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b69dc1a0-b324-4860-a747-9ad7495939b1 | scenescape-text-driven-consistent-scene | 2302.01133 | null | https://arxiv.org/abs/2302.01133v2 | https://arxiv.org/pdf/2302.01133v2.pdf | SceneScape: Text-Driven Consistent Scene Generation | We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained tex... | ['Tali Dekel', 'Yoni Kasten', 'Amit Abecasis', 'Rafail Fridman'] | 2023-02-02 | null | null | null | null | ['video-generation', 'scene-generation', 'perpetual-view-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.6650567e-01 3.8490978e-01 2.7234721e-01 -2.1983030e-01
-5.4872942e-01 -7.3662120e-01 8.0577445e-01 -4.3809295e-01
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2.4329603e-01 4.6856752e-01 1.5295707e-01 -2.3908782e-01
3.4995976e-01... | [9.307686805725098, -2.7883713245391846] |
1b79305b-50e7-4aef-b996-e66143242a62 | demystifying-code-summarization-models | 2102.04625 | null | https://arxiv.org/abs/2102.04625v3 | https://arxiv.org/pdf/2102.04625v3.pdf | WheaCha: A Method for Explaining the Predictions of Models of Code | Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use fo... | ['Ke Wang', 'Linzhang Wang', 'Yu Wang'] | 2021-02-09 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 1.48386672e-01 3.19704145e-01 -5.86005628e-01 -5.87855756e-01
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2.81368941e-01 2.69337118e-01 8.76459628e-02 -2.72986323... | [7.524317741394043, 7.836153030395508] |
d273d7f7-01c6-45e6-8725-1f06a141e7e9 | matra-a-multilingual-attentive | 2208.10801 | null | https://arxiv.org/abs/2208.10801v1 | https://arxiv.org/pdf/2208.10801v1.pdf | MATra: A Multilingual Attentive Transliteration System for Indian Scripts | Transliteration is a task in the domain of NLP where the output word is a similar-sounding word written using the letters of any foreign language. Today this system has been developed for several language pairs that involve English as either the source or target word and deployed in several places like Google Translate... | ['Bhavesh Laddagiri', 'Yash Raj'] | 2022-08-23 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-2.93740690e-01 -1.44146591e-01 -2.14455947e-01 -1.21224880e-01
-1.17109537e+00 -8.81018162e-01 8.06715846e-01 -1.49767786e-01
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6.31998658e-01 9.80874181e-01 1.20937772e-01 -8.36987078... | [11.213993072509766, 10.334821701049805] |
7d0af43c-1936-473a-9647-ee5416cfffc8 | teaching-large-language-models-to-self-debug | 2304.05128 | null | https://arxiv.org/abs/2304.05128v1 | https://arxiv.org/pdf/2304.05128v1.pdf | Teaching Large Language Models to Self-Debug | Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose Self... | ['Denny Zhou', 'Nathanael Schärli', 'Maxwell Lin', 'Xinyun Chen'] | 2023-04-11 | null | null | null | null | ['program-repair', 'text-to-sql', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [-3.67561206e-02 3.63566607e-01 -3.42760623e-01 -3.45008284e-01
-1.29970694e+00 -7.13775337e-01 1.04559921e-01 4.28091764e-01
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-3.38160433e-02 2.47199804e-01 1.15637578e-01 -2.15579033... | [7.9317216873168945, 7.6912689208984375] |
467e8231-88db-46f6-875a-9d20bd5e092f | personalized-federated-learning-via-amortized | 2307.02222 | null | https://arxiv.org/abs/2307.02222v1 | https://arxiv.org/pdf/2307.02222v1.pdf | Personalized Federated Learning via Amortized Bayesian Meta-Learning | Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform w... | ['Yue Yu', 'Hui Wang', 'Zenglin Xu', 'Dun Zeng', 'Shaogao Lv', 'Shiyu Liu'] | 2023-07-05 | null | null | null | null | ['meta-learning', 'federated-learning', 'personalized-federated-learning'] | ['methodology', 'methodology', 'methodology'] | [-2.78749824e-01 6.89065382e-02 -1.95722222e-01 -7.05283940e-01
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2.23919123e-01 6.07557058e-01 7.38034099e-02 5.85962772... | [5.836982250213623, 6.336577415466309] |
c6e50a86-994b-4515-aa73-09d855ac6cf8 | modeling-biological-face-recognition-with | 2208.06681 | null | https://arxiv.org/abs/2208.06681v2 | https://arxiv.org/pdf/2208.06681v2.pdf | Modeling biological face recognition with deep convolutional neural networks | Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and consequently, recent efforts have started to transfer this achievement to research on biological face recognition. In... | ['Walter R. Gruber', 'Leonard E. van Dyck'] | 2022-08-13 | null | null | null | null | ['face-detection', 'face-identification'] | ['computer-vision', 'computer-vision'] | [ 4.38401282e-01 3.28560881e-02 -3.75856049e-02 -3.81822795e-01
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4.12332593e-03 -5.28981462e-02 -4.18855160e-01 -1.23627044... | [10.326226234436035, 2.239854335784912] |
48b78bbe-77f2-45bd-b024-f1768d12f6a8 | daliid-distortion-adaptive-learned-invariance | 2302.05753 | null | https://arxiv.org/abs/2302.05753v1 | https://arxiv.org/pdf/2302.05753v1.pdf | DaliID: Distortion-Adaptive Learned Invariance for Identification Models | In unconstrained scenarios, face recognition and person re-identification are subject to distortions such as motion blur, atmospheric turbulence, or upsampling artifacts. To improve robustness in these scenarios, we propose a methodology called Distortion-Adaptive Learned Invariance for Identification (DaliID) models. ... | ['Terrance E. Boult', 'Gabriel Bertocco', 'Wes Robbins'] | 2023-02-11 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 3.90486538e-01 -4.08375025e-01 1.04727596e-01 -5.07084191e-01
-3.51539195e-01 -5.71766436e-01 8.92647088e-01 -4.65688407e-01
-4.05697376e-01 3.76876980e-01 4.37554687e-01 -5.85705182e-03
-3.06090266e-01 -3.52948964e-01 -4.03050870e-01 -6.17401302e-01
-1.42731756e-01 6.39139190e-02 -3.97611380e-01 -8.96167010... | [13.362154006958008, 0.8177219033241272] |
cc57e014-72d0-4f79-9615-7ecf0c260d13 | inesc-id-sentiment-analysis-without-hand | null | null | https://aclanthology.org/S15-2109 | https://aclanthology.org/S15-2109.pdf | INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces | null | ['Ramon Astudillo', 'Wang Ling', 'Silvio Amir', 'Isabel Trancoso', 'Mario J. Silva', 'Bruno Martins'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.220873832702637, 3.8006834983825684] |
e8699a3b-faeb-419a-95b8-8a199e40bc09 | generating-coherent-spontaneous-speech-and | 2101.05684 | null | https://arxiv.org/abs/2101.05684v1 | https://arxiv.org/pdf/2101.05684v1.pdf | Generating coherent spontaneous speech and gesture from text | Embodied human communication encompasses both verbal (speech) and non-verbal information (e.g., gesture and head movements). Recent advances in machine learning have substantially improved the technologies for generating synthetic versions of both of these types of data: On the speech side, text-to-speech systems are n... | ['Jonas Beskow', 'Taras Kucherenko', 'Gustav Eje Henter', 'Éva Székely', 'Simon Alexanderson'] | 2021-01-14 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 2.98414111e-01 4.72097993e-01 2.43565202e-01 -2.49488518e-01
-1.09116495e+00 -5.14129996e-01 1.30977261e+00 -7.54512429e-01
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1.33608624e-01 5.49714506e-01 -3.63888517e-02 -3.45674664... | [5.610383987426758, -0.10062926262617111] |
e693f4a8-c642-453c-9abf-976c0460f778 | discovering-entity-knowledge-bases-on-the-web | null | null | https://aclanthology.org/W16-1302 | https://aclanthology.org/W16-1302.pdf | Discovering Entity Knowledge Bases on the Web | null | ['Will Radford', 'Andrew Chisholm', 'Ben Hachey'] | 2016-06-01 | null | null | null | ws-2016-6 | ['person-recognition'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.408065319061279, 3.7329864501953125] |
8656c2b8-8ea5-4c16-8ca8-f9f09150e33f | adversarial-multitask-learning-for-joint-1 | 1910.12702 | null | https://arxiv.org/abs/1910.12702v1 | https://arxiv.org/pdf/1910.12702v1.pdf | Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling | Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources. In this paper we explore the use o... | ['Nizar Habash', 'Nasser Zalmout'] | 2019-10-28 | adversarial-multitask-learning-for-joint | https://aclanthology.org/P19-1173 | https://aclanthology.org/P19-1173.pdf | acl-2019-7 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.06714034e-01 -8.20087343e-02 1.54337689e-01 -4.92287636e-01
