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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9086cbf8-9ee7-4808-a9c3-270a0fdece10 | key-frame-extraction-with-attention-based | 2306.13176 | null | https://arxiv.org/abs/2306.13176v1 | https://arxiv.org/pdf/2306.13176v1.pdf | Key Frame Extraction with Attention Based Deep Neural Networks | Automatic keyframe detection from videos is an exercise in selecting scenes that can best summarize the content for long videos. Providing a summary of the video is an important task to facilitate quick browsing and content summarization. The resulting photos are used for automated works (e.g. summarizing security foot... | ['Senem Tanberk', 'Samed Arslan'] | 2023-06-21 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 2.29031086e-01 -5.13357460e-01 -3.01830441e-01 8.52539092e-02
-8.71460557e-01 -2.65765190e-01 3.07112873e-01 4.14281934e-01
-3.09274107e-01 3.76813620e-01 3.84999335e-01 4.42671776e-01
-1.71147659e-01 -4.53305870e-01 -6.09275043e-01 -9.92171764e-01
-1.19007573e-01 -5.00365309e-02 2.88011104e-01 1.36771366... | [10.227903366088867, 0.49886563420295715] |
b2d47e49-9e16-4714-a7a9-5fbe71a5e829 | the-hci-aspects-of-public-deployment-of | 2306.04765 | null | https://arxiv.org/abs/2306.04765v1 | https://arxiv.org/pdf/2306.04765v1.pdf | The HCI Aspects of Public Deployment of Research Chatbots: A User Study, Design Recommendations, and Open Challenges | Publicly deploying research chatbots is a nuanced topic involving necessary risk-benefit analyses. While there have recently been frequent discussions on whether it is responsible to deploy such models, there has been far less focus on the interaction paradigms and design approaches that the resulting interfaces should... | ['Jason Weston', 'Y-Lan Boureau', 'Melanie Kambadur', 'Moya Chen', 'Mojtaba Komeili', 'Kurt Shuster', 'Benjamin Babcock', 'Giuliano Morse', 'Joshua Lane', 'William Ngan', 'Morteza Behrooz'] | 2023-06-07 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-1.07702911e-01 7.45200753e-01 7.03137666e-02 -2.70199269e-01
-1.88538596e-01 -5.27066588e-01 5.03201067e-01 1.27661362e-01
-3.05787563e-01 4.86550927e-01 7.59993792e-01 -7.18704402e-01
-2.72032231e-01 -2.34502599e-01 -1.84025958e-01 -9.53884199e-02
3.38754267e-01 1.20818086e-01 -1.43937826e-01 -2.57060885... | [12.272294044494629, 7.794983863830566] |
144144b3-5c19-4a6b-8e4b-2b2c8526a090 | modeling-text-with-graph-convolutional | 1802.00985 | null | http://arxiv.org/abs/1802.00985v2 | http://arxiv.org/pdf/1802.00985v2.pdf | Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval | Cross-modal information retrieval aims to find heterogeneous data of various
modalities from a given query of one modality. The main challenge is to map
different modalities into a common semantic space, in which distance between
concepts in different modalities can be well modeled. For cross-modal
information retrieva... | ['Yuhang Lu', 'Jing Yu', 'Li Guo', 'Zengchang Qin', 'Yanbing Liu', 'Weifeng Zhang', 'Jianlong Tan'] | 2018-02-03 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 1.17475711e-01 -1.94383606e-01 -1.86752498e-01 -1.95870683e-01
-1.00664127e+00 -2.87288994e-01 8.31780732e-01 4.44263667e-01
-4.34371412e-01 9.82543081e-02 1.88879520e-01 6.01962656e-02
-3.05643529e-01 -8.41247201e-01 -4.38462436e-01 -5.55710375e-01
2.80728519e-01 3.51735383e-01 7.08388761e-02 -3.15580428... | [10.815652847290039, 1.2604072093963623] |
9a55522b-d7f0-4120-a682-5039339bd707 | a-comparison-of-three-heart-rate-detection | 2101.09144 | null | https://arxiv.org/abs/2101.09144v4 | https://arxiv.org/pdf/2101.09144v4.pdf | A comparison of three heart rate detection algorithms over ballistocardiogram signals | Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging because the signal morphology can vary between and within-subjects. Also, it differs from one sensor to another. Hence, it is essential to evaluate HR detection algorithms across several datasets and under different experimental setups. In t... | ['Bessam Abdulrazak', 'Ibrahim Sadek'] | 2021-01-22 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.55320990e-01 -3.03526133e-01 3.14597607e-01 1.68068763e-02
-4.76211727e-01 -1.99621141e-01 -2.02611297e-01 4.10964526e-03
-4.38328624e-01 8.42601776e-01 -1.23528138e-01 -9.40643251e-02
-1.40635625e-01 -4.83834952e-01 1.63954303e-01 -9.29114759e-01
-3.56715590e-01 -2.02293888e-01 2.06834167e-01 8.80321786... | [13.961009979248047, 3.0244035720825195] |
6e5b9ef8-fd26-4693-8c1a-64e5ebca671f | cvefixes-automated-collection-of | 2107.08760 | null | https://arxiv.org/abs/2107.08760v1 | https://arxiv.org/pdf/2107.08760v1.pdf | CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software | Data-driven research on the automated discovery and repair of security vulnerabilities in source code requires comprehensive datasets of real-life vulnerable code and their fixes. To assist in such research, we propose a method to automatically collect and curate a comprehensive vulnerability dataset from Common Vulner... | ['Leon Moonen', 'Amara Naseer', 'Guru Prasad Bhandari'] | 2021-07-19 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-5.02380610e-01 -2.25359574e-01 -2.19961450e-01 -1.58593491e-01
-8.83374393e-01 -1.32181430e+00 -1.12404600e-01 1.05081546e+00
1.22149229e-01 7.06724375e-02 3.78720701e-01 -8.72586489e-01
-4.43284452e-01 -1.15588701e+00 -5.77113688e-01 2.88891252e-02
-2.10013688e-01 -3.29569578e-01 3.74975801e-01 -3.73993725... | [7.085632801055908, 7.779716968536377] |
d5baf002-923c-42cd-a29f-e9dd494e9513 | a-slot-is-not-built-in-one-utterance-spoken | null | null | https://openreview.net/forum?id=AjLizwlktx | https://openreview.net/pdf?id=AjLizwlktx | A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots | A slot value might be provided segment by segment over multiple-turn interactions in a dialog, especially for some important information such as phone numbers and names. It is a common phenomenon in daily life, but little attention has been paid to it in previous work. To fill the gap, this paper defines a new task nam... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sstod'] | ['natural-language-processing'] | [-3.89403671e-01 3.13779175e-01 -2.42515817e-01 -8.88433814e-01
-4.04568672e-01 -8.75598907e-01 8.63971055e-01 -3.66111398e-01
-3.91234368e-01 1.30318630e+00 7.08488703e-01 -5.14759481e-01
2.16575101e-01 -3.73205662e-01 1.52063802e-01 -3.02590162e-01
2.80116290e-01 1.21634936e+00 7.49388218e-01 -1.01111686... | [12.757564544677734, 7.8600382804870605] |
2608763c-6269-4e7d-b650-07edbaa6201e | learning-discriminative-representations-for | 2011.02120 | null | https://arxiv.org/abs/2011.02120v1 | https://arxiv.org/pdf/2011.02120v1.pdf | Learning Discriminative Representations for Fine-Grained Diabetic Retinopathy Grading | Diabetic retinopathy (DR) is one of the leading causes of blindness. However, no specific symptoms of early DR lead to a delayed diagnosis, which results in disease progression in patients. To determine the disease severity levels, ophthalmologists need to focus on the discriminative parts of the fundus images. In rece... | ['Yupeng Xu', 'Shaorong Xie', 'Zhijie Wen', 'Liyan Ma', 'Li Tian'] | 2020-11-04 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-1.84386298e-02 -3.52966249e-01 -2.00293288e-01 -7.23386168e-01
-5.29755712e-01 -2.91622460e-01 1.99625790e-01 -7.25594312e-02
-3.44353557e-01 6.63210928e-01 3.84321839e-01 -2.29908392e-01
-2.12293997e-01 -6.38037741e-01 -2.14748591e-01 -9.36797976e-01
3.12616050e-01 -1.21797631e-02 9.56894681e-02 2.21332163... | [15.810710906982422, -3.982178211212158] |
d291a2d6-df28-4b83-a7e0-54d11e8d4c84 | few-shot-scene-classification-of-optical | null | null | https://www.webofscience.com/wos/woscc/full-record/WOS:000818847600008 | https://ieeexplore.ieee.org/document/9799778 | Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks | Small data hold big artificial intelligence (AI) potential. As one of the promising small data AI approaches, few-shot learning has the goal to learn a model efficiently that can recognize novel classes with extremely limited training samples. Therefore, it is critical to accumulate useful prior knowledge obtained from... | ['Tiancan)', 'TC (Mei', 'Can) [1] ; Mei', 'C (Li', 'Yu) [1] ; Li', 'Y (Wan', 'Yongjun) [1] ; Wan', 'YJ (Zhang', 'Zhi) [1] ; Zhang', 'Z (Gao', 'Hong) [1] ; Gao', 'H (Ji', 'Ji'] | 2022-07-06 | null | null | null | journal-2022-7 | ['scene-classification'] | ['computer-vision'] | [ 5.74737191e-01 -4.84076925e-02 -2.31910974e-01 -5.15778661e-01
-9.77105737e-01 -2.59301513e-01 3.25107276e-01 -2.29575559e-01
-2.10545152e-01 6.72000706e-01 -1.41614318e-01 -1.54310599e-01
-3.53106141e-01 -9.79691446e-01 -8.07148635e-01 -8.81070256e-01
9.22511145e-02 1.53962806e-01 1.24022178e-02 -1.33097991... | [9.92615032196045, 2.6768438816070557] |
f6ad9a21-7be2-482d-a610-c5d414c04f2d | levin-tree-search-with-context-models | 2305.16945 | null | https://arxiv.org/abs/2305.16945v2 | https://arxiv.org/pdf/2305.16945v2.pdf | Levin Tree Search with Context Models | Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansions before reaching a goal node, depending on the quality of the policy. This guarantee can be used as a loss function, which we call the LTS ... | ['Levi H. S. Lelis', 'Marcus Hutter', 'Laurent Orseau'] | 2023-05-26 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 1.66659907e-01 3.99805784e-01 -6.25979364e-01 1.25750184e-01
-1.13448071e+00 -7.47020662e-01 2.02027470e-01 3.09775949e-01
-6.04156911e-01 8.67816925e-01 8.37421566e-02 -6.80205584e-01
-5.24592459e-01 -7.77809620e-01 -1.17024875e+00 -9.93649006e-01
-4.66233075e-01 8.46868813e-01 2.17117146e-01 -3.91919538... | [5.088741302490234, 3.00252628326416] |
1ecf34b8-06f2-445c-bd33-3515a30be60b | a-tree-transducer-model-for-grammatical-error | null | null | https://aclanthology.org/W13-3606 | https://aclanthology.org/W13-3606.pdf | A Tree Transducer Model for Grammatical Error Correction | null | ['Jan Buys', 'Brink van der Merwe'] | 2013-08-01 | null | null | null | ws-2013-8 | ['grammatical-error-detection'] | ['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... | [-7.478485584259033, 3.7403757572174072] |
b8a86210-f90b-49e2-92d0-4be6ed865ff8 | generative-ai-for-programming-education | 2306.17156 | null | https://arxiv.org/abs/2306.17156v2 | https://arxiv.org/pdf/2306.17156v2.pdf | Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors | Generative AI and large language models hold great promise in enhancing computing education by powering next-generation educational technologies for introductory programming. Recent works have studied these models for different scenarios relevant to programming education; however, these works are limited for several re... | ['Gustavo Soares', 'Adish Singla', 'Rupak Majumdar', 'Tobias Kohn', 'Sumit Gulwani', 'José Cambronero', 'Victor-Alexandru Pădurean', 'Tung Phung'] | 2023-06-29 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-3.69277596e-01 1.66081652e-01 1.55051365e-01 -2.87573010e-01
-6.81404054e-01 -8.17755520e-01 3.78164232e-01 4.48427379e-01
-2.18518674e-01 4.29265797e-01 -2.17556000e-01 -8.28960836e-01
-1.16760932e-01 -1.05105388e+00 -9.31851029e-01 -1.59689620e-01
-2.82935381e-01 6.11750782e-01 7.19097018e-01 -6.04137301... | [9.511303901672363, 7.398095607757568] |
882c38ef-0c3e-455a-923c-ff339e3aae55 | hyperedge2vec-distributed-representations-for | null | null | https://openreview.net/forum?id=rJ5C67-C- | https://openreview.net/pdf?id=rJ5C67-C- | Hyperedge2vec: Distributed Representations for Hyperedges | Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are \enquote{sets of individuals}; \enquote{item sets} in pattern mining are sets; and for various types of ... | ['Ankit Sharma', 'Jaideep Srivastava', 'Shafiq Joty', 'Himanshu Kharkwal'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.30010769e-01 2.28632912e-01 -1.25716865e-01 -2.07804680e-01
1.38496071e-01 -7.32923806e-01 7.76934087e-01 5.59768736e-01
-2.64349520e-01 5.55190802e-01 3.45704108e-01 -3.10674667e-01
-7.17608333e-01 -1.46839893e+00 -7.45253980e-01 -6.16204500e-01
-5.69923222e-01 9.08202291e-01 -3.72029021e-02 -4.13134813... | [7.329822540283203, 6.499244213104248] |
298f98b8-055c-4f9f-aef6-d684c72f3b62 | dynamical-softassign-and-adaptive-parameter | 2208.08233 | null | https://arxiv.org/abs/2208.08233v2 | https://arxiv.org/pdf/2208.08233v2.pdf | Dynamical softassign and adaptive parameter tuning for graph matching | This paper studies a framework, projected fixed-point method, for graph matching. The framework contains a class of popular graph matching algorithms, including graduated assignment (GA), integer projected fixed-point method (IPFP) and doubly stochastic projected fixed-point method (DSPFP). We propose an adaptive strat... | ['Shengxin Zhu', 'Qiang Niu', 'Binrui Shen'] | 2022-08-17 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 2.08509430e-01 1.52064875e-01 -2.39981145e-01 3.94428708e-02
-6.67548478e-01 -4.96439397e-01 6.05121553e-01 1.52923957e-01
-1.13839246e-01 6.41240478e-01 -3.18788379e-01 -3.00156534e-01
-8.50467324e-01 -1.10806429e+00 -9.11411941e-01 -9.00973797e-01
