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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7d220314-1cf6-4123-b503-b3f2e47c7aad | fera-2017-addressing-head-pose-in-the-third | 1702.04174 | null | http://arxiv.org/abs/1702.04174v1 | http://arxiv.org/pdf/1702.04174v1.pdf | FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge | The field of Automatic Facial Expression Analysis has grown rapidly in recent
years. However, despite progress in new approaches as well as benchmarking
efforts, most evaluations still focus on either posed expressions, near-frontal
recordings, or both. This makes it hard to tell how existing expression
recognition app... | ['László A. Jeni', 'Enrique Sánchez-Lozano', 'Jeffrey M. Girard', 'Jeffrey F. Cohn', 'Maja Pantic', 'Michel F. Valstar', 'Lijun Yin', 'Zheng Zhang'] | 2017-02-14 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 5.05815089e-01 -3.34956527e-01 -5.75102381e-02 -7.97768533e-01
-7.09686100e-01 -5.72331309e-01 6.62472844e-01 -6.01229370e-01
-4.25242186e-01 5.36585689e-01 2.75914997e-01 3.80254656e-01
2.61160046e-01 -2.41014212e-02 -1.02690093e-01 -7.58560359e-01
-3.94421369e-01 -1.26896665e-01 -5.48233330e-01 -1.20915316... | [13.572493553161621, 1.9241000413894653] |
d3b57d45-c1e9-4619-9efe-6102ef6b2e5a | unsupervised-feature-rich-clustering | null | null | https://aclanthology.org/C12-2028 | https://aclanthology.org/C12-2028.pdf | Unsupervised Feature-Rich Clustering | null | ['Vladimir Eidelman'] | 2012-12-01 | unsupervised-feature-rich-clustering-1 | https://aclanthology.org/C12-2028 | https://aclanthology.org/C12-2028.pdf | coling-2012-12 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.348635673522949, 3.813462972640991] |
ad619c90-7d0a-4f63-a845-232860210a39 | improving-traffic-sign-recognition-by-active | 2111.14426 | null | https://arxiv.org/abs/2111.14426v1 | https://arxiv.org/pdf/2111.14426v1.pdf | Improving traffic sign recognition by active search | We describe an iterative active-learning algorithm to recognise rare traffic signs. A standard ResNet is trained on a training set containing only a single sample of the rare class. We demonstrate that by sorting the samples of a large, unlabeled set by the estimated probability of belonging to the rare class, we can e... | ['N. Gustafsson', 'E. Werner', 'B. Mehlig', 'H. Gustafsson', 'S. Jaghouar'] | 2021-11-29 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.38847154e-01 3.37916672e-01 -5.19903362e-01 -5.06092846e-01
-9.49846864e-01 -3.01401943e-01 6.11450016e-01 -6.06712289e-02
-6.91282749e-01 9.61792409e-01 -1.87092572e-02 -2.49747381e-01
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5.82027473e-02 6.92172587e-01 6.91782653e-01 1.69679940... | [9.240924835205078, 1.278090476989746] |
c2fd8fe5-cc85-4b4c-be25-d17bf25743a3 | hodinet-high-order-discrepant-interaction | 2307.00954 | null | https://arxiv.org/abs/2307.00954v1 | https://arxiv.org/pdf/2307.00954v1.pdf | HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection | RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. However, these features contribute differently to the final ... | ['Yan-Feng Wu', 'Fu Guo', 'Xiao Jin', 'Jing Xu', 'Kang Yi'] | 2023-07-03 | null | null | null | null | ['rgb-d-salient-object-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 7.79929459e-02 -6.62529692e-02 -4.80276858e-03 -3.99349272e-01
-8.45148683e-01 -1.65548325e-01 5.19084275e-01 9.79243517e-02
-3.01155627e-01 3.35026979e-01 3.92963797e-01 3.27594317e-02
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3.39319617e-01 -4.02465433e-01 7.57444561e-01 -4.24493015... | [9.693520545959473, -0.8006700873374939] |
cf77bae4-bfcb-44b0-8f75-ebbd7b45bb43 | multi-channel-target-speech-extraction-with | 2010.09191 | null | https://arxiv.org/abs/2010.09191v1 | https://arxiv.org/pdf/2010.09191v1.pdf | Multi-channel target speech extraction with channel decorrelation and target speaker adaptation | The end-to-end approaches for single-channel target speech extraction have attracted widespread attention. However, the studies for end-to-end multi-channel target speech extraction are still relatively limited. In this work, we propose two methods for exploiting the multi-channel spatial information to extract the tar... | ['Yijie Li', 'Yanhua Long', 'Xinyuan Zhou', 'Jiangyu Han'] | 2020-10-19 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 2.95431077e-01 -9.58045274e-02 -1.63212046e-01 -1.95401534e-01
-1.84254730e+00 -5.03561437e-01 4.28824484e-01 -3.46632779e-01
-3.98674637e-01 6.38979375e-01 6.97148740e-01 -4.70647484e-01
3.54977101e-01 1.42845809e-01 -5.22250712e-01 -9.35266137e-01
1.87984202e-02 -4.38594669e-01 1.83495656e-01 -3.46176662... | [14.805537223815918, 5.99949312210083] |
76b0dfb9-a7fc-477e-8bba-304faf345a9c | attention-mechanism-exploits-temporal | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Attention_Mechanism_Exploits_Temporal_Contexts_Real-Time_3D_Human_Pose_Reconstruction_CVPR_2020_paper.pdf | Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction | We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be ... | [' Vijayan Asari', ' Sen-ching Cheung', ' Chen Chen', ' He Wang', ' Ju Shen', 'Ruixu Liu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.15280369e-01 -3.11194569e-01 5.89838177e-02 -2.69691527e-01
-4.87462342e-01 -3.64352167e-01 3.75402778e-01 -3.49743098e-01
-8.37203264e-01 5.40371358e-01 1.37246937e-01 2.75254756e-01
1.31526574e-01 -1.73163742e-01 -9.53143954e-01 -5.29522121e-01
-2.16683656e-01 3.13204914e-01 3.05672020e-01 6.65055290... | [7.3248090744018555, -0.6032341122627258] |
5342213a-642f-4406-82a4-656cc0745484 | modeling-multi-hop-question-answering-as-2 | 2205.09226 | null | https://arxiv.org/abs/2205.09226v1 | https://arxiv.org/pdf/2205.09226v1.pdf | Modeling Multi-hop Question Answering as Single Sequence Prediction | Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,... | ['Caiming Xiong', 'Nitish Shirish Keskar', 'Yingbo Zhou', 'Kazuma Hashimoto', 'Semih Yavuz'] | 2022-05-18 | modeling-multi-hop-question-answering-as-1 | https://aclanthology.org/2022.acl-long.69 | https://aclanthology.org/2022.acl-long.69.pdf | acl-2022-5 | ['multi-hop-question-answering', 'generative-question-answering', 'passage-retrieval'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [-1.64659947e-01 5.72726130e-01 1.65149987e-01 -2.08409280e-01
-1.69665611e+00 -7.76507556e-01 8.46621513e-01 6.48983046e-02
8.79535675e-02 9.70142126e-01 9.47264612e-01 -6.37941539e-01
-1.31609932e-01 -9.90723372e-01 -8.25522006e-01 -1.47434762e-02
5.05673826e-01 8.50523591e-01 5.63930631e-01 -8.59613180... | [11.083868980407715, 7.952037811279297] |
19bfc6ee-0a9c-4a2c-bb4a-7862842d49a6 | star-net-improving-single-image-desnowing | 2303.09988 | null | https://arxiv.org/abs/2303.09988v1 | https://arxiv.org/pdf/2303.09988v1.pdf | Star-Net: Improving Single Image Desnowing Model With More Efficient Connection and Diverse Feature Interaction | Compared to other severe weather image restoration tasks, single image desnowing is a more challenging task. This is mainly due to the diversity and irregularity of snow shape, which makes it extremely difficult to restore images in snowy scenes. Moreover, snow particles also have a veiling effect similar to haze or mi... | ['Binling Nie', 'Xuesong Yin', 'Yuanqi Chang', 'Jiawei Mao'] | 2023-03-17 | null | null | null | null | ['single-image-desnowing'] | ['computer-vision'] | [ 2.49164939e-01 -4.33109373e-01 5.34070671e-01 -1.48589820e-01
-2.55241305e-01 -3.82971525e-01 1.45436794e-01 -2.82765865e-01
-4.08451259e-02 5.66943884e-01 2.54358530e-01 -2.76867539e-01
1.21805035e-01 -1.06630087e+00 -6.40806317e-01 -1.05046308e+00
1.98081702e-01 -1.10809349e-01 6.07748747e-01 -6.96401238... | [10.922208786010742, -3.2027337551116943] |
091f870e-27fb-465e-b1ab-0c2beb9dbc95 | a-self-boosting-framework-for-automated | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.pdf | A Self-Boosting Framework for Automated Radiographic Report Generation | Automated radiographic report generation is a challenging task since it requires to generate paragraphs describing fine-grained visual differences of cases, especially for those between the diseased and the healthy. Existing image captioning methods commonly target at generic images, and lack mechanism to meet this... | ['Xiu Li', 'Lei Wang', 'Luping Zhou', 'Zhanyu Wang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['medical-report-generation', 'word-similarity'] | ['medical', 'natural-language-processing'] | [ 3.67268324e-01 4.44195837e-01 -2.91413873e-01 -2.60862291e-01
-1.24858510e+00 -1.84204906e-01 4.58273292e-01 2.66553342e-01
-1.42825007e-01 7.74395943e-01 5.05345881e-01 -4.91077626e-05
1.76935971e-01 -8.76451969e-01 -7.38248050e-01 -9.38375294e-01
2.72927016e-01 2.53934115e-01 1.48348778e-01 -1.21015441... | [15.040847778320312, -1.4163368940353394] |
ac38ded2-23cc-463f-9cf1-2a3c8c2f4aaa | conditionally-invariant-representation | 2307.00558 | null | https://arxiv.org/abs/2307.00558v1 | https://arxiv.org/pdf/2307.00558v1.pdf | Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity | This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. Our approach identifies both spurious and invariant latent features necessary for achieving accurate reconstruction by placing distinct conditional pri... | ['Fabian J. Theis', 'Soroor Hediyeh-Zadeh', 'Ferdinand Kapl', 'Hananeh Aliee'] | 2023-07-02 | null | null | null | null | ['data-integration', 'benchmarking', 'benchmarking'] | ['knowledge-base', 'miscellaneous', 'robots'] | [ 4.57570761e-01 -1.77798405e-01 -4.71861452e-01 -4.21002150e-01
-8.44944060e-01 -5.91779888e-01 8.86815727e-01 4.80725288e-01
2.38898601e-02 1.02674806e+00 5.31008303e-01 4.18303674e-03
-4.37021911e-01 -6.32553637e-01 -8.55942428e-01 -1.10980463e+00
4.27295417e-02 5.41794240e-01 -2.60830522e-01 2.80446529... | [6.869443416595459, 5.201504707336426] |
39670fcb-55ff-4fc0-83a1-f39159fc85a9 | saliency-guided-textured-contact-lens-aware | null | null | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/html/Parzianello_Saliency-Guided_Textured_Contact_Lens-Aware_Iris_Recognition_WACVW_2022_paper.html | https://openaccess.thecvf.com/content/WACV2022W/MAP-A/papers/Parzianello_Saliency-Guided_Textured_Contact_Lens-Aware_Iris_Recognition_WACVW_2022_paper.pdf | Saliency-Guided Textured Contact Lens-Aware Iris Recognition | Iris recognition requires an adequate level of the iris texture being visible to perform a reliable matching. In case when a textured contact lens covers the iris, a false non-match is reported or a presentation attack is detected. There are, however, scenarios in which one wants to maximize the probability of a correc... | ['Adam Czajka', 'Lucas Parzianello'] | 2022-01-03 | null | null | null | proceedings-of-the-ieee-cvf-winter-conference-1 | ['iris-segmentation'] | ['medical'] | [ 8.04268122e-01 2.51283288e-01 -2.59820342e-01 -9.84830707e-02
-3.13964397e-01 -4.57347542e-01 3.19564342e-01 -1.99875578e-01
-1.74701229e-01 3.17371190e-01 1.76831096e-01 -2.56082863e-01
-3.45110059e-01 -2.73229450e-01 -6.03977501e-01 -9.29306805e-01
-9.32321846e-02 2.82260895e-01 -2.92403042e-01 3.71490300... | [3.7392876148223877, -3.6347038745880127] |
bf7b43d5-e1e0-4c08-8b9b-c36e9c573d42 | gal-geometric-adversarial-loss-for-single | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Li_Jiang_GAL_Geometric_Adversarial_ECCV_2018_paper.pdf | GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction | In this paper, we present a framework for reconstructing a point-based 3D model of an object from a single view image. Distance metrics, like Chamfer distance, were used in previous work to measure the difference of two point sets and serve as the loss function in point-based reconstruction. However, such point-point l... | ['Shaoshuai Shi', 'Xiaojuan Qi', 'Jiaya Jia', 'Li Jiang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['3d-object-reconstruction'] | ['computer-vision'] | [-3.94152477e-03 3.91470164e-01 3.11050117e-01 -2.10146070e-01
-6.63702905e-01 -7.09120631e-01 4.81712490e-01 -1.72511145e-01
1.32253721e-01 3.04419786e-01 -1.91946372e-01 2.87885349e-02
4.19588536e-02 -9.50248241e-01 -1.05085289e+00 -4.43504781e-01
2.72412926e-01 4.89554495e-01 5.11573493e-01 -1.35458753... | [8.6577787399292, -3.4898130893707275] |
a6a46fb5-f60b-48c0-9c8a-9dabf89740b0 | attribution-aware-weight-transfer-a-warm | 2210.07207 | null | https://arxiv.org/abs/2210.07207v1 | https://arxiv.org/pdf/2210.07207v1.pdf | Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation | In class-incremental semantic segmentation (CISS), deep learning architectures suffer from the critical problems of catastrophic forgetting and semantic background shift. Although recent works focused on these issues, existing classifier initialization methods do not address the background shift problem and assign the ... | ['Didier Stricker', 'Joost Van de Weijer', 'René Schuster', 'Dipam Goswami'] | 2022-10-13 | null | null | null | null | ['overlapped-100-5', 'overlapped-5-3', 'overlapped-10-1', 'overlapped-15-5', 'overlapped-100-10', 'class-incremental-semantic-segmentation', 'overlapped-15-1', 'overlapped-50-50', 'overlapped-100-50', 'overlapped-14-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.43532872e-01 -1.79790750e-01 1.10923275e-02 -4.44670290e-01
-3.91079664e-01 -2.97443599e-01 3.76765907e-01 1.64310232e-01
-7.76216805e-01 8.40763748e-01 -4.47447002e-01 -2.58695364e-01
3.64382446e-01 -7.26022482e-01 -6.45688593e-01 -8.23625326e-01
3.65441829e-01 4.54665691e-01 1.14580214e+00 -1.26229227... | [9.416951179504395, 1.9453972578048706] |
38f43025-8c5b-488b-b1f9-5af0f9539df6 | autoregressive-language-model-for-zero-shot | null | null | https://openreview.net/forum?id=5cU6H8EAxpO | https://openreview.net/pdf?id=5cU6H8EAxpO | Autoregressive Language Model for Zero-shot Constrained Keyphrase Generation | Recently, most of the state-of-the-art keyphrase prediction models are based on a supervised generative model.
It shows significantly better than before. Nevertheless, it still faces domain robustness and building datasets on high-resource.
To overcome these limitations, unsupervised methods have also been critical an... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.03907675e-01 -3.54715511e-02 -4.13789451e-01 1.36921540e-01
-9.42202806e-01 -8.58948469e-01 9.78544176e-01 3.08889002e-01
-4.07976776e-01 1.10412288e+00 4.10700798e-01 -2.50001580e-01
-5.39786369e-02 -8.63569856e-01 -6.08866990e-01 -4.34867144e-01
3.24196398e-01 7.42311418e-01 6.91097140e-01 -5.72119653... | [12.265238761901855, 8.894455909729004] |
f69b6453-1489-4a49-aa02-eb843169d34d | towards-preemptive-detection-of-depression | 2011.05249 | null | https://arxiv.org/abs/2011.05249v1 | https://arxiv.org/pdf/2011.05249v1.pdf | Towards Preemptive Detection of Depression and Anxiety in Twitter | Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not exp... | ['Luis Espinosa-Anke', 'Jose Camacho Collados', 'David Owen'] | 2020-11-10 | null | https://aclanthology.org/2020.smm4h-1.12 | https://aclanthology.org/2020.smm4h-1.12.pdf | smm4h-coling-2020-12 | ['anxiety-detection'] | ['medical'] | [ 1.66070566e-01 2.27510825e-01 -4.51274693e-01 -6.88671410e-01
-1.04412055e+00 -6.02119088e-01 7.24582136e-01 1.10657966e+00
-5.83594680e-01 4.73866791e-01 7.70739973e-01 -5.89663327e-01
-1.04861468e-01 -5.97306967e-01 1.24812596e-01 -5.92520870e-02
-9.46347639e-02 5.30077755e-01 -2.83328325e-01 -2.62106776... | [8.801128387451172, 10.309511184692383] |
b7175e5d-c915-4832-917f-ab0074e7875f | 0-nu-beta-beta-nuclear-matrix-elements | 1808.05016 | null | http://arxiv.org/abs/1808.05016v1 | http://arxiv.org/pdf/1808.05016v1.pdf | $0\nu\beta\beta$ nuclear matrix elements, neutrino potentials and $\mathrm{SU}(4)$ symmetry | Intimate relation between the Gamow-Teller part of the matrix element
$M^{0\nu}_\mathrm{GT}$ and the $2\nu\beta\beta$ closure matrix element
$M^{2\nu}_\mathrm{cl}$ is explained and explored. If the corresponding radial
dependence $C^{2\nu}_\mathrm{cl}(r)$ would be known, $M^{0\nu}$ corresponding
to any mechanism respon... | [] | 2018-08-15 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.31859571e-01 3.03750575e-01 -1.22730680e-01 -1.48840055e-01
-5.77319264e-01 -1.08847618e-01 4.80031133e-01 -1.65734544e-01
-6.24720812e-01 1.04936504e+00 -3.92916024e-01 -7.00107396e-01
-4.85772163e-01 -1.15609288e+00 -4.56517994e-01 -1.53905058e+00
-3.29952925e-01 4.83830631e-01 1.94780439e-01 -7.03646541... | [6.040604114532471, 4.600096225738525] |
5e61f00c-999b-4b17-9218-fb06a4c9d9b2 | spring-a-high-resolution-high-detail-dataset | 2303.01943 | null | https://arxiv.org/abs/2303.01943v1 | https://arxiv.org/pdf/2303.01943v1.pdf | Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo | While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation methodology. Hence, we introduce Spring $-$ a large, high-resolution, high-detail, computer-generated... | ['Andrés Bruhn', 'Yaroslava Nalivayko', 'Azin Jahedi', 'Jenny Schmalfuss', 'Lukas Mehl'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.pdf | cvpr-2023-1 | ['stereo-depth-estimation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-2.00758308e-01 -5.62824011e-01 -1.23017328e-02 -5.98198809e-02
-8.25976670e-01 -6.93951368e-01 6.72872305e-01 -2.55209774e-01
-1.85990557e-01 9.74361479e-01 5.60564280e-01 -4.42940481e-02
1.79843932e-01 -7.03104317e-01 -5.31752110e-01 -5.20757258e-01
-3.42850506e-01 2.06097007e-01 4.06351328e-01 -3.18545312... | [8.726853370666504, -1.9655331373214722] |
23140b88-2143-4a35-9655-31d0dfb0bdb6 | distributed-dynamic-safe-screening-algorithms | 2204.10981 | null | https://arxiv.org/abs/2204.10981v1 | https://arxiv.org/pdf/2204.10981v1.pdf | Distributed Dynamic Safe Screening Algorithms for Sparse Regularization | Distributed optimization has been widely used as one of the most efficient approaches for model training with massive samples. However, large-scale learning problems with both massive samples and high-dimensional features widely exist in the era of big data. Safe screening is a popular technique to speed up high-dimens... | ['Heng Huang', 'Wenhan Xian', 'Xidong Wu', 'Runxue Bao'] | 2022-04-23 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.58039004e-01 -4.08767343e-01 -2.73374736e-01 -2.67819583e-01
-7.08321214e-01 -7.50815570e-02 -5.73220477e-02 1.39114812e-01
-3.08099866e-01 8.29985559e-01 -2.65817493e-01 -5.25690168e-02
-3.71859521e-01 -6.44702435e-01 -6.81310475e-01 -9.13041472e-01
-1.14596769e-01 4.72503006e-01 2.93235242e-01 2.00937688... | [6.835245609283447, 4.663538455963135] |
adc8682c-f3cf-464c-8a8b-f6c9dadac9a9 | explicit-box-detection-unifies-end-to-end | 2302.01593 | null | https://arxiv.org/abs/2302.01593v1 | https://arxiv.org/pdf/2302.01593v1.pdf | Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation | This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information. Different from previous one-stage methods, ED-Pose re-considers this task as two ex... | ['Lei Zhang', 'Ruimao Zhang', 'Feng Li', 'Shilong Liu', 'Ailing Zeng', 'Jie Yang'] | 2023-02-03 | null | null | null | null | ['keypoint-detection', '2d-human-pose-estimation', 'human-detection', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.23537976e-01 3.32083628e-02 1.03103079e-01 -1.73457608e-01
-1.12449682e+00 -4.08920765e-01 4.86067653e-01 2.28382006e-01
-8.71693313e-01 3.11237276e-01 3.10761422e-01 3.33862484e-01
3.78353059e-01 -5.70577741e-01 -8.29965472e-01 -3.21327984e-01
-1.12313963e-01 6.62714660e-01 6.38854265e-01 -2.40168422... | [7.253607273101807, -0.7411671876907349] |
6304e45f-ebe6-490b-b376-211e3238f598 | optimizing-temporal-resolution-of | 2210.10208 | null | https://arxiv.org/abs/2210.10208v1 | https://arxiv.org/pdf/2210.10208v1.pdf | Optimizing Temporal Resolution Of Convolutional Recurrent Neural Networks For Sound Event Detection | In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher se... | ['Hugo Van hamme', 'Wim Boes'] | 2022-10-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-7.23980889e-02 4.07596350e-01 1.84834808e-01 -2.54444569e-01
-1.16287208e+00 -5.87371707e-01 6.03226125e-01 9.96772274e-02
-8.29846740e-01 5.65086842e-01 2.32642710e-01 -1.51142359e-01
-1.01834744e-01 -3.08097035e-01 -6.16528809e-01 -7.19771981e-01
-2.51039565e-01 1.15773212e-02 6.19446278e-01 -9.43046510... | [15.181229591369629, 5.184020519256592] |
72d2d1ad-bec8-486d-98b8-f51bdf0f2c8d | ssgan-secure-steganography-based-on | 1707.01613 | null | http://arxiv.org/abs/1707.01613v4 | http://arxiv.org/pdf/1707.01613v4.pdf | SSGAN: Secure Steganography Based on Generative Adversarial Networks | In this paper, a novel strategy of Secure Steganograpy based on Generative
Adversarial Networks is proposed to generate suitable and secure covers for
steganography. The proposed architecture has one generative network, and two
discriminative networks. The generative network mainly evaluates the visual
quality of the g... | ['Xiao-Yu Zhang', 'Yinlong Qian', 'Jing Dong', 'Haichao Shi', 'Wei Wang'] | 2017-07-06 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.29175526e-01 3.77127938e-02 2.62259752e-01 9.22547802e-02
-2.28602141e-01 -3.41712177e-01 5.37193954e-01 -8.25025439e-01
-1.06374197e-01 5.68912864e-01 -8.60054642e-02 -4.57551837e-01
3.94851953e-01 -1.40526450e+00 -4.91626322e-01 -1.20220816e+00
9.45809297e-03 -2.14047998e-01 2.74132580e-01 -4.05648500... | [4.297282695770264, 8.05553913116455] |
8a51ebce-868f-4133-86ca-b20c03c73efa | semantic-parsing-via-staged-query-graph | null | null | https://aclanthology.org/P15-1128 | https://aclanthology.org/P15-1128.pdf | Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base | null | ['Ming-Wei Chang', 'Wen-tau Yih', 'Xiaodong He', 'Jianfeng Gao'] | 2015-07-01 | semantic-parsing-via-staged-query-graph-1 | https://aclanthology.org/P15-1128 | https://aclanthology.org/P15-1128.pdf | ijcnlp-2015-7 | ['knowledge-base-question-answering'] | ['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... | [-7.282231330871582, 3.796398401260376] |
bad92d84-db2a-445c-b069-940c40d9b35b | analysis-of-cnn-based-remote-ppg-to | 1911.02736 | null | https://arxiv.org/abs/1911.02736v2 | https://arxiv.org/pdf/1911.02736v2.pdf | Analysis of CNN-based remote-PPG to understand limitations and sensitivities | Deep learning based on Convolutional Neural Network (CNN) has shown promising results in various vision-based applications, recently also in camera-based vital signs monitoring. The CNN-based Photoplethysmography (PPG) extraction has, so far, been focused on performance rather than understanding. In this paper, we try ... | ['Gerard de Haan', 'Wenjin Wang', 'Qi Zhan'] | 2019-11-07 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 7.00854361e-02 -1.96182474e-01 1.61473304e-01 -2.69988328e-01
-3.93627852e-01 -1.76061362e-01 7.57907405e-02 -7.28780851e-02
-5.46755731e-01 7.10887372e-01 3.62497307e-02 -1.77160412e-01
-1.25162303e-01 -3.56594890e-01 -2.04460353e-01 -1.06151664e+00
-2.24704117e-01 -2.76191175e-01 -1.53498892e-02 -5.81369363... | [13.92258358001709, 2.864889621734619] |
044872b8-b350-4d06-a797-8fc079b00dc1 | 3d-shape-generation-with-grid-based-implicit-1 | 2107.10607 | null | https://arxiv.org/abs/2107.10607v1 | https://arxiv.org/pdf/2107.10607v1.pdf | 3D Shape Generation with Grid-based Implicit Functions | Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it i... | ['Leif Kobbelt', 'Isaak Lim', 'Moritz Ibing'] | 2021-07-22 | 3d-shape-generation-with-grid-based-implicit | http://openaccess.thecvf.com//content/CVPR2021/html/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ibing_3D_Shape_Generation_With_Grid-Based_Implicit_Functions_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-generation'] | ['computer-vision'] | [ 1.46724686e-01 4.30001497e-01 2.75290489e-01 1.33620664e-01
-6.80775285e-01 -8.52727652e-01 1.05335951e+00 -1.55319959e-01
-2.56078858e-02 1.09390855e+00 2.27348462e-01 1.74608767e-01
5.29943146e-02 -1.28653228e+00 -8.72950792e-01 -1.11012924e+00
2.24884957e-01 7.84362435e-01 5.52125499e-02 -1.03195824... | [11.549866676330566, -0.3476146161556244] |
1bbd18d7-3ab5-4594-9e71-88a367527290 | report-from-the-nsf-future-directions-1 | 2203.10012 | null | https://arxiv.org/abs/2203.10012v1 | https://arxiv.org/pdf/2203.10012v1.pdf | Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challenges | This is a report on the NSF Future Directions Workshop on Automatic Evaluation of Dialog. The workshop explored the current state of the art along with its limitations and suggested promising directions for future work in this important and very rapidly changing area of research. | ['Chen Zhang', 'Yizhe Zhang', 'Zhou Yu', 'Yi-Ting Yeh', 'David Traum', 'Samira Shaikh', 'Verena Rieser', 'Zekang Li', 'Dilek Hakkani-Tur', 'Kallirroi Georgila', 'Milica Gasic', 'Maxine Eskenazi', 'Jan Deriu', "Luis Fernando D'Haro", 'Jinho Choi', 'Shikib Mehri'] | 2022-03-18 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-1.50486277e-02 4.57698733e-01 -2.71798819e-01 -9.58905280e-01
-7.95062244e-01 -8.33455741e-01 7.32522786e-01 1.45277828e-02
-6.47355735e-01 9.67465401e-01 8.23444307e-01 -4.22887385e-01
2.99911737e-01 -2.50836790e-01 2.73475237e-02 5.84276356e-02
1.05775136e-04 1.03762591e+00 6.66632414e-01 -7.19994545... | [12.866443634033203, 7.955621719360352] |
7d2c4931-bbe0-48e6-b6d9-f26ef9bc42db | unsupervised-anomaly-detection-in-medical-1 | 2305.19867 | null | https://arxiv.org/abs/2305.19867v1 | https://arxiv.org/pdf/2305.19867v1.pdf | Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model | It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a d... | ['Chen Chen', 'Jing Hua', 'Umar Khalid', 'Hasan Iqbal'] | 2023-05-31 | null | null | null | null | ['unsupervised-anomaly-detection', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 4.89589125e-01 5.49281180e-01 -6.06514402e-02 -4.09469783e-01
-9.26400065e-01 -1.81745827e-01 8.02422106e-01 1.10361040e-01
-2.27044031e-01 5.30418932e-01 2.71120787e-01 -3.19658279e-01
2.17540696e-01 -4.93842155e-01 -6.55972183e-01 -7.53147006e-01
-2.55351931e-01 5.08277833e-01 1.69296965e-01 2.30837330... | [14.352910995483398, -2.139075756072998] |
f186a9ce-2bb4-48db-b5c5-cfd47bcdf92f | waco-word-aligned-contrastive-learning-for | 2212.09359 | null | https://arxiv.org/abs/2212.09359v3 | https://arxiv.org/pdf/2212.09359v3.pdf | WACO: Word-Aligned Contrastive Learning for Speech Translation | End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model's performance closely correlates with its embedding similarity between speech and source... | ['Lei LI', 'Rong Ye', 'Siqi Ouyang'] | 2022-12-19 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.63009280e-01 1.34671196e-01 -3.52986336e-01 -3.23362589e-01
-1.81762397e+00 -7.26102233e-01 9.13850248e-01 -3.06474358e-01
-3.68570685e-01 6.58793688e-01 7.43573010e-01 -7.68932819e-01
6.85212851e-01 -1.16204366e-01 -6.53929472e-01 -4.00514811e-01
2.93141544e-01 7.07024932e-01 1.13529118e-03 -3.56980741... | [14.499320983886719, 7.186354160308838] |
96e3845d-c5ad-4490-b922-81686d1b2926 | nlx-gpt-a-model-for-natural-language | 2203.05081 | null | https://arxiv.org/abs/2203.05081v1 | https://arxiv.org/pdf/2203.05081v1.pdf | NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks | Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, high-level and fine-grained. Current NLE models explain the decision-making process of a vision or vision-language model (a.k.a., task model),... | ['Nikos Deligiannis', 'Tanmoy Mukherjee', 'Fawaz Sammani'] | 2022-03-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['visual-entailment'] | ['reasoning'] | [ 2.51807570e-01 8.28175902e-01 -7.87569433e-02 -6.65701926e-01
-6.84119999e-01 -4.25769746e-01 8.36004794e-01 -1.44560516e-01
1.52178749e-01 5.84264874e-01 4.07607675e-01 -7.57479727e-01
-1.40319504e-02 -5.76567769e-01 -8.74024272e-01 -3.53207082e-01
5.68501294e-01 8.37674797e-01 -4.60168384e-02 -2.49772612... | [10.841211318969727, 1.9665685892105103] |
62b27646-608f-407e-a85b-b1514463b690 | random-projections-for-adversarial-attack | 2012.06405 | null | https://arxiv.org/abs/2012.06405v2 | https://arxiv.org/pdf/2012.06405v2.pdf | Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis | Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be well-defined, attacker strategies may still vary widely within those constraints. Therefore, detection should be considered as an open-set prob... | ['Philippe Burlina', 'Neil Fendley', 'Nathan Drenkow'] | 2020-12-11 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 4.24071133e-01 -3.41029555e-01 -1.28385857e-01 -6.61525526e-04
-1.11386979e+00 -1.48378325e+00 8.17482710e-01 3.20581309e-02
-2.41544858e-01 3.04161251e-01 -3.87950912e-02 -3.99233788e-01
-9.32301357e-02 -5.16281903e-01 -5.32577157e-01 -7.08125651e-01
-3.70054603e-01 1.36926910e-03 1.57177165e-01 -1.22734383... | [5.678063869476318, 7.870211124420166] |
c3a63fe9-e392-4c8d-bca4-1eadf2294f09 | a-multi-view-context-aware-approach-to | 1704.01759 | null | http://arxiv.org/abs/1704.01759v2 | http://arxiv.org/pdf/1704.01759v2.pdf | A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization | Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view)... | ['Yang Liu', 'Mahinthan Chandramohan', 'Annamalai Narayanan', 'Lihui Chen'] | 2017-04-06 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.01807222e-01 -5.60534537e-01 -9.14058268e-01 9.38667208e-02
-6.40481412e-01 -1.09448624e+00 8.09032679e-01 1.59837455e-01
2.83803910e-01 -1.25485435e-02 -2.87162475e-02 -6.24774337e-01
-1.59331590e-01 -6.21834397e-01 -3.82173836e-01 -4.39865947e-01
-3.56493562e-01 -9.26862955e-02 6.35766983e-01 -4.54210117... | [14.414860725402832, 9.676582336425781] |
3c477458-02d9-478b-b71f-095bc1736061 | causal-discovery-for-gene-regulatory-network | 2301.01110 | null | https://arxiv.org/abs/2301.01110v1 | https://arxiv.org/pdf/2301.01110v1.pdf | Causal Discovery for Gene Regulatory Network Prediction | Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and ... | ['Jacob Rast'] | 2023-01-03 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.86616850e-01 1.58101887e-01 -2.97652543e-01 -1.76367909e-02
6.47346258e-01 -7.47231901e-01 1.04919565e+00 6.13740385e-01
1.62187800e-01 1.24077630e+00 2.86293328e-01 -6.17845297e-01
-4.10851717e-01 -8.83846700e-01 -5.17543256e-01 -7.94295847e-01
-4.80592012e-01 3.16444188e-01 1.06476948e-01 2.13702093... | [6.642040729522705, 5.362929821014404] |
978ba999-71ad-4235-9d0d-52f8411c5750 | estimation-beyond-data-reweighting-kernel | 2305.10898 | null | https://arxiv.org/abs/2305.10898v2 | https://arxiv.org/pdf/2305.10898v2.pdf | Estimation Beyond Data Reweighting: Kernel Method of Moments | Moment restrictions and their conditional counterparts emerge in many areas of machine learning and statistics ranging from causal inference to reinforcement learning. Estimators for these tasks, generally called methods of moments, include the prominent generalized method of moments (GMM) which has recently gained att... | ['Jia-Jie Zhu', 'Bernhard Schölkopf', 'Yassine Nemmour', 'Heiner Kremer'] | 2023-05-18 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 1.45500168e-01 2.42955670e-01 -5.91723144e-01 -5.57059348e-01
-9.96610343e-01 -2.81982392e-01 7.97563612e-01 6.84989765e-02
-6.48041666e-01 1.04571784e+00 1.44042403e-01 -3.78100365e-01
-5.97113550e-01 -5.70130706e-01 -8.39604855e-01 -6.87270463e-01
-2.78460354e-01 4.42832738e-01 -4.88787070e-02 3.16845745... | [7.257648468017578, 4.160569190979004] |
7e291a25-f073-47b7-abfa-ecfb3e02884b | an-asymmetric-loss-with-anomaly-detection | 2302.10889 | null | https://arxiv.org/abs/2302.10889v1 | https://arxiv.org/pdf/2302.10889v1.pdf | An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction | Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we prop... | ['Mariette Awad', 'Maha Issa', 'Jihan Ghanim'] | 2023-02-05 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-6.28426224e-02 -1.51371330e-01 3.63425255e-01 -3.29804540e-01
-3.67699236e-01 -4.76725310e-01 5.52738845e-01 5.14463186e-01
-1.58869103e-01 1.16951358e+00 1.21002994e-01 -3.93983006e-01
-6.54772818e-01 -1.13649750e+00 -6.05341136e-01 -1.15788114e+00
-3.71990412e-01 4.44810092e-01 -2.24849224e-01 -7.26479143... | [6.130898475646973, 2.8131866455078125] |
6bb1dacb-df14-449b-9fd5-22421d737438 | generating-a-graph-colouring-heuristic-with | 2304.04051 | null | https://arxiv.org/abs/2304.04051v1 | https://arxiv.org/pdf/2304.04051v1.pdf | Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks | The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour. In this work we investigate whether deep reinforcement learning can be used to discover a competitive construction heuristic for graph colouring. Our proposed approa... | ['Juergen Branke', 'Giovanni Montana', 'George Watkins'] | 2023-04-08 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 1.91286922e-01 5.30392826e-01 -3.73383552e-01 -6.85342704e-04
-2.47777015e-01 -5.58032155e-01 4.41423774e-01 2.78469563e-01
-8.02722797e-02 7.20769167e-01 -6.75344989e-02 -6.19121552e-01
-5.69382668e-01 -1.05248547e+00 -6.25037253e-01 -7.19904900e-01
-6.25801504e-01 7.73605227e-01 1.52601287e-01 -1.67926386... | [5.270968914031982, 3.116010904312134] |
8c6787d9-b652-4741-8aba-806f6eb2dcbc | voxformer-sparse-voxel-transformer-for-camera | 2302.12251 | null | https://arxiv.org/abs/2302.12251v2 | https://arxiv.org/pdf/2302.12251v2.pdf | VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion | Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics fr... | ['Anima Anandkumar', 'Chen Feng', 'Sanja Fidler', 'Jose M. Alvarez', 'Chaowei Xiao', 'Christopher Choy', 'Zhiding Yu', 'Yiming Li'] | 2023-02-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_VoxFormer_Sparse_Voxel_Transformer_for_Camera-Based_3D_Semantic_Scene_Completion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 1.17933087e-01 2.53163636e-01 2.47249246e-01 -3.90539527e-01
-3.57103765e-01 -1.16304301e-01 5.22342622e-01 2.54976917e-02
-8.56444836e-02 4.59398180e-01 3.25770915e-01 1.42298630e-02
3.19623649e-01 -1.04956138e+00 -8.30874324e-01 -7.30635762e-01
7.54755959e-02 6.42204523e-01 3.64252269e-01 3.65606025... | [8.524521827697754, -3.065643310546875] |
5aa0ac73-8c03-4c88-a0b2-e99f2077ac25 | bionavi-np-biosynthesis-navigator-for-natural | 2105.13121 | null | https://arxiv.org/abs/2105.13121v1 | https://arxiv.org/pdf/2105.13121v1.pdf | BioNavi-NP: Biosynthesis Navigator for Natural Products | Nature, a synthetic master, creates more than 300,000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs. To date, there are fewer than 30,000 validated NPs compounds involved in about 33,000 known enzyme catalytic reactions, and even fewer biosynthetic p... | ['Ruibo Wu', 'Yuedong Yang', 'Connor W. Coley', 'Binghong Chen', 'Chengtao Li', 'Tao Zeng', 'Shuangjia Zheng'] | 2021-05-26 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.04532540e-01 9.52245221e-02 -4.39959764e-01 1.79153949e-01
-2.48037547e-01 -1.31596279e+00 6.75093591e-01 4.15964693e-01
1.31034940e-01 1.20960104e+00 3.92096341e-02 -8.52490187e-01
-4.47911210e-02 -6.89745188e-01 -8.26447427e-01 -7.41563320e-01
-9.81340930e-02 6.19486630e-01 3.33431065e-01 -4.32083517... | [4.469828128814697, 6.12613582611084] |
eed64737-9909-40fd-b6e1-4a27227def33 | ea-2e-improving-consistency-with-event | null | null | https://aclanthology.org/2022.findings-naacl.202 | https://aclanthology.org/2022.findings-naacl.202.pdf | EA^2E: Improving Consistency with Event Awareness for Document-Level Argument Extraction | Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore ca... | ['Heng Ji', 'Qiusi Zhan', 'Qi Zeng'] | null | null | null | null | findings-naacl-2022-7 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 3.22911322e-01 7.39020586e-01 -6.24324799e-01 -4.58249152e-01
-9.81959999e-01 -6.62270367e-01 1.19477046e+00 9.12273943e-01
-3.70360345e-01 1.28208339e+00 8.93663764e-01 -4.47335839e-01
-2.99034387e-01 -9.09558654e-01 -9.10412371e-01 7.74153471e-02
-1.86589081e-02 4.46656495e-01 7.08615661e-01 -9.36108604... | [9.085515975952148, 9.21395492553711] |
f121a176-1128-4d5f-9c93-253742b06b7d | uncertainty-estimation-for-deep-learning | 2305.07618 | null | https://arxiv.org/abs/2305.07618v1 | https://arxiv.org/pdf/2305.07618v1.pdf | Uncertainty Estimation for Deep Learning Image Reconstruction using a Local Lipschitz Metric | The use of deep learning approaches for image reconstruction is of contemporary interest in radiology, especially for approaches that solve inverse problems associated with imaging. In deployment, these models may be exposed to input distributions that are widely shifted from training data, due in part to data biases o... | ['Matthew S. Rosen', 'Bruce R. Rosen', 'Neha Koonjoo', 'Jeremiah Z. Liu', 'Bo Zhu', 'Danyal F. Bhutto'] | 2023-05-12 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.95922780e-01 4.55866575e-01 -2.05530033e-01 -6.88476324e-01
-1.57155216e+00 -4.40164328e-01 3.93200815e-01 4.40210074e-01
-6.46719277e-01 8.91787410e-01 2.47318119e-01 -4.84956563e-01
-5.46361566e-01 -6.00380242e-01 -1.03040504e+00 -9.51212883e-01
-3.55979949e-02 6.92633212e-01 2.76435409e-02 3.81699741... | [13.717344284057617, -2.254343032836914] |
f0c54348-182d-4ff2-bd15-5d90ddbde840 | is-chatgpt-a-good-nlg-evaluator-a-preliminary | 2303.04048 | null | https://arxiv.org/abs/2303.04048v2 | https://arxiv.org/pdf/2303.04048v2.pdf | Is ChatGPT a Good NLG Evaluator? A Preliminary Study | Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable performance on various NLP tasks in terms of automatic evaluation metrics. However, the ability of ChatGPT to serve as an evaluation metric is stil... | ['Jianfeng Qu', 'Zengkui Sun', 'Jie zhou', 'Jinan Xu', 'Zhixu Li', 'Haoxiang Shi', 'Fandong Meng', 'Yunlong Liang', 'Jiaan Wang'] | 2023-03-07 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 1.16184326e-02 3.25615138e-01 -1.06237233e-01 -2.67108291e-01
-1.20281315e+00 -6.27843559e-01 9.91993845e-01 3.98118079e-01
-3.77936184e-01 8.82272720e-01 6.53654218e-01 -3.48154396e-01
1.13883786e-01 -7.32015073e-01 -3.19456398e-01 -4.97051775e-01
3.79943192e-01 8.29729378e-01 2.81543285e-01 -3.02867889... | [11.823302268981934, 9.014461517333984] |
27f8be08-8f3a-42f5-825b-b5504f34c325 | knowledge-guided-representation-learning-and | 2306.09302 | null | https://arxiv.org/abs/2306.09302v1 | https://arxiv.org/pdf/2306.09302v1.pdf | Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science | An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different soil processes that limit our understanding of soil. Process-based models and analyzing observed data provide two avenues for improving our ... | ['Ranveer Chandra', 'Emre Kiciman', 'John Crawford', 'Robert Ness', 'Andy Neal', 'Rishabh Tushir', 'Licheng Liu', 'Swati Sharma', 'Somya Sharma'] | 2023-06-15 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 5.93092203e-01 3.40494782e-01 -5.84677756e-01 -5.58118485e-02
-1.96905226e-01 -4.47322339e-01 8.32134724e-01 6.39386714e-01
2.09632978e-01 1.16464174e+00 6.38910532e-01 -8.24584484e-01
-5.91839075e-01 -1.69351113e+00 -1.18776345e+00 -6.93725228e-01
-5.33033729e-01 3.06027770e-01 2.32271090e-01 -2.32370794... | [7.822916030883789, 5.198968887329102] |
362c5c73-6ab2-48a0-8d0b-445c5ce4c7d4 | beyond-chemical-language-a-multimodal | 2306.14919 | null | https://arxiv.org/abs/2306.14919v1 | https://arxiv.org/pdf/2306.14919v1.pdf | Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction | We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection method that identifies physicochemical features based on their direct ... | ['Dmitry Zubarev', 'Kristin Schmidt', 'Dan Sanders', 'Renato Cerqueira', 'Karen Fiorela Aquino Gutierrez', 'Emilio Vital Brazil', 'Eduardo Soares'] | 2023-06-22 | null | null | null | null | ['property-prediction', 'molecular-property-prediction'] | ['medical', 'miscellaneous'] | [ 5.55273294e-01 -5.01859844e-01 -4.88471150e-01 7.67448321e-02
-6.84186876e-01 -6.52877748e-01 5.47334313e-01 1.09159553e+00
-2.17325091e-01 8.91272247e-01 4.08491552e-01 -4.78319377e-01
-6.20272994e-01 -9.37180579e-01 -7.59797275e-01 -1.18341756e+00
-4.27577555e-01 -6.84736818e-02 -8.50003436e-02 -9.99406800... | [5.057693958282471, 5.869029521942139] |
ed282fb4-5177-4857-a779-43cecf44e78f | dspdet3d-dynamic-spatial-pruning-for-3d-small | 2305.03716 | null | https://arxiv.org/abs/2305.03716v2 | https://arxiv.org/pdf/2305.03716v2.pdf | DSPDet3D: Dynamic Spatial Pruning for 3D Small Object Detection | Fine-grained 3D object detection is a core ability for agents to understand their 3D environment and interact with surrounding objects. However, current methods and benchmarks mainly focus on relatively large stuff. 3D object detectors still struggle on small objects due to weak geometric information. With in-depth stu... | ['Jiwen Lu', 'Jie zhou', 'Hongmin Liu', 'Ziwei Wang', 'Zhihao Sun', 'Xiuwei Xu'] | 2023-05-05 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-2.06224144e-01 -2.81068474e-01 2.15476066e-01 -1.08488396e-01
-2.34337181e-01 -4.70527440e-01 5.15411019e-01 6.96786568e-02
-5.30184150e-01 1.19482286e-01 -9.93387252e-02 -3.27517748e-01
2.38410920e-01 -1.03977704e+00 -8.59154642e-01 -3.98512691e-01
-2.52341151e-01 7.56811142e-01 1.03882194e+00 -1.31075472... | [7.735630035400391, -2.584165573120117] |
cc24d388-1648-4fb2-9f25-c5e43024de00 | vernacular-search-query-translation-with | 2208.03711 | null | https://arxiv.org/abs/2208.03711v1 | https://arxiv.org/pdf/2208.03711v1.pdf | Vernacular Search Query Translation with Unsupervised Domain Adaptation | With the democratization of e-commerce platforms, an increasingly diversified user base is opting to shop online. To provide a comfortable and reliable shopping experience, it's important to enable users to interact with the platform in the language of their choice. An accurate query translation is essential for Cross-... | ['Nikesh Garera', 'Mandar Kulkarni'] | 2022-08-07 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.73542613e-01 -4.81868058e-01 -5.39070845e-01 -5.37444711e-01
-1.66624713e+00 -1.04724789e+00 4.13501531e-01 -8.47364366e-02
-8.15141022e-01 6.34529650e-01 1.34907097e-01 -6.02219701e-01
3.37629318e-01 -5.43092370e-01 -6.05853677e-01 -1.39365613e-01
5.83180249e-01 1.05663037e+00 2.16992751e-01 -9.40262794... | [11.561062812805176, 10.045476913452148] |
34b5d72a-64b9-47e0-95a2-72e460bb7cae | an-end-to-end-strategy-for-recovering-a-free | 2305.16845 | null | https://arxiv.org/abs/2305.16845v1 | https://arxiv.org/pdf/2305.16845v1.pdf | An end-to-end strategy for recovering a free-form potential from a snapshot of stellar coordinates | New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning. Here we present an end-to-end strategy for recovering a free-form analytical potential from a mere snapshot of stellar posi... | ['Foivos I. Diakogiannis', 'Rodrigo Ibata', 'Wassim Tenachi'] | 2023-05-26 | null | null | null | null | ['symbolic-regression'] | ['knowledge-base'] | [-1.31849468e-01 1.41765684e-01 8.52296650e-02 -1.59360111e-01
-2.36661881e-01 -6.42645001e-01 9.52478409e-01 -1.98906541e-01
-1.61587528e-03 7.71785855e-01 -3.04605484e-01 -6.03739381e-01
-1.38798431e-01 -8.35720420e-01 -7.53713429e-01 -9.51062799e-01
-1.37425318e-01 7.15957284e-01 -4.19680448e-03 -3.63107502... | [6.591766834259033, 3.498807668685913] |
95eecb1c-b77d-4d7c-a531-cec114c23b01 | iterative-refinement-graph-neural-network-for-1 | 2110.04624 | null | https://arxiv.org/abs/2110.04624v3 | https://arxiv.org/pdf/2110.04624v3.pdf | Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design | Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the... | ['Tommi Jaakkola', 'Regina Barzilay', 'Jeremy Wohlwend', 'Wengong Jin'] | 2021-10-09 | iterative-refinement-graph-neural-network-for | https://openreview.net/forum?id=LI2bhrE_2A | https://openreview.net/pdf?id=LI2bhrE_2A | iclr-2022-4 | ['protein-design'] | ['medical'] | [ 4.04485017e-01 -7.31869647e-03 -2.00413510e-01 -4.82904196e-01
-4.11050439e-01 -1.12838781e+00 2.54824519e-01 -3.78503241e-02
-1.27632409e-01 1.14883983e+00 3.97610664e-01 -9.02031243e-01
2.34329030e-01 -5.52847266e-01 -9.53191817e-01 -8.18882287e-01
-1.84866950e-01 9.90338385e-01 -3.61855025e-04 -3.31118971... | [4.774374008178711, 5.5745368003845215] |
8aa820d7-1a9e-4c7f-93d8-ca8898456c27 | gsrformer-grounded-situation-recognition | 2208.08965 | null | https://arxiv.org/abs/2208.08965v4 | https://arxiv.org/pdf/2208.08965v4.pdf | GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement | Grounded Situation Recognition (GSR) aims to generate structured semantic summaries of images for "human-like" event understanding. Specifically, GSR task not only detects the salient activity verb (e.g. buying), but also predicts all corresponding semantic roles (e.g. agent and goods). Inspired by object detection and... | ['Alexander G. Hauptmann', 'Teruko Mitamura', 'SiYao Li', 'Qi Dai', 'Zhi-Qi Cheng'] | 2022-08-18 | null | null | null | null | ['grounded-situation-recognition'] | ['computer-vision'] | [ 7.08274901e-01 2.63036102e-01 -1.34901568e-01 -5.90444028e-01
-3.90515268e-01 -4.85394478e-01 7.79400706e-01 2.88087279e-01
-4.47585344e-01 5.36471188e-01 4.47039276e-01 -6.99200332e-02
-6.22683065e-03 -9.00164664e-01 -8.13312650e-01 -5.95432162e-01
3.02334696e-01 3.80326509e-01 3.35248053e-01 -2.84733832... | [10.114749908447266, 1.147128701210022] |
6a643a70-9449-4ba8-a5d9-e734edfcd4c4 | changer-feature-interaction-is-what-you-need | 2209.08290 | null | https://arxiv.org/abs/2209.08290v1 | https://arxiv.org/pdf/2209.08290v1.pdf | Changer: Feature Interaction is What You Need for Change Detection | Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts "where" the change has occurred. Different from other dense prediction tasks, a meaningful consideration for change detection is the interaction between bi-temporal features. With this motiv... | ['Zhe Li', 'Kaiyu Li', 'Sheng Fang'] | 2022-09-17 | null | null | null | null | ['building-change-detection-for-remote-sensing'] | ['miscellaneous'] | [-1.04618989e-01 -4.72577631e-01 2.82869011e-01 -6.63559020e-01
-2.45684475e-01 -6.58947289e-01 1.09521282e+00 1.29548982e-01
-4.73749608e-01 4.44733411e-01 2.01534718e-01 -1.66628912e-01
-2.11714029e-01 -1.05574834e+00 -5.19237041e-01 -7.04904795e-01
-2.97125727e-01 2.24549044e-02 4.53117996e-01 -6.75187647... | [9.703120231628418, -1.2552931308746338] |
39accfca-1187-40df-b63c-b6ba92e0e55a | how-to-guide-your-learner-imitation-learning | 2303.02073 | null | https://arxiv.org/abs/2303.02073v1 | https://arxiv.org/pdf/2303.02073v1.pdf | How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement | Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from compounding error, low sample efficiency, and high hyper-parameter sensitivity. In contrast, active imitation learning methods solicit exper... | ['Yang Yu', 'Zongzhang Zhang', 'Ruifeng Chen', 'Shengyi Jiang', 'Tianyuan Liu', 'Xinyu Zhang', 'Feng Xu', 'Xu-Hui Liu'] | 2023-03-03 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.26151526e-01 4.51591879e-01 -6.37490809e-01 8.38615671e-02
-7.75421858e-01 -5.64397037e-01 3.03598136e-01 -1.74397483e-01
-6.90477192e-01 9.01487947e-01 -4.11550999e-01 -2.89294183e-01
-3.96760195e-01 -4.83137071e-01 -8.50766003e-01 -8.60828340e-01
2.41461605e-01 4.38033998e-01 3.53933126e-01 -6.09404966... | [4.134297847747803, 2.0021755695343018] |
9af0cbde-315e-4025-a4f3-2590890b49c7 | self-evolution-learning-for-mixup-enhance | 2305.13547 | null | https://arxiv.org/abs/2305.13547v1 | https://arxiv.org/pdf/2305.13547v1.pdf | Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks | Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks. However, most of the mixup methods do not consider the varying degree of learning difficulty in di... | ['DaCheng Tao', 'Dongsheng Li', 'Xin Niu', 'Zhiliang Tian', 'Liang Ding', 'Qihuang Zhong', 'Haoqi Zheng'] | 2023-05-22 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.44741583e-01 2.24346176e-01 -3.68376285e-01 -3.68232191e-01
-3.18817794e-01 -1.88118219e-01 5.19545019e-01 4.93197590e-01
-4.79969323e-01 7.63860524e-01 -7.32237007e-04 -9.17823240e-02
5.83534352e-02 -8.65042090e-01 -1.83948338e-01 -8.20611298e-01
5.74657381e-01 5.09560645e-01 1.63767666e-01 -1.72649264... | [10.454832077026367, 7.49127197265625] |
7733e2e2-568e-4bf1-bc5e-3ee21151768f | document-layout-analysis-via-dynamic-residual | 2104.02874 | null | https://arxiv.org/abs/2104.02874v1 | https://arxiv.org/pdf/2104.02874v1.pdf | Document Layout Analysis via Dynamic Residual Feature Fusion | The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval. However, it is a challenge to build a DLA system because the training data is... | ['Liang He', 'Jing Yang', 'Xiangcheng Du', 'Ziling Hu', 'Xingjiao Wu'] | 2021-04-07 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 8.09303522e-02 -6.50761902e-01 -1.73722319e-02 -3.36759388e-01
-4.35696512e-01 -4.91050899e-01 4.27716315e-01 -2.91664153e-01
-1.64581329e-01 2.16036901e-01 2.55212098e-01 -1.96552038e-01
-4.05548364e-01 -6.49609268e-01 -4.03561264e-01 -6.38111115e-01
4.40401256e-01 4.52889279e-02 1.89876840e-01 1.99797135... | [11.659172058105469, 2.232804298400879] |
250744fd-7d38-4cb8-a4ee-b70734a73c9b | self-supervised-learning-of-geometrically | 1804.01552 | null | http://arxiv.org/abs/1804.01552v1 | http://arxiv.org/pdf/1804.01552v1.pdf | Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection | Self-supervision can dramatically cut back the amount of manually-labelled
data required to train deep neural networks. While self-supervision has usually
been considered for tasks such as image classification, in this paper we aim at
extending it to geometry-oriented tasks such as semantic matching and part
detection.... | ['Diane Larlus', 'Andrea Vedaldi', 'Samuel Albanie', 'David Novotny'] | 2018-04-04 | self-supervised-learning-of-geometrically-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Novotny_Self-Supervised_Learning_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Novotny_Self-Supervised_Learning_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['unsupervised-landmark-detection'] | ['computer-vision'] | [ 6.06528878e-01 5.03305733e-01 -1.76614538e-01 -7.67695129e-01
-6.92056477e-01 -4.22890872e-01 8.80648911e-01 3.12342972e-01
-6.59909308e-01 3.44657421e-01 1.36740297e-01 -1.50534838e-01
8.54173973e-02 -8.88394415e-01 -1.11957252e+00 -5.81230700e-01
1.45276338e-01 6.91362083e-01 3.75507355e-01 -1.76912472... | [9.468605995178223, 1.3080921173095703] |
d39490c4-2457-45d1-935b-31691beda649 | how-to-memorize-a-random-60-bit-string | null | null | https://aclanthology.info/papers/N15-1180/n15-1180 | https://www.aclweb.org/anthology/N15-1180 | How to Memorize a Random 60-Bit String | null | ['Marjan Ghazvininejad', 'Kevin Knight'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['2048'] | ['playing-games'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
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b0982b7c-9aec-48d3-be6e-69342f4963a9 | parsing-entire-discourses-as-very-long | null | null | https://aclanthology.org/Q13-1026 | https://aclanthology.org/Q13-1026.pdf | Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning | Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context o... | ['Mark Johnson', 'Minh-Thang Luong', 'Michael C. Frank'] | 2013-01-01 | null | null | null | tacl-2013-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.93445933e-01 9.10069346e-01 -1.82467937e-01 -7.26912618e-01
-8.10177982e-01 -7.01888621e-01 4.29075181e-01 9.28208888e-01
-4.16828632e-01 6.91883743e-01 6.97071493e-01 -5.10457695e-01
1.97686747e-01 -8.59069645e-01 -9.54964519e-01 -3.21958214e-01
-9.30715259e-03 5.23428261e-01 3.85297924e-01 -3.00367802... | [10.6866455078125, 9.406256675720215] |
4c25b312-22a9-4c69-a613-d1ac2cb158f6 | visual-detection-with-context-for-document | null | null | https://aclanthology.org/D19-1348 | https://aclanthology.org/D19-1348.pdf | Visual Detection with Context for Document Layout Analysis | We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting hi... | ['Shinjae Yoo', 'Carlos Soto'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['document-layout-analysis', 'literature-mining'] | ['computer-vision', 'natural-language-processing'] | [ 1.54441625e-01 -1.72877405e-02 -3.92338604e-01 -2.13873133e-01
-1.22387803e+00 -1.15644026e+00 5.99642873e-01 6.22752905e-01
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-1.49821620e-02 -4.59431797e-01 -9.01999116e-01 -1.86337397e-01
4.38414589e-02 1.70642599e-01 2.08406284e-01 4.79269773... | [11.63255500793457, 2.8112621307373047] |
ee06f84f-0039-4087-9455-1e0234052719 | promptkg-a-prompt-learning-framework-for | 2210.00305 | null | https://arxiv.org/abs/2210.00305v2 | https://arxiv.org/pdf/2210.00305v2.pdf | LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings | Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present. In... | ['Huajun Chen', 'Feiyu Xiong', 'Shumin Deng', 'Bozhong Tian', 'Siyuan Cheng', 'Jintian Zhang', 'Yuqi Zhu', 'Ningyu Zhang', 'Xiaohan Wang', 'Zhoubo Li', 'Xin Xie'] | 2022-10-01 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-0.793565 0.5608073 -0.5951951 0.03425942 -0.38141155 -0.47682914
0.41430163 0.52183646 0.04666169 0.77993083 0.5102051 -0.46707144
-0.20703803 -1.3824457 -0.7334117 -0.25340602 -0.23344518 0.768559
0.29616833 -0.33746204 -0.13246228 0.02527981 -1.0487984 0.08226863
0.9345312 0.90652627 0.41... | [8.864853858947754, 7.963493824005127] |
48a4220d-ee8a-4ae9-8a80-1fd2b50e2be7 | learning-dynamic-word-embeddings-with-drift | 1907.09169 | null | https://arxiv.org/abs/1907.09169v1 | https://arxiv.org/pdf/1907.09169v1.pdf | Learning dynamic word embeddings with drift regularisation | Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the Dynamic Bernoulli Embeddings model to learn dynamic word embeddings, in order to identify notable properties of the model. The comparison i... | ['Alexandre Allauzen', 'Syrielle Montariol'] | 2019-07-22 | null | null | null | null | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-3.93331200e-01 -2.19698980e-01 -4.83587205e-01 -3.41995358e-01
7.33674839e-02 -1.00283897e+00 1.25608277e+00 4.38894004e-01
-1.26741457e+00 4.56930310e-01 8.43677521e-01 -4.29449052e-01
-8.18357840e-02 -6.38161242e-01 1.94695853e-02 -3.29383850e-01
-1.72470674e-01 4.09805864e-01 1.20076753e-01 -4.89967704... | [10.20109748840332, 8.946676254272461] |
c7ca8eab-a8c1-4cdd-a8af-e0270abe517b | better-long-range-dependency-by-bootstrapping | 1905.11978 | null | https://arxiv.org/abs/1905.11978v2 | https://arxiv.org/pdf/1905.11978v2.pdf | Better Long-Range Dependency By Bootstrapping A Mutual Information Regularizer | In this work, we develop a novel regularizer to improve the learning of long-range dependency of sequence data. Applied on language modelling, our regularizer expresses the inductive bias that sequence variables should have high mutual information even though the model might not see abundant observations for complex lo... | ['Peng Xu', 'Yanshuai Cao'] | 2019-05-28 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 2.73095369e-01 4.83013421e-01 -4.48610127e-01 -5.38372517e-01
-7.17846215e-01 -6.74355268e-01 4.19611096e-01 8.87687132e-02
-2.44560152e-01 1.14063668e+00 3.82282913e-01 -5.61938345e-01
-2.86129676e-02 -7.04971313e-01 -8.33473146e-01 -8.13333273e-01
-1.15333319e-01 3.09190124e-01 5.66707458e-04 -1.35816380... | [11.388154983520508, 9.207175254821777] |
219b5e20-2827-4e37-9901-38f99e510e18 | data-augmentation-with-symbolic-to-real-image | 1907.12902 | null | https://arxiv.org/abs/1907.12902v1 | https://arxiv.org/pdf/1907.12902v1.pdf | Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition | Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of ... | ['Matias Valdenegro-Toro', 'Nour Soufi'] | 2019-07-17 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.34343553e-01 1.08171083e-01 -8.03561136e-02 -3.21759135e-01
-5.31495750e-01 -4.41721946e-01 6.79377675e-01 -8.64017189e-01
-2.46254370e-01 1.00115752e+00 -2.32124940e-01 -5.46257913e-01
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2.36807331e-01 4.81776267e-01 3.09241176e-01 -5.52363455... | [8.072647094726562, -0.8077100515365601] |
94f4cd5d-9f6a-4a8b-ac32-dfbc753e59a9 | rating-distributions-and-bayesian-inference | null | null | https://aclanthology.org/W18-2807 | https://aclanthology.org/W18-2807.pdf | Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use | We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second ... | ['Thomas Kluth', 'Holger Schultheis'] | 2018-07-01 | null | null | null | ws-2018-7 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-7.16077909e-02 4.17283326e-01 1.05233140e-01 -7.69654572e-01
-8.17499638e-01 -9.90752637e-01 7.41882026e-01 4.53704834e-01
-5.09846330e-01 3.31434727e-01 3.58864397e-01 -8.53738487e-01
-6.73702180e-01 -9.71561849e-01 -5.16807616e-01 -1.16709536e-02
-2.39547924e-03 4.68500584e-01 3.00509185e-01 -2.34803677... | [9.691948890686035, 7.451372146606445] |
5e661236-7cc9-4ea0-8d12-bb8e0b127bf9 | robust-single-image-reflection-removal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Song_Robust_Single_Image_Reflection_Removal_Against_Adversarial_Attacks_CVPR_2023_paper.pdf | Robust Single Image Reflection Removal Against Adversarial Attacks | This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we firs... | ['Jianfeng Lu', 'Wenqi Ren', 'Zhaoxin Fan', 'Wenhan Luo', 'Kaihao Zhang', 'Zhenyuan Zhang', 'Zhenbo Song'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['reflection-removal'] | ['computer-vision'] | [ 4.36807573e-01 -4.12909955e-01 2.87350535e-01 1.22630410e-01
-1.31074035e+00 -9.77505028e-01 5.45366585e-01 -5.88069618e-01
-6.56106994e-02 4.24684644e-01 3.87799263e-01 -1.82824120e-01
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5.97010814e-02 -4.95439202e-01 4.06149104e-02 -5.36725700... | [5.5446085929870605, 7.970531463623047] |
cf1f6487-f3e7-4f1c-80cd-c119ec037f0f | transducing-sentences-to-syntactic-feature | null | null | https://aclanthology.org/W13-3205 | https://aclanthology.org/W13-3205.pdf | Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to ``Parse''? | null | ["Lorenzo Dell{'}Arciprete", 'Fabio Massimo Zanzotto'] | 2013-08-01 | null | null | null | ws-2013-8 | ['graph-similarity'] | ['graphs'] | [-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... | [-7.379051208496094, 3.688793659210205] |
8aba4ddf-d1c4-4f0a-9260-32c20912d28f | mmd-b-fair-learning-fair-representations-with | 2211.07907 | null | https://arxiv.org/abs/2211.07907v3 | https://arxiv.org/pdf/2211.07907v3.pdf | MMD-B-Fair: Learning Fair Representations with Statistical Testing | We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between representations of different sensitive groups, while preserving information about the target attributes. Mini... | ['Danica J. Sutherland', 'Namrata Deka'] | 2022-11-15 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 4.31698650e-01 5.12079060e-01 -2.79471278e-01 -5.95093846e-01
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-2.04962984e-01 -8.96808386e-01 -9.76600170e-01 -9.53538477e-01
-3.20390671e-01 4.60526258e-01 9.74529088e-02 -6.55844286... | [6.034641265869141, 6.949220180511475] |
400fab34-7401-4b4c-adbb-e37f98d7f09f | smartchoices-augmenting-software-with-learned | 2304.13033 | null | https://arxiv.org/abs/2304.13033v1 | https://arxiv.org/pdf/2304.13033v1.pdf | SmartChoices: Augmenting Software with Learned Implementations | We are living in a golden age of machine learning. Powerful models are being trained to perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying those models in existing software systems remains difficult. In this paper we present SmartCho... | ['Vincent Tjeng', 'Justin Sybrandt', 'Nikhil Sarda', 'Ruoyan Qin', 'Efi Kokiopoulou', 'Tzu-Kuo Huang', 'Emily Donahue', 'Eric Chen', 'Gabor Bartok', 'Daniel Golovin'] | 2023-04-12 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.84276223e-01 1.99055359e-01 -3.35986376e-01 -5.39576292e-01
-3.46463531e-01 -4.37625021e-01 1.48962989e-01 -1.45769820e-01
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5.12892939e-02 -2.86648482e-01 -4.44778949e-01 -2.43244711e-02
8.12592655e-02 1.19581409e-01 3.03687632e-01 -3.41455132... | [8.01183032989502, 7.4305100440979] |
d281e4b4-b13d-4e6b-a3a4-b01586b16cbd | audio-representation-learning-by-distilling | 2302.02845 | null | https://arxiv.org/abs/2302.02845v1 | https://arxiv.org/pdf/2302.02845v1.pdf | Audio Representation Learning by Distilling Video as Privileged Information | Deep audio representation learning using multi-modal audio-visual data often leads to a better performance compared to uni-modal approaches. However, in real-world scenarios both modalities are not always available at the time of inference, leading to performance degradation by models trained for multi-modal inference.... | ['Ali Etemad', 'Amirhossein Hajavi'] | 2023-02-06 | null | null | null | null | ['speaker-recognition', 'speech-emotion-recognition'] | ['speech', 'speech'] | [ 4.18310910e-01 1.78737924e-01 1.11154772e-01 -3.06367010e-01
-1.11116159e+00 -5.53043008e-01 5.80862224e-01 6.29269779e-02
-4.34107214e-01 5.65727413e-01 1.02312081e-01 -1.29793838e-01
1.46464810e-01 -6.59615159e-01 -9.14410174e-01 -9.25649047e-01
2.49017760e-01 5.22650957e-01 9.93203446e-02 1.25710428... | [14.99648666381836, 5.077201843261719] |
6b74ce97-9f6a-4dd8-ad1f-23efa6bdaaf4 | deep-reinforcement-learning-based-optimal-1 | 2304.03489 | null | https://arxiv.org/abs/2304.03489v1 | https://arxiv.org/pdf/2304.03489v1.pdf | Deep Reinforcement Learning Based Optimal Infinite-Horizon Control of Probabilistic Boolean Control Networks | In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs). Compared with the existing literatures, the proposed method is model-free, namely, the system model and the initial states needn't to b... | ['Zheng-Guang Wu', 'Fangfei Li', 'Jingjie Ni'] | 2023-04-07 | null | null | null | null | ['q-learning'] | ['methodology'] | [-2.02037036e-01 1.26312271e-01 -4.11502838e-01 3.60780925e-01
-3.72833401e-01 -2.38072336e-01 1.57349810e-01 -6.42689764e-02
-5.08504868e-01 1.28260183e+00 -2.73738980e-01 -5.21343887e-01
-6.38145506e-01 -1.20170534e+00 -6.26345217e-01 -1.12464476e+00
-1.92241207e-01 4.06205863e-01 5.43355703e-01 -1.74921572... | [4.463194847106934, 2.2165398597717285] |
1ffb21e5-f710-42f9-9ad8-dfafa1ea2635 | describing-image-focused-in-cognitive-and | 2202.05331 | null | https://arxiv.org/abs/2202.05331v2 | https://arxiv.org/pdf/2202.05331v2.pdf | Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs | Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the growth in assistive systems availability, there is a lack of services that support specific tasks, such as understanding the image context presented in ... | ['Michel Melo Silva', 'Fabio Ribeiro Cerqueira', 'Marcos Henrique Fonseca Ribeiro', 'Daniel Louzada Fernandes'] | 2022-02-10 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 1.39600232e-01 3.64473522e-01 2.68394619e-01 -4.31376576e-01
-4.36916143e-01 -3.41815531e-01 5.68238258e-01 1.38000816e-01
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-3.80177423e-02 -3.95739824e-01 -5.47381461e-01 1.42107569e-02
4.01899278e-01 4.02320027e-01 2.28080839e-01 -3.23991239... | [10.878764152526855, 0.8369085192680359] |
d208443d-590d-44e7-8739-682b474e8c2b | thunder-a-fast-coordinate-selection-solver | null | null | http://proceedings.neurips.cc/paper/2020/hash/11348e03e23b137d55d94464250a67a2-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/11348e03e23b137d55d94464250a67a2-Paper.pdf | Thunder: a Fast Coordinate Selection Solver for Sparse Learning | L1 regularization has been broadly employed to pursue model sparsity. Despite the non-smoothness, people have developed efficient algorithms by leveraging the sparsity and convexity of the problems. In this paper, we propose a novel active incremental approach to further improve the efficiency of the solvers. We show t... | ['Ping Li', 'Weijie Zhao', 'Shaogang Ren'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['sparse-learning'] | ['methodology'] | [-1.44049674e-01 5.76674156e-02 -6.81550443e-01 -3.08278669e-02
-7.30697811e-01 -2.00283244e-01 1.81726933e-01 -2.40244523e-01
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-2.54406363e-01 -3.49089772e-01 -5.47837973e-01 -6.92788959e-01
-2.37212554e-01 -5.52704073e-02 -1.32817209e-01 1.23017058... | [6.981679439544678, 4.503215789794922] |
3a092a4d-ba6f-48d9-8f03-fa382f649563 | introducing-rezojdm16k-a-french | null | null | https://aclanthology.org/2022.lrec-1.553 | https://aclanthology.org/2022.lrec-1.553.pdf | Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction | Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge... | ['Mathieu Lafourcade', 'Lawrence Carbon', 'Helene Jacquenet', 'Kevin Cousot', 'Mohammad Javad Saeedizade', 'Waleed Ragheb', 'Mehdi Mirzapour'] | null | null | null | null | lrec-2022-6 | ['knowledge-graph-embedding'] | ['graphs'] | [-3.15212667e-01 5.26435435e-01 -1.00838804e+00 1.08230084e-01
-2.26677433e-01 -8.17363322e-01 8.17643762e-01 5.44002354e-01
-1.86181724e-01 1.12344193e+00 3.23193192e-01 -6.60593450e-01
-6.85431242e-01 -1.31667137e+00 -5.53160548e-01 3.83541584e-02
4.52288464e-02 8.53353441e-01 6.48589253e-01 -4.02634531... | [8.838347434997559, 7.943588733673096] |
d9c0478e-94b6-454c-873a-66ae656df6ba | vision-based-vehicle-speed-estimation-for-its | 2101.06159 | null | https://arxiv.org/abs/2101.06159v2 | https://arxiv.org/pdf/2101.06159v2.pdf | Vision-based Vehicle Speed Estimation: A Survey | The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective me... | ['Iván García Daza', 'Antonio Hernández Martínez', 'David Fernández Llorca'] | 2021-01-15 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [-1.08476639e-01 -4.90358382e-01 -6.50063276e-01 -3.00741732e-01
-3.20496887e-01 -3.39273542e-01 6.65684581e-01 1.47014009e-02
-6.23569787e-01 6.41759992e-01 -3.02535534e-01 -3.67747962e-01
-4.10526991e-01 -1.02191234e+00 -1.15476474e-01 -7.55039036e-01
1.98402584e-01 3.37386400e-01 5.64610481e-01 -7.22779632... | [7.917357921600342, -1.0188539028167725] |
1d131e13-7691-49dc-aad0-08cead05966e | background-matters-enhancing-out-of | 2303.08727 | null | https://arxiv.org/abs/2303.08727v1 | https://arxiv.org/pdf/2303.08727v1.pdf | Background Matters: Enhancing Out-of-distribution Detection with Domain Features | Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-world scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground semantic feat... | ['Chunhua Shen', 'Guansong Pang', 'Choubo Ding'] | 2023-03-15 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 2.57094562e-01 -1.24207832e-01 -5.40871561e-01 -3.50245595e-01
-7.54484117e-01 -7.57125497e-01 8.12084019e-01 -6.30341051e-03
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1.25231966e-02 -8.60025823e-01 -8.26917648e-01 -8.81344259e-01
8.27213284e-03 3.79598975e-01 4.05895472e-01 3.77805531... | [9.362041473388672, 1.6373825073242188] |
bafcfe63-686a-402d-80d2-19bef70adcd0 | hknas-classification-of-hyperspectral-imagery | 2304.11701 | null | https://arxiv.org/abs/2304.11701v1 | https://arxiv.org/pdf/2304.11701v1.pdf | HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search | Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, increasing searching time and restricting model performances. To tackle these issues, in this paper,... | ['DaCheng Tao', 'Liangpei Zhang', 'Bo Du', 'Di Wang'] | 2023-04-23 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.76848704e-01 -5.97005069e-01 -9.90199819e-02 -3.20722997e-01
-1.91947475e-01 -4.76809323e-01 3.00091580e-02 -2.84925878e-01
-5.26951551e-01 3.33019048e-01 -1.44008011e-01 -5.00731230e-01
-5.22604525e-01 -9.39395964e-01 -3.10403824e-01 -1.03364491e+00
1.68807268e-01 -1.96600825e-01 2.45015964e-01 -1.04907371... | [9.987753868103027, -1.575551986694336] |
eb3be988-1770-4d5a-a2da-1b48ba6b5693 | cyber-resilient-automatic-generation-control | 2208.11163 | null | https://arxiv.org/abs/2208.11163v2 | https://arxiv.org/pdf/2208.11163v2.pdf | Cyber-resilient Automatic Generation Control for Systems of AC Microgrids | In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micro-AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory... | ['Marija Ilic', 'Dan Wu', 'Tong Huang'] | 2022-08-23 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-2.44395465e-01 1.64631531e-01 4.56580609e-01 4.69688118e-01
-3.54924560e-01 -1.32616127e+00 6.30835116e-01 2.42254540e-01
3.29688281e-01 7.52778530e-01 -4.47177291e-02 -3.67609441e-01
-4.07694608e-01 -6.80697501e-01 -6.27094328e-01 -1.03483415e+00
-6.58618093e-01 -1.21020228e-01 2.00565532e-01 -3.81430507... | [5.60982084274292, 2.539811849594116] |
25017358-834b-4ffb-a948-b385bbb311b8 | botsim-an-end-to-end-bot-simulation-toolkit | 2211.15916 | null | https://arxiv.org/abs/2211.15916v2 | https://arxiv.org/pdf/2211.15916v2.pdf | BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systems | We introduce BotSIM, a modular, open-source Bot SIMulation environment with dialog generation, user simulation and conversation analytics capabilities. BotSIM aims to serve as a one-stop solution for large-scale data-efficient end-to-end evaluation, diagnosis and remediation of commercial task-oriented dialog (TOD) sys... | ['Steven Hoi', 'Junnan Li', 'Shafiq Joty', 'Guangsen Wang'] | 2022-11-29 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-8.33881199e-01 -3.07331264e-01 -1.54753262e-02 6.54340312e-02
-1.98142499e-01 -8.59852672e-01 5.15581965e-01 -2.47631624e-01
-3.02843720e-01 4.65929776e-01 -1.23636566e-01 -9.23378110e-01
1.67331621e-01 -4.14501756e-01 3.99764925e-01 -3.15224946e-01
2.75973022e-01 8.60120773e-01 7.68358409e-01 -5.71361959... | [12.718998908996582, 7.946619033813477] |
dd1a17e7-6942-4ba1-8676-b6968ac0b7d5 | flag3d-a-3d-fitness-activity-dataset-with | 2212.04638 | null | https://arxiv.org/abs/2212.04638v2 | https://arxiv.org/pdf/2212.04638v2.pdf | FLAG3D: A 3D Fitness Activity Dataset with Language Instruction | With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision. While a variety of new tasks and algorithms have been proposed recently, there are growing hunger for data resources involved in high-quality data, fine-grained labels, and div... | ['Xiu Li', 'Jie zhou', 'Jiwen Lu', 'Yongming Rao', 'Wenxun Dai', 'Bin Yang', 'Aoyang Liu', 'Jinpeng Liu', 'Yansong Tang'] | 2022-12-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_FLAG3D_A_3D_Fitness_Activity_Dataset_With_Language_Instruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-action-generation', 'human-mesh-recovery', 'action-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.54572630e-02 -5.98194063e-01 -2.86412686e-01 4.60033752e-02
-7.52251387e-01 -1.16267405e-01 1.93002313e-01 -3.43005270e-01
-2.72055268e-01 6.28533423e-01 6.67428136e-01 3.29902202e-01
-6.48182184e-02 -5.29304743e-01 -6.97004139e-01 -5.35208106e-01
-1.71254605e-01 4.62675214e-01 5.62648952e-01 -3.80755395... | [7.195827007293701, -0.67093825340271] |
474537bb-2acd-4b11-82cf-b8c5981a3d9c | lggnet-learning-from-local-global-graph | 2105.02786 | null | https://arxiv.org/abs/2105.02786v3 | https://arxiv.org/pdf/2105.02786v3.pdf | LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface | Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-glo... | ['Qiuhao Zeng', 'Chengxuan Tong', 'Cuntai Guan', 'Neethu Robinson', 'Yi Ding'] | 2021-05-05 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-1.12396970e-01 -2.23854795e-01 3.83330643e-01 -1.78500712e-01
1.77766964e-01 -2.27550179e-01 3.66885811e-01 5.20973317e-02
-1.35984734e-01 8.11488986e-01 1.67198792e-01 -1.65601626e-01
-8.58980060e-01 -6.67452753e-01 -5.04203916e-01 -6.82766318e-01
-7.04090655e-01 -3.74784879e-02 -8.66203979e-02 -1.82852313... | [12.949873924255371, 3.51086163520813] |
28ca0aba-92ce-4695-876d-584cb5bea756 | image-processing-using-multi-code-gan-prior | 1912.07116 | null | https://arxiv.org/abs/1912.07116v2 | https://arxiv.org/pdf/1912.07116v2.pdf | Image Processing Using Multi-Code GAN Prior | Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructi... | ['Yujun Shen', 'Bolei Zhou', 'Jinjin Gu'] | 2019-12-15 | image-processing-using-multi-code-gan-prior-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Gu_Image_Processing_Using_Multi-Code_GAN_Prior_CVPR_2020_paper.pdf | cvpr-2020-6 | ['blind-face-restoration'] | ['computer-vision'] | [ 8.66972208e-01 2.10224301e-01 -9.36395079e-02 -2.11634204e-01
-8.63949537e-01 -5.67180693e-01 7.90700316e-01 -6.30491138e-01
7.21267052e-03 8.62502694e-01 3.08055311e-01 -1.01306848e-01
3.58814806e-01 -9.88775074e-01 -1.14627314e+00 -8.79333794e-01
5.79460680e-01 1.49461284e-01 -2.97188282e-01 -6.40481338... | [11.611298561096191, -0.5253480672836304] |
aded787b-ce7f-4450-ade2-8761f81b4c79 | vision-language-models-in-remote-sensing | 2305.05726 | null | https://arxiv.org/abs/2305.05726v1 | https://arxiv.org/pdf/2305.05726v1.pdf | Vision-Language Models in Remote Sensing: Current Progress and Future Trends | The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide us with intelligent solutions that are more similar to human thinking, enabling us to use general artificial intelligence to... | ['Xiao Xiang Zhu', 'Zhenghang Yuan', 'Xiang Li', 'Yuan Hu', 'Congcong Wen'] | 2023-05-09 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 6.87758803e-01 1.42067537e-01 6.72132745e-02 -3.84558439e-01
-4.06686187e-01 -7.73456514e-01 6.37613416e-01 1.85274303e-01
-6.20450377e-02 3.49323988e-01 8.60043839e-02 -8.80471468e-01
-4.91623804e-02 -1.13081098e+00 -5.78828692e-01 -7.45160580e-01
1.47876784e-01 4.93997514e-01 2.86758970e-02 -1.38266250... | [9.70201587677002, -1.1297796964645386] |
838ea1c9-13f8-4cc9-a50b-adb1d00d3b7c | monoloco-monocular-3d-pedestrian-localization | 1906.06059 | null | https://arxiv.org/abs/1906.06059v2 | https://arxiv.org/pdf/1906.06059v2.pdf | MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation | We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss function based on the Laplace distribution. Our architecture is... | ['Sven Kreiss', 'Alexandre Alahi', 'Lorenzo Bertoni'] | 2019-06-14 | monoloco-monocular-3d-pedestrian-localization-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Bertoni_MonoLoco_Monocular_3D_Pedestrian_Localization_and_Uncertainty_Estimation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Bertoni_MonoLoco_Monocular_3D_Pedestrian_Localization_and_Uncertainty_Estimation_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-depth-estimation'] | ['computer-vision'] | [-6.35053590e-02 -9.73440055e-03 -4.23640087e-02 -6.33288980e-01
-1.03127861e+00 -4.57917988e-01 4.84846205e-01 5.24437346e-04
-5.59945464e-01 1.19461167e+00 -5.04624322e-02 -3.59820813e-01
9.08296630e-02 -4.31657344e-01 -1.19749045e+00 -3.03183913e-01
-5.23938119e-01 5.98458529e-01 3.14786881e-02 4.22920972... | [7.1921467781066895, -1.1385163068771362] |
c5625381-7498-4558-bf6e-bf73f4d65928 | achieving-better-kinship-recognition-through | 2006.11739 | null | https://arxiv.org/abs/2006.11739v1 | https://arxiv.org/pdf/2006.11739v1.pdf | Achieving Better Kinship Recognition Through Better Baseline | Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a resu... | ['Andrei Shadrikov'] | 2020-06-21 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 9.15050283e-02 9.94879007e-02 -7.24089891e-02 -1.17004681e+00
-4.15394783e-01 -4.43125337e-01 7.18866944e-01 -3.85411024e-01
-1.24361098e-01 5.56850672e-01 1.34411663e-01 1.11391217e-01
-1.58329293e-01 -5.61593473e-01 -5.91841698e-01 -7.86894083e-01
-3.06228608e-01 7.56286800e-01 2.79311184e-02 -4.09911901... | [13.283685684204102, 0.8207560777664185] |
558511ed-89d0-41a7-b47b-2cef27348ca0 | deep-face-video-inpainting-via-uv-mapping | 2109.00681 | null | https://arxiv.org/abs/2109.00681v2 | https://arxiv.org/pdf/2109.00681v2.pdf | Deep Face Video Inpainting via UV Mapping | This paper addresses the problem of face video inpainting. Existing video inpainting methods target primarily at natural scenes with repetitive patterns. They do not make use of any prior knowledge of the face to help retrieve correspondences for the corrupted face. They therefore only achieve sub-optimal results, part... | ['Kwan-Yee K. Wong', 'GuanYing Chen', 'Chaofeng Chen', 'Zhenfang Chen', 'Wenqi Yang'] | 2021-09-02 | null | null | null | null | ['facial-inpainting', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 1.77511424e-01 -8.79683942e-02 -1.42242268e-01 -4.49830621e-01
-6.16222322e-01 -2.98600852e-01 2.91785717e-01 -7.28700757e-01
-1.72671303e-01 6.41334713e-01 1.52024522e-01 2.71535903e-01
4.50962484e-01 -3.44636947e-01 -9.74226713e-01 -6.17696345e-01
3.16730440e-01 1.96427643e-01 -2.44460732e-01 -6.31007403... | [12.790802955627441, -0.24970608949661255] |
d0535116-d520-4129-8311-3cf2bf029c40 | brain-in-a-vat-on-missing-pieces-towards | 2307.03762 | null | https://arxiv.org/abs/2307.03762v1 | https://arxiv.org/pdf/2307.03762v1.pdf | Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models | In this perspective paper, we first comprehensively review existing evaluations of Large Language Models (LLMs) using both standardized tests and ability-oriented benchmarks. We pinpoint several problems with current evaluation methods that tend to overstate the capabilities of LLMs. We then articulate what artificial ... | ['Song-Chun Zhu', 'Chi Zhang', 'Yuxi Ma'] | 2023-07-07 | null | null | null | null | ['unity'] | ['computer-vision'] | [ 1.18988469e-01 5.43915153e-01 -6.36507198e-02 -1.28153443e-01
-2.87150055e-01 -7.88717151e-01 1.14987671e+00 1.34390011e-01
-3.75805914e-01 5.25015593e-01 6.04172409e-01 -1.19094290e-01
-6.98278010e-01 -9.57952678e-01 -4.67994303e-01 -3.49157989e-01
1.50291011e-01 8.41518223e-01 -1.10208832e-01 -5.03104031... | [9.307820320129395, 6.826945781707764] |
c87135a3-99cf-45b3-a48d-3a123729f8c8 | gkd-generalized-knowledge-distillation-for | 2306.13649 | null | https://arxiv.org/abs/2306.13649v1 | https://arxiv.org/pdf/2306.13649v1.pdf | GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models | Knowledge distillation is commonly used for compressing neural networks to reduce their inference cost and memory footprint. However, current distillation methods for auto-regressive models, such as generative language models (LMs), suffer from two key issues: (1) distribution mismatch between output sequences during t... | ['Olivier Bachem', 'Matthieu Geist', 'Sabela Ramos', 'Piotr Stanczyk', 'Nino Vieillard', 'Rishabh Agarwal'] | 2023-06-23 | null | null | null | null | ['machine-translation', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 3.35307270e-01 2.85980076e-01 -2.71278769e-01 -2.34011158e-01
-8.59999120e-01 -6.34283721e-01 4.57819760e-01 1.31082237e-01
-5.84456027e-01 8.98795426e-01 2.12958902e-01 -7.24282384e-01
7.62156993e-02 -8.64951015e-01 -1.06548774e+00 -6.06298149e-01
3.73610824e-01 9.29730415e-01 -5.71714938e-02 5.67413233... | [11.174793243408203, 8.583617210388184] |
1ace3a0c-1f87-4865-ade9-8be726551b55 | survivalgan-generating-time-to-event-data-for | 2302.12749 | null | https://arxiv.org/abs/2302.12749v1 | https://arxiv.org/pdf/2302.12749v1.pdf | SurvivalGAN: Generating Time-to-Event Data for Survival Analysis | Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data... | ['Mihaela van der Schaar', 'Pietro Lio', 'Fergus Imrie', 'Bogdan Cebere', 'Alexander Norcliffe'] | 2023-02-24 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.32882816e-02 2.47439176e-01 -4.52299386e-01 -3.77814472e-01
-1.13983953e+00 -6.04706287e-01 6.72875285e-01 4.11347210e-01
-1.39286280e-01 1.17705429e+00 6.10759437e-01 -5.51014006e-01
-2.63828307e-01 -8.84871483e-01 -4.73812282e-01 -7.68182814e-01
-3.43211740e-01 7.18897581e-01 -3.87682289e-01 9.73961949... | [7.766798973083496, 5.571503162384033] |
cba72fb7-7c7e-43be-9331-08b76a70403d | ahp-learning-to-negative-sample-for-hyperedge | 2204.06353 | null | https://arxiv.org/abs/2204.06353v2 | https://arxiv.org/pdf/2204.06353v2.pdf | AHP: Learning to Negative Sample for Hyperedge Prediction | Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future or missing hyperedges bears significant implications for many applications ... | ['Kijung Shin', 'Chanyoung Park', 'Seungwoo Lee', 'Hyunjin Hwang'] | 2022-04-13 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 5.97640015e-02 5.31799972e-01 -4.39268023e-01 -1.22290030e-01
-1.93787709e-01 -7.94459760e-01 4.63322699e-01 6.77202269e-02
1.17536359e-01 9.93319809e-01 -7.07477480e-02 -4.31031495e-01
-7.68092871e-02 -1.37596357e+00 -8.38408113e-01 -7.07593501e-01
-1.43958718e-01 5.62791884e-01 1.48787335e-01 -3.42839122... | [7.339439392089844, 6.259860992431641] |
0936d963-e457-4186-b808-33da6791694d | rademacher-random-projections-with-tensor | 2110.13970 | null | https://arxiv.org/abs/2110.13970v3 | https://arxiv.org/pdf/2110.13970v3.pdf | Rademacher Random Projections with Tensor Networks | Random projection (RP) have recently emerged as popular techniques in the machine learning community for their ability in reducing the dimension of very high-dimensional tensors. Following the work in [30], we consider a tensorized random projection relying on Tensor Train (TT) decomposition where each element of the c... | ['Guillaume Rabusseau', 'Beheshteh T. Rakhshan'] | 2021-10-26 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-5.97659200e-02 1.61361292e-01 1.63042799e-01 1.69737577e-01
-5.25160193e-01 -8.30309331e-01 5.58061242e-01 -4.92747873e-01
-2.62210310e-01 4.02746290e-01 7.86348403e-01 -5.95307767e-01
-5.42986333e-01 -5.34516037e-01 -6.61046565e-01 -1.05039740e+00
-4.99786496e-01 8.90450597e-01 -7.20391870e-02 -1.23766497... | [7.08745813369751, 4.764685153961182] |
e6e4c72f-a36e-4c9d-ad0d-b49f75af4262 | improved-automated-lesion-segmentation-in | 2210.07761 | null | https://arxiv.org/abs/2210.07761v1 | https://arxiv.org/pdf/2210.07761v1.pdf | Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation | Numerous oncology indications have extensively quantified metabolically active tumors using positron emission tomography (PET) and computed tomography (CT). F-fluorodeoxyglucose-positron emission tomography (FDG-PET) is frequently utilized in clinical practice and clinical drug research to detect and measure metabolica... | ['Bulat Ibragimov', 'Sepideh Amiri'] | 2022-10-14 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 0.38892177 -0.06699484 -0.6774278 -0.5189342 -1.0396608 -0.57858974
0.24651226 0.18766946 -0.84926 1.0005683 -0.12257627 -0.7884153
-0.01401369 -0.9453487 -0.37602606 -0.8584934 0.16273479 0.8133331
0.2468079 0.171901 -0.12898013 0.766147 -0.7917309 0.37142637
0.6188783 0.8577867 0.47... | [14.795733451843262, -2.5039162635803223] |
00c78745-7a87-4390-8898-a63e8a40f787 | efficient-person-search-an-anchor-free | 2109.00211 | null | https://arxiv.org/abs/2109.00211v1 | https://arxiv.org/pdf/2109.00211v1.pdf | Efficient Person Search: An Anchor-Free Approach | Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing to the ROI-Align operation, this pipeline yields promising accuracy as re-id features a... | ['Xiaokang Yang', 'Shengcai Liao', 'Jie Qin', 'Jinpeng Li', 'Yichao Yan'] | 2021-09-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.17713404e-01 -3.61729294e-01 -1.37290552e-01 -2.56291091e-01
-7.47081935e-01 -3.70665878e-01 6.64515555e-01 -1.08279191e-01
-6.50044739e-01 2.61799604e-01 3.64788771e-01 3.78559440e-01
-5.45356274e-02 -5.20831525e-01 -4.69193369e-01 -4.01509285e-01
9.27284360e-02 3.90824914e-01 3.07437867e-01 -3.31876092... | [14.787038803100586, 0.8036001324653625] |
f332bd38-1208-4033-b473-4d5287296cce | resolving-spatial-time-conflicts-in-a-set-of | 1608.02763 | null | http://arxiv.org/abs/1608.02763v1 | http://arxiv.org/pdf/1608.02763v1.pdf | Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths | We study the multi-agent path finding problem (MAPF) for a group of agents
which are allowed to move into arbitrary directions on a 2D square grid. We
focus on centralized conflict resolution for independently computed plans. We
propose an algorithm that eliminates conflicts by using local re-planning and
introducing t... | ['Konstantin Yakovlev', 'Anton Andreychuk'] | 2016-08-09 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.52285931e-02 2.68141091e-01 -3.85160595e-02 7.47217163e-02
-6.81222856e-01 -7.66948819e-01 5.45202136e-01 4.58663046e-01
-7.08672822e-01 1.53532696e+00 2.04787366e-02 -1.79709822e-01
-7.28760540e-01 -1.16850269e+00 -1.56254932e-01 -5.24924874e-01
-8.97135317e-01 1.37690723e+00 8.87916327e-01 -4.45118994... | [4.986667156219482, 1.7268034219741821] |
2652486a-116b-448a-886d-226b3b86f434 | one-shot-domain-adaptation-for-person-re | 1811.10144 | null | https://arxiv.org/abs/1811.10144v3 | https://arxiv.org/pdf/1811.10144v3.pdf | Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification | Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in the samples from the target domain for learning to conduct person re-ID in an unsupervised manner. Concretely, we propose a Self-similarity Gr... | ['Yuqian Zhou', 'Yunchao Wei', 'Honghui Shi', 'Guanshuo Wang', 'Yang Fu', 'Thomas Huang'] | 2018-11-26 | self-similarity-grouping-a-simple | http://openaccess.thecvf.com/content_ICCV_2019/html/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Fu_Self-Similarity_Grouping_A_Simple_Unsupervised_Cross_Domain_Adaptation_Approach_for_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.06107134e-03 -4.19158153e-02 -1.24871828e-01 -4.41571981e-01
-5.78655958e-01 -6.61618471e-01 8.15867901e-01 -1.36000523e-02
-4.43520129e-01 6.49473548e-01 2.87857622e-01 2.46057644e-01
1.25902385e-01 -4.69026893e-01 -5.05150259e-01 -5.52299917e-01
2.22808525e-01 7.82773316e-01 1.19156636e-01 -9.74375382... | [14.817155838012695, 1.105860710144043] |
f8135c70-3aec-4c5b-baf6-ce1cc8f6f92a | pp-yolov2-a-practical-object-detector | 2104.10419 | null | https://arxiv.org/abs/2104.10419v1 | https://arxiv.org/pdf/2104.10419v1.pdf | PP-YOLOv2: A Practical Object Detector | Being effective and efficient is essential to an object detector for practical use. To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged. This paper will analyze a collection of refinements and empir... | ['Osamu Yoshie', 'Yanjun Ma', 'dianhai yu', 'Xiaoguang Hu', 'Qiwen Liu', 'Shumin Han', 'Qingqing Dang', 'Kaipeng Deng', 'Xiang Long', 'Xiaying Bai', 'Wenyu Lv', 'Xinxin Wang', 'Xin Huang'] | 2021-04-21 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-5.37196994e-01 -1.33364454e-01 -1.25495449e-01 -1.58452764e-01
-7.65225708e-01 -3.62576693e-01 3.24788928e-01 -1.10865682e-01
-6.53044403e-01 2.80049235e-01 -3.59588891e-01 -4.44658905e-01
5.38405001e-01 -6.54020965e-01 -8.69443119e-01 -4.30909693e-01
1.35480165e-01 1.11041032e-01 9.22004938e-01 6.76639527... | [8.709386825561523, -0.14886309206485748] |
6253219d-d97e-447c-8055-f2e8bfed1d11 | on-approximate-nearest-neighbour-selection | 2108.11480 | null | https://arxiv.org/abs/2108.11480v1 | https://arxiv.org/pdf/2108.11480v1.pdf | On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval | Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represe... | ['Nicola Tonellotto', 'Craig Macdonald'] | 2021-08-25 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-2.41685122e-01 -3.23118031e-01 -1.86396122e-01 -1.24723896e-01
-1.19827604e+00 -7.84975648e-01 8.70389402e-01 9.80386138e-01
-8.41021001e-01 5.75963914e-01 5.11872709e-01 -1.02154255e-01
-6.14777148e-01 -7.77504206e-01 -2.28378922e-01 -4.75987464e-01
-2.17860550e-01 7.53456652e-01 4.76572037e-01 -1.82274491... | [11.453065872192383, 7.6040120124816895] |
0b867a97-8792-488e-8385-81ee5bffb4ae | a-self-reasoning-framework-for-anomaly | 2008.11887 | null | https://arxiv.org/abs/2008.11887v1 | https://arxiv.org/pdf/2008.11887v1.pdf | A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels | Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations of anomalous events, obtaining video-level annotations is quite fast and cheap though such high-level labels may contain significant noise.... | ['Seung-Ik Lee', 'Arif Mahmood', 'Hochul Shin', 'Muhammad Zaigham Zaheer'] | 2020-08-27 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 2.85485983e-01 -1.90793350e-01 1.86014563e-01 -4.34722424e-01
-3.14277440e-01 -2.53903151e-01 4.89711732e-01 4.01990980e-01
-3.84992033e-01 4.52394366e-01 1.05761349e-01 -2.08426356e-01
2.03197211e-01 -5.33509374e-01 -8.51708531e-01 -7.76068032e-01
-2.96743572e-01 -7.32409135e-02 5.44021726e-01 2.03431547... | [7.8534064292907715, 1.5488550662994385] |
3bcb1410-a6ad-40aa-871b-f7811aed6e5b | generation-guided-multi-level-unified-network | 2303.07748 | null | https://arxiv.org/abs/2303.07748v1 | https://arxiv.org/pdf/2303.07748v1.pdf | Generation-Guided Multi-Level Unified Network for Video Grounding | Video grounding aims to locate the timestamps best matching the query description within an untrimmed video. Prevalent methods can be divided into moment-level and clip-level frameworks. Moment-level approaches directly predict the probability of each transient moment to be the boundary in a global perspective, and the... | ['Fan Yang', 'Hezheng Lin', 'Dong Shen', 'Xiangyu Wu', 'Xing Cheng'] | 2023-03-14 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 1.27049275e-02 -4.40361261e-01 -6.26940370e-01 -1.75927445e-01
-1.20518863e+00 -3.29165012e-01 5.96474767e-01 2.91480720e-01
-2.26442024e-01 5.78446388e-01 3.67182672e-01 1.57953113e-01
-1.68540418e-01 -8.74533534e-01 -6.91093028e-01 -6.47402823e-01
-2.52846897e-01 1.46397457e-01 7.83502638e-01 -2.37592697... | [9.897262573242188, 0.5823878049850464] |
5799e1a6-be83-4795-b3e0-7dfe46df7e70 | span-based-joint-entity-and-relation-1 | null | null | https://aclanthology.org/2020.coling-main.8 | https://aclanthology.org/2020.coling-main.8.pdf | Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations | Span-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span semantic representations are shared in both entity recognition and relation extraction, while existing mod... | ['Huijun Liu', 'Yusong Tan', 'Qingbo Wu', 'Jun Ma', 'Shasha Li', 'Jie Yu', 'Bin Ji'] | 2020-12-01 | null | null | null | coling-2020-8 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-9.64505449e-02 3.78719240e-01 -6.72923625e-01 -4.64427918e-01
-8.66839051e-01 -2.86532938e-01 5.85856199e-01 5.85733831e-01
-3.03370297e-01 1.19918990e+00 7.19260991e-01 -7.37933666e-02
-4.37609144e-02 -1.33550525e+00 -6.38408840e-01 2.49362871e-01
-9.13052857e-02 7.78217852e-01 3.22163522e-01 -2.75486946... | [9.344921112060547, 8.679740905761719] |
5d9b7c7c-df01-4def-9032-2254f7c4fc0c | clta-contents-and-length-based-temporal | 2103.10567 | null | https://arxiv.org/abs/2103.10567v1 | https://arxiv.org/pdf/2103.10567v1.pdf | CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition | Few-shot action recognition has attracted increasing attention due to the difficulty in acquiring the properly labelled training samples. Current works have shown that preserving spatial information and comparing video descriptors are crucial for few-shot action recognition. However, the importance of preserving tempor... | ['Wenbo He', 'Yangdi Lu', 'Yang Bo'] | 2021-03-18 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.81747210e-01 -4.05961931e-01 -4.24017847e-01 -2.38727614e-01
-8.43241632e-01 -7.54694194e-02 7.29670644e-01 -3.25864255e-01
-5.54939687e-01 4.46542054e-01 2.47284248e-01 3.08802873e-01
-2.37779841e-01 -2.22867072e-01 -7.18244731e-01 -9.93595898e-01
-1.95931450e-01 3.75793837e-02 4.99785244e-01 3.48893888... | [8.366703033447266, 0.6829637885093689] |
3fa21ff2-eec9-4c1a-a6f6-e900beb6d6bb | language-agnostic-representation-from | null | null | https://aclanthology.org/2021.emnlp-main.612 | https://aclanthology.org/2021.emnlp-main.612.pdf | Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation | We propose a method to distill a language-agnostic meaning embedding from a multilingual sentence encoder. By removing language-specific information from the original embedding, we retrieve an embedding that fully represents the sentence’s meaning. The proposed method relies only on parallel corpora without any human a... | ['Makoto Onizuka', 'Yuki Arase', 'Tomoyuki Kajiwara', 'Nattapong Tiyajamorn'] | null | null | null | null | emnlp-2021-11 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [-2.45809625e-03 -1.48557112e-01 -2.35642970e-01 -5.98658919e-01
-1.48846734e+00 -8.83800089e-01 6.25686049e-01 7.49642670e-01
-8.32484365e-01 7.88401902e-01 6.30167365e-01 -3.49054933e-01
3.19728583e-01 -6.71729684e-01 -7.92772055e-01 -3.27029139e-01
3.20640922e-01 3.28306019e-01 -1.98448360e-01 -5.10603726... | [11.066627502441406, 9.847050666809082] |
f9c17d92-5ea6-4537-b8dc-130232db6315 | ellipse-r-cnn-learning-to-infer-elliptical | 2001.11584 | null | https://arxiv.org/abs/2001.11584v2 | https://arxiv.org/pdf/2001.11584v2.pdf | Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion | Images of heavily occluded objects in cluttered scenes, such as fruit clusters in trees, are hard to segment. To further retrieve the 3D size and 6D pose of each individual object in such cases, bounding boxes are not reliable from multiple views since only a little portion of the object's geometry is captured. We intr... | ['Volkan Isler', 'Cheng Peng', 'Pravakar Roy', 'Wenbo Dong'] | 2020-01-30 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-5.57966642e-02 3.81120592e-01 1.27855897e-01 -1.99593797e-01
-4.57683116e-01 -8.37221444e-01 1.68157116e-01 -7.25586638e-02
-4.13726233e-02 1.64740637e-01 -3.81127208e-01 -2.49778330e-01
7.54403323e-02 -4.44581300e-01 -9.71453726e-01 -4.19664323e-01
-7.14190464e-05 7.97553003e-01 5.43654799e-01 3.42171133... | [7.488414764404297, -2.6472692489624023] |
8d717434-f222-4247-ac20-c793f30d917f | survey-image-mixing-and-deleting-for-data | 2106.07085 | null | https://arxiv.org/abs/2106.07085v4 | https://arxiv.org/pdf/2106.07085v4.pdf | Survey: Image Mixing and Deleting for Data Augmentation | Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting and enhance their generalization and performance, various methods have been suggested in the literature, including dropout, regularization, label smoothing, etc. One such method is augmentation which introduces different types ... | ['Ajmal Mian', 'Kashif Javed', 'Munawar Hayat', 'Saeed Anwar', 'Humza Naveed'] | 2021-06-13 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.96137410e-01 8.55188295e-02 -2.63859183e-01 -5.15651584e-01
-1.08431280e-02 -2.54250526e-01 3.60384375e-01 8.05719048e-02
-5.66552877e-01 6.29253924e-01 5.39678372e-02 -4.42687832e-02
1.11826502e-01 -5.81607401e-01 -7.13634253e-01 -1.06090665e+00
4.34231311e-02 -1.26631811e-01 -8.10745060e-02 1.95393890... | [9.39210033416748, 2.0842912197113037] |
7a7f6d7e-bbc3-4c9f-9249-097e1667fd19 | multi-scale-deformable-alignment-and-content | 2306.16544 | null | https://arxiv.org/abs/2306.16544v1 | https://arxiv.org/pdf/2306.16544v1.pdf | Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression | The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional ... | ['A. Murat Tekalp', 'O. Ugur Ulas', 'M. Akin Yilmaz'] | 2023-06-28 | null | null | null | null | ['video-compression', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 5.77509642e-01 -1.01903059e-01 -6.81249499e-01 -5.09895205e-01
-8.61297905e-01 -2.00239450e-01 3.78569037e-01 -4.00177479e-01
-1.41426027e-01 4.00785267e-01 8.54853868e-01 5.68131842e-02
-2.45309740e-01 -4.83117819e-01 -8.20727110e-01 -6.33412957e-01
-4.16051745e-01 9.13437232e-02 4.26577330e-01 -1.97871421... | [11.353140830993652, -1.6269787549972534] |
704dd086-76f4-469b-8a33-2e14b3af10fd | zero-shot-learning-for-audio-based-music | 1907.02670 | null | https://arxiv.org/abs/1907.02670v2 | https://arxiv.org/pdf/1907.02670v2.pdf | Zero-shot Learning for Audio-based Music Classification and Tagging | Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels. This intrinsically cannot handle unseen labels such as newly added music genres or semantic words that users arbitrarily choose for music retrieval. Zero-shot learning can address this problem ... | ['Juhan Nam', 'Jiyoung Park', 'Jeong Choi', 'Jongpil Lee'] | 2019-07-05 | null | null | null | null | ['multi-label-zero-shot-learning', 'music-classification'] | ['computer-vision', 'music'] | [ 3.70084316e-01 -1.00760102e-01 -3.57016504e-01 -4.62873906e-01
-1.04288852e+00 -8.21956038e-01 2.70115495e-01 2.06569448e-01
-5.52981973e-01 4.67937112e-01 4.17115718e-01 3.63601089e-01
-5.23027480e-01 -6.57853067e-01 -3.24464262e-01 -7.23283470e-01
-5.21412725e-03 5.97580791e-01 2.15373188e-01 1.22454213... | [15.464028358459473, 5.1002302169799805] |
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