-1.26753759e+00 -9.87388313e-01 3.80756855e-01 4.40778099e-02
-7.30492353e-01 6.53798759e-01 2.01868623e-01 -3.29030514e-01
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-2.13826478e-01 7.88800836e-01 -1.76570162e-01 -6.68726325... | [10.647099494934082, 9.9813814163208] |
d0bd699f-7bbd-4a58-81db-2ac10c568cd7 | towards-controllable-protein-design-with | 2201.07338 | null | https://arxiv.org/abs/2201.07338v2 | https://arxiv.org/pdf/2201.07338v2.pdf | Controllable Protein Design with Language Models | The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make t... | ['Birte Höcker', 'Noelia Ferruz'] | 2022-01-18 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.62822449e-01 2.52486378e-01 -5.03044836e-02 -5.69808841e-01
-2.82817513e-01 -1.08994865e+00 5.51695287e-01 3.62132460e-01
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-1.72222089e-02 5.83592713e-01 1.47034079e-01 -7.05889344... | [4.683996677398682, 5.579418659210205] |
97b7f0d5-4ad1-408a-ab15-f069a4036dcc | uniadapter-unified-parameter-efficient | 2302.06605 | null | https://arxiv.org/abs/2302.06605v2 | https://arxiv.org/pdf/2302.06605v2.pdf | UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling | Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the standard full fine-tuning paradigm becomes unsustainable due to heavy computational and storage costs. This paper proposes ... | ['Wei Zhan', 'Masayoshi Tomizuka', 'Zhiwu Lu', 'Guoxing Yang', 'Yuqi Huo', 'Mingyu Ding', 'Haoyu Lu'] | 2023-02-13 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-1.16545416e-01 -4.23895746e-01 -3.60157192e-01 -2.25719213e-01
-1.22569644e+00 -8.54347885e-01 7.62649357e-01 -2.78215289e-01
-6.21293843e-01 3.43547702e-01 3.03526849e-01 -2.52367139e-01
2.26932675e-01 -3.77395183e-01 -7.86312997e-01 -6.14540637e-01
4.21906382e-01 4.27521378e-01 1.38680384e-01 -3.09020668... | [10.365816116333008, 1.6268411874771118] |
5587c862-5d72-4885-855b-4ce4d885eee3 | learning-canonical-shape-space-for-category | 2001.09322 | null | https://arxiv.org/abs/2001.09322v3 | https://arxiv.org/pdf/2001.09322v3.pdf | Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation | We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative... | ['Zheng Wang', 'Jun Li', 'Dengsheng Chen', 'Kai Xu'] | 2020-01-25 | learning-canonical-shape-space-for-category-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-representation', 'generating-3d-point-clouds'] | ['computer-vision', 'computer-vision'] | [ 2.73128343e-03 2.51197487e-01 2.09642097e-01 -5.42478740e-01
-1.09263575e+00 -9.28203464e-01 7.25976169e-01 -4.10958290e-01
-2.49010120e-02 -1.42268822e-01 8.53043422e-02 2.34093308e-01
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3.65796447e-01 1.27112520e+00 -1.93457697e-02 2.80299544... | [8.226242065429688, -3.230858325958252] |
ca1b71d4-80e9-401d-9aca-8c0649bc7c74 | a-framework-for-depth-estimation-and-relative | 1908.00309 | null | https://arxiv.org/abs/1908.00309v1 | https://arxiv.org/pdf/1908.00309v1.pdf | A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision | The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localiz... | ['Romulo T. Rodrigues', 'A. Pedro Aguiar', 'Pedro Miraldo', 'Dimos V. Dimarogonas'] | 2019-08-01 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [-1.14880748e-01 1.90119475e-01 2.20766664e-01 -3.12938631e-01
-3.87151897e-01 -5.46729624e-01 7.17316628e-01 3.91118407e-01
-8.81133974e-01 5.62800825e-01 -3.29396516e-01 8.30169395e-02
-2.38849193e-01 -6.18388712e-01 -7.81662703e-01 -7.38934219e-01
-1.09100856e-01 1.17806721e+00 3.59816611e-01 -3.21639717... | [7.284296035766602, -2.1049301624298096] |
c073a96a-85ce-4a76-b88d-b486768d5f4c | the-effect-of-wearing-a-face-mask-on-face | 2110.11283 | null | https://arxiv.org/abs/2110.11283v4 | https://arxiv.org/pdf/2110.11283v4.pdf | The Effect of Wearing a Face Mask on Face Image Quality | Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. Th... | ['Naser Damer', 'Florian Kirchbuchner', 'Biying Fu'] | 2021-10-21 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.73142180e-01 8.35526213e-02 3.42416316e-01 -4.31681305e-01
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-2.40220517e-01 5.44383749e-02 -7.74812326e-02 -1.09372012... | [13.070234298706055, 0.9680819511413574] |
b8efc4bb-9562-4d8f-99d4-8aac95052477 | an-end-to-end-multi-module-audio-deepfake | 2307.00729 | null | https://arxiv.org/abs/2307.00729v1 | https://arxiv.org/pdf/2307.00729v1.pdf | An End-to-End Multi-Module Audio Deepfake Generation System for ADD Challenge 2023 | The task of synthetic speech generation is to generate language content from a given text, then simulating fake human voice.The key factors that determine the effect of synthetic speech generation mainly include speed of generation, accuracy of word segmentation, naturalness of synthesized speech, etc. This paper build... | ['Zhuoyue Chen', 'Yibo Duan', 'Qilong Yuan', 'Sheng Zhao'] | 2023-07-03 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.31415480e-02 3.52293521e-01 1.43357351e-01 -2.17416003e-01
-1.05598819e+00 -4.91074771e-01 9.28702772e-01 -8.28884065e-01
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6.04029179e-01 3.89546961e-01 -4.48999740e-03 -4.74099606... | [15.04190731048584, 6.507950782775879] |
a7806385-baa2-4e7f-8d32-b449123d6f26 | gender-biases-in-automatic-evaluation-metrics | 2305.14711 | null | https://arxiv.org/abs/2305.14711v1 | https://arxiv.org/pdf/2305.14711v1.pdf | Gender Biases in Automatic Evaluation Metrics: A Case Study on Image Captioning | Pretrained model-based evaluation metrics have demonstrated strong performance with high correlations with human judgments in various natural language generation tasks such as image captioning. Despite the impressive results, their impact on fairness is under-explored -- it is widely acknowledged that pretrained models... | ['Nanyun Peng', 'Asli Celikyilmaz', 'Tianlu Wang', 'Zi-Yi Dou', 'Haoyi Qiu'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 3.68233770e-01 4.95414078e-01 -4.35779244e-01 -8.83080602e-01
-6.01526618e-01 -4.01118815e-01 8.79563987e-01 3.28100324e-01
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3.08024764e-01 4.59708512e-01 -7.11523890e-01 -2.31833115... | [12.599200248718262, 1.3271886110305786] |
5dfb005b-6dbc-4738-b746-8e23547462fc | a-user-centered-interactive-human-in-the-loop | 2304.01774 | null | https://arxiv.org/abs/2304.01774v1 | https://arxiv.org/pdf/2304.01774v1.pdf | A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System | Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the l... | ['Rob Procter', 'Yulan He', 'Du Liu', 'Lama Alqazlan', 'Zheng Fang'] | 2023-04-04 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.93687975e-01 5.55310965e-01 5.48444688e-02 -6.23565733e-01
-8.39048624e-01 -6.87685728e-01 7.05792904e-01 7.91662872e-01
-3.85894477e-01 4.76436198e-01 3.59345943e-01 -5.03664255e-01
-6.63712844e-02 -6.53634667e-01 -1.70224473e-01 -9.33423564e-02
1.39928296e-01 9.72412765e-01 7.08912015e-01 -3.59610319... | [10.424726486206055, 7.169796943664551] |
6ce34e36-319f-4475-a786-83765e27889e | deep-reinforcement-learning-with-stacked | 2010.11655 | null | https://arxiv.org/abs/2010.11655v3 | https://arxiv.org/pdf/2010.11655v3.pdf | Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games | We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with te... | ['Chengqi Zhang', 'Joey Tianyi Zhou', 'Yali Du', 'Ling Chen', 'Meng Fang', 'Yunqiu Xu'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/bf65417dcecc7f2b0006e1f5793b7143-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/bf65417dcecc7f2b0006e1f5793b7143-Paper.pdf | neurips-2020-12 | ['text-based-games'] | ['playing-games'] | [-8.21372196e-02 5.79125822e-01 -3.97920758e-02 -1.06434144e-01
-1.36188954e-01 -4.61445928e-01 8.39189768e-01 8.90345648e-02
-4.04765517e-01 9.36937869e-01 4.18272793e-01 -5.68868279e-01
-2.44232137e-02 -1.37921691e+00 -4.63636130e-01 -2.16823280e-01
-5.92319369e-02 7.56861746e-01 5.11953712e-01 -7.88207114... | [3.8094332218170166, 1.3131780624389648] |
8ed2bb36-d9f2-4857-9a0a-6fdf7d2bfbc2 | similarities-and-differences-between-stimulus | 1612.06975 | null | http://arxiv.org/abs/1612.06975v1 | http://arxiv.org/pdf/1612.06975v1.pdf | Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks | Deep convolutional neural networks (CNNs) trained for object classification
have a number of striking similarities with the primate ventral visual stream.