-1.71055317e-01 8.05916131e-01 6.05661750e-01 -3.39967996... | [8.071150779724121, -2.0523314476013184] |
53b7d5c8-3967-4612-a5ef-6a6a9d3a38f0 | leveraging-temporal-context-in-low | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fosco_Leveraging_Temporal_Context_in_Low_Representational_Power_Regimes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fosco_Leveraging_Temporal_Context_in_Low_Representational_Power_Regimes_CVPR_2023_paper.pdf | Leveraging Temporal Context in Low Representational Power Regimes | Computer vision models are excellent at identifying and exploiting regularities in the world. However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with ... | ['Aude Oliva', 'Emilie Josephs', 'SouYoung Jin', 'Camilo L. Fosco'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['action-anticipation', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 5.67940533e-01 -9.64635238e-02 -4.10194993e-01 -3.23785990e-01
-3.76335055e-01 -5.26958704e-01 8.22687566e-01 -1.57373235e-01
-6.23520017e-01 4.18332577e-01 8.62767339e-01 1.30338566e-02
-1.44813016e-01 -1.52723774e-01 -8.19598496e-01 -5.16530752e-01
-3.56611013e-01 -5.62418476e-02 -5.99572808e-02 4.43715639... | [8.283102989196777, 0.5676915049552917] |
772c4364-bcea-4d6c-a2f3-a08afd52be56 | kernel-estimation-from-salient-structure-for | 1212.1073 | null | http://arxiv.org/abs/1212.1073v2 | http://arxiv.org/pdf/1212.1073v2.pdf | Kernel Estimation from Salient Structure for Robust Motion Deblurring | Blind image deblurring algorithms have been improving steadily in the past
years. Most state-of-the-art algorithms, however, still cannot perform
perfectly in challenging cases, especially in large blur setting. In this
paper, we focus on how to estimate a good kernel estimate from a single blurred
image based on the i... | ['Xianfeng GU', 'Zhixun Su', 'Jinshan Pan', 'Risheng Liu'] | 2012-12-05 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.44908899e-01 -6.41494930e-01 4.21192855e-01 -2.07093800e-03
-4.68567520e-01 -3.59109432e-01 2.04360068e-01 -3.68992031e-01
-1.38025671e-01 7.14008093e-01 5.67904174e-01 1.31712809e-01
-4.24070805e-01 -3.27194870e-01 -5.26348054e-01 -9.84138250e-01
1.42328680e-01 -5.74238777e-01 2.85337418e-01 1.79616198... | [11.558659553527832, -2.6910159587860107] |
2352e875-eb7a-487f-950f-829023df687c | bayesian-phylogenetic-cognate-prediction | null | null | https://aclanthology.org/2022.sigtyp-1.8 | https://aclanthology.org/2022.sigtyp-1.8.pdf | Bayesian Phylogenetic Cognate Prediction | In Jäger (2019) a computational framework was defined to start from parallel word lists of related languages and infer the corresponding vocabulary of the shared proto-language. The SIGTYP 2022 Shared Task is closely related. The main difference is that what is to be reconstructed is not the proto-form but an unknown w... | ['Gerhard Jäger'] | null | null | null | null | naacl-sigtyp-2022-7 | ['cognate-prediction'] | ['natural-language-processing'] | [-7.92762041e-02 6.87200278e-02 -1.45959169e-01 -1.89681813e-01
-4.19336736e-01 -7.16744006e-01 1.11052835e+00 9.03310999e-02
-7.98586071e-01 1.10883498e+00 5.46716452e-01 -6.32383585e-01
-3.12959015e-01 -5.99197090e-01 -2.35978886e-01 -2.27602407e-01
1.30339518e-01 7.98802078e-01 5.66679180e-01 -7.48316705... | [10.40386962890625, 10.06480598449707] |
9e02206c-7f8c-4bcd-85f6-0ae045455e7c | leveraging-pre-trained-acoustic-feature | null | null | https://ieeexplore.ieee.org/document/9980083 | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThAM1-3/1570839332.pdf | Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts Tasks | Understanding humans’ emotions is a challenge for computers. Nowadays, research on speech emotion recognition has been conducted progressively. Instead of a speech, affective information may lay on short vocal bursts (i.e., cry when sad). In this study, we evaluated a recent self-supervised learning model to extract ac... | ['Akira Sasou', 'Bagus Tris Atmaja'] | 2022-12-21 | null | null | null | apsipa-2022-12 | ['speech-emotion-recognition'] | ['speech'] | [ 5.19722849e-02 8.19417387e-02 1.45720318e-01 -7.59194076e-01
-4.25340921e-01 -2.83681959e-01 4.77090359e-01 1.35957133e-02
-6.37742639e-01 4.39863563e-01 2.32264102e-01 5.09997867e-02
4.58914697e-01 -2.46011421e-01 -3.33057046e-01 -5.77167690e-01
-1.53884873e-01 -4.09086682e-02 -3.36543135e-02 -2.04686701... | [13.574618339538574, 5.796138286590576] |
5060d52a-38aa-4773-b867-f3f549469bec | towards-automated-melanoma-screening-proper | 1604.04024 | null | http://arxiv.org/abs/1604.04024v3 | http://arxiv.org/pdf/1604.04024v3.pdf | Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results | In this paper we survey, analyze and criticize current art on automated
melanoma screening, reimplementing a baseline technique, and proposing two
novel ones. Melanoma, although highly curable when detected early, ends as one
of the most dangerous types of cancer, due to delayed diagnosis and treatment.
Its incidence i... | ['Eduardo Valle', 'Flávia Vasques Bittencourt', 'Micael Carvalho', 'Sandra Avila', 'Michel Fornaciali'] | 2016-04-14 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 6.10544205e-01 4.92100120e-01 -1.71509370e-01 3.17900665e-02
-9.46989536e-01 -6.03694379e-01 5.71514726e-01 6.90704942e-01
-9.44784045e-01 6.05943561e-01 3.20488960e-01 -5.69421470e-01
-6.59145117e-02 -5.21217644e-01 -2.77591228e-01 -7.63136804e-01
2.13569015e-01 6.23877287e-01 3.66602421e-01 -3.07920814... | [15.605868339538574, -2.9316909313201904] |
075a43fd-620b-4b90-a708-3fe41279accd | clustering-macroeconomic-time-series | 1807.04004 | null | http://arxiv.org/abs/1807.04004v2 | http://arxiv.org/pdf/1807.04004v2.pdf | Clustering Macroeconomic Time Series | The data mining technique of time series clustering is well established in
many fields. However, as an unsupervised learning method, it requires making
choices that are nontrivially influenced by the nature of the data involved.
The aim of this paper is to verify usefulness of the time series clustering
method for macr... | [] | 2018-07-18 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-1.99747354e-01 -4.85275209e-01 -4.22848538e-02 -2.35169366e-01
-2.44502038e-01 -6.47042572e-01 8.81063402e-01 5.59001267e-01
-3.99201691e-01 5.46414435e-01 2.17062503e-01 -7.94102788e-01
-7.79687822e-01 -8.83550942e-01 -7.38785118e-02 -8.00969362e-01
-3.51449192e-01 5.29544711e-01 -1.85222179e-01 -2.59014815... | [7.235757827758789, 3.410902976989746] |
447da6b9-47c1-4e65-bcf1-ac0784ec1934 | uni6d-a-unified-cnn-framework-without | 2203.14531 | null | https://arxiv.org/abs/2203.14531v2 | https://arxiv.org/pdf/2203.14531v2.pdf | Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation | As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use different backbones to extract features for RGB and depth images. They use a 2D CNN for RGB images and a per-pixel point cloud network for dep... | ['Liwei Wu', 'Rui Zhao', 'Ye Zheng', 'Hao Chen', 'Donghai Li', 'Xiaoke Jiang'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Jiang_Uni6D_A_Unified_CNN_Framework_Without_Projection_Breakdown_for_6D_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 1.26542151e-01 1.08427003e-01 -3.17063145e-02 -4.99066472e-01
-5.22396088e-01 -4.89085257e-01 2.17918977e-01 -2.07967237e-01
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3.67587268e-01 -9.22780275e-01 -9.42055762e-01 -6.06652677e-01
3.26882988e-01 1.57064274e-01 3.32290381e-01 -1.06009841... | [7.97607946395874, -2.7779479026794434] |
b1c3733d-cc9c-4fa6-82c8-6d6338a28986 | self-supervised-neural-topic-modeling | null | null | https://aclanthology.org/2021.findings-emnlp.284 | https://aclanthology.org/2021.findings-emnlp.284.pdf | Self-Supervised Neural Topic Modeling | Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level semantic concept. In this paper, we propose a new light-weight Self-Sup... | ['Carsten Eickhoff', 'Martin Jaggi', 'Seyed Ali Bahrainian'] | null | null | null | null | findings-emnlp-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-4.79281694e-02 1.94932714e-01 -4.70711261e-01 -4.90818977e-01
-6.89456403e-01 -9.20494646e-02 9.16723549e-01 6.08133912e-01
-9.46643874e-02 5.23706555e-01 6.66140556e-01 -1.52487420e-02
-3.51692468e-01 -1.01252115e+00 -2.99913228e-01 -8.81842017e-01
-2.67260909e-01 5.99009573e-01 1.13811120e-01 2.49085665... | [10.403322219848633, 6.977249622344971] |
3ea8c976-c580-4390-99f0-ca68975a406c | abstractive-text-summarization-for-resumes | 2306.13315 | null | https://arxiv.org/abs/2306.13315v1 | https://arxiv.org/pdf/2306.13315v1.pdf | Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM | Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recen... | ['Senem Tanberk', 'Aysu Deliahmetoglu', 'Sena Nur Cavsak', 'Öykü Berfin Mercan'] | 2023-06-23 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23360041e-02 1.01302914e-01 -3.22653353e-01 -2.32751906e-01
-7.58386910e-01 -2.78926998e-01 7.38889515e-01 9.74407434e-01
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1.49964377e-01 9.27258730e-02 1.74916163e-02 -6.19998097... | [12.574074745178223, 9.570383071899414] |
81c258b9-478a-4978-9b01-434b366a97db | information-plane-analysis-of-deep-neural | 1909.11396 | null | https://arxiv.org/abs/1909.11396v1 | https://arxiv.org/pdf/1909.11396v1.pdf | Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels | Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization ability. However, it is by no means obvious how to estimate mutual information (MI) between each hidden layer and the input/desired output, ... | ['Michael Kampffmeyer', 'Sigurd Løkse', 'Kristoffer Wickstrøm', 'Shujian Yu', 'Robert Jenssen', 'Jose Principe'] | 2019-09-25 | null | null | null | null | ['information-plane'] | ['methodology'] | [-1.20235030e-02 1.92163572e-01 2.11380683e-02 -7.70130306e-02
9.89772156e-02 -6.31550968e-01 5.07116318e-01 -3.70822139e-02
-5.59202611e-01 5.42601347e-01 -2.94818670e-01 -3.95616770e-01
-6.72982872e-01 -7.76364684e-01 -7.11786628e-01 -9.94312108e-01
-5.11472166e-01 1.77555770e-01 2.63531506e-01 -8.63950048... | [7.923905849456787, 3.6034035682678223] |
d1628128-5eb4-4b04-a5ed-dba8980eaf16 | conquer-contextual-query-aware-ranking-for | 2109.10016 | null | https://arxiv.org/abs/2109.10016v1 | https://arxiv.org/pdf/2109.10016v1.pdf | CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval | This paper tackles a recently proposed Video Corpus Moment Retrieval task. This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel CONtextual QUery-awarE Ranking~(CONQUER) model for effective moment localization ... | ['Wing Kwong Chan', 'Chong-Wah Ngo', 'Zhijian Hou'] | 2021-09-21 | null | null | null | null | ['moment-retrieval', 'corpus-video-moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.32245183e-01 -6.75854087e-01 -5.86922646e-01 -2.17543021e-01
-1.81835604e+00 -6.00292206e-01 7.73889303e-01 2.35120878e-02
-5.60491800e-01 3.49577278e-01 6.52872264e-01 3.91777515e-01
-3.73608410e-01 -2.84960449e-01 -7.03189194e-01 -6.60517812e-01
-2.93510556e-01 1.17957637e-01 3.32022935e-01 -7.70948678... | [10.174355506896973, 0.7909061312675476] |
1df31a27-f755-485b-b1de-a207503fb14a | a-rank-corrected-procedure-for-matrix | 1210.3709 | null | http://arxiv.org/abs/1210.3709v3 | http://arxiv.org/pdf/1210.3709v3.pdf | A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients | For the problems of low-rank matrix completion, the efficiency of the
widely-used nuclear norm technique may be challenged under many circumstances,
especially when certain basis coefficients are fixed, for example, the low-rank
correlation matrix completion in various fields such as the financial market
and the low-ra... | ['Shaohua Pan', 'Defeng Sun', 'Weimin Miao'] | 2012-10-13 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.87217790e-01 1.35078445e-01 -1.42946452e-01 -6.27601072e-02
-1.03846943e+00 -3.53456110e-01 2.45371372e-01 -7.03941807e-02
-4.50605005e-01 1.09231591e+00 1.86113775e-01 -1.68106139e-01
-7.17707813e-01 -5.48500240e-01 -6.24755740e-01 -1.01912212e+00
5.68682998e-02 1.40005589e-01 -3.04421604e-01 -2.31755152... | [6.908297061920166, 4.6042351722717285] |
17f667d7-4709-4957-96ef-25689555c1b0 | stylegan2-distillation-for-feed-forward-image | 2003.03581 | null | https://arxiv.org/abs/2003.03581v2 | https://arxiv.org/pdf/2003.03581v2.pdf | StyleGAN2 Distillation for Feed-forward Image Manipulation | StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was explicitly trained to have disentangled directions in latent space, which allows efficient image manipulation by varying latent factors. Editing existing images requires embedding a given image into the latent space of StyleGAN2. La... | ['Evgeny Kashin', 'Vladimir Ivashkin', 'Yuri Viazovetskyi'] | 2020-03-07 | stylegan2-distillation-for-feed-forward-image-1 | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3992_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670171.pdf | eccv-2020-8 | ['image-morphing'] | ['computer-vision'] | [ 6.26705825e-01 6.02055252e-01 1.33057281e-01 -3.90413672e-01
-5.50077021e-01 -6.58917725e-01 9.88212585e-01 -6.12417400e-01
-2.37282336e-01 8.06209087e-01 1.99532494e-01 -1.70766547e-01
4.68345284e-01 -8.15569580e-01 -9.49511826e-01 -7.09095180e-01
1.39360234e-01 5.37044525e-01 -4.11038280e-01 -1.52811378... | [11.910778045654297, -0.312527596950531] |
aa10e2ee-3180-484f-b0a4-8a896f60fc37 | end-to-end-chinese-landscape-painting | 2011.05552 | null | https://arxiv.org/abs/2011.05552v1 | https://arxiv.org/pdf/2011.05552v1.pdf | End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks | Current GAN-based art generation methods produce unoriginal artwork due to their dependence on conditional input. Here, we propose Sketch-And-Paint GAN (SAPGAN), the first model which generates Chinese landscape paintings from end to end, without conditional input. SAPGAN is composed of two GANs: SketchGAN for generati... | ['Alice Xue'] | 2020-11-11 | null | null | null | null | ['chinese-landscape-painting-generation'] | ['computer-vision'] | [ 7.69013464e-01 4.65943336e-01 3.29621792e-01 -1.83766469e-01
-1.03170061e+00 -9.19216335e-01 1.03010166e+00 -1.07234395e+00
2.69673049e-01 1.23842692e+00 3.83302510e-01 -3.28124404e-01
5.64177871e-01 -1.25242281e+00 -9.73337770e-01 -3.99386704e-01
7.34597564e-01 6.93812966e-01 -3.66601199e-01 -1.54887080... | [11.643706321716309, -0.3147982656955719] |
eeed8cb3-7fc4-4396-b2c6-8e4dc204ce90 | dao-shi-ping-jie-wang-shu-ju-yi-zai-githubbei | null | null | https://kgco.github.io/RateMySupervisor/html/index.html | https://github.com/kgco/RateMySupervisor | 导师评价网数据已在Github备份，可直接检索导师个人信息！ | 永久免费开源的导师评价数据、数据爬虫、无需编程基础的展示网页以及新信息补充平台。从导师评价网获取的原始数据，本平台开源存储数据以备各种你懂得的不可抗力因素。 项目名称：RateMySupervisor，链接： https://github.com/kgco/RateMySupervisor
在线浏览：可以直接访问这个GitHub Pages页面，打开即可在线浏览数据。导师评价信息 https://kgco.github.io/RateMySupervisor/html/index.html 网页前端加载出来之后，要加载一个20MB左右的js数据文件，由于网络原因可能速度比较慢，所以会有一小段时间下拉列表里没有数据，请耐心等待... | ['导师评价网'] | 2020-08-25 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.01411295e+00 -4.14208382e-01 -1.54846027e-01 3.28896344e-01
-1.15597939e+00 -6.90958261e-01 7.78430626e-02 1.42859125e+00
-1.23165540e-01 7.69456089e-01 5.56540310e-01 -2.23837733e-01
-2.11709693e-01 -1.18935001e+00 -7.84280717e-01 -7.98518300e-01
-8.43119562e-01 1.80020773e+00 8.07940006e-01 -7.58795083... | [-3.315948724746704, 6.907647132873535] |
d80da344-65c0-4612-8961-d05de6a968ae | efficient-approach-of-using-cnn-based | 2209.13005 | null | https://arxiv.org/abs/2209.13005v1 | https://arxiv.org/pdf/2209.13005v1.pdf | Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition | Due to digitalization in everyday life, the need for automatically recognizing handwritten digits is increasing. Handwritten digit recognition is essential for numerous applications in various industries. Bengali ranks the fifth largest language in the world with 265 million speakers (Native and non-native combined) an... | ['Md Sakib Ullah Sourav', 'Md Mostak Shaikh', 'Zubaer Haque', 'Jannatul Nayeem', 'Rejwan Bin Sulaiman', 'Musarrat Saberin Nipun', 'Shabbir Ahmed Shuvo', 'Muntarin Islam'] | 2022-09-19 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.79266500e-01 -4.71236885e-01 -9.66278985e-02 -4.06399965e-01
-8.56595412e-02 -5.71751893e-01 5.89528263e-01 -3.23973805e-01
-5.41100979e-01 7.16273487e-01 2.91503131e-01 -6.56835496e-01
1.34870157e-01 -8.61581802e-01 -1.97761104e-01 -4.49173003e-01
3.02105725e-01 4.93593514e-01 2.66837716e-01 -3.10712606... | [11.84997844696045, 2.694119453430176] |
52303221-cb38-4f68-b0a2-7a000c27bf8a | mvt-multi-view-vision-transformer-for-3d | 2110.13083 | null | https://arxiv.org/abs/2110.13083v1 | https://arxiv.org/pdf/2110.13083v1.pdf | MVT: Multi-view Vision Transformer for 3D Object Recognition | Inspired by the great success achieved by CNN in image recognition, view-based methods applied CNNs to model the projected views for 3D object understanding and achieved excellent performance. Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiv... | ['Ping Li', 'Tan Yu', 'Shuo Chen'] | 2021-10-25 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-3.82897079e-01 -2.03788400e-01 -1.77121922e-01 -2.53122300e-01
-3.81474227e-01 -5.55748701e-01 7.13423729e-01 -5.99334598e-01
4.63288337e-01 -1.26428396e-01 3.82485092e-01 -2.40233272e-01
5.94117977e-02 -9.43382680e-01 -1.09217846e+00 -4.66839999e-01
3.17606181e-01 1.37602240e-01 1.60176501e-01 7.78211653... | [8.254111289978027, -3.6521849632263184] |
526bc039-7bb7-4bdd-ae2e-195db2d8bc4c | learning-to-cluster-faces-via-confidence-and | 2004.00445 | null | https://arxiv.org/abs/2004.00445v2 | https://arxiv.org/pdf/2004.00445v2.pdf | Learning to Cluster Faces via Confidence and Connectivity Estimation | Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide range of applications including face annotation and retrieval. Recent works show that supervised clustering can result in noticeable performance gain. However, they usually involve heuristic steps and require numerous overlapped... | ['Rui Zhao', 'Dapeng Chen', 'Lei Yang', 'Chen Change Loy', 'Xiaohang Zhan', 'Dahua Lin'] | 2020-04-01 | learning-to-cluster-faces-via-confidence-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Learning_to_Cluster_Faces_via_Confidence_and_Connectivity_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Learning_to_Cluster_Faces_via_Confidence_and_Connectivity_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['face-clustering', 'connectivity-estimation'] | ['computer-vision', 'graphs'] | [-2.07793966e-01 3.47889364e-02 -4.15961266e-01 -6.51913106e-01
-4.63678271e-01 -4.72954571e-01 2.84649253e-01 -2.07852628e-02
-1.89187694e-02 2.32429013e-01 -6.72516376e-02 5.36536449e-04
-1.81973651e-01 -7.66584754e-01 -5.65940440e-01 -8.02641511e-01
-2.30989262e-01 5.60687065e-01 8.13238695e-02 4.33410645... | [13.464945793151855, 1.0667654275894165] |
e62ca7e3-e7d7-43ea-b217-535082cb14e4 | histogram-of-cell-types-deep-learning-for | 2107.02293 | null | https://arxiv.org/abs/2107.02293v2 | https://arxiv.org/pdf/2107.02293v2.pdf | Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology | Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with high inter-observer variability. This may lead to a delayed or incorrect diagnosis, leav... | ['Clinton JV Campbell', 'Hamid R. Tizhoosh', 'Ronan Foley', 'Monalisa Sur', 'Catherine Ross', 'Taher Dehkharghanian', 'Youqing Mu', 'Rohollah Moosavi Tayebi'] | 2021-07-05 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.30757606e-02 -2.70674229e-01 -2.19156578e-01 2.56150775e-03
-1.22906566e+00 -6.31492376e-01 2.52237111e-01 1.26868606e+00
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1.87506810e-01 -1.09849691e+00 -4.73221168e-02 -1.13633931e+00
1.87623635e-01 1.23019505e+00 4.72826883e-02 5.33940434... | [15.00773811340332, -3.1477739810943604] |
391811f0-d72a-4eac-a7b2-cf3c1e738377 | do-graph-neural-networks-learn-traditional | 2211.09912 | null | https://arxiv.org/abs/2211.09912v1 | https://arxiv.org/pdf/2211.09912v1.pdf | Do graph neural networks learn traditional jet substructure? | At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particl... | ['Javier Duarte', 'Raghav Kansal', 'Farouk Mokhtar'] | 2022-11-17 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.61927426e-01 3.41973573e-01 8.20128247e-02 -3.38551551e-01
-2.17211649e-01 -7.96390355e-01 9.99219358e-01 6.41378045e-01
-3.21377844e-01 5.98167360e-01 2.81525493e-01 -5.30413032e-01
-2.12744102e-01 -8.98419976e-01 -6.52195513e-01 -6.84545636e-01
-4.35821682e-01 1.27982354e+00 6.30477786e-01 1.30728912... | [15.706509590148926, 2.9187138080596924] |
7d84eacb-05f6-4994-899f-81d60cf96974 | protodiff-learning-to-learn-prototypical | 2306.14770 | null | https://arxiv.org/abs/2306.14770v1 | https://arxiv.org/pdf/2306.14770v1.pdf | ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion | Prototype-based meta-learning has emerged as a powerful technique for addressing few-shot learning challenges. However, estimating a deterministic prototype using a simple average function from a limited number of examples remains a fragile process. To overcome this limitation, we introduce ProtoDiff, a novel framework... | ['Cees Snoek', 'Shengcai Liao', 'Zehao Xiao', 'Yingjun Du'] | 2023-06-26 | null | null | null | null | ['meta-learning', 'few-shot-learning'] | ['methodology', 'methodology'] | [ 2.22031489e-01 -3.26020658e-01 -3.47199410e-01 -2.90288091e-01
-8.52774799e-01 -2.48138279e-01 7.23914504e-01 7.48688262e-03
-2.33712733e-01 4.15870070e-01 8.75404850e-03 -1.19899407e-01
-1.01406097e-01 -6.67724967e-01 -5.91566980e-01 -6.02950931e-01
1.19713627e-01 3.47394884e-01 -1.40403407e-02 -3.92292738... | [9.993277549743652, 2.9673967361450195] |
d4d837b8-8914-43af-9b92-1e237f923798 | impact-of-content-features-for-automatic | 1704.03289 | null | http://arxiv.org/abs/1704.03289v1 | http://arxiv.org/pdf/1704.03289v1.pdf | Impact Of Content Features For Automatic Online Abuse Detection | Online communities have gained considerable importance in recent years due to
the increasing number of people connected to the Internet. Moderating user
content in online communities is mainly performed manually, and reducing the
workload through automatic methods is of great financial interest for community
maintainer... | ['Linares Georges LIA', 'Dufour Richard LIA', 'Labatut Vincent LIA', 'Papegnies Etienne LIA'] | 2017-04-11 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 0.21221358 -0.26755 0.20950758 -0.10596668 -0.44514272 -0.68792635
0.67683786 0.67835885 -0.65040433 0.44812712 0.17784023 -0.44429982
0.02727143 -0.7722462 0.10149755 -0.25157046 -0.11107598 0.2451156
0.5518023 -0.53860694 0.7909761 0.38208765 -1.6993729 0.3889654
1.026556 0.72858 0.06... | [8.570852279663086, 10.33877944946289] |
368413c1-e1db-4ba4-b80e-19d3a9ca5587 | cuts-neural-causal-discovery-from-irregular | 2302.07458 | null | https://arxiv.org/abs/2302.07458v1 | https://arxiv.org/pdf/2302.07458v1.pdf | CUTS: Neural Causal Discovery from Irregular Time-Series Data | Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when ... | ['Qionghai Dai', 'Kunlun He', 'Jinli Suo', 'Zongren Li', 'Tingxiong Xiao', 'Runzhao Yang', 'Yuxiao Cheng'] | 2023-02-15 | null | null | null | null | ['causal-discovery', 'irregular-time-series'] | ['knowledge-base', 'time-series'] | [ 3.56383175e-01 4.43880111e-01 -3.85643065e-01 -3.57192785e-01
-2.29711220e-01 -2.52218634e-01 7.42890120e-01 2.32083928e-02
2.95080841e-01 1.13230515e+00 6.48670912e-01 -6.55269921e-01
-5.59698761e-01 -9.72911716e-01 -8.97217512e-01 -6.59794271e-01
-6.80415571e-01 7.32260048e-01 -2.39892215e-01 2.86543727... | [7.841884136199951, 5.2841691970825195] |
554ad404-c9f2-42f5-9925-e4cbdf386984 | identity-preserving-talking-face-generation | 2305.08293 | null | https://arxiv.org/abs/2305.08293v1 | https://arxiv.org/pdf/2305.08293v1.pdf | Identity-Preserving Talking Face Generation with Landmark and Appearance Priors | Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for training or fine-tuning. Existing person-generic methods have difficulty in generating realistic and lip-synced videos while preserving identi... | ['Guanbin Li', 'Liang Lin', 'Gangming Zhao', 'Pengxu Wei', 'Yinqi Cai', 'Chaowei Fang', 'Weizhi Zhong'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhong_Identity-Preserving_Talking_Face_Generation_With_Landmark_and_Appearance_Priors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhong_Identity-Preserving_Talking_Face_Generation_With_Landmark_and_Appearance_Priors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.76649407e-01 -9.12829563e-02 3.10753882e-02 -3.86385441e-01
-8.83037567e-01 -4.54856515e-01 4.72284466e-01 -6.53546393e-01
1.34112924e-01 4.73357856e-01 4.30383086e-01 5.36097944e-01
3.21758509e-01 -5.13594508e-01 -5.43588758e-01 -7.83764243e-01
2.18022645e-01 -4.51641381e-02 -1.21911746e-02 -5.44673316... | [13.230057716369629, -0.40445783734321594] |
3ef8802b-5710-4f49-bddd-1fa6c07433f5 | learning-dynamical-human-joint-affinity-for | 2109.07353 | null | https://arxiv.org/abs/2109.07353v1 | https://arxiv.org/pdf/2109.07353v1.pdf | Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos | Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human skeleton. This may reduce adaptation capacity of GCN to tackle complex spatio-temporal pose variations in videos. To alleviate this problem,... | ['Yu Qiao', 'Zhe Wang', 'Tianyu Luan', 'Zhipeng Zhou', 'Yali Wang', 'Junhao Zhang'] | 2021-09-15 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-4.37998325e-01 -2.64156222e-01 -1.55782923e-01 1.49282049e-02
-8.17053542e-02 -2.44643673e-01 4.55404557e-02 -3.79180223e-01
-4.92399633e-01 3.53523254e-01 3.93703192e-01 1.63383067e-01
-2.36352161e-01 -4.94910181e-01 -7.50337839e-01 -4.99049425e-01
-5.03307104e-01 5.75770140e-01 4.91412848e-01 -2.11381376... | [7.241910457611084, -0.4355258643627167] |
0f398cff-9da0-4b8b-81f2-5c927aeeb02f | prior-based-sampling-for-adaptive-lidar | 2304.07099 | null | https://arxiv.org/abs/2304.07099v1 | https://arxiv.org/pdf/2304.07099v1.pdf | Prior based Sampling for Adaptive LiDAR | We propose SampleDepth, a Convolutional Neural Network (CNN), that is suited for an adaptive LiDAR. Typically,LiDAR sampling strategy is pre-defined, constant and independent of the observed scene. Instead of letting a LiDAR sample the scene in this agnostic fashion, SampleDepth determines, adaptively, where it is best... | ['Shai Avidan', 'Amit Shomer'] | 2023-04-14 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 4.01115030e-01 8.77063349e-02 -1.77114293e-01 -6.64656758e-01
-4.75247651e-01 -3.10791761e-01 5.83733439e-01 1.12869851e-01
-7.59667456e-01 6.25977814e-01 -2.27717414e-01 -1.13721967e-01
-5.20805828e-02 -1.20577884e+00 -7.53060043e-01 -3.98493916e-01
7.98432231e-02 9.86999989e-01 4.28534091e-01 9.16895717... | [8.395977973937988, -2.558716297149658] |
e75be01b-71f7-4f48-96cb-fbec2800d9c7 | improving-response-diversity-through | null | null | https://aclanthology.org/2022.rocling-1.37 | https://aclanthology.org/2022.rocling-1.37.pdf | Improving Response Diversity through Commonsense-Aware Empathetic Response Generation | Due to the lack of conversation practice, the main challenge for the second-language learners is speaking. Our goal is to develop a chatbot to encourage individuals to reflect, describe, analyse and communicate what they read as well as improve students’ English expression skills. In this paper, we exploit COMMET, an i... | ['Chia-Hui Chang', 'Tzu-Hsien Huang'] | null | null | null | null | rocling-2022-11 | ['empathetic-response-generation'] | ['natural-language-processing'] | [-6.91219121e-02 7.23603606e-01 2.07943127e-01 -3.17608446e-01
-7.57625043e-01 -6.66377068e-01 7.00853527e-01 -7.28950649e-02
-2.99566299e-01 1.27854621e+00 7.81641245e-01 -1.39026716e-02
2.56310731e-01 -7.89407611e-01 1.74804516e-02 -3.89743447e-01
8.12854230e-01 4.95791584e-01 2.24693143e-03 -9.86715972... | [13.11286735534668, 7.694706916809082] |
ffffb61a-0ecc-4576-851e-23c433a7b8cf | pygsl-a-graph-structure-learning-toolkit | 2211.03583 | null | https://arxiv.org/abs/2211.03583v1 | https://arxiv.org/pdf/2211.03583v1.pdf | pyGSL: A Graph Structure Learning Toolkit | We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. The implementations are written in GPU-friendly ways, allowing one to scale to much larger network tasks. A common interface is introduced for... | ['Gonzalo Mateos', 'Max Wasserman'] | 2022-11-07 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-3.52842152e-01 9.15369317e-02 -5.67168117e-01 -2.27633864e-01
-4.24945623e-01 -7.59101748e-01 5.26695967e-01 3.80146295e-01
-2.60212123e-01 4.40551937e-01 1.76556751e-01 -6.92320943e-01
6.25265241e-02 -1.09046626e+00 -6.15126073e-01 -4.04528737e-01
-4.29294467e-01 5.13049603e-01 3.69007885e-01 -3.80567089... | [6.976859092712402, 5.923220634460449] |
5cccc5bc-23b1-405d-abe3-5d086acb6b1b | semantically-aware-attentive-neural | 1812.03402 | null | https://arxiv.org/abs/1812.03402v2 | https://arxiv.org/pdf/1812.03402v2.pdf | Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization | We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearance features, the semantic layout of a scene is generally more invariant to appearance variations. We use this intuition and propose a novel ... | ['Han-Pang Chiu', 'Rakesh Kumar', 'Karan Sikka', 'Supun Samarasekera', 'Zachary Seymour'] | 2018-12-08 | null | null | null | null | ['deep-attention', 'image-based-localization', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-1.96420223e-01 -1.48893610e-01 1.84759706e-01 -5.42881787e-01
-8.17888379e-01 -3.09575230e-01 4.65202689e-01 1.88511714e-01
-4.14414555e-01 3.18301678e-01 2.68942297e-01 1.67239960e-02
8.62384811e-02 -4.22323495e-01 -8.53275120e-01 -2.88872242e-01
-4.66976345e-01 -1.08196974e-01 3.58775444e-02 -5.46229109... | [7.942440509796143, -2.087804079055786] |
f47c6013-dd0a-42ce-b4f6-0d785ad6c378 | generative-adversarial-nets-for-multiple-text | 1712.09127 | null | http://arxiv.org/abs/1712.09127v1 | http://arxiv.org/pdf/1712.09127v1.pdf | Generative Adversarial Nets for Multiple Text Corpora | Generative adversarial nets (GANs) have been successfully applied to the
artificial generation of image data. In terms of text data, much has been done
on the artificial generation of natural language from a single corpus. We
consider multiple text corpora as the input data, for which there can be two
applications of G... | ['Baiyang Wang', 'Diego Klabjan'] | 2017-12-25 | null | https://openreview.net/forum?id=BkexaxBKPB | https://openreview.net/pdf?id=BkexaxBKPB | null | ['cross-corpus'] | ['computer-vision'] | [ 5.84499538e-01 3.37786913e-01 1.47571340e-01 -2.00077340e-01
-1.01076424e+00 -5.11039913e-01 1.15712786e+00 -3.48850101e-01
-3.44144613e-01 7.79865444e-01 6.04695857e-01 -2.04202473e-01
5.05860627e-01 -9.95624840e-01 -5.35935879e-01 -6.51915491e-01
2.93666452e-01 6.79389000e-01 -1.46177739e-01 -4.47244525... | [11.901880264282227, 9.40964412689209] |
56ff792a-9328-4ebc-874f-8fe54323a5de | a-hierarchical-neural-network-for-information | null | null | https://aclanthology.org/W16-4403 | https://aclanthology.org/W16-4403.pdf | A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences | This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental res... | ['Hisako Asano', 'Ryuichiro Higashinaka', 'Kugatsu Sadamitsu', 'Yukinori Homma', 'Yoshihiro Matsuo', 'Kyosuke Nishida'] | 2016-12-01 | null | null | null | ws-2016-12 | ['product-recommendation'] | ['miscellaneous'] | [ 5.36095023e-01 -1.66137796e-02 -5.14897227e-01 -1.02147722e+00
-1.84548825e-01 -5.55609941e-01 1.20566092e-01 5.82509756e-01
-4.14207309e-01 6.91273987e-01 4.65222210e-01 -4.92249340e-01
-8.25856775e-02 -1.04442859e+00 -3.10520679e-01 -2.47524187e-01
-1.46245092e-01 3.02195668e-01 6.97309710e-03 -4.97931927... | [11.125703811645508, 7.394969463348389] |
11cdfe38-6ad2-410b-b1b5-e25cf60c4934 | certified-graph-unlearning | 2206.09140 | null | https://arxiv.org/abs/2206.09140v2 | https://arxiv.org/pdf/2206.09140v2.pdf | Certified Graph Unlearning | Graph-structured data is ubiquitous in practice and often processed using graph neural networks (GNNs). With the adoption of recent laws ensuring the ``right to be forgotten'', the problem of graph data removal has become of significant importance. To address the problem, we introduce the first known framework for \emp... | ['Olgica Milenkovic', 'Chao Pan', 'Eli Chien'] | 2022-06-18 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 6.48180962e-01 5.79894960e-01 -2.50410765e-01 3.80511805e-02
-4.14175242e-01 -4.70556825e-01 2.12799534e-01 5.15026867e-01
-5.53266943e-01 8.39622617e-01 -3.29578578e-01 -7.26948321e-01
-6.27764344e-01 -1.15464032e+00 -9.73257959e-01 -5.77364981e-01
-7.01279879e-01 3.57661813e-01 1.90962017e-01 -1.23428114... | [7.018658638000488, 6.074635982513428] |
e9c76248-79bc-488d-b863-1cb55fd79648 | localeyenet-deep-attention-framework-for | 2303.12728 | null | https://arxiv.org/abs/2303.12728v1 | https://arxiv.org/pdf/2303.12728v1.pdf | LocalEyenet: Deep Attention framework for Localization of Eyes | Development of human machine interface has become a necessity for modern day machines to catalyze more autonomy and more efficiency. Gaze driven human intervention is an effective and convenient option for creating an interface to alleviate human errors. Facial landmark detection is very crucial for designing a robust ... | ['Akshansh Gupta', 'Somsukla Maiti'] | 2023-03-13 | null | null | null | null | ['deep-attention', 'facial-landmark-detection', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-3.01642984e-01 2.08457828e-01 2.78630495e-01 -6.83475912e-01
-1.47340655e-01 -8.95025730e-02 3.53611499e-01 -3.91554952e-01
-3.63891900e-01 4.23808128e-01 1.28338769e-01 1.91820338e-01
-9.28740650e-02 -3.73896599e-01 -5.85087061e-01 -5.41941702e-01
7.37578794e-02 1.35984123e-01 9.55407247e-02 -3.58566970... | [14.116055488586426, 0.047562167048454285] |
b784ce38-38d1-436c-84f7-46002e6db8fe | audio-driven-facial-animation-by-joint-end-to | null | null | https://research.nvidia.com/publication/2017-07_audio-driven-facial-animation-joint-end-end-learning-pose-and-emotion | https://dl.acm.org/doi/pdf/10.1145/3072959.3073658 | Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion | We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in f... | ['Jaakko Lehtinen', 'Antti Herva', 'Samuli Laine', 'Timo Aila', 'Tero Karras'] | 2017-07-30 | null | null | null | siggraph-2017-7 | ['face-model'] | ['computer-vision'] | [-7.41087124e-02 3.09978932e-01 9.66230035e-03 -2.96930224e-01
-6.24961853e-01 -7.99292088e-01 4.82728750e-01 -4.92757529e-01
7.38795241e-03 1.50718451e-01 1.30172849e-01 1.61367714e-01
4.93505806e-01 -2.87258416e-01 -5.75247526e-01 -4.19822127e-01
-3.69080782e-01 5.25564194e-01 -2.69508958e-01 -1.35246873... | [13.176382064819336, -0.4039621353149414] |
0491b97d-809d-449a-b9d7-ec8ec8cdadc9 | ais-a-nonlinear-activation-function-for | 2111.13861 | null | https://arxiv.org/abs/2111.13861v1 | https://arxiv.org/pdf/2111.13861v1.pdf | AIS: A nonlinear activation function for industrial safety engineering | In the task of Chinese named entity recognition based on deep learning, activation function plays an irreplaceable role, it introduces nonlinear characteristics into neural network, so that the fitted model can be applied to various tasks. However, the information density of industrial safety analysis text is relativel... | ['Dong Gao', 'Beike Zhang', 'Zhenhua Wang'] | 2021-11-27 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-4.05768305e-01 -2.55216777e-01 1.97565332e-02 -3.64152521e-01
1.16928540e-01 -8.94779712e-03 1.03345998e-01 -1.56547680e-01
-6.64221168e-01 5.57006061e-01 4.08279151e-03 -3.35328430e-01
2.72271801e-02 -8.80037367e-01 -4.93855327e-01 -9.44553077e-01
3.57511759e-01 1.17431834e-01 5.00923514e-01 -2.80594081... | [9.769743919372559, 9.692291259765625] |
fd69658e-2532-4722-83e0-c02bbd9dcfcb | claster-clustering-with-reinforcement | 2101.07042 | null | https://arxiv.org/abs/2101.07042v3 | https://arxiv.org/pdf/2101.07042v3.pdf | CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition | Zero-shot action recognition is the task of recognizingaction classes without visual examples, only with a seman-tic embedding which relates unseen to seen classes. Theproblem can be seen as learning a function which general-izes well to instances of unseen classes without losing dis-crimination between classes. Neural... | ['Marcus Rohrbach', 'Frank Keller', 'Laura Sevilla-Lara', 'Shreyank N Gowda'] | 2021-01-18 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 7.05184415e-02 9.28983390e-02 -4.53508884e-01 -2.34480813e-01
-8.14161181e-01 -4.07564074e-01 6.78796113e-01 1.41192049e-01
-5.59587479e-01 6.57461703e-01 4.43062223e-02 8.20055008e-02
-1.67690516e-01 -8.17540407e-01 -7.75272846e-01 -7.34915316e-01
-2.04188917e-02 7.43480206e-01 4.89752948e-01 -2.28482902... | [8.72701358795166, 1.1684004068374634] |
177af928-9c1a-449a-b9c2-4d10fd0ed08a | deep-instance-segmentation-and-visual | 2211.07977 | null | https://arxiv.org/abs/2211.07977v2 | https://arxiv.org/pdf/2211.07977v2.pdf | Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System | The game of Jenga represents an inspiring benchmark for developing innovative manipulation solutions for complex tasks. Indeed, it encouraged the study of novel robotics methods to successfully extract blocks from the tower. A Jenga game round undoubtedly embeds many traits of complex industrial or surgical manipulatio... | ['Marcello Chiaberge', 'Francesco Salvetti', 'Simone Angarano', 'Mauro Martini', 'Giulio Pugliese', 'Luca Marchionna'] | 2022-11-15 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [-1.08948156e-01 3.37490797e-01 -1.03946142e-01 3.02994251e-01
-3.72602493e-01 -8.23726475e-01 9.66795906e-02 -4.40171473e-02
-5.05454361e-01 3.34926665e-01 -6.79013193e-01 -3.80809575e-01
-4.28956836e-01 -3.79063219e-01 -8.82788241e-01 -6.38664007e-01
-5.45353293e-02 7.56783187e-01 1.97402194e-01 -5.95525146... | [5.944459915161133, -0.7116377949714661] |
6c88180b-f65e-431e-917c-9a462fb8d741 | semattack-natural-textual-attacks-via-1 | 2205.01287 | null | https://arxiv.org/abs/2205.01287v3 | https://arxiv.org/pdf/2205.01287v3.pdf | SemAttack: Natural Textual Attacks via Different Semantic Spaces | Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks. However, existing attack methods either suffer from low attack success rates or fail to search efficiently in the exponentially large perturbation space. We propose an efficient and effective framework SemAttack to... | ['Bo Li', 'Yu Cheng', 'Xiangyu Liu', 'Chejian Xu', 'Boxin Wang'] | 2022-05-03 | null | https://aclanthology.org/2022.findings-naacl.14 | https://aclanthology.org/2022.findings-naacl.14.pdf | findings-naacl-2022-7 | ['adversarial-text'] | ['adversarial'] | [-9.13253650e-02 -1.03481352e-01 9.22998637e-02 6.55692667e-02
-9.29911911e-01 -1.25722945e+00 7.56288826e-01 -2.02374622e-01
-4.54639554e-01 6.91934347e-01 1.85554862e-01 -5.49777746e-01
3.04835200e-01 -1.03713071e+00 -8.63009810e-01 -5.85826993e-01
3.96297842e-01 4.88310814e-01 2.04716429e-01 -5.88887632... | [6.005535125732422, 8.089773178100586] |
8032c8a0-8d71-433d-9dfd-9ef9078a2f06 | multilinear-wavelets-a-statistical-shape | 1401.2818 | null | http://arxiv.org/abs/1401.2818v2 | http://arxiv.org/pdf/1401.2818v2.pdf | Multilinear Wavelets: A Statistical Shape Space for Human Faces | We present a statistical model for $3$D human faces in varying expression,
which decomposes the surface of the face using a wavelet transform, and learns
many localized, decorrelated multilinear models on the resulting coefficients.
Using this model we are able to reconstruct faces from noisy and occluded $3$D
face sca... | ['Timo Bolkart', 'Stefanie Wuhrer', 'Alan Brunton'] | 2014-01-13 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-1.51613001e-02 -9.05505046e-02 -1.52327148e-02 -5.78240275e-01
-8.22141230e-01 -5.74110985e-01 3.08087915e-01 -5.72583139e-01
-4.94802147e-02 3.95529807e-01 2.63904065e-01 3.34376961e-01
-2.84960240e-01 -4.45300132e-01 -5.62091947e-01 -7.25157619e-01
-3.23541850e-01 3.96945029e-01 -2.23487809e-01 7.16507658... | [13.163264274597168, -0.015258114784955978] |
a174bc10-6df9-4443-a19e-315cd43b54ec | an-ensemble-approach-for-facial-expression | 2203.12891 | null | https://arxiv.org/abs/2203.12891v1 | https://arxiv.org/pdf/2203.12891v1.pdf | An Ensemble Approach for Facial Expression Analysis in Video | Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper will introduce the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on s... | ['Soo-Hyung Kim', 'Van-Thong Huynh', 'Hong-Hai Nguyen'] | 2022-03-24 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.22160682e-01 -8.19485486e-02 8.12132508e-02 -7.05113530e-01
-3.94346446e-01 -1.98384225e-01 4.20894653e-01 1.17287494e-01
-5.35710931e-01 7.77097225e-01 2.82786995e-01 5.01570761e-01
3.23626906e-01 -3.48266959e-01 3.76460738e-02 -7.69449949e-01
-3.84010732e-01 -2.07843900e-01 -2.55590796e-01 -2.99950600... | [13.490983963012695, 2.6867470741271973] |
99ca63ee-3dd3-47b3-969f-72273d9c1868 | short-segment-heart-sound-classification | 1810.11573 | null | http://arxiv.org/abs/1810.11573v1 | http://arxiv.org/pdf/1810.11573v1.pdf | Short-segment heart sound classification using an ensemble of deep convolutional neural networks | This paper proposes a framework based on deep convolutional neural networks
(CNNs) for automatic heart sound classification using short-segments of
individual heart beats. We design a 1D-CNN that directly learns features from
raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional
time-frequency fea... | ['Sh-Hussain Salleh', 'Chee-Ming Ting', 'Fuad Noman', 'Hernando Ombao'] | 2018-10-27 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.42893542e-02 -1.30097777e-01 -6.11678734e-02 -4.62668955e-01
-6.91542923e-01 -1.93379924e-01 -9.73230824e-02 1.61149770e-01
-5.87839603e-01 6.18165612e-01 4.97781783e-02 -2.99339116e-01
-1.15985431e-01 -7.19407499e-01 -8.37721080e-02 -6.31888688e-01
-6.20836794e-01 -8.54675565e-03 -6.15469329e-02 2.52573252... | [14.283003807067871, 3.3641231060028076] |
f36202c8-96ea-4f70-a4e4-a7dc9fe2da92 | me-d2n-multi-expert-domain-decompositional | 2210.05280 | null | https://arxiv.org/abs/2210.05280v1 | https://arxiv.org/pdf/2210.05280v1.pdf | ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning | Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core challenge of CD-FSL lies in the domain gap between the source and novel target datasets. Though many attempts have been made for CD-FSL without... | ['Yu-Gang Jiang', 'Jingjing Chen', 'Yanwei Fu', 'Yu Xie', 'Yuqian Fu'] | 2022-10-11 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.58264124e-01 2.47370720e-01 -4.52391207e-01 -4.96575147e-01
-8.96898687e-01 -5.92221200e-01 3.69882137e-01 2.22786721e-02
-2.25589156e-01 8.78567219e-01 -1.35102257e-01 -1.73294678e-01
-2.67194092e-01 -8.65833700e-01 -6.44622326e-01 -5.35304844e-01
6.14257872e-01 6.34184897e-01 6.59845054e-01 -4.37428176... | [10.128108024597168, 2.9794766902923584] |
143eb561-9a7c-4219-8d64-fdd418c9841c | morphological-analysis-and-disambiguation-for-1 | null | null | https://aclanthology.org/2020.lrec-1.480 | https://aclanthology.org/2020.lrec-1.480.pdf | Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods | In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian ... | ['Nizar Habash', 'Salam Khalifa', 'Nasser Zalmout'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['morphological-disambiguation', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-4.20987725e-01 -2.80362576e-01 2.76031464e-01 -1.53640494e-01
-4.65590209e-01 -1.11918652e+00 4.24855441e-01 7.74981618e-01
-7.65849948e-01 6.10840738e-01 -1.87771954e-02 -9.05512214e-01
-3.98841426e-02 -7.93789148e-01 1.25301391e-01 -4.51191247e-01
-8.64948891e-03 7.88643897e-01 4.07456934e-01 -1.04188323... | [10.35727310180664, 10.38680648803711] |
9644caf4-46c2-4f55-8f4f-32ffa7a9d5fd | unsupervised-semantic-segmentation-with-self | 2207.05027 | null | https://arxiv.org/abs/2207.05027v2 | https://arxiv.org/pdf/2207.05027v2.pdf | Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations | In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-s... | ['Thomas Brox', 'Francesco Locatello', 'Yi Zhu', 'Matthaeus Kleindessner', 'Andrii Zadaianchuk'] | 2022-07-11 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 5.39069712e-01 5.23977220e-01 -1.81636244e-01 -5.61208844e-01
-5.13681591e-01 -5.52875638e-01 2.94338167e-01 1.59508169e-01
-5.57775438e-01 7.54358590e-01 -3.82074326e-01 -5.75529039e-02
-4.41593900e-02 -5.07625341e-01 -7.56508708e-01 -4.55619335e-01
-1.19769417e-01 8.55799377e-01 1.04449427e+00 -3.81097756... | [9.532651901245117, 0.5669164657592773] |
2d8ad2c9-d739-491f-8a9e-7f29260617a7 | 3d-model-shapenet-core-classification-using | 2205.15869 | null | https://arxiv.org/abs/2205.15869v1 | https://arxiv.org/pdf/2205.15869v1.pdf | 3D-model ShapeNet Core Classification using Meta-Semantic Learning | Understanding 3D point cloud models for learning purposes has become an imperative challenge for real-world identification such as autonomous driving systems. A wide variety of solutions using deep learning have been proposed for point cloud segmentation, object detection, and classification. These methods, however, of... | ['Hamid R. Arabnia', 'Beshoy Morkos', 'M. Hadi Amini', 'Farzan Shenavarmasouleh', 'Cheng Chen', 'Farid Ghareh Mohammadi'] | 2022-05-28 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-3.19338180e-02 -2.69892395e-01 -1.65335432e-01 -5.16520023e-01
-8.71877432e-01 -6.52870595e-01 7.42186725e-01 5.07649891e-02
-2.81752378e-01 -1.90138206e-01 -5.90996563e-01 -3.64001900e-01
-5.78333996e-02 -8.06961060e-01 -1.13050807e+00 -4.49572265e-01
6.83103353e-02 1.04491389e+00 6.11229360e-01 -1.67606529... | [7.976344585418701, -3.1036345958709717] |
0e4cd476-039c-4e21-af88-839ae7435fc0 | optimized-views-photogrammetry-precision | 2206.12216 | null | https://arxiv.org/abs/2206.12216v1 | https://arxiv.org/pdf/2206.12216v1.pdf | Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao | UAVs have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure and accurate data acquisition brings great challenges to 3D modeling and scene updating. Optimal trajectory planning of UAVs an... | ['San Jiang', 'Wenshuai Yu', 'Qingquan Li'] | 2022-06-24 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-2.33776733e-01 -4.80394542e-01 9.54398140e-02 -1.73206069e-03
-6.93520755e-02 -2.86926001e-01 2.71399051e-01 -2.64884949e-01
-3.60809825e-02 4.94173318e-01 -5.54723918e-01 -5.97434223e-01
-4.13414717e-01 -1.57729328e+00 -4.23186511e-01 -6.45892620e-01
1.69615299e-01 7.07343936e-01 2.36706749e-01 -6.00011587... | [8.34218692779541, -2.669382095336914] |
f858a6c7-2a5b-4987-879b-9e526adc7038 | ptw-pivotal-tuning-watermarking-for-pre | 2304.07361 | null | https://arxiv.org/abs/2304.07361v2 | https://arxiv.org/pdf/2304.07361v2.pdf | PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators | Deepfakes refer to content synthesized using deep generators, which, when misused, have the potential to erode trust in digital media. Synthesizing high-quality deepfakes requires access to large and complex generators only a few entities can train and provide. The threat is malicious users that exploit access to the p... | ['Florian Kerschbaum', 'Nils Lukas'] | 2023-04-14 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 3.85226727e-01 6.53257430e-01 -2.02978075e-01 4.94946390e-01
-1.08700633e+00 -1.43118441e+00 5.95278144e-01 -1.97472155e-01
-3.91843438e-01 5.75630903e-01 1.78229511e-02 -7.55645633e-01
4.59273309e-01 -9.33796287e-01 -1.08256495e+00 -6.02965593e-01
-3.73315632e-01 -3.17816615e-01 2.83301204e-01 -2.04853475... | [5.609605312347412, 7.776146411895752] |
74eb599b-4932-4e0a-bbc4-756049876ad2 | spatial-probabilistic-pulsatility-model-for | 1607.08129 | null | http://arxiv.org/abs/1607.08129v1 | http://arxiv.org/pdf/1607.08129v1.pdf | Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems | Photolethysmographic imaging (PPGI) is a widefield non-contact biophotonic
technology able to remotely monitor cardiovascular function over anatomical
areas. Though spatial context can provide increased physiological insight,
existing PPGI systems rely on coarse spatial averaging with no anatomical
priors for assessing... | ['David A. Clausi', 'Alexander Wong', 'Robert Amelard'] | 2016-07-27 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 8.44317600e-02 1.75843108e-02 1.28220618e-01 -1.98271230e-01
-7.40359843e-01 -4.55508828e-01 8.22429061e-02 -4.50217463e-02
-2.61987716e-01 1.13109779e+00 4.45344746e-01 4.61346805e-02
-4.06894326e-01 -5.68395495e-01 -1.90192387e-01 -8.16279590e-01
-9.38078582e-01 8.96350175e-05 -5.67486733e-02 4.76632208... | [14.026573181152344, 2.9612128734588623] |
280c530a-f497-438d-8dac-daf29b331d39 | divide-and-conquer-text-semantic-matching-1 | 2203.02898 | null | https://arxiv.org/abs/2203.02898v1 | https://arxiv.org/pdf/2203.02898v1.pdf | Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents | Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence g... | ['Daniel Wang', 'Haixiang Li', 'Meng Tang', 'Qi Zhang', 'Junzhe Wang', 'Tao Gui', 'Hongwei Liu', 'Yicheng Zou'] | 2022-03-06 | null | https://aclanthology.org/2022.findings-acl.287 | https://aclanthology.org/2022.findings-acl.287.pdf | findings-acl-2022-5 | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 4.78837222e-01 -1.46578044e-01 -5.27747273e-01 -5.76864898e-01
-6.99337363e-01 -4.28766608e-01 9.54972565e-01 9.70954835e-01
-5.94974041e-01 3.66011381e-01 5.34107566e-01 -4.43781793e-01
-4.08330001e-03 -1.05551004e+00 -4.86987203e-01 -1.12689503e-01
5.65580249e-01 4.67979997e-01 6.38939261e-01 -2.86574453... | [10.963156700134277, 8.14614200592041] |
8b68dcc5-6975-4ede-a85d-bd7ddbbcb637 | drt-a-lightweight-single-image-deraining | 2204.11385 | null | https://arxiv.org/abs/2204.11385v1 | https://arxiv.org/pdf/2204.11385v1.pdf | DRT: A Lightweight Single Image Deraining Recursive Transformer | Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision ta... | ['Yang Liu', 'Saeed Anwar', 'Yuanchu Liang'] | 2022-04-25 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.42139435e-01 -2.03727365e-01 6.71385899e-02 -3.21204782e-01
-5.69996834e-01 5.30210920e-02 3.36836159e-01 -4.49794918e-01
-4.94769573e-01 4.59750146e-01 -1.30434290e-01 -6.44462645e-01
1.13856927e-01 -6.24997437e-01 -8.58602345e-01 -1.10236156e+00
3.24783713e-01 -1.56580769e-02 3.34086150e-01 -1.57844394... | [11.092427253723145, -2.2643020153045654] |
2d507d4e-b99d-4ef6-98cb-b370e4868e3b | basic-tasks-of-sentiment-analysis | 1710.06536 | null | http://arxiv.org/abs/1710.06536v1 | http://arxiv.org/pdf/1710.06536v1.pdf | Basic tasks of sentiment analysis | Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, ... | ['Soujanya Poria', 'Iti Chaturvedi', 'Erik Cambria'] | 2017-10-18 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.64767528e-01 2.62106866e-01 -3.53838921e-01 -6.65881932e-01
-3.15320849e-01 -9.30299520e-01 3.92001033e-01 7.81865656e-01
3.78854247e-03 6.29111469e-01 5.30418456e-01 -3.65983099e-01
3.34200233e-01 -5.91553569e-01 3.45554315e-02 -7.98952162e-01
4.84491736e-01 8.58924389e-02 -9.67717618e-02 -4.56273317... | [11.313948631286621, 6.728762149810791] |
560e8fc6-7605-4b9a-9395-4a62a82221b5 | proactively-reducing-the-hate-intensity-of | 2206.04007 | null | https://arxiv.org/abs/2206.04007v1 | https://arxiv.org/pdf/2206.04007v1.pdf | Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization | Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible for several geopolitical and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization, that aims to weaken the intensity of hatred exhib... | ['Tanmoy Chakraborty', 'Md Shad Akhtar', 'Mohammad Aflah Khan', 'Manjot Bedi', 'Sarah Masud'] | 2022-06-08 | null | null | null | null | ['hate-speech-normalization', 'hate-span-identification', 'hate-intensity-prediction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 6.97716996e-02 -1.75286923e-02 7.47996569e-02 1.32025003e-01
-5.23805320e-01 -8.06328118e-01 5.93984187e-01 1.67428270e-01
-1.90882310e-01 4.00255978e-01 5.91062307e-01 -1.12220071e-01
1.10260911e-01 -4.25865710e-01 -3.53712022e-01 -6.36488080e-01
2.02268630e-01 -2.45457292e-01 -5.82092926e-02 -4.62519228... | [8.742560386657715, 10.552167892456055] |
80f81fb8-c837-410b-b5b5-e17dd4c5b6c8 | towards-hardware-aware-tractable-learning-of | null | null | http://papers.nips.cc/paper/9525-towards-hardware-aware-tractable-learning-of-probabilistic-models | http://papers.nips.cc/paper/9525-towards-hardware-aware-tractable-learning-of-probabilistic-models.pdf | Towards Hardware-Aware Tractable Learning of Probabilistic Models | Smart portable applications increasingly rely on edge computing due to privacy and latency concerns. But guaranteeing always-on functionality comes with two major challenges: heavily resource-constrained hardware; and dynamic application conditions. Probabilistic models present an ideal solution to these challenges: th... | ['Guy Van Den Broeck', 'Wannes Meert', 'Marian Verhelst', 'Laura I. Galindez Olascoaga', 'Nimish Shah'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['small-data'] | ['computer-vision'] | [ 3.50504547e-01 -1.92651153e-01 -7.53691792e-01 -3.85766923e-01
-9.66374159e-01 -5.42147756e-01 1.28918812e-01 -1.35231659e-01
-2.96162874e-01 7.52666771e-01 -3.71034332e-02 -6.44305170e-01
-3.93540919e-01 -5.27057707e-01 -8.53829682e-01 -5.99594176e-01
-2.89589614e-01 2.11983427e-01 6.29261509e-02 5.10613739... | [5.945688247680664, 6.050426483154297] |
6dae7119-8ef8-421f-ac6a-e2edc01274da | the-cmu-submission-for-the-shared-task-on | null | null | https://aclanthology.org/W14-3909 | https://aclanthology.org/W14-3909.pdf | The CMU Submission for the Shared Task on Language Identification in Code-Switched Data | null | ['Chu-Cheng Lin', 'Waleed Ammar', 'Chris Dyer', 'Lori Levin'] | 2014-10-01 | null | null | null | ws-2014-10 | ['learning-word-embeddings'] | ['methodology'] | [-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... | [-7.332990646362305, 3.7051520347595215] |
4b1a0294-99bf-4692-b3ab-dfcf07b03019 | fast-inference-and-transfer-of-compositional | 2205.12648 | null | https://arxiv.org/abs/2205.12648v1 | https://arxiv.org/pdf/2205.12648v1.pdf | Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization | We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph that defines a set of subtasks and their dependencies that are unknown to the agent. Different from the previous m... | ['Honglak Lee', 'Aleksandra Faust', 'Izzeddin Gur', 'lyubing qiang', 'Jongwook Choi', 'Hyunjae Woo', 'Sungryull Sohn'] | 2022-05-25 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 4.39542145e-01 5.97937047e-01 -7.73373339e-03 -7.35518038e-02
-6.16470993e-01 -6.15140021e-01 6.38812065e-01 -1.46248728e-01
-5.73483884e-01 1.01845384e+00 8.84479731e-02 -2.13217214e-01
-2.84075081e-01 -5.88078797e-01 -9.20635402e-01 -7.32566357e-01
-2.39036471e-01 9.01599765e-01 7.24601686e-01 -5.78943312... | [4.163809299468994, 1.3641831874847412] |
fce9866d-e914-4753-b1e0-7073860d5be0 | sparql-query-generation-for-complex-question | null | null | http://www.dialog-21.ru/media/5088/evseevdaplusarkhipov-myu-048.pdf | http://www.dialog-21.ru/media/5088/evseevdaplusarkhipov-myu-048.pdf | SPARQL query generation for complex question answering with BERT and BiLSTM-based model | In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base. Unlike simple questions, which require extraction of single fact from the knowledge base, complex questions are based on more than one triplet and need logical or comparative reasoning. The proposed ques... | ['Arkhipov M. Yu.', 'Evseev D. A.'] | 2020-06-17 | null | null | null | proceedings-of-the-international-conference | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.19253534e-01 7.35213995e-01 1.24584779e-01 -4.52683926e-01
-6.47597849e-01 -9.98248518e-01 4.89384025e-01 7.70994604e-01
-5.49084842e-01 1.18500376e+00 1.43702462e-01 -6.80078506e-01
-7.05311716e-01 -1.51437747e+00 -4.01868314e-01 3.27997178e-01
2.77571678e-01 1.21968091e+00 1.10390437e+00 -7.03631282... | [10.390316009521484, 8.035091400146484] |
928f8b53-e7de-45e8-a60f-6d340a05c759 | darts-deceiving-autonomous-cars-with-toxic | 1802.06430 | null | http://arxiv.org/abs/1802.06430v3 | http://arxiv.org/pdf/1802.06430v3.pdf | DARTS: Deceiving Autonomous Cars with Toxic Signs | Sign recognition is an integral part of autonomous cars. Any
misclassification of traffic signs can potentially lead to a multitude of
disastrous consequences, ranging from a life-threatening accident to even a
large-scale interruption of transportation services relying on autonomous cars.
In this paper, we propose and... | ['Chawin Sitawarin', 'Mung Chiang', 'Arsalan Mosenia', 'Arjun Nitin Bhagoji', 'Prateek Mittal'] | 2018-02-18 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.96480644e-01 -1.58001959e-01 2.57479310e-01 -1.64824069e-01
-4.85999346e-01 -1.31252289e+00 8.90592098e-01 -7.23114073e-01
-3.35730672e-01 5.59361279e-01 -6.77991390e-01 -9.03154433e-01
4.05249037e-02 -9.10299897e-01 -9.80931520e-01 -8.64246726e-01
5.77288307e-02 -2.40217745e-02 6.65953159e-01 -4.49168473... | [5.4310302734375, 7.7906880378723145] |
74ae6e50-bcb6-4028-9d5c-c0a13ec56e2b | interface-adjustable-angular-margin-inter | 2210.02018 | null | https://arxiv.org/abs/2210.02018v2 | https://arxiv.org/pdf/2210.02018v2.pdf | InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition | In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by... | ['Shan Zhao', 'Yang Yang', 'Anning Pan', 'Pan Tan', 'Mengzhen Li', 'Jiaxuan Chen', 'Meng Sang'] | 2022-10-05 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 7.56476298e-02 -8.90114605e-02 -3.46699446e-01 -9.71861064e-01
-1.70139119e-01 -4.97220196e-02 4.22779024e-01 -1.55622870e-01
-4.95476216e-01 4.52892810e-01 4.53163907e-02 -1.22673690e-01
-1.12816691e-01 -7.21446574e-01 -5.39328873e-01 -7.17484593e-01
-2.72768624e-02 2.71183671e-03 6.42375350e-02 2.44748630... | [13.243943214416504, 0.7299585938453674] |
6846fec6-846e-45cc-9654-631e5600c231 | deep-learning-for-real-time-crime-forecasting-1 | 1707.03340 | null | http://arxiv.org/abs/1707.03340v1 | http://arxiv.org/pdf/1707.03340v1.pdf | Deep Learning for Real Time Crime Forecasting | Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatica... | ['Andrea L. Bertozzi', 'Duo Zhang', 'P. Jeffery Brantingham', 'Duanhao Zhang', 'Bao Wang'] | 2017-07-09 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 4.76967469e-02 -6.85606122e-01 -1.49592841e-02 -5.83852470e-01
-9.61416721e-01 -2.94664055e-01 5.48611879e-01 4.67025250e-01
-5.91297030e-01 8.30401778e-01 8.38812113e-01 -2.67128319e-01
-3.73164028e-01 -1.02745557e+00 -6.93501294e-01 -2.56424308e-01
-3.06469411e-01 9.46014822e-02 -4.09968905e-02 -2.56454706... | [6.741401672363281, 1.9824378490447998] |
a8c47f6f-4a55-4a11-9466-83c164b35e28 | a-pre-training-oracle-for-predicting | 2106.03233 | null | https://arxiv.org/abs/2106.03233v1 | https://arxiv.org/pdf/2106.03233v1.pdf | A Pre-training Oracle for Predicting Distances in Social Networks | In this paper, we propose a novel method to make distance predictions in real-world social networks. As predicting missing distances is a difficult problem, we take a two-stage approach. Structural parameters for families of synthetic networks are first estimated from a small set of measurements of a real-world network... | ['Rasika Karkare', 'Anura Jayasumana', 'Randy Paffenroth', 'Gunjan Mahindre'] | 2021-06-06 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 3.08344334e-01 6.89221501e-01 -3.68643492e-01 -4.27409947e-01
-4.92417961e-01 -5.61075151e-01 3.90879571e-01 1.23575449e-01
-2.83107877e-01 8.61384571e-01 -2.11049885e-01 -6.08937025e-01
-5.58018565e-01 -1.21769440e+00 -1.03823626e+00 -5.42231739e-01
-5.15135109e-01 1.07015073e+00 3.18114579e-01 -4.10200238... | [6.8495259284973145, 5.725025177001953] |
ecf3c231-f561-42cd-9a08-8acdd9c1a598 | ai-enhanced-3d-rf-representation-using-low | null | null | https://doi.org/10.1145/3274783.3275210 | https://www.semanticscholar.org/paper/AI-Enhanced-3D-RF-Representation-Using-Low-Cost-Fang-Nirjon/36075af2129b590dfa128f5de5a159395952549e | AI-Enhanced 3D RF Representation Using Low-Cost mmWave Radar | This paper introduces a system that takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77 GHz mm Wave radar and produces an enhanced 3D RF representation of a scene. Such a system can be used in scenarios where camera and other types of sensors do not work, or their performance is impacted due to bad l... | ['Shiwei Fang', 'Shahriar Nirjon'] | 2018-11-04 | null | null | null | sensys-18-proceedings-of-the-16th-acm | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 6.42049134e-01 2.46747985e-01 3.98355544e-01 -4.60563451e-01
-4.73294914e-01 -5.79914331e-01 3.28920543e-01 -4.68743831e-01
-3.06933701e-01 8.90865803e-01 5.17934598e-02 -4.11259741e-01
-3.85236025e-01 -9.88405824e-01 -9.99276713e-02 -7.69675910e-01
3.69595550e-02 6.59071267e-01 7.88418110e-03 -1.29369333... | [6.725404739379883, 0.7338322997093201] |
60b71a66-f195-49b8-88b0-2b6c77714fe2 | adversarial-domain-adaptation-for-duplicate | 1809.02255 | null | http://arxiv.org/abs/1809.02255v1 | http://arxiv.org/pdf/1809.02255v1.pdf | Adversarial Domain Adaptation for Duplicate Question Detection | We address the problem of detecting duplicate questions in forums, which is
an important step towards automating the process of answering new questions. As
finding and annotating such potential duplicates manually is very tedious and
costly, automatic methods based on machine learning are a viable alternative.
However,... | ['Tao Lei', 'Darsh J Shah', 'Preslav Nakov', 'Salvatore Romeo', 'Alessandro Moschitti'] | 2018-09-07 | adversarial-domain-adaptation-for-duplicate-1 | https://aclanthology.org/D18-1131 | https://aclanthology.org/D18-1131.pdf | emnlp-2018-10 | ['question-similarity'] | ['natural-language-processing'] | [-8.90399888e-02 2.88970739e-01 1.50765553e-01 -3.95967394e-01
-1.30192411e+00 -1.24180508e+00 5.91960371e-01 3.37236375e-01
-4.88761604e-01 1.08528626e+00 3.52481455e-01 -4.36297119e-01
2.94531047e-01 -6.06781244e-01 -8.47159505e-01 -6.56611696e-02
5.16365528e-01 8.78332973e-01 6.10416889e-01 -5.23291647... | [11.360297203063965, 8.158501625061035] |
ebf426fa-67fc-4704-8c56-60d6211687c0 | piqa-reasoning-about-physical-commonsense-in | 1911.11641 | null | https://arxiv.org/abs/1911.11641v1 | https://arxiv.org/pdf/1911.11641v1.pdf | PIQA: Reasoning about Physical Commonsense in Natural Language | To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems. While recent pretrained models (such as BERT) have made progress on question answering over more abstract domains - suc... | ['Yejin Choi', 'Rowan Zellers', 'Ronan Le Bras', 'Jianfeng Gao', 'Yonatan Bisk'] | 2019-11-26 | null | null | null | null | ['physical-commonsense-reasoning'] | ['reasoning'] | [ 2.38308072e-01 4.09445047e-01 -4.76056308e-01 -1.14617705e-01
-5.14583945e-01 -8.10313344e-01 8.06738079e-01 2.37664834e-01
-3.69560242e-01 1.12959075e+00 3.46223146e-01 -8.15358818e-01
-5.07240772e-01 -1.06981432e+00 -7.51295865e-01 -2.65420407e-01
3.74422640e-01 4.78790700e-01 3.65453988e-01 -6.87295020... | [9.766639709472656, 7.540696144104004] |
f9f35c2a-6378-4d5b-b4f0-aaa95ca53e97 | efficient-cross-lingual-transfer-for-chinese | 2305.11540 | null | https://arxiv.org/abs/2305.11540v1 | https://arxiv.org/pdf/2305.11540v1.pdf | Efficient Cross-Lingual Transfer for Chinese Stable Diffusion with Images as Pivots | Diffusion models have made impressive progress in text-to-image synthesis. However, training such large-scale models (e.g. Stable Diffusion), from scratch requires high computational costs and massive high-quality text-image pairs, which becomes unaffordable in other languages. To handle this challenge, we propose IAP,... | ['Maosong Sun', 'Zhiyuan Liu', 'Wenhao Li', 'Yutong Chen', 'Xiaoyuan Yi', 'Xu Han', 'Jinyi Hu'] | 2023-05-19 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 1.05366446e-01 -9.46475118e-02 -7.21585900e-02 -1.89040512e-01
-8.31277072e-01 -5.28714716e-01 7.35710621e-01 -5.33289909e-01
-5.32385349e-01 6.26082301e-01 5.99904478e-01 -1.62401125e-01
4.11555529e-01 -7.15566337e-01 -8.10536146e-01 -6.59412563e-01
5.49803793e-01 1.29774123e-01 3.10027182e-01 -1.86398268... | [11.469698905944824, -0.2290440946817398] |
79c73c7a-9db1-42bf-8b6d-3293d7b17d7a | an-understanding-oriented-robust-machine | 2207.00187 | null | https://arxiv.org/abs/2207.00187v1 | https://arxiv.org/pdf/2207.00187v1.pdf | An Understanding-Oriented Robust Machine Reading Comprehension Model | Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.... | ['Qi Ma', 'Chunchao Liu', 'Jiaqi Wang', 'Bingchao Wang', 'Shilei Liu', 'Bochao Li', 'Yongkang Liu', 'Feiliang Ren'] | 2022-07-01 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 1.69583634e-01 3.73441502e-02 1.27279554e-02 -4.58927691e-01
-1.10199273e+00 -4.37037438e-01 4.77526754e-01 3.72045189e-01
-3.66169721e-01 3.77768040e-01 5.01360774e-01 -4.40130562e-01
-1.56008378e-01 -7.24976003e-01 -9.10100639e-01 -2.34562516e-01
4.84124839e-01 2.83413351e-01 5.33467352e-01 -5.52112281... | [11.266475677490234, 8.16019058227539] |
a479b21f-b69a-406a-90ff-c4ead739be29 | all-points-matter-entropy-regularized | 2305.15832 | null | https://arxiv.org/abs/2305.15832v1 | https://arxiv.org/pdf/2305.15832v1.pdf | All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation | Pseudo-labels are widely employed in weakly supervised 3D segmentation tasks where only sparse ground-truth labels are available for learning. Existing methods often rely on empirical label selection strategies, such as confidence thresholding, to generate beneficial pseudo-labels for model training. This approach may,... | ['DaCheng Tao', 'Chaoyue Wang', 'Shanshan Zhao', 'Zhe Chen', 'Liyao Tang'] | 2023-05-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 3.03898931e-01 4.45312023e-01 -5.46106994e-01 -5.88713288e-01
-1.24770129e+00 -8.17863166e-01 4.16656286e-01 -2.62361411e-02
-3.18260372e-01 6.79582715e-01 -2.01371461e-01 -2.04010651e-01
4.25250083e-01 -3.34729940e-01 -7.49423802e-01 -1.00189972e+00
3.69738489e-01 5.34721851e-01 4.08035517e-02 4.42991316... | [9.443851470947266, 1.2663640975952148] |
c599fa29-6eb0-4310-aa01-05bca80c5759 | unsupervised-algorithm-for-disaggregating-low | 1908.10713 | null | https://arxiv.org/abs/1908.10713v1 | https://arxiv.org/pdf/1908.10713v1.pdf | Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households | Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power consumption of an entire house. This information allows households t... | ['Jordan Holweger', 'Marina Dorokhova', 'Lionel Bloch', 'Christophe Ballif', 'Nicolas Wyrsch'] | 2019-08-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.10167491e-01 1.96309522e-01 -1.74083650e-01 -2.80232042e-01
-4.29346353e-01 -5.93318939e-01 5.39396286e-01 3.47503424e-01
-1.00887872e-01 7.68535674e-01 -3.78009155e-02 -2.14863151e-01
-3.46917599e-01 -1.17676795e+00 -2.06906900e-01 -1.04850650e+00
3.84842642e-02 6.79337919e-01 -1.24328233e-01 4.07169044... | [5.988373279571533, 2.5779459476470947] |
94b48805-e7e3-44b5-bf03-a21b9d2eef41 | rumor-detection-on-twitter-with-tree | null | null | https://aclanthology.org/P18-1184 | https://aclanthology.org/P18-1184.pdf | Rumor Detection on Twitter with Tree-structured Recursive Neural Networks | Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based o... | ['Kam-Fai Wong', 'Wei Gao', 'Jing Ma'] | 2018-07-01 | null | null | null | acl-2018-7 | ['rumour-detection'] | ['natural-language-processing'] | [-1.54781103e-01 -1.34273171e-01 -4.11226839e-01 -5.79424977e-01
-3.00891817e-01 -6.85887039e-02 7.96832561e-01 3.86783689e-01
-9.66247246e-02 4.73954558e-01 7.49580562e-01 -6.56006813e-01
1.44554138e-01 -9.75638747e-01 -2.43659750e-01 -2.41240129e-01
-4.03159410e-01 4.50378567e-01 3.36674690e-01 -7.05537081... | [8.2064790725708, 10.164151191711426] |
8420cc08-fcf9-48be-8c5f-f6fe9388b4f4 | learning-view-priors-for-single-view-3d | 1811.10719 | null | http://arxiv.org/abs/1811.10719v2 | http://arxiv.org/pdf/1811.10719v2.pdf | Learning View Priors for Single-view 3D Reconstruction | There is some ambiguity in the 3D shape of an object when the number of
observed views is small. Because of this ambiguity, although a 3D object
reconstructor can be trained using a single view or a few views per object,
reconstructed shapes only fit the observed views and appear incorrect from the
unobserved viewpoint... | ['Tatsuya Harada', 'Hiroharu Kato'] | 2018-11-26 | learning-view-priors-for-single-view-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kato_Learning_View_Priors_for_Single-View_3D_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kato_Learning_View_Priors_for_Single-View_3D_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 8.43233839e-02 5.15908659e-01 -6.32020310e-02 -5.11321247e-01
-5.39458752e-01 -7.83940017e-01 5.58271527e-01 -6.38340473e-01
4.28832024e-01 2.75761604e-01 8.83201733e-02 1.55218840e-01
1.13610454e-01 -8.05653036e-01 -1.08089030e+00 -4.37117249e-01
3.04132670e-01 1.04974103e+00 4.00876790e-01 3.91692162... | [8.725756645202637, -2.9726388454437256] |
203f1be2-62a4-495e-b291-0f5ae08caad5 | relational-reasoning-via-set-transformers | 2209.09845 | null | https://arxiv.org/abs/2209.09845v3 | https://arxiv.org/pdf/2209.09845v3.pdf | Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL | The cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfortunately, the theoretical understanding of this MARL problem is lacking due to the curse of many agents and the limited exploration of the ... | ['Zhaoran Wang', 'Zhuoran Yang', 'Vincent Y. F. Tan', 'Kaixin Wang', 'Boyi Liu', 'Fengzhuo Zhang'] | 2022-09-20 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-6.03361465e-02 1.92577407e-01 -1.18931299e-02 1.58681154e-01
-5.53925157e-01 -7.77799428e-01 7.47733057e-01 1.56331137e-01
-4.22798783e-01 8.49370599e-01 -3.96150947e-01 -3.65981162e-01
-1.25354600e+00 -6.41041636e-01 -9.69760656e-01 -1.10576057e+00
-6.21795356e-01 6.50491714e-01 2.76342541e-01 -6.22734129... | [3.784022808074951, 2.1405839920043945] |
66f35b3f-f970-48c2-99cc-4063171e10bc | real-time-3d-indoor-human-image-capturing | null | null | https://arxiv.org/abs/1812.07099 | https://arxiv.org/pdf/1812.07099.pdf | Real Time 3D Indoor Human Image Capturing Based on FMCW Radar | Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy. This work proposes a deep learning radio imaging solution to visualize real-time user indoor activities. The proposed solution uses a low-power, MIMO Frequency ... | ['Saeed ALI-AlQarni', 'Wenjun Shi', 'Shaoen Wu', 'Nan Zhang', 'Honggang Wang', 'Hangqing Guo'] | 2019-08-05 | null | null | null | 2019-ieee-international-conference-on-2 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 4.21456784e-01 -4.26309496e-01 8.37020636e-01 -2.21966848e-01
-3.26730102e-01 -4.23309244e-02 2.77215213e-01 -4.76420820e-01
-4.39666152e-01 4.83248800e-01 4.35100272e-02 -3.36026639e-01
-1.05533786e-01 -9.20211136e-01 -4.46652442e-01 -8.18314075e-01
-1.71426624e-01 3.13415155e-02 -3.27510536e-01 7.82784745... | [6.858942031860352, 0.5047256946563721] |
4c541186-dc51-4730-88be-e0a0ceb194c1 | masked-rpca-sparse-and-low-rank-decomposition | 1909.08049 | null | https://arxiv.org/abs/1909.08049v1 | https://arxiv.org/pdf/1909.08049v1.pdf | Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection | Foreground detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance system. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and ... | ['Amirhossein Khalilian-Gourtani', 'Yao Wang', 'Shervin Minaee'] | 2019-09-17 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 7.88918972e-01 -1.82802007e-01 1.99335262e-01 2.76036263e-01
-1.64558157e-01 -4.54104841e-01 4.78789002e-01 -5.15578032e-01
3.03962585e-02 5.87209821e-01 3.94617021e-01 3.98748219e-02
-1.28073990e-01 -4.13262099e-01 -5.06103754e-01 -1.28458512e+00
-1.71744600e-01 3.15568119e-01 5.90953350e-01 2.59191066... | [9.021259307861328, -0.8195126056671143] |
6210c00d-d9c9-4110-b2e1-c555f9231e97 | ai-imagery-and-the-overton-window | 2306.00080 | null | https://arxiv.org/abs/2306.00080v2 | https://arxiv.org/pdf/2306.00080v2.pdf | AI Imagery and the Overton Window | AI-based text-to-image generation has undergone a significant leap in the production of visually comprehensive and aesthetic imagery over the past year, to the point where differentiating between a man-made piece of art and an AI-generated image is becoming more difficult. Generative Models such as Stable Diffusion, Mi... | ['Sarah K. Amer'] | 2023-05-31 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 3.93932134e-01 5.00569999e-01 3.16223204e-01 1.74234867e-01
-4.45814542e-02 -8.90227795e-01 9.74009097e-01 -1.87670901e-01
-1.38825610e-01 5.24304748e-01 5.42375803e-01 -3.12283099e-01
-8.14476907e-02 -7.29787230e-01 -2.88128138e-01 -3.89242232e-01
4.50153977e-01 4.15051132e-01 -1.63772598e-01 -4.44055259... | [9.326991081237793, 6.3547234535217285] |
61a8593e-17af-4a03-bd2d-6b9924973eb6 | mvtn-multi-view-transformation-network-for-3d | 2011.13244 | null | https://arxiv.org/abs/2011.13244v3 | https://arxiv.org/pdf/2011.13244v3.pdf | MVTN: Multi-View Transformation Network for 3D Shape Recognition | Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance on 3D shape recognition. Those methods learn different ways to aggregate information from multiple views. However, the camera view-points for those views tend to be heuristically set and fixed for all shapes. To circumve... | ['Bernard Ghanem', 'Silvio Giancola', 'Abdullah Hamdi'] | 2020-11-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hamdi_MVTN_Multi-View_Transformation_Network_for_3D_Shape_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hamdi_MVTN_Multi-View_Transformation_Network_for_3D_Shape_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-retrieval', '3d-shape-recognition', '3d-classification', 'multi-view-3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.74404794e-01 -2.72491395e-01 9.66356024e-02 -5.45843959e-01
-8.70797038e-01 -9.17977810e-01 8.42888415e-01 -4.17321950e-01
1.02770858e-01 -2.09268153e-01 1.19852237e-01 -2.96958297e-01
1.48581401e-01 -8.47366154e-01 -8.55382860e-01 -5.08150160e-01
2.37823054e-01 8.79298508e-01 7.00274110e-02 -1.87023386... | [8.271098136901855, -3.581665515899658] |
cc23d227-9366-4b18-894e-bef3f3168144 | forecasting-west-nile-virus-with-graph-neural | 2212.11367 | null | https://arxiv.org/abs/2212.11367v1 | https://arxiv.org/pdf/2212.11367v1.pdf | Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data | Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the under... | ['Rebecca Smith', 'William Brown', 'Bo Li', 'Trevor Harris', 'Adam Tonks'] | 2022-12-21 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 1.59593955e-01 8.86314660e-02 -3.13752919e-01 -2.21175432e-01
3.95961940e-01 -2.37509310e-01 6.37154043e-01 7.25964904e-01
-1.04010655e-02 7.55679965e-01 4.34135526e-01 -1.26180995e+00
-3.01256835e-01 -1.52204835e+00 -5.78889906e-01 -5.49123824e-01
-6.69888139e-01 3.05509865e-01 -2.70601481e-01 -5.20406127... | [6.708925247192383, 2.725522518157959] |
f64de075-589d-4af7-9057-95344f8c8568 | reduction-of-subjective-listening-effort-for | 2111.01914 | null | https://arxiv.org/abs/2111.01914v1 | https://arxiv.org/pdf/2111.01914v1.pdf | Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks | Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal. This can result in a reduced satisfaction and increased listening effort of the listeners. Since the broa... | ['Bernd T. Meyer', 'Jan Rennies', 'Ragini Sinha', 'Hannah Baumgartner', 'Rainer Huber', 'Nils L. Westhausen'] | 2021-11-02 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 3.99373084e-01 5.10699004e-02 5.55429101e-01 -6.91891760e-02
-1.16558218e+00 -1.60310000e-01 4.90983669e-03 2.61125714e-01
-6.13073826e-01 5.36677659e-01 6.55491531e-01 -6.48617595e-02
-2.51322299e-01 -4.72765148e-01 -2.05334023e-01 -8.03534627e-01
-1.39995907e-02 -3.02699149e-01 2.48179853e-01 -5.65080702... | [15.097996711730957, 5.780060768127441] |
5108f6af-b307-4c0b-b175-fd89b82d83d8 | rethinking-image-restoration-for-object | null | null | https://openreview.net/forum?id=se2oxj-6Nz | https://openreview.net/pdf?id=se2oxj-6Nz | Rethinking Image Restoration for Object Detection | Although image restoration has achieved significant progress, its potential to assist object detectors in adverse imaging conditions lacks enough attention. It is reported that the existing image restoration methods cannot improve the object detector performance and sometimes even reduce the detection performance. To a... | ['Xiaochun Cao', 'Tao Wang', 'Wenqi Ren', 'Shangquan Sun'] | 2022-11-01 | null | null | null | nips-2022-11 | ['image-dehazing'] | ['computer-vision'] | [ 7.02804387e-01 -2.72599667e-01 3.33663732e-01 -4.16189246e-02
-1.11563611e+00 -2.85810798e-01 5.52955031e-01 -2.29544535e-01
-2.91273206e-01 5.86429060e-01 1.32152811e-01 -1.42939687e-01
2.16709659e-01 -6.53444886e-01 -7.15387166e-01 -9.44904685e-01
3.97706330e-01 1.61922332e-02 5.93623221e-01 -2.62745410... | [10.781844139099121, -2.530362844467163] |
c6af1d88-8fbf-4d04-aac4-d281527f3c9d | beyond-detection-visual-realism-assessment-of | 2306.05985 | null | https://arxiv.org/abs/2306.05985v1 | https://arxiv.org/pdf/2306.05985v1.pdf | Beyond Detection: Visual Realism Assessment of Deepfakes | In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns. This paper presents an effective measure to assess the visual realism of DeepFake videos. We utilize an ensemble of two Convolutional Neural Network (C... | ['Borut Batagelj', 'Vitomir Štruc', 'Peter Peer', 'Luka Dragar'] | 2023-06-09 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-2.38232315e-01 -2.64839083e-02 2.24105213e-02 -5.14501333e-01
-3.89060974e-01 -3.75255883e-01 5.72734773e-01 -4.69450355e-01
-7.64741361e-01 3.70758384e-01 3.39544922e-01 -3.89088780e-01
2.25728552e-04 -4.61199522e-01 -5.57376504e-01 -9.80351046e-02
-3.00164878e-01 -5.17135561e-01 -3.60449851e-01 -1.79296225... | [13.355147361755371, 1.6094558238983154] |
12f9e452-9414-4b4d-a7d0-c69400a27ec9 | spoken-language-identification-with | null | null | https://www.researchgate.net/publication/301828095_Spoken_Language_Identification_with_Phonotactics_Methods_on_Minangkabau_Sundanese_and_Javanese_Languages | https://www.researchgate.net/publication/301828095_Spoken_Language_Identification_with_Phonotactics_Methods_on_Minangkabau_Sundanese_and_Javanese_Languages | Spoken Language Identification with Phonotactics Methods on Minangkabau, Sundanese, and Javanese Languages | Research in the field of spoken language identification (spoken LID) on local languages helps to extend the outreach of
technology to local language speakers. This research also contributes to the preservation of local languages. In this paper, we
report our work on identifying spoken data in three local Indonesian l... | ['and Mirna Adriani', 'Amalia Zahra', 'Nur Endah Safitri'] | 2016-10-01 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-5.56753129e-02 -1.94282621e-01 -1.73196241e-01 -5.92530429e-01
-8.62012088e-01 -7.90602565e-01 8.31249774e-01 -3.50476891e-01
-5.51660359e-01 8.70152533e-01 5.59123456e-01 -7.29831338e-01
3.17418247e-01 -3.92879486e-01 -3.03314537e-01 -5.23627698e-01
2.75611430e-01 7.39547789e-01 5.01086637e-02 -2.63637006... | [14.243820190429688, 6.84208869934082] |
0b4b8e3c-b369-427a-bcf0-72c83c16a166 | predicting-gaze-in-egocentric-video-by | 1803.09125 | null | http://arxiv.org/abs/1803.09125v3 | http://arxiv.org/pdf/1803.09125v3.pdf | Predicting Gaze in Egocentric Video by Learning Task-dependent Attention Transition | We present a new computational model for gaze prediction in egocentric videos
by exploring patterns in temporal shift of gaze fixations (attention
transition) that are dependent on egocentric manipulation tasks. Our assumption
is that the high-level context of how a task is completed in a certain way has
a strong influ... | ['Zhenqiang Li', 'Minjie Cai', 'Yifei Huang', 'Yoichi Sato'] | 2018-03-24 | predicting-gaze-in-egocentric-video-by-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Huang_Predicting_Gaze_in_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Huang_Predicting_Gaze_in_ECCV_2018_paper.pdf | eccv-2018-9 | ['eye-tracking'] | ['computer-vision'] | [ 2.91249037e-01 9.98048782e-02 -1.84883028e-01 -3.40336651e-01
1.11799031e-01 -3.08134053e-02 3.65282029e-01 -2.63411552e-01
-1.78844005e-01 3.97075444e-01 4.66821462e-01 2.81291828e-02
-1.44511327e-01 -1.04925089e-01 -8.90685380e-01 -6.87194228e-01
-1.12849995e-01 -2.14520663e-01 3.24937195e-01 -2.43613735... | [13.975506782531738, 0.04987473413348198] |
844a1811-5df0-4632-b58c-76151c6cb079 | sentence-level-sign-language-recognition | 2211.14447 | null | https://arxiv.org/abs/2211.14447v1 | https://arxiv.org/pdf/2211.14447v1.pdf | Sentence-Level Sign Language Recognition Framework | We present two solutions to sentence-level SLR. Sentence-level SLR required mapping videos of sign language sentences to sequences of gloss labels. Connectionist Temporal Classification (CTC) has been used as the classifier level of both models. CTC is used to avoid pre-segmenting the sentences into individual words. T... | ['Atra Akandeh'] | 2022-11-13 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.86657560e-01 -5.60391508e-02 -1.21301927e-01 -2.93747187e-01
-7.48807669e-01 -4.71098483e-01 4.61915433e-01 -4.90064561e-01
-9.52974677e-01 4.92087245e-01 4.15088058e-01 -9.06844065e-02
2.73369700e-01 -1.98547825e-01 -5.92035830e-01 -7.42495000e-01
-8.70331470e-03 2.42665812e-01 6.16339445e-01 6.88844686... | [9.17330551147461, -6.480647087097168] |
dbb7e8c5-cd09-4662-8673-d78770ce14eb | gradual-test-time-adaptation-by-self-training | 2208.07736 | null | https://arxiv.org/abs/2208.07736v2 | https://arxiv.org/pdf/2208.07736v2.pdf | Introducing Intermediate Domains for Effective Self-Training during Test-Time | Experiencing domain shifts during test-time is nearly inevitable in practice and likely results in a severe performance degradation. To overcome this issue, test-time adaptation continues to update the initial source model during deployment. A promising direction are methods based on self-training which have been shown... | ['Bin Yang', 'Mario Döbler', 'Robert A. Marsden'] | 2022-08-16 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.99441957e-01 -3.28776181e-01 -1.90327555e-01 -4.72063243e-01
-8.89018178e-01 -7.22482920e-01 5.09496033e-01 6.37069941e-02
-5.65244973e-01 1.06833887e+00 -1.23863205e-01 -2.46676639e-01
2.43706405e-01 -5.80794930e-01 -8.53043616e-01 -5.85418642e-01
-6.38652146e-02 6.62642837e-01 8.19813848e-01 -3.47484082... | [9.70718765258789, 1.5189100503921509] |
a3519b25-43e7-4166-bb37-4c94e9305cf3 | smile-and-laugh-expressions-detection-based | 2101.01874 | null | https://arxiv.org/abs/2101.01874v1 | https://arxiv.org/pdf/2101.01874v1.pdf | Smile and Laugh Expressions Detection Based on Local Minimum Key Points | In this paper, a smile and laugh facial expression is presented based on dimension reduction and description process of the key points. The paper has two main objectives; the first is to extract the local critical points in terms of their apparent features, and the second is to reduce the system's dependence on trainin... | ['Majid Harouni', 'Mina Mohammadi Dashti'] | 2021-01-06 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.86203891e-01 8.72619301e-02 6.80763274e-02 -1.37771279e-01
-1.83300421e-01 -1.71536624e-01 4.44927186e-01 1.54091239e-01
-6.85157031e-02 2.65924603e-01 -1.91119611e-02 3.08300883e-01
-4.64039594e-01 -6.17228627e-01 -6.51279911e-02 -9.88946795e-01
1.00315206e-01 6.49886802e-02 1.06660753e-01 -2.33824924... | [13.13843822479248, 0.6967126727104187] |
517c3b6b-6597-4e96-92c3-e6902137815a | mime-mutual-information-minimisation | 2001.05636 | null | https://arxiv.org/abs/2001.05636v1 | https://arxiv.org/pdf/2001.05636v1.pdf | MIME: Mutual Information Minimisation Exploration | We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn. We propose a counter-intuitive solution that we call Mutual Information Minimising Exploration (MIME) where an agent learns a latent rep... | ['Craig Atkinson', 'Lech Szymanski', 'Brendan McCane', 'Haitao Xu'] | 2020-01-16 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-1.56778827e-01 5.94443202e-01 2.38305889e-02 -1.72527730e-01
-5.18457532e-01 -4.80755240e-01 9.45063591e-01 5.14088273e-02
-6.81629956e-01 1.18612266e+00 -4.76680025e-02 -1.95068657e-01
-4.10463363e-01 -7.18861222e-01 -8.94372582e-01 -8.35682392e-01
-8.98718297e-01 6.80827677e-01 9.42029059e-02 -6.72251582... | [3.9274983406066895, 1.6894569396972656] |
d328dab2-4802-4c56-a517-5375ba672e55 | deep-kalman-filters | 1511.05121 | null | http://arxiv.org/abs/1511.05121v2 | http://arxiv.org/pdf/1511.05121v2.pdf | Deep Kalman Filters | Kalman Filters are one of the most influential models of time-varying
phenomena. They admit an intuitive probabilistic interpretation, have a simple
functional form, and enjoy widespread adoption in a variety of disciplines.
Motivated by recent variational methods for learning deep generative models, we
introduce a uni... | ['Rahul G. Krishnan', 'Uri Shalit', 'David Sontag'] | 2015-11-16 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.89140674e-02 1.40277073e-01 -2.35037014e-01 -2.51366645e-01
-5.31438828e-01 -2.59416342e-01 8.83190215e-01 -2.70478010e-01
-4.17819738e-01 1.19162309e+00 8.31701458e-01 -4.37142491e-01
-5.48997700e-01 -8.29045534e-01 -8.46444368e-01 -7.62318850e-01
-3.58033657e-01 3.79751056e-01 -3.48118275e-01 4.36408781... | [8.04527759552002, 5.439292907714844] |
fd636e57-cb86-44de-bbf5-73ef58a73e2e | study-on-the-tea-market-in-india | 2304.07851 | null | https://arxiv.org/abs/2304.07851v1 | https://arxiv.org/pdf/2304.07851v1.pdf | Study on the tea market in India | India's tea business has a long history and plays a significant role in the economics of the nation. India is the world's second-largest producer of tea, with Assam and Darjeeling being the most well-known tea-growing regions. Since the British introduced tea cultivation to India in the 1820s, the nation has produced t... | ['Sreayans Jain', 'Harshita Sachdev', 'Devansh Khandelwal', 'Adarsh Damani', 'Adit Vinod Nair'] | 2023-04-16 | null | null | null | null | ['culture'] | ['speech'] | [-4.06936586e-01 -3.14013034e-01 -6.01200938e-01 3.16221505e-01
-5.22079766e-01 -9.00440753e-01 4.78688538e-01 4.39581633e-01
-2.68020686e-02 8.12058985e-01 1.34664133e-01 -4.49179202e-01
1.65515229e-01 -1.07600427e+00 -1.41894743e-01 -1.01611745e+00
1.65681019e-01 1.39103800e-01 -1.15016922e-01 -6.04545295... | [9.09794807434082, 6.205521583557129] |
be6aa59f-04a8-4639-adf9-d199ff6679e4 | detecting-approximate-reflection-symmetry-in | 1706.08801 | null | http://arxiv.org/abs/1706.08801v6 | http://arxiv.org/pdf/1706.08801v6.pdf | Detecting Approximate Reflection Symmetry in a Point Set using Optimization on Manifold | We propose an algorithm to detect approximate reflection symmetry present in
a set of volumetrically distributed points belonging to $\mathbb{R}^d$
containing a distorted reflection symmetry pattern. We pose the problem of
detecting approximate reflection symmetry as the problem of establishing
correspondences between ... | ['Rajendra Nagar', 'Shanmuganathan Raman'] | 2017-06-27 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 2.48614773e-01 2.87613720e-02 3.34112912e-01 -1.87033296e-01
-7.87015259e-01 -4.48290139e-01 7.62118459e-01 -9.25450176e-02
-2.63668180e-01 -1.43776059e-01 2.99018063e-02 3.41793551e-04
-3.01282555e-01 -5.62370539e-01 -7.26866961e-01 -5.60952604e-01
-3.74490879e-02 5.46871185e-01 6.93777055e-02 -2.33976111... | [8.05241584777832, -2.3514676094055176] |
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