01 -6.86434448e-01 4.21760418e-02
-3.46994847e-01 8.62201929e-01 1.10756300e-01 -3.02169710... | [4.394287586212158, 1.103908896446228] |
5f5e5c35-7d83-4faa-9316-4f6a2262e3e2 | sketching-without-worrying-noise-tolerant | 2203.14817 | null | https://arxiv.org/abs/2203.14817v1 | https://arxiv.org/pdf/2203.14817v1.pdf | Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval | Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models that predomin... | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Aneeshan Sain', 'Abdullah Faiz Ur Rahman Khilji', 'Subhadeep Koley', 'Ayan Kumar Bhunia'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bhunia_Sketching_Without_Worrying_Noise-Tolerant_Sketch-Based_Image_Retrieval_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bhunia_Sketching_Without_Worrying_Noise-Tolerant_Sketch-Based_Image_Retrieval_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.06748605e-01 -4.57343161e-02 -8.28548074e-02 -1.23109315e-02
-1.06057274e+00 -8.47698569e-01 7.74836957e-01 -1.39272377e-01
-1.83139771e-01 2.21529201e-01 3.51371169e-01 -2.33271420e-01
-1.80825174e-01 -7.06613362e-01 -5.46920896e-01 -6.02677345e-01
3.43783289e-01 2.38690168e-01 2.77188029e-02 -4.41198528... | [11.655597686767578, 0.5566099882125854] |
66a94325-98d0-4414-810e-2c72b2f5b68d | towards-imperceptible-document-manipulations | 2305.01860 | null | https://arxiv.org/abs/2305.01860v1 | https://arxiv.org/pdf/2305.01860v1.pdf | Towards Imperceptible Document Manipulations against Neural Ranking Models | Adversarial attacks have gained traction in order to identify potential vulnerabilities in neural ranking models (NRMs), but current attack methods often introduce grammatical errors, nonsensical expressions, or incoherent text fragments, which can be easily detected. Additionally, current methods rely heavily on the u... | ['Yingfei Sun', 'Le Sun', 'Zheng Ye', 'Ben He', 'Xuanang Chen'] | 2023-05-03 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.60813785e-01 1.95542276e-01 3.24753821e-01 -1.71558067e-01
-1.03946817e+00 -1.10090518e+00 9.89492595e-01 1.04260251e-01
-2.91329682e-01 6.15144849e-01 2.25114241e-01 -3.18655103e-01
1.40393987e-01 -6.10103965e-01 -9.04590011e-01 -4.09426957e-01
3.25649641e-02 3.33912641e-01 2.56352481e-02 -5.35163701... | [6.089151382446289, 8.15313720703125] |
abab3cca-9763-46b7-8bb7-26d1b85bb352 | real-esrgan-training-real-world-blind-super | 2107.10833 | null | https://arxiv.org/abs/2107.10833v2 | https://arxiv.org/pdf/2107.10833v2.pdf | Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data | Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is ... | ['Ying Shan', 'Chao Dong', 'Liangbin Xie', 'Xintao Wang'] | 2021-07-22 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 5.26697636e-01 -4.27336335e-01 6.11402243e-02 3.09211351e-02
-6.36186779e-01 -3.51461381e-01 6.01905286e-01 -1.02514541e+00
8.66273344e-02 1.01405728e+00 5.59886336e-01 -1.43968761e-01
1.17158093e-01 -3.38108212e-01 -5.39600134e-01 -8.66610110e-01
7.54962265e-02 -2.61228263e-01 -1.29344612e-02 -4.83140081... | [11.411760330200195, -2.1670775413513184] |
59b3c6d2-da17-4854-b073-b905ed82d397 | pixelsteganalysis-destroying-hidden | 1902.11113 | null | http://arxiv.org/abs/1902.11113v2 | http://arxiv.org/pdf/1902.11113v2.pdf | PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation | Steganography is the science of unnoticeably concealing a secret message
within a certain image, called a cover image. The cover image with the secret
message is called a stego image. Steganography is commonly used for illegal
purposes such as terrorist activities and pornography. To thwart covert
communications and tr... | ['Hyun-Soo Choi', 'Dahuin Jung', 'Sungroh Yoon', 'Ho Bae'] | 2019-01-30 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 8.92667770e-01 1.35036409e-01 3.55125107e-02 1.95318460e-01
-1.92826405e-01 -1.19939655e-01 5.52525878e-01 -4.41464037e-01
-3.17666829e-01 5.63769400e-01 -2.13638961e-01 -6.02681518e-01
4.56442446e-01 -1.18900478e+00 -1.05810559e+00 -1.23900855e+00
-4.44593549e-01 -9.99838114e-02 7.71216974e-02 -6.33175254... | [4.306778907775879, 8.058269500732422] |
9a84435b-adb7-4059-ac9d-852d7ea746af | whole-body-tumor-segmentation-of-18f-fdg-pet | 2210.08068 | null | https://arxiv.org/abs/2210.08068v1 | https://arxiv.org/pdf/2210.08068v1.pdf | Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks | Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cos... | ['Lei Xiang', 'Xinrui Zhan', 'Ludovic Sibille'] | 2022-10-14 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.53120446e-01 3.93376082e-01 -4.63236779e-01 -4.68745977e-01
-9.31020975e-01 -4.64615554e-01 4.80643839e-01 3.93720984e-01
-8.86300147e-01 9.23219323e-01 -3.96615453e-02 -4.90230978e-01
1.18066393e-01 -6.40293241e-01 -3.35841984e-01 -1.00560844e+00
1.06504537e-01 1.02084661e+00 3.54710191e-01 4.32382911... | [14.719883918762207, -2.4950523376464844] |
29b45e59-fa00-41cc-b49b-76d8f9713a2d | temporal-cycle-consistency-learning | 1904.07846 | null | http://arxiv.org/abs/1904.07846v1 | http://arxiv.org/pdf/1904.07846v1.pdf | Temporal Cycle-Consistency Learning | We introduce a self-supervised representation learning method based on the
task of temporal alignment between videos. The method trains a network using
temporal cycle consistency (TCC), a differentiable cycle-consistency loss that
can be used to find correspondences across time in multiple videos. The
resulting per-fra... | ['Yusuf Aytar', 'Debidatta Dwibedi', 'Jonathan Tompson', 'Andrew Zisserman', 'Pierre Sermanet'] | 2019-04-16 | temporal-cycle-consistency-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Dwibedi_Temporal_Cycle-Consistency_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Dwibedi_Temporal_Cycle-Consistency_Learning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-alignment'] | ['computer-vision'] | [ 7.40203559e-02 -2.71546364e-01 -3.31147939e-01 -4.84190613e-01
-5.79246998e-01 -6.70228124e-01 6.64248049e-01 1.32358328e-01
-2.96086371e-01 2.20826477e-01 4.56793875e-01 2.84702510e-01
-2.35636368e-01 -2.72132933e-01 -8.25723052e-01 -4.38939720e-01
-7.47729778e-01 4.74583134e-02 2.64145613e-01 1.12730205... | [8.69175910949707, 0.7312559485435486] |
d15988fa-cfd5-436e-ac57-a9e6f8dc2eee | temporal-feature-networks-for-cnn-based | 2103.12213 | null | https://arxiv.org/abs/2103.12213v1 | https://arxiv.org/pdf/2103.12213v1.pdf | Temporal Feature Networks for CNN based Object Detection | For reliable environment perception, the use of temporal information is essential in some situations. Especially for object detection, sometimes a situation can only be understood in the right perspective through temporal information. Since image-based object detectors are currently based almost exclusively on CNN arch... | ['J. Marius Zöllner', 'Tassilo Wald', 'Michael Weber'] | 2021-03-22 | null | null | null | null | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 1.97496489e-01 -2.84337223e-01 1.88353341e-02 -4.66633946e-01
-6.14959635e-02 -3.87877941e-01 9.88608301e-01 2.93452233e-01
-9.87867415e-01 3.84757161e-01 -2.44284391e-01 -1.72451720e-01
-4.11777943e-01 -5.49859464e-01 -4.65965211e-01 -7.44925320e-01
-3.15334529e-01 1.33233830e-01 1.02047360e+00 -4.04511303... | [8.446159362792969, -0.39331290125846863] |
1ca48970-b24a-47c4-b4bc-a38221699f82 | deep-implicit-templates-for-3d-shape | 2011.14565 | null | https://arxiv.org/abs/2011.14565v2 | https://arxiv.org/pdf/2011.14565v2.pdf | Deep Implicit Templates for 3D Shape Representation | Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationshi... | ['Yebin Liu', 'Qionghai Dai', 'Tao Yu', 'Zerong Zheng'] | 2020-11-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zheng_Deep_Implicit_Templates_for_3D_Shape_Representation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-representation'] | ['computer-vision'] | [ 8.21439326e-02 6.18921295e-02 5.11342846e-03 -5.62726378e-01
-4.03732747e-01 -5.12787223e-01 7.51365483e-01 -1.01244509e-01
2.20656261e-01 3.27621430e-01 2.67167866e-01 -1.40227586e-01
-3.01911503e-01 -1.13644278e+00 -7.36664116e-01 -7.34662414e-01
5.70680857e-01 6.30469561e-01 1.61207736e-01 4.72009443... | [8.584914207458496, -3.6599552631378174] |
375b858d-6f62-4d85-b549-c813e1fc1f83 | coarse-to-fine-q-attention-with-tree | 2204.12471 | null | https://arxiv.org/abs/2204.12471v2 | https://arxiv.org/pdf/2204.12471v2.pdf | Coarse-to-fine Q-attention with Tree Expansion | Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at each layer in the hierarchy. Although effective, Q-attention suffers from "coarse ambiguity" - when voxelization is significantly coarse, i... | ['Pieter Abbeel', 'Stephen James'] | 2022-04-26 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.78989077e-01 3.81197006e-01 1.44106746e-01 1.43952891e-01
-1.02905619e+00 -4.69626695e-01 9.41321999e-02 2.05201566e-01
-2.05742404e-01 7.05880702e-01 1.44725934e-01 -1.30105674e-01
-4.42725748e-01 -7.30081141e-01 -7.46488094e-01 -4.54269677e-01
-9.36753973e-02 9.74647045e-01 3.83973300e-01 -4.17002052... | [4.737096309661865, 0.592918336391449] |
89b3cc43-b77c-4a40-828e-c613ac11ffcf | domain-agnostic-few-shot-learning-for | 2111.00007 | null | https://arxiv.org/abs/2111.00007v1 | https://arxiv.org/pdf/2111.00007v1.pdf | Domain Agnostic Few-Shot Learning For Document Intelligence | Few-shot learning aims to generalize to novel classes with only a few samples with class labels. Research in few-shot learning has borrowed techniques from transfer learning, metric learning, meta-learning, and Bayesian methods. These methods also aim to train models from limited training samples, and while encouraging... | ['Glenn Fung', 'Eric Bunch', 'Jaya Krishna Mandivarapu'] | 2021-10-29 | null | null | null | null | ['document-image-classification', 'cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.66850781e-01 -7.84296840e-02 -5.00741243e-01 -6.43591285e-01
-9.08179700e-01 -1.28603667e-01 9.44897652e-01 2.71000355e-01
-1.26074657e-01 8.20371568e-01 -7.31319189e-02 7.55416080e-02
-3.23805392e-01 -8.52903187e-01 -5.35946131e-01 -5.50651848e-01
2.66802102e-01 7.01517463e-01 6.05336368e-01 -2.50270694... | [10.024373054504395, 3.0845348834991455] |
2e8e0a96-c717-4c9f-9853-f257f0af013d | using-context-information-for-dialog-act | null | null | https://aclanthology.org/D17-1231 | https://aclanthology.org/D17-1231.pdf | Using Context Information for Dialog Act Classification in DNN Framework | Previous work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent studies explored using deep learning neural networks for DA classification, however, it is not clear y... | ['Yang Liu', 'Kun Han', 'Zhao Tan', 'Yun Lei'] | 2017-09-01 | null | null | null | emnlp-2017-9 | ['dialog-act-classification'] | ['natural-language-processing'] | [ 1.29325375e-01 1.43087640e-01 -1.90875277e-01 -7.51324713e-01
-1.36170134e-01 -5.84465027e-01 6.47235930e-01 -3.07964701e-02
-3.38290840e-01 7.96098351e-01 8.05739641e-01 -6.36000633e-01
5.79954624e-01 -5.58385551e-01 1.53826401e-01 -6.47251487e-01
9.06460881e-02 4.60426718e-01 2.98531830e-01 -5.03692210... | [12.815994262695312, 7.723491668701172] |
9021bcb9-38a4-4805-994d-e3740ed61f50 | densely-constrained-depth-estimator-for | 2207.10047 | null | https://arxiv.org/abs/2207.10047v3 | https://arxiv.org/pdf/2207.10047v3.pdf | Densely Constrained Depth Estimator for Monocular 3D Object Detection | Estimating accurate 3D locations of objects from monocular images is a challenging problem because of lacking depth. Previous work shows that utilizing the object's keypoint projection constraints to estimate multiple depth candidates boosts the detection performance. However, the existing methods can only utilize vert... | ['Yuntao Chen', 'Zhaoxiang Zhang', 'JiaWei He', 'Yingyan Li'] | 2022-07-20 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-2.47333631e-01 -2.90509135e-01 -4.62921798e-01 -1.92305773e-01
-4.11588460e-01 -4.55725849e-01 3.57881784e-01 -2.29093418e-01
-3.62155855e-01 2.80480534e-01 1.33313552e-01 -1.88845679e-01
3.75979871e-01 -8.05642307e-01 -4.41016793e-01 -4.71139193e-01
4.03116584e-01 2.67430991e-01 1.05709636e+00 2.24008322... | [7.936544418334961, -2.490060567855835] |
46bf72af-c734-47d4-982a-813469ad30af | offline-reinforcement-learning-with-implicit | 2110.06169 | null | https://arxiv.org/abs/2110.06169v1 | https://arxiv.org/pdf/2110.06169v1.pdf | Offline Reinforcement Learning with Implicit Q-Learning | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because m... | ['Sergey Levine', 'Ashvin Nair', 'Ilya Kostrikov'] | 2021-10-12 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.02139607e-01 2.37439856e-01 -5.68358004e-01 -2.00510040e-01
-1.00738406e+00 -1.04507267e+00 2.84367710e-01 1.89058006e-01
-9.29067314e-01 1.05252528e+00 6.87862486e-02 -5.00147879e-01
-1.95771307e-01 -8.18367064e-01 -1.00817466e+00 -9.73903656e-01
-1.84682176e-01 7.37255096e-01 -3.20505574e-02 -1.68185562... | [4.106921672821045, 2.2142112255096436] |
66472b8e-8a1f-4d90-a49f-ec35c5a244ba | lastustaln-at-complex-word-identification-cwi | null | null | https://aclanthology.org/W18-0517 | https://aclanthology.org/W18-0517.pdf | LaSTUS/TALN at Complex Word Identification (CWI) 2018 Shared Task | This paper presents the participation of the LaSTUS/TALN team in the Complex Word Identification (CWI) Shared Task 2018 in the English monolingual track . The purpose of the task was to determine if a word in a given sentence can be judged as complex or not by a certain target audience. For the English track, task orga... | ["Ahmed Abura{'}ed", 'Horacio Saggion'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [ 4.92943600e-02 -1.51631432e-02 1.10795513e-01 -4.70786631e-01
-8.42610478e-01 -9.95800018e-01 7.68524945e-01 7.21076310e-01
-1.23469460e+00 5.81765413e-01 4.53898787e-01 -6.82923138e-01
2.26226106e-01 -3.43705922e-01 -2.94971019e-01 -2.17402637e-01
2.00005785e-01 7.62172103e-01 -9.36635137e-02 -4.05224830... | [10.527769088745117, 10.440348625183105] |
3b82a0e6-2295-4813-b76d-ac591e7780af | explainability-by-parsing-neural-module-tree | 1812.03299 | null | https://arxiv.org/abs/1812.03299v3 | https://arxiv.org/pdf/1812.03299v3.pdf | Learning to Assemble Neural Module Tree Networks for Visual Grounding | Visual grounding, a task to ground (i.e., localize) natural language in images, essentially requires composite visual reasoning. However, existing methods over-simplify the composite nature of language into a monolithic sentence embedding or a coarse composition of subject-predicate-object triplet. In this paper, we pr... | ['Zheng-Jun Zha', 'Feng Wu', 'Daqing Liu', 'Hanwang Zhang'] | 2018-12-08 | learning-to-assemble-neural-module-tree | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Learning_to_Assemble_Neural_Module_Tree_Networks_for_Visual_Grounding_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Learning_to_Assemble_Neural_Module_Tree_Networks_for_Visual_Grounding_ICCV_2019_paper.pdf | iccv-2019-10 | ['natural-language-visual-grounding'] | ['reasoning'] | [-9.62772465e-04 6.92402542e-01 -7.05057979e-02 -5.01583695e-01
-6.22862041e-01 -4.68051523e-01 5.19403517e-01 1.58396974e-01
-1.20380215e-01 3.31195682e-01 3.93873364e-01 -5.13138950e-01
3.01773220e-01 -8.05897534e-01 -1.01229537e+00 -4.77196604e-01
1.44205302e-01 1.32212117e-01 -7.15115666e-02 1.79947615... | [10.598921775817871, 1.6051007509231567] |
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