In particular, activity in early, intermediate, and late layers is closely
related to activity in V1, V4, and the inferotemporal cortex (IT). This study
further comp... | [] | 2016-12-21 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.11418523e-01 -4.82603461e-01 -3.65699120e-02 -4.25632715e-01
-9.65194181e-02 -1.00574672e+00 6.02223277e-01 4.05412525e-01
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-2.30224982e-01 -2.73766398e-01 -6.88989043e-01 -6.29190505e-01
-2.70302027e-01 -3.29446167e-01 5.54234445e-01 2.99285911... | [9.640077590942383, 2.44645619392395] |
e075b844-1b25-4968-8b7a-1bf20fea6fed | vid2seq-large-scale-pretraining-of-a-visual | 2302.14115 | null | https://arxiv.org/abs/2302.14115v2 | https://arxiv.org/pdf/2302.14115v2.pdf | Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the ... | ['Cordelia Schmid', 'Josef Sivic', 'Ivan Laptev', 'Jordi Pont-Tuset', 'Antoine Miech', 'Paul Hongsuck Seo', 'Arsha Nagrani', 'Antoine Yang'] | 2023-02-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.82069319e-01 4.09065425e-01 -5.83283365e-01 -3.05028379e-01
-1.14437795e+00 -6.79474533e-01 7.94611931e-01 -4.39323753e-01
-2.70986855e-01 9.25745547e-01 1.06044388e+00 -1.96545627e-02
6.96295798e-01 -3.20097208e-01 -1.19639230e+00 -3.47805053e-01
-1.24511443e-01 4.28270906e-01 6.52553663e-02 1.80727184... | [10.47775936126709, 0.7202789187431335] |
b343024a-9649-4036-931c-66a767cd31eb | kimera-an-open-source-library-for-real-time | 1910.02490 | null | https://arxiv.org/abs/1910.02490v3 | https://arxiv.org/pdf/1910.02490v3.pdf | Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping | We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM, VINS- Mono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling in 3D. Kimera is d... | ['Luca Carlone', 'Antoni Rosinol', 'Yun Chang', 'Marcus Abate'] | 2019-10-06 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-3.26387644e-01 -2.92878002e-01 -1.99781433e-01 -3.13853145e-01
-7.21716940e-01 -6.95699215e-01 5.11847615e-01 5.53841926e-02
-2.95904577e-01 4.47450966e-01 -1.18324660e-01 -4.30638999e-01
-1.25070075e-02 -7.66024649e-01 -8.41566682e-01 -1.25133514e-01
-1.15747258e-01 1.08474290e+00 3.59566689e-01 -2.75250643... | [7.388444900512695, -2.2035486698150635] |
ff5ea051-5264-4331-b118-325591bcbf6d | probabilistic-time-series-forecasting-for | 2211.13729 | null | https://arxiv.org/abs/2211.13729v2 | https://arxiv.org/pdf/2211.13729v2.pdf | Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments | With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link im... | ['Lauritz Thamsen', 'Odej Kao', 'Soeren Becker', 'Babak Sistani Zadeh Aghdam', 'Dominik Scheinert'] | 2022-11-24 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 2.96341062e-01 1.38847679e-01 -5.38404472e-02 -1.94097817e-01
-1.70526639e-01 -6.80916369e-01 6.07472122e-01 3.67515951e-01
-2.95970440e-01 7.87217557e-01 -4.96788844e-02 -4.85552102e-01
-6.76800430e-01 -8.68648648e-01 -4.66657788e-01 -4.06350553e-01
-1.40840366e-01 5.77074409e-01 7.46086419e-01 2.86707520... | [6.861713409423828, 2.6971137523651123] |
576d916e-b409-46e1-9102-bc82b6307aff | correlating-edge-pose-with-parsing | 2005.01431 | null | https://arxiv.org/abs/2005.01431v1 | https://arxiv.org/pdf/2005.01431v1.pdf | Correlating Edge, Pose with Parsing | According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirmed through the concatenation of their features with the parsing features. Driven by the insights, this paper studies how human semantic boun... | ['Xiaodong Xie', 'Chi Su', 'Ziwei Zhang', 'Liang Zheng'] | 2020-05-04 | correlating-edge-pose-with-parsing-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Correlating_Edge_Pose_With_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Correlating_Edge_Pose_With_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 9.11063701e-02 1.44527495e-01 -3.83250862e-01 -4.37510282e-01
-6.63773477e-01 -4.41109449e-01 5.08485973e-01 1.28791824e-01
-2.80034035e-01 3.06624562e-01 7.83064723e-01 2.08586618e-01
-2.47005045e-01 -6.09258473e-01 -6.85977817e-01 -3.82749826e-01
-2.94398040e-01 -3.79949771e-02 4.26344395e-01 -2.28486493... | [7.9938225746154785, -0.3066132068634033] |
7f9ee0cb-1321-44df-b66b-2213f7c3bbdc | specifying-object-attributes-and-relations-in | 1909.05379 | null | https://arxiv.org/abs/1909.05379v2 | https://arxiv.org/pdf/1909.05379v2.pdf | Specifying Object Attributes and Relations in Interactive Scene Generation | We introduce a method for the generation of images from an input scene graph. The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs. In addition, the e... | ['Oron Ashual', 'Lior Wolf'] | 2019-09-11 | specifying-object-attributes-and-relations-in-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.pdf | iccv-2019-10 | ['layout-to-image-generation', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 2.36733183e-01 1.11516409e-01 1.22000456e-01 -3.74010384e-01
-2.57238925e-01 -1.02923167e+00 5.17288089e-01 1.62715942e-01
3.95584218e-02 3.00272882e-01 2.29162648e-01 -3.10539126e-01
1.19739987e-01 -1.11564672e+00 -7.09634602e-01 -4.32876199e-01
1.56098366e-01 1.23699404e-01 4.87245053e-01 -6.66636601... | [11.2797269821167, -0.2892415523529053] |
a2db815e-2a79-4317-8bd4-9556294b39e9 | endowing-language-models-with-multimodal | 2206.13163 | null | https://arxiv.org/abs/2206.13163v1 | https://arxiv.org/pdf/2206.13163v1.pdf | Endowing Language Models with Multimodal Knowledge Graph Representations | We propose a method to make natural language understanding models more parameter efficient by storing knowledge in an external knowledge graph (KG) and retrieving from this KG using a dense index. Given (possibly multilingual) downstream task data, e.g., sentences in German, we retrieve entities from the KG and use the... | ['Iacer Calixto', 'Clara Vania', 'Kyunghyun Cho', 'Houda Alberts', 'Yibo Liu', 'Yash R. Deshpande', 'Ningyuan Huang'] | 2022-06-27 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.59579742e-01 5.18111229e-01 -2.67472506e-01 -2.57299066e-01
-1.18130708e+00 -8.59997392e-01 5.10285199e-01 6.19292438e-01
-6.56703830e-01 7.23905861e-01 4.20309037e-01 -1.85930982e-01
-2.05109157e-02 -9.36138511e-01 -8.88644755e-01 -2.05591798e-01
-6.24569133e-02 7.45867431e-01 -3.72110493e-02 -3.12616855... | [9.039839744567871, 8.028904914855957] |
017a7f89-b157-40a2-91ac-088d63580ce8 | meta-learning-of-interface-conditions-for | 2210.12669 | null | https://arxiv.org/abs/2210.12669v2 | https://arxiv.org/pdf/2210.12669v2.pdf | Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks | Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply different PINNs to solve the problem in each subdomain, and stitch the subdomains at the interface. Thereby, they can further alleviate the problem ... | ['Shandian Zhe', 'Robert M. Kirby', 'Akil Narayan', 'Conor Tillinghast', 'Yiming Xu', 'Michael Penwarden', 'Shibo Li'] | 2022-10-23 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-4.65329736e-02 -1.63755164e-01 -3.58533412e-01 2.03714937e-01
-1.15045595e+00 -7.18948781e-01 3.00088793e-01 4.05282192e-02
-2.87785232e-01 1.06233251e+00 -1.26345530e-01 -4.96201158e-01
-6.54239178e-01 -9.35015261e-01 -9.34728801e-01 -1.04457474e+00
1.95777461e-01 1.03157365e+00 1.32061124e-01 7.08073229... | [6.621880054473877, 3.951761245727539] |
6a2cbcc6-3f1d-4518-9ef7-506fbb0f83cf | vecchia-gaussian-process-ensembles-on | 2305.17063 | null | https://arxiv.org/abs/2305.17063v1 | https://arxiv.org/pdf/2305.17063v1.pdf | Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks | For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification, while deep neural networks (DNNs) excel at representation learning. We propose to synergistically combine these two approaches in a hybrid method consisting of an ensemble of GPs built on the output of hidden layers of a... | ['Matthias Katzfuss', 'Felix Jimenez'] | 2023-05-26 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [-2.83073664e-01 3.03764135e-01 1.94011450e-01 -4.78119522e-01
-6.31996930e-01 -4.94986504e-01 9.14794445e-01 1.02874219e-01
-1.51849091e-01 8.95832658e-01 1.62980720e-01 -3.37751925e-01
-2.33141884e-01 -1.14024484e+00 -7.95342803e-01 -1.00685227e+00
-5.08119427e-02 6.23138547e-01 5.28453514e-02 1.76449373... | [7.2266740798950195, 3.8163340091705322] |
2cd8cb59-60ce-4f84-887e-8a45cae8e9cb | a-graphical-point-process-framework-for | 2302.06075 | null | https://arxiv.org/abs/2302.06075v1 | https://arxiv.org/pdf/2302.06075v1.pdf | A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution | Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of onl... | ['Lingzhou Xue', 'Amirhossein Meisami', 'Arava Sai Kumar', 'James W. Snyder Jr.', 'Qian Chen', 'Jun Tao'] | 2023-02-13 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.57442109e-02 -2.83188939e-01 -9.00411189e-01 -5.59186578e-01
-4.98451173e-01 -6.60524547e-01 6.25685513e-01 4.73649055e-01
-6.91366047e-02 3.30835432e-01 -8.00022557e-02 -5.33711255e-01
-5.82115948e-01 -1.00230861e+00 -6.60013735e-01 -2.02889934e-01
-1.05836347e-01 4.79161859e-01 9.53349918e-02 -2.69310027... | [9.584177017211914, 5.3930792808532715] |
7cfcb90a-6526-48b4-9e20-b9e0a017ccd2 | camera-calibration-from-a-single-imaged | 2307.00689 | null | https://arxiv.org/abs/2307.00689v1 | https://arxiv.org/pdf/2307.00689v1.pdf | Camera Calibration from a Single Imaged Ellipsoid: A Moon Calibration Algorithm | This work introduces a method that applies images of the extended bodies in the solar system to spacecraft camera calibration. The extended bodies consist of planets and moons that are well-modeled by triaxial ellipsoids. When imaged, the triaxial ellipsoid projects to a conic section which is generally an ellipse. Thi... | ['Mason A. Peck', 'Kalani R. Danas Rivera'] | 2023-07-02 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 4.71555889e-02 2.96376526e-01 2.77606845e-01 -3.88269946e-02
-6.27910718e-02 -1.03860343e+00 7.98445880e-01 -1.02544272e+00
-3.77514899e-01 5.74253678e-01 -5.10710716e-01 -2.46119365e-01
2.73116380e-01 -3.66194189e-01 -6.85567200e-01 -5.79535365e-01
4.05347198e-01 1.07825315e+00 1.08108394e-01 2.50495404... | [7.8988752365112305, -2.246901512145996] |
bc46ea45-e9e6-45bf-b288-42c4a07d8abf | representation-flow-for-action-recognition | 1810.01455 | null | https://arxiv.org/abs/1810.01455v3 | https://arxiv.org/pdf/1810.01455v3.pdf | Representation Flow for Action Recognition | In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to capture the `flow' of any representation channel within a convolutional neural network for action recognition. Its parameters for... | ['Michael S. Ryoo', 'AJ Piergiovanni'] | 2018-10-02 | representation-flow-for-action-recognition-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Piergiovanni_Representation_Flow_for_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Piergiovanni_Representation_Flow_for_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['activity-recognition-in-videos'] | ['computer-vision'] | [ 1.03606479e-02 -3.00506204e-01 -6.31993294e-01 -2.52106577e-01
-8.99123698e-02 -4.16841298e-01 5.87545872e-01 -6.15146220e-01
-3.96368295e-01 6.53414130e-01 6.55616522e-01 -1.64673254e-01
5.59321083e-02 -6.21222854e-01 -6.10916495e-01 -4.14428055e-01
-3.26340079e-01 -1.46998778e-01 5.62544502e-02 1.40353963... | [8.517139434814453, 0.09249679744243622] |
d81aa905-b5b0-447d-8f3e-92e216473418 | fine-grained-temporal-relation-extraction-1 | null | null | https://aclanthology.org/2021.wnut-1.5 | https://aclanthology.org/2021.wnut-1.5.pdf | Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks | Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors. In this wor... | ['Thien Huu Nguyen', 'Minh Van Nguyen', 'Minh Tran Phu'] | null | null | null | null | wnut-acl-2021-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-1.70539960e-01 3.57633919e-01 -3.09509337e-01 -7.43709266e-01
-5.37314117e-01 -4.18239653e-01 6.93042696e-01 6.68067038e-01
-6.09590471e-01 8.10742795e-01 7.16892362e-01 -1.62433878e-01
-2.15091988e-01 -9.21279252e-01 -4.82094496e-01 -4.13884163e-01
-3.63307297e-01 3.82776111e-01 4.45656985e-01 -1.68484882... | [9.108587265014648, 9.13388729095459] |
5f263d46-1b2d-4c90-b724-0e3070cf8ed3 | combatant-tamilnlp-acl2022-fine-grained | null | null | https://aclanthology.org/2022.dravidianlangtech-1.34 | https://aclanthology.org/2022.dravidianlangtech-1.34.pdf | COMBATANT@TamilNLP-ACL2022: Fine-grained Categorization of Abusive Comments using Logistic Regression | With the widespread usage of social media and effortless internet access, millions of posts and comments are generated every minute. Unfortunately, with this substantial rise, the usage of abusive language has increased significantly in these mediums. This proliferation leads to many hazards such as cyber-bullying, vul... | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain', 'Mahathir Bishal', 'Alamgir Hossain'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['abusive-language'] | ['natural-language-processing'] | [-3.58455956e-01 -2.45813683e-01 -7.86939338e-02 4.23277915e-02
-6.75115049e-01 -4.28116500e-01 6.51344538e-01 2.20744133e-01
-6.11594915e-01 9.05288458e-01 -9.97941643e-02 -6.02825642e-01
3.73565257e-01 -5.92007458e-01 -1.14044137e-01 -3.60493958e-01
1.86192319e-01 1.18948057e-01 3.17699820e-01 -6.70670569... | [8.855563163757324, 10.580623626708984] |
6c5d0527-3b68-41c6-9f7e-3bf8d2b095b5 | event-intensity-stereo-estimating-depth-by | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Mostafavi_Event-Intensity_Stereo_Estimating_Depth_by_the_Best_of_Both_Worlds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Mostafavi_Event-Intensity_Stereo_Estimating_Depth_by_the_Best_of_Both_Worlds_ICCV_2021_paper.pdf | Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds | Event cameras can report scene movements as an asynchronous stream of data called the events. Unlike traditional cameras, event cameras have very low latency (microseconds vs milliseconds) very high dynamic range (140dB vs 60 dB), and low power consumption, as they report changes of a scene and not a complete frame... | ['Jonghyun Choi', 'Kuk-Jin Yoon', 'Mohammad Mostafavi'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 5.78152001e-01 -5.16076446e-01 1.49447545e-01 -4.89292353e-01
-4.18901652e-01 -6.32028282e-01 4.73290890e-01 5.72010726e-02
-8.02210331e-01 7.68466592e-01 1.63452908e-01 3.00377104e-02
2.26957127e-01 -8.44849527e-01 -7.70288408e-01 -5.71619630e-01
9.05544162e-02 -1.01808771e-01 9.58642662e-01 2.76195377... | [8.810954093933105, -1.5987322330474854] |
60d7fbcc-0675-4980-a582-1160bac4ea57 | haar-wavelet-feature-compression-for | 2110.04824 | null | https://arxiv.org/abs/2110.04824v1 | https://arxiv.org/pdf/2110.04824v1.pdf | Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks | Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these... | ['Eran Treister', 'Benjamin Bodner', 'Moshe Eliasof'] | 2021-10-10 | null | null | null | null | ['feature-compression', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 2.43582159e-01 1.61267251e-01 -1.32126614e-01 -1.52912870e-01
-3.57724637e-01 -3.61039340e-01 3.73710483e-01 4.80805278e-01
-4.93907154e-01 3.04950953e-01 -4.52918321e-01 -3.23852092e-01
-8.34336877e-03 -1.20228159e+00 -9.04030502e-01 -4.61865991e-01
-3.44833523e-01 3.68715137e-01 5.19421875e-01 -9.27604884... | [7.920679092407227, -3.4888179302215576] |
a7e70e25-2346-4ee9-8351-0ea927f2c4f1 | person-identification-and-body-mass-index-a | 1811.07173 | null | http://arxiv.org/abs/1811.07173v2 | http://arxiv.org/pdf/1811.07173v2.pdf | Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers | Obtaining a smart surveillance requires a sensing system that can capture
accurate and detailed information for the human walking style. The radar
micro-Doppler ($\boldsymbol{\mu}$-D) analysis is proved to be a reliable metric
for studying human locomotions. Thus, $\boldsymbol{\mu}$-D signatures can be
used to identify... | ['Fady Aziz', 'Urs Schneider', 'Sherif Abdulatif', 'Karim Armanious', 'Bernhard Kleiner', 'Bin Yang'] | 2018-11-17 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.17994286e-01 -2.75289029e-01 1.74649045e-01 -3.44259918e-01
-3.11959863e-01 1.45919949e-01 5.62520027e-02 -2.94584692e-01
-5.19250393e-01 5.44283032e-01 9.74515751e-02 -1.31866157e-01
-6.65933311e-01 -1.01342392e+00 -3.77076566e-01 -9.16635215e-01
-9.24498379e-01 4.76024225e-02 -1.19856872e-01 -3.99870396... | [14.093668937683105, 1.517244577407837] |
2cdb0a0e-579f-43ac-9602-241a62f400d6 | graph-to-tree-learning-for-solving-math-word | null | null | https://aclanthology.org/2020.acl-main.362 | https://aclanthology.org/2020.acl-main.362.pdf | Graph-to-Tree Learning for Solving Math Word Problems | While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution exp... | ['Ee-Peng Lim', 'Roy Ka-Wei Lee', 'Yi Bin', 'Jipeng Zhang', 'Jie Shao', 'Yan Wang', 'Lei Wang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.75735797e-03 1.26334623e-01 -2.47793317e-01 -4.41616476e-01
-8.76650870e-01 -4.02591109e-01 2.74212152e-01 2.62365073e-01
-6.82021081e-02 9.11419570e-01 4.33846802e-01 -5.86656988e-01
1.22892745e-01 -1.20254838e+00 -8.52237821e-01 -1.27434596e-01
2.20886692e-01 3.83779526e-01 -1.60036281e-01 -4.00467187... | [9.74813461303711, 7.510909080505371] |
362b2a6d-2edd-44ae-8893-3fba67d213da | towards-real-time-6d-pose-estimation-of | 2211.03211 | null | https://arxiv.org/abs/2211.03211v1 | https://arxiv.org/pdf/2211.03211v1.pdf | Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray | Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purp... | ['Fons van der Sommen', 'Peter H. N. de With', 'Lena Filatova', 'Joel de Bruijn', 'Christiaan G. A. Viviers'] | 2022-11-06 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.43411714e-02 1.74407646e-01 2.51933057e-02 -3.84689599e-01
-1.13824880e+00 -2.27101058e-01 1.92083865e-01 8.68503526e-02
-6.49138451e-01 2.55742133e-01 -5.47310531e-01 -2.34463885e-01
-1.82816625e-01 -7.02023447e-01 -8.78066659e-01 -4.71381664e-01
-1.71490788e-01 1.27055907e+00 1.34755149e-01 -2.09378362... | [13.571736335754395, -2.7943899631500244] |
ae2e4605-3e80-46d0-b98e-dcee03e1d43f | nerf-textbf-2-neural-radio-frequency-radiance | 2305.06118 | null | https://arxiv.org/abs/2305.06118v1 | https://arxiv.org/pdf/2305.06118v1.pdf | NeRF$^\textbf{2}$: Neural Radio-Frequency Radiance Fields | Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., refl... | ['Lei Yang', 'Qingrui Pan', 'Zhenlin An', 'Xiaopeng Zhao'] | 2023-05-10 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 2.48550773e-01 -7.46181682e-02 4.74049926e-01 -1.74311191e-01
-3.81740361e-01 -1.88996866e-02 1.64362006e-02 -5.15510142e-01
-3.79608095e-01 9.55069005e-01 -4.28687423e-01 -7.80470550e-01
-4.43470538e-01 -1.21196580e+00 -9.14885759e-01 -9.75751221e-01
-5.40293932e-01 -1.55138537e-01 -1.01639867e-01 -2.68056154... | [6.3335041999816895, 1.222572922706604] |
4cd1dfbd-8550-4198-bd31-53403662e7ab | machine-learning-approaches-for-non-intrusive | 2203.16538 | null | https://arxiv.org/abs/2203.16538v1 | https://arxiv.org/pdf/2203.16538v1.pdf | Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use | Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios include but are not limited to: elderly people living alone, people suffering from dementia, home quarantine. The majority of... | ['Dimitris Vrakas', 'Athanasios Lentzas'] | 2022-03-30 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 4.16196018e-01 8.13553408e-02 -5.54227903e-02 -1.23678699e-01
-5.27201355e-01 -3.29303354e-01 5.16498864e-01 9.50266197e-02
-1.97398365e-01 1.23011947e+00 3.49333704e-01 3.41820568e-02
-1.73860833e-01 -1.05720448e+00 -9.76031646e-02 -1.01905429e+00
1.00525446e-01 2.86752880e-01 -6.08524717e-02 9.05451179... | [5.9858903884887695, 2.545330762863159] |
547f7aa3-eaac-4348-9053-dddb89b92b8b | deep-learning-for-extracting-protein-protein | 1706.01556 | null | http://arxiv.org/abs/1706.01556v2 | http://arxiv.org/pdf/1706.01556v2.pdf | Deep learning for extracting protein-protein interactions from biomedical literature | State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-b... | ['Yifan Peng', 'Zhiyong Lu'] | 2017-06-05 | deep-learning-for-extracting-protein-protein-1 | https://aclanthology.org/W17-2304 | https://aclanthology.org/W17-2304.pdf | ws-2017-8 | ['cross-corpus'] | ['computer-vision'] | [-2.02307284e-01 -3.17909569e-01 -3.54179561e-01 -5.49909472e-01
-7.58658707e-01 -3.51865143e-01 3.23499888e-01 5.14767706e-01
-6.04675710e-01 1.03130889e+00 1.41982615e-01 -2.38144621e-01
-4.78222109e-02 -6.46744609e-01 -9.95985925e-01 -8.99793684e-01
-3.13863546e-01 5.31520605e-01 1.68407753e-01 -1.46672815... | [4.753305912017822, 5.702038288116455] |
27d681a7-744f-43fb-aca5-99451dc562b0 | cafs-class-adaptive-framework-for-semi | 2303.11606 | null | https://arxiv.org/abs/2303.11606v1 | https://arxiv.org/pdf/2303.11606v1.pdf | CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation | Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by selftraining with pseudo-labeling pixels having high confidences for unlabeled images. However, using ... | ['Dong-Geol Choi', 'Minseok Seo', 'Yooseung Wang', 'Hyeoncheol Noh', 'Jingi Ju'] | 2023-03-21 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.09788316e-01 4.57894623e-01 -5.51829875e-01 -1.05474186e+00
-1.17506742e+00 -6.35460854e-01 2.93787539e-01 5.07520363e-02
-5.73890746e-01 8.53050947e-01 -3.72212261e-01 -7.94487521e-02
2.46135384e-01 -6.42305791e-01 -8.75567377e-01 -6.99218154e-01
6.45042121e-01 6.12160802e-01 3.59051794e-01 4.07542050... | [9.563941955566406, 1.0263577699661255] |
0c82cc7a-3ada-4f2b-92e4-9e8e72383193 | spatio-temporal-prediction-in-video-coding-by-1 | 2207.09729 | null | https://arxiv.org/abs/2207.09729v1 | https://arxiv.org/pdf/2207.09729v1.pdf | Spatio-temporal prediction in video coding by non-local means refined motion compensation | The prediction step is a very important part of hybrid video codecs. In this contribution, a novel spatio-temporal prediction algorithm is introduced. For this, the prediction is carried out in two steps. Firstly, a preliminary temporal prediction is conducted by motion compensation. Afterwards, spatial refinement is c... | ['André Kaup', 'Thomas Richter', 'Jürgen Seiler'] | 2022-07-20 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.60609519e-01 9.02089998e-02 -5.00137098e-02 -8.29023123e-02
-3.05417269e-01 -7.54139572e-02 5.08629858e-01 3.81056637e-01
-4.32991058e-01 7.61919320e-01 3.66530061e-01 -1.80507004e-01
1.72059704e-02 -5.79334915e-01 -4.10468340e-01 -9.26394701e-01
-4.21760418e-02 -2.91567177e-01 8.28274906e-01 8.05888921... | [11.258515357971191, -2.0711774826049805] |
54b0305a-c4d8-46f9-8b83-83c24198e66f | nerfbk-a-high-quality-benchmark-for-nerf | 2306.06300 | null | https://arxiv.org/abs/2306.06300v2 | https://arxiv.org/pdf/2306.06300v2.pdf | NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction | This paper introduces a new real and synthetic dataset called NeRFBK specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. High-quality 3D reconstruction has significant potential in various fields, and advancements in image-based algorithms make it essential to evaluate new advanced ... | ['Fabio Remondino', 'Ziyang Yan', 'Gabriele Mazzacca', 'Simone Rigon', 'Ali Karami'] | 2023-06-09 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [-7.36554414e-02 -5.80262065e-01 1.99265748e-01 -3.07258844e-01
-8.42608988e-01 -6.78640783e-01 7.28673697e-01 -7.50809684e-02
-2.98412263e-01 6.54622078e-01 1.83060989e-01 8.00439529e-03
-2.62978375e-01 -8.74509454e-01 -6.12672508e-01 -5.85061669e-01
-2.54572242e-01 4.44241285e-01 3.09282303e-01 -2.69152373... | [8.534247398376465, -2.469073534011841] |
088b4a8d-e3cc-44cf-abcf-22947ef83f6f | learning-logic-programs-by-discovering-where | 2202.09806 | null | https://arxiv.org/abs/2202.09806v2 | https://arxiv.org/pdf/2202.09806v2.pdf | Learning logic programs by discovering where not to search | The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers where not to search. We use given BK to discover constraints on hypothes... | ['Céline Hocquette', 'Andrew Cropper'] | 2022-02-20 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 2.22248331e-01 5.00228763e-01 -6.62918329e-01 -3.40844005e-01
-4.57726479e-01 -8.25379908e-01 2.45452598e-01 3.68270576e-01
-9.32829268e-03 1.16573083e+00 -3.12236130e-01 -9.74341810e-01
2.54312940e-02 -1.41185212e+00 -1.10914004e+00 7.27650672e-02
-3.87592226e-01 6.79413438e-01 9.13852096e-01 -2.84259375... | [8.777582168579102, 7.113070011138916] |
a28cd2e7-253b-44d1-9eb8-42ab0d703bb4 | partially-latent-factors-based-multi-view | 2201.01050 | null | https://arxiv.org/abs/2201.01050v2 | https://arxiv.org/pdf/2201.01050v2.pdf | Partially latent factors based multi-view subspace learning | Multi-view subspace clustering always performs well in high-dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering. This paper first propose a novel matrix... | ['Jian-wei Liu', 'Jin-zhong Chen', 'Ze-Yu Liu', 'Run-kun Lu'] | 2022-01-04 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.54016399e-01 -5.63235700e-01 -1.56146288e-01 -1.72570392e-01
-6.74724638e-01 -6.94018185e-01 6.32913530e-01 -5.36477268e-01
9.90239829e-02 1.80100381e-01 7.57325351e-01 2.24057913e-01
-3.89537483e-01 -2.69370377e-01 -1.09445110e-01 -1.20698965e+00
4.29835767e-01 3.40851009e-01 -9.67747197e-02 2.61650294... | [8.215710639953613, 4.567084312438965] |
26d0b87a-fb75-4f66-b1d1-2ed6ce9cdd0b | vision-based-pose-estimation-for-augmented | 1806.09316 | null | http://arxiv.org/abs/1806.09316v1 | http://arxiv.org/pdf/1806.09316v1.pdf | Vision-based Pose Estimation for Augmented Reality : A Comparison Study | Augmented reality aims to enrich our real world by inserting 3D virtual
objects. In order to accomplish this goal, it is important that virtual
elements are rendered and aligned in the real scene in an accurate and visually
acceptable way. The solution of this problem can be related to a pose
estimation and 3D camera l... | ['Nadia Zenati', 'Samir Otmane', 'Hayet Belghit', 'Abdelkader Bellarbi'] | 2018-06-25 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.07713190e-02 -1.57878980e-01 2.47879907e-01 -1.63160250e-01
-3.16519499e-01 -7.65972078e-01 5.17004251e-01 1.58117294e-01
-1.23907402e-01 5.74213803e-01 9.95337218e-02 -3.91787291e-01
-7.41434097e-02 -6.00086272e-01 -4.08960164e-01 -1.11608831e-02
3.16644795e-02 5.08315325e-01 3.68140399e-01 -6.02487683... | [7.90024995803833, -1.8462049961090088] |
7136c879-2159-40b9-972f-76846aae9c65 | improving-the-transferability-of-adversarial-6 | 2303.15735 | null | https://arxiv.org/abs/2303.15735v1 | https://arxiv.org/pdf/2303.15735v1.pdf | Improving the Transferability of Adversarial Samples by Path-Augmented Method | Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model... | ['Michael R. Lyu', 'Yuxin Su', 'Xiaosen Wang', 'Weibin Wu', 'Yichen Li', 'Wenxuan Wang', 'Jen-tse Huang', 'Jianping Zhang'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-augmentation'] | ['computer-vision'] | [ 4.97802228e-01 -8.93664062e-02 -1.00036100e-01 -7.45568350e-02
-7.19575107e-01 -7.73445427e-01 8.03828359e-01 -1.51683778e-01
-4.87344503e-01 5.64525068e-01 -8.04366097e-02 -4.44738746e-01
2.83988684e-01 -1.02301037e+00 -9.43449020e-01 -7.47936010e-01
1.99668318e-01 1.96061507e-01 4.85191882e-01 -3.48170102... | [5.567678928375244, 7.90447473526001] |
5c0ea7da-528d-402f-8378-8fd0c0758afb | ace-an-actor-ensemble-algorithm-for | 1811.02696 | null | http://arxiv.org/abs/1811.02696v1 | http://arxiv.org/pdf/1811.02696v1.pdf | ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search | In this paper, we propose an actor ensemble algorithm, named ACE, for
continuous control with a deterministic policy in reinforcement learning. In
ACE, we use actor ensemble (i.e., multiple actors) to search the global maxima
of the critic. Besides the ensemble perspective, we also formulate ACE in the
option framework... | ['Shangtong Zhang', 'Hao Chen', 'Hengshuai Yao'] | 2018-11-06 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-6.65178150e-02 4.68085438e-01 -3.97027194e-01 -1.01584233e-01
-6.37458324e-01 -4.85777795e-01 4.18677151e-01 1.33692518e-01
-3.06272596e-01 1.21105063e+00 1.00127935e-01 -1.88241243e-01
-3.52546036e-01 -6.97227836e-01 -6.16805136e-01 -9.47391927e-01
-2.94348776e-01 4.08251494e-01 -6.90521374e-02 -4.99682307... | [4.161385536193848, 2.185845375061035] |
58327c45-4562-4d7e-9f28-d5e784836ba5 | query2particles-knowledge-graph-reasoning | 2204.12847 | null | https://arxiv.org/abs/2204.12847v1 | https://arxiv.org/pdf/2204.12847v1.pdf | Query2Particles: Knowledge Graph Reasoning with Particle Embeddings | Answering complex logical queries on incomplete knowledge graphs (KGs) with missing edges is a fundamental and important task for knowledge graph reasoning. The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space. Then the answer entities are s... | ['Yangqiu Song', 'Hongming Zhang', 'ZiHao Wang', 'Jiaxin Bai'] | 2022-04-27 | null | https://aclanthology.org/2022.findings-naacl.207 | https://aclanthology.org/2022.findings-naacl.207.pdf | findings-naacl-2022-7 | ['complex-query-answering'] | ['knowledge-base'] | [-4.39568043e-01 2.58675307e-01 -4.96890843e-01 -3.86260867e-01
-6.49866939e-01 -5.57254374e-01 3.72635007e-01 2.91706443e-01
-1.24567881e-01 5.18054664e-01 1.75987676e-01 -1.37518764e-01
-7.86410630e-01 -1.45052671e+00 -7.18886912e-01 -1.85189784e-01
8.35599471e-03 1.02782655e+00 8.54836285e-01 -3.96131903... | [9.071069717407227, 7.688830852508545] |
8a858e92-f321-4bb0-b421-b591ca1cc0a2 | clusternet-deep-hierarchical-cluster-network | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_ClusterNet_Deep_Hierarchical_Cluster_Network_With_Rigorously_Rotation-Invariant_Representation_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_ClusterNet_Deep_Hierarchical_Cluster_Network_With_Rigorously_Rotation-Invariant_Representation_for_CVPR_2019_paper.pdf | ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis | Current neural networks for 3D object recognition are vulnerable to 3D rotation. Existing works mostly rely on massive amounts of rotation-augmented data to alleviate the problem, which lacks solid guarantee of the 3D rotation invariance. In this paper, we address the issue by introducing a novel point cloud representa... | [' Liang Lin', ' Meng Wang', ' Tianshui Chen', ' Ruijia Xu', ' Guanbin Li', 'Chao Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-object-classification', '3d-object-recognition'] | ['computer-vision', 'computer-vision'] | [-3.50803971e-01 -2.52990752e-01 -2.43196279e-01 -2.17882931e-01
-2.31308058e-01 -4.88939553e-01 3.78677964e-01 -2.31889337e-01
4.09394838e-02 -1.47573696e-02 -1.16625980e-01 -3.54149133e-01
-2.67380387e-01 -5.69753349e-01 -9.36093628e-01 -8.55350375e-01
1.38885733e-02 1.64497003e-01 1.24824628e-01 -2.35398188... | [7.9073166847229, -3.576603412628174] |
78de44e9-8799-4d9e-95d3-de86294ab627 | end-to-end-document-classification-and-key | 2306.00750 | null | https://arxiv.org/abs/2306.00750v1 | https://arxiv.org/pdf/2306.00750v1.pdf | End-to-End Document Classification and Key Information Extraction using Assignment Optimization | We propose end-to-end document classification and key information extraction (KIE) for automating document processing in forms. Through accurate document classification we harness known information from templates to enhance KIE from forms. We use text and layout encoding with a cosine similarity measure to classify vis... | ["Mairead O'Cuinn", 'Rachel Heyburn', 'Bradley Savage', 'Liam Madigan', 'Joana Cavadas', 'Ciaran Cooney'] | 2023-06-01 | null | null | null | null | ['document-classification', 'key-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.25561321e-01 -5.28433025e-02 -7.64791444e-02 -3.38162184e-01
-1.54309392e+00 -1.06197357e+00 5.31829834e-01 6.63075924e-01
-3.78145456e-01 3.01075459e-01 2.00360253e-01 -2.53915519e-01
-6.64976776e-01 -5.28194785e-01 -5.42714298e-01 -3.00211757e-01
-3.86815593e-02 2.57629067e-01 -2.36959279e-01 8.30826387... | [11.754863739013672, 2.741575241088867] |
a2d50fd9-9657-43d8-a374-74ac29f6767c | dyrep-learning-representations-over-dynamic | null | null | https://openreview.net/forum?id=HyePrhR5KX | https://openreview.net/pdf?id=HyePrhR5KX | DyRep: Learning Representations over Dynamic Graphs | Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundam... | ['Prasenjeet Biswal', 'Hongyuan Zha', 'Rakshit Trivedi', 'Mehrdad Farajtabar'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['dynamic-link-prediction'] | ['graphs'] | [ 1.69458538e-01 4.61583465e-01 -2.05899075e-01 2.41443608e-03
-3.44415680e-02 -7.17776656e-01 1.09560049e+00 2.34270960e-01
2.18433559e-01 1.16535135e-01 5.07838666e-01 -4.21861202e-01
-4.79085892e-01 -1.11635971e+00 -7.27926791e-01 -7.34873354e-01
-8.38214159e-01 7.74977505e-01 6.75425753e-02 -3.58002901... | [7.113889694213867, 5.950300693511963] |
18c24a5d-a148-4b94-b2ac-cb0632df565e | deepsnr-a-deep-learning-foundation-for | 2207.04749 | null | https://arxiv.org/abs/2207.04749v1 | https://arxiv.org/pdf/2207.04749v1.pdf | DeepSNR: A deep learning foundation for offline gravitational wave detection | All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipelines in experiments at LIGO is the matched-filter algorithm, which pr... | ['Haydn Vestal', 'Gianni Martire', 'Alexey Bobrick', 'Luke Sellers', 'Manfred Paulini', 'Michael Andrews'] | 2022-07-11 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-2.76883274e-01 -1.99053168e-01 2.90745497e-01 -3.12880039e-01
-1.05185592e+00 -4.90988016e-01 1.21255398e+00 1.17222384e-01
-2.53967732e-01 2.55662590e-01 -4.84414920e-02 -6.98146999e-01
-3.65617007e-01 -8.19676757e-01 -4.78747517e-01 -1.12066877e+00
-5.05967081e-01 7.75024593e-01 5.13665259e-01 -1.91857386... | [7.566976547241211, 3.11861252784729] |
987ef3fe-78b0-40db-bc2b-78eff76ca8e1 | reliability-scores-from-saliency-map-clusters | 2305.15149 | null | https://arxiv.org/abs/2305.15149v1 | https://arxiv.org/pdf/2305.15149v1.pdf | Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower | Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation,... | ['Ribana Roscher', 'Jana Kierdorf'] | 2023-05-24 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.79446155e-01 2.41464406e-01 -4.90614146e-01 -4.11164522e-01
-2.99184084e-01 -8.36022317e-01 1.82948083e-01 6.21955812e-01
-7.01859891e-02 3.37085724e-01 -2.55752951e-01 -2.96961039e-01
-2.58995175e-01 -8.28218043e-01 -4.87848580e-01 -5.86165369e-01
-9.69403014e-02 2.76551276e-01 1.38375431e-01 -2.32517093... | [9.110373497009277, -1.5408707857131958] |
ba6a716a-2e9f-4e54-90e2-93ee934dafd0 | unsupervised-deep-one-class-classification | 2302.06048 | null | https://arxiv.org/abs/2302.06048v1 | https://arxiv.org/pdf/2302.06048v1.pdf | Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics | One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In practice, however, abnormal samples are often mixed in a training dataset, and they detrimentally affect the training of deep models, which li... | ['Jun Kyun Choi', 'Jongmin Yu', 'Junsik Kim', 'Minkyung Kim'] | 2023-02-13 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.41185576e-02 -2.09199414e-01 2.06417684e-02 -7.71557033e-01
-3.85255337e-01 -2.22582117e-01 5.57801127e-01 2.23930210e-01
-1.07101992e-01 2.98361093e-01 -2.37603217e-01 -3.45989019e-01
-1.09611042e-01 -7.72292256e-01 -7.38748431e-01 -8.94602537e-01
-1.11016497e-01 4.63838220e-01 5.07924631e-02 4.25471552... | [7.597194194793701, 2.3646860122680664] |
b153efa0-da9e-446a-a193-d8cec569d6bd | frame-rate-up-conversion-detection-based-on | 2103.13674 | null | https://arxiv.org/abs/2103.13674v1 | https://arxiv.org/pdf/2103.13674v1.pdf | Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features | With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continuity of videos with a lower frame-rate and is used by malicious counter... | ['Heung-Kyu Lee', 'Myung-Joon Kwon', 'Wonhyuk Ahn', 'In-Jae Yu', 'Seung-Hun Nam', 'Minseok Yoon'] | 2021-03-25 | null | null | null | null | ['video-forensics'] | ['computer-vision'] | [ 2.18458220e-01 -6.27162397e-01 -2.23625228e-01 4.26722690e-02
-7.03006923e-01 -5.02119958e-01 1.95771575e-01 -2.60799468e-01
-4.63880867e-01 6.99167788e-01 -3.19212288e-01 -4.18195367e-01
1.31015345e-01 -5.91290414e-01 -9.04453397e-01 -6.38907492e-01
-2.46412531e-01 -4.18085158e-01 3.98763031e-01 1.84771597... | [12.385322570800781, 0.9806836247444153] |
00661dfc-d586-4fca-8423-d311941a913d | composite-optimization-algorithms-for-sigmoid | 2303.00589 | null | https://arxiv.org/abs/2303.00589v3 | https://arxiv.org/pdf/2303.00589v3.pdf | Composite Optimization Algorithms for Sigmoid Networks | In this paper, we use composite optimization algorithms to solve sigmoid networks. We equivalently transfer the sigmoid networks to a convex composite optimization and propose the composite optimization algorithms based on the linearized proximal algorithms and the alternating direction method of multipliers. Under the... | ['Qi Ye', 'Huixiong Chen'] | 2023-03-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.37690723e-01 1.14920512e-01 -1.84887558e-01 -2.75352508e-01
-4.52459008e-01 -3.73909622e-01 1.24652542e-01 -1.98745832e-01
-7.68546283e-01 1.14729834e+00 -2.98763156e-01 -4.90931988e-01
-2.28973970e-01 -5.35539150e-01 -8.59639585e-01 -8.93432200e-01
1.14963189e-01 1.85127437e-01 -1.29807934e-01 -4.03647631... | [6.934177398681641, 4.236011981964111] |
f48376dc-0505-4699-a6a1-12c9299ce46d | counterfactual-analysis-in-dynamic-models | 2205.13832 | null | https://arxiv.org/abs/2205.13832v4 | https://arxiv.org/pdf/2205.13832v4.pdf | Counterfactual Analysis in Dynamic Latent State Models | We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the ``abduction, action, and prediction'' approach to answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2) the model is dynami... | ['Raghav Singal', 'Martin Haugh'] | 2022-05-27 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.49659708e-01 5.94815493e-01 -8.88615191e-01 -1.22849112e-02
-5.68418324e-01 -5.06265461e-01 8.02163184e-01 1.07409462e-01
-3.03693175e-01 1.15311170e+00 6.66902959e-01 -1.16782272e+00
-5.15295804e-01 -7.45933890e-01 -6.61102533e-01 -4.22511548e-01
-7.46602237e-01 4.74728823e-01 -9.85084847e-02 -1.25121698... | [8.148226737976074, 5.500607490539551] |
4573bad9-8e27-49ce-91f2-5b6f4b673a3d | dereverberation-of-autoregressive-envelopes | 2108.05520 | null | https://arxiv.org/abs/2108.05520v2 | https://arxiv.org/pdf/2108.05520v2.pdf | Dereverberation of Autoregressive Envelopes for Far-field Speech Recognition | The task of speech recognition in far-field environments is adversely affected by the reverberant artifacts that elicit as the temporal smearing of the sub-band envelopes. In this paper, we develop a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The sub-band envelopes are der... | ['Sriram Ganapathy', 'Rohit Kumar', 'Anirudh Sreeram', 'Anurenjan Purushothaman'] | 2021-08-12 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.86963379e-01 -4.00655597e-01 8.10385406e-01 -2.30090916e-01
-1.17507541e+00 -6.44393921e-01 1.47174910e-01 -1.48022071e-01
-2.03673527e-01 4.76591855e-01 4.87400949e-01 -4.79996622e-01
-1.51593145e-02 -1.70484006e-01 -7.14779139e-01 -8.18496823e-01
-3.86247188e-01 -4.65716422e-01 -2.41678268e-01 -1.77791357... | [15.087634086608887, 5.929826736450195] |
4db229e6-5da7-4ea8-bf53-4231ca1c8ee4 | the-clip-model-is-secretly-an-image-to-prompt | 2305.12716 | null | https://arxiv.org/abs/2305.12716v1 | https://arxiv.org/pdf/2305.12716v1.pdf | The CLIP Model is Secretly an Image-to-Prompt Converter | The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to incorporating implicit information from reference images. Existing method... | ['Lingqiao Liu', 'Haoxuan Ding', 'Chunna Tian', 'Yuxuan Ding'] | 2023-05-22 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 8.08558881e-01 -2.18925737e-02 -1.90963671e-02 -2.36515984e-01
-6.97417617e-01 -6.36281490e-01 9.70984161e-01 -4.84888516e-02
-4.67691272e-01 4.82524008e-01 -2.29155302e-01 -4.04454648e-01
3.37463580e-02 -4.74691927e-01 -5.33398986e-01 -6.85325325e-01
4.53509986e-01 -1.73413660e-02 2.11316481e-01 -1.97844267... | [11.381202697753906, -0.34713679552078247] |
3718bb34-7c21-492e-8b60-78c52eeea53b | hierarchically-attentive-rnn-for-album | 1708.02977 | null | http://arxiv.org/abs/1708.02977v1 | http://arxiv.org/pdf/1708.02977v1.pdf | Hierarchically-Attentive RNN for Album Summarization and Storytelling | We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive ... | ['Tamara L. Berg', 'Licheng Yu', 'Mohit Bansal'] | 2017-08-09 | hierarchically-attentive-rnn-for-album-1 | https://aclanthology.org/D17-1101 | https://aclanthology.org/D17-1101.pdf | emnlp-2017-9 | ['visual-storytelling'] | ['natural-language-processing'] | [ 0.28775823 0.09659904 -0.08084155 -0.38920984 -1.3287857 -0.4789913
0.97876 -0.02431096 -0.07183661 0.4934779 1.0995289 0.2834496
0.394337 -0.6085551 -0.84389967 -0.3432947 0.16380003 0.65689826
-0.10954198 -0.07580213 0.31091392 0.06621649 -1.76309 1.0153388
0.38206205 0.9246202 0.676... | [11.198999404907227, 0.7032565474510193] |
d91ff9e8-0819-4a33-ba4f-e6a12713f495 | general-automatic-human-shape-and-motion | 1607.08659 | null | http://arxiv.org/abs/1607.08659v2 | http://arxiv.org/pdf/1607.08659v2.pdf | General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues | Markerless motion capture algorithms require a 3D body with properly
personalized skeleton dimension and/or body shape and appearance to
successfully track a person. Unfortunately, many tracking methods consider
model personalization a different problem and use manual or semi-automatic
model initialization, which great... | ['Hans-Peter Seidel', 'Christian Richardt', 'Nadia Robertini', 'Helge Rhodin', 'Dan Casas', 'Christian Theobalt'] | 2016-07-28 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 4.25417610e-02 6.80757174e-03 1.87438160e-01 5.79826161e-02
-2.68711060e-01 -4.48424488e-01 3.94019186e-01 -1.36457562e-01
-4.65255231e-01 5.52100241e-01 -8.55560601e-02 4.58339334e-01
2.61950344e-01 -6.96488917e-01 -5.62655687e-01 -6.15527689e-01
1.32962927e-01 9.32720184e-01 3.36205393e-01 3.18358094... | [7.20846700668335, -1.1189587116241455] |
baa4941f-0943-430a-ace2-f6a951ea6acb | dynamic-neural-network-decoupling | 1906.01166 | null | https://arxiv.org/abs/1906.01166v2 | https://arxiv.org/pdf/1906.01166v2.pdf | Interpretable Neural Network Decoupling | The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the exploitation of their full potential. Towards network interpretation, previous endeavors mainly resort to the single filter analysis, which h... | ['Ling Shao', 'Yuchao Li', 'Yongjian Wu', 'Baochang Zhang', 'Shaohui Lin', 'Rongrong Ji', 'Feiyue Huang', 'Chenqian Yan'] | 2019-06-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2426_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600647.pdf | eccv-2020-8 | ['network-interpretation'] | ['computer-vision'] | [ 5.41771770e-01 1.87127829e-01 -1.23632848e-01 -2.84773976e-01
3.49709660e-01 -5.23388505e-01 4.21992153e-01 -1.29090205e-01
-3.54017824e-01 3.54647368e-01 5.65841496e-02 -5.39374113e-01
-2.19871610e-01 -7.58886576e-01 -6.80657923e-01 -9.13860381e-01
3.53277385e-01 -3.13348889e-01 3.40474434e-02 -1.48949102... | [9.224493026733398, 2.547226905822754] |
0c92f779-226d-46a8-afe3-4b1ee26ac07c | a-search-based-dynamic-reranking-model-for | null | null | https://aclanthology.org/P16-1132 | https://aclanthology.org/P16-1132.pdf | A Search-Based Dynamic Reranking Model for Dependency Parsing | null | ['Shu-Jian Huang', 'Jia-Jun Chen', 'Xin-yu Dai', 'Yue Zhang', 'Hao Zhou', 'Junsheng Zhou'] | 2016-08-01 | null | null | null | acl-2016-8 | ['transition-based-dependency-parsing'] | ['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... | [-7.310357093811035, 3.703533411026001] |
df760dc7-0744-4980-bff3-4c1aeddaaf6b | meta-ordinal-weighting-net-for-improving-lung | 2102.00456 | null | https://arxiv.org/abs/2102.00456v1 | https://arxiv.org/pdf/2102.00456v1.pdf | Meta ordinal weighting net for improving lung nodule classification | The progression of lung cancer implies the intrinsic ordinal relationship of lung nodules at different stages-from benign to unsure then to malignant. This problem can be solved by ordinal regression methods, which is between classification and regression due to its ordinal label. However, existing convolutional neural... | ['Junping Zhang', 'Hongming Shan', 'Yiming Lei'] | 2021-01-31 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 4.34336454e-01 2.35148221e-01 -1.04017460e+00 -6.39623284e-01
-8.39546323e-01 2.76465744e-01 6.73282504e-01 7.12851360e-02
-4.26934212e-01 7.27801979e-01 4.37453389e-01 3.78478914e-02
-5.54481685e-01 -7.96617985e-01 -4.24696088e-01 -7.01299965e-01
-8.25816095e-02 7.02912509e-01 2.20244825e-01 7.04025012... | [15.289863586425781, -2.162858486175537] |
717abd79-d7c8-4f98-8254-b5f645bc4e0a | unarxive-a-large-scholarly-data-set-with | null | null | https://link.springer.com/article/10.1007%2Fs11192-020-03382-z | https://www.aifb.kit.edu/images/f/f9/UnarXive_Scientometrics2020.pdf | unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata | In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the ... | ['Michael Färber', 'Tarek Saier'] | 2020-03-02 | null | null | null | scientometrics-2020-3 | ['scientific-results-extraction', 'scientific-concept-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-2.85298616e-01 -2.38319457e-01 -6.73198760e-01 4.88735139e-02
-8.15461874e-01 -8.82176042e-01 1.13077629e+00 7.92341948e-01
-2.54140109e-01 1.02345634e+00 6.34025633e-01 -4.91761208e-01
-5.62216282e-01 -8.22599590e-01 -3.36408496e-01 -2.62349427e-01
1.96927056e-01 3.29360306e-01 9.07577574e-02 9.28127989... | [9.575181007385254, 8.215286254882812] |
7deed062-9903-4a86-9a6b-3af3f1644bf7 | an-aerial-weed-detection-system-for-green | null | null | https://doi.org/10.37221/eaef.13.2_42 | https://www.jstage.jst.go.jp/article/eaef/13/2/13_42/_pdf/-char/en | An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm | The real-time object detection system You Only Look Once (specifically YOLOv3) has recently shown remarkable speed, making it potentially suitable for Unmanned Aerial Vehicle (UAV) precision spraying. In this study, YOLO-WEED, a weed detection system based on YOLOv3, was developed. The dataset, derived from a five-minu... | ['Tofael Ahamed', 'Addie Ira Borja Parico'] | 2020-01-01 | null | null | null | engineering-in-agriculture-environment-and | ['real-time-object-detection'] | ['computer-vision'] | [-4.46425751e-02 -7.08838940e-01 -1.17634073e-01 4.53300804e-01
1.64661095e-01 -9.36870813e-01 9.52205583e-02 1.10930137e-01
-4.06042993e-01 3.50681335e-01 -1.12030637e+00 -9.46785212e-01
6.85096383e-02 -7.20624268e-01 -2.17143297e-01 -6.83639526e-01
-4.45976764e-01 -3.80441397e-01 7.00494409e-01 -2.31611252... | [8.403450965881348, -1.0154520273208618] |
7c645db4-e8b7-425c-9c10-aa4c9e781644 | deciphering-speech-a-zero-resource-approach | 2111.06799 | null | https://arxiv.org/abs/2111.06799v3 | https://arxiv.org/pdf/2111.06799v3.pdf | Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR | We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from... | ['Peter Bell', 'Electra Wallington', 'Ondrej Klejch'] | 2021-11-12 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [ 6.17351353e-01 3.95679444e-01 2.47330591e-01 -5.15648961e-01
-1.55292177e+00 -8.46982777e-01 5.27843893e-01 -2.17049643e-01
-5.67320704e-01 7.81533301e-01 9.37823951e-02 -6.65871680e-01
5.37508845e-01 -3.29238027e-01 -8.32750976e-01 -5.86039543e-01
2.54382432e-01 7.91025162e-01 3.51692252e-02 -2.44062036... | [14.408053398132324, 6.895720958709717] |
7e5a19e7-c8b4-4038-a4cd-27a0c328bb23 | fenet-a-frequency-extraction-network-for | 2101.02873 | null | https://arxiv.org/abs/2101.02873v1 | https://arxiv.org/pdf/2101.02873v1.pdf | FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection | Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the cost... | ['Xiangliang Zhang', 'Lizhen Cui', 'Hongxu Chen', 'Tong Chen', 'Hongzhi Yin', 'Guanhua Ye'] | 2021-01-08 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 1.74508423e-01 -2.71326303e-01 -2.62370706e-01 -1.49262846e-01
-1.43175334e-01 -2.98960030e-01 -6.18301511e-01 -2.03065306e-01
-2.92411119e-01 6.87716961e-01 -3.08013391e-02 -1.46047667e-01
-2.85950035e-01 -7.14101732e-01 2.45943502e-01 -8.27885866e-01
4.32597958e-02 -2.70546407e-01 -8.21592584e-02 9.78792161... | [13.933869361877441, 3.0542564392089844] |
0246edf7-c6fd-4819-903f-1d521bb4f02c | de-identification-of-french-unstructured | 2209.09631 | null | https://arxiv.org/abs/2209.09631v1 | https://arxiv.org/pdf/2209.09631v1.pdf | De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks | Unstructured textual data are at the heart of health systems: liaison letters between doctors, operating reports, coding of procedures according to the ICD-10 standard, etc. The details included in these documents make it possible to get to know the patient better, to better manage him or her, to better study the patho... | ['Christophe Guyeux', 'Azzedine Rahmani', 'Philippe Selles', 'David Laiymani', 'Maxime Coulmeau', 'Jean-François Couchot', 'Yakini Tchouka'] | 2022-09-16 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 2.17496291e-01 4.42425698e-01 -2.29640193e-02 -2.50190794e-01
-5.84853053e-01 -5.49803376e-01 5.15173256e-01 7.90374398e-01
-7.52383530e-01 9.18770432e-01 1.30066171e-01 -2.92436659e-01
-4.74442959e-01 -8.71152878e-01 -3.10422510e-01 -8.32711279e-01
1.86667904e-01 9.21616435e-01 -8.97880420e-02 -9.95430648... | [6.84993314743042, 7.058649063110352] |
63ddfddb-32f9-44d5-81c8-8331e97fe41b | dptext-detr-towards-better-scene-text | 2207.04491 | null | https://arxiv.org/abs/2207.04491v2 | https://arxiv.org/pdf/2207.04491v2.pdf | DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer | Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query model... | ['DaCheng Tao', 'Bo Du', 'Juhua Liu', 'Shanshan Zhao', 'Jing Zhang', 'Maoyuan Ye'] | 2022-07-10 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.70771554e-02 -3.11783224e-01 -8.25064778e-02 2.91183982e-02
-7.84313619e-01 -5.17516673e-01 6.98042512e-01 -4.20910269e-02
-2.53568679e-01 9.02239531e-02 7.45243952e-02 -2.37758219e-01
9.49982256e-02 -8.49521160e-01 -8.65671575e-01 -5.60837090e-01
5.27675807e-01 2.20487788e-01 6.13978088e-01 -3.51233155... | [12.089644432067871, 2.2512810230255127] |
b16953a4-009b-414d-8542-c7b33eb7aadb | dp-gan-diversity-promoting-generative | 1802.01345 | null | http://arxiv.org/abs/1802.01345v3 | http://arxiv.org/pdf/1802.01345v3.pdf | DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text | Existing text generation methods tend to produce repeated and "boring"
expressions. To tackle this problem, we propose a new text generation model,
called Diversity-Promoting Generative Adversarial Network (DP-GAN). The
proposed model assigns low reward for repeatedly generated text and high reward
for "novel" and flue... | ['Xu sun', 'Jingjing Xu', 'Junyang Lin', 'Xuancheng Ren'] | 2018-02-05 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 2.92057872e-01 4.09712583e-01 -2.10299179e-01 -1.40631393e-01
-1.09375918e+00 -6.04394376e-01 1.10434926e+00 -4.78028446e-01
2.14629211e-02 1.50743032e+00 5.14872253e-01 -2.66963661e-01
6.47885203e-01 -9.31102276e-01 -2.89782852e-01 -5.62494218e-01
6.17675900e-01 5.91206312e-01 -3.41130763e-01 -5.50970137... | [11.913419723510742, 9.140331268310547] |
4a86567c-ba5c-46ba-9f87-2525aaa195ea | multimodal-few-shot-object-detection-with | 2204.07841 | null | https://arxiv.org/abs/2204.07841v3 | https://arxiv.org/pdf/2204.07841v3.pdf | Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting | We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection, which are complementary to each other by definition. Most of the previous works on multi-modal FSOD are fine-tuning-based which are inefficient for online applications. ... | ['Shiyuan Huang', 'Jiawei Ma', 'Long Chen', 'Shih-Fu Chang', 'Rama Chellappa', 'Guangxing Han'] | 2022-04-16 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 3.36238027e-01 -1.28458768e-01 -4.61668074e-01 -4.01495218e-01
-8.96512151e-01 -3.78958941e-01 7.86352456e-01 4.13225263e-01
-2.86979675e-01 3.37351978e-01 1.57004416e-01 1.47414267e-01
-5.63535281e-02 -9.38551247e-01 -6.32096767e-01 -4.90109861e-01
3.75971287e-01 2.45974287e-01 7.02694356e-01 -2.89434999... | [10.00744915008545, 2.551547050476074] |
61ca9b8e-b663-4e60-8ef9-6f8a3fccc58b | vcvw-3d-a-virtual-construction-vehicles-and | 2305.17927 | null | https://arxiv.org/abs/2305.17927v1 | https://arxiv.org/pdf/2305.17927v1.pdf | VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations | Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligence (AI) applications only applicable to some scenarios that only require 2D information. However, most advanced applications usually require... | ['Xiaowei Luo', 'Yuexiong Ding'] | 2023-05-29 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 4.82260995e-02 -1.15387484e-01 1.00530095e-01 -4.08344269e-02
-1.55682549e-01 -3.27641606e-01 5.24486184e-01 2.18417630e-01
1.52569972e-02 1.24959059e-01 -2.23645806e-01 -5.03877878e-01
-3.53878677e-01 -7.87610888e-01 -2.86965698e-01 -6.40750289e-01
4.51380350e-02 4.79901820e-01 7.00422227e-01 -4.17709589... | [7.62137508392334, -1.4041985273361206] |
30028906-d58b-4343-834c-bd91e689a007 | calibration-assessment-and-boldness | 2305.03780 | null | https://arxiv.org/abs/2305.03780v2 | https://arxiv.org/pdf/2305.03780v2.pdf | Calibration Assessment and Boldness-Recalibration for Binary Events | Probability predictions are essential to inform decision making in medicine, economics, image classification, sports analytics, entertainment, and many other fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., far from the base rate of the event. Predictions that satis... | ['Christopher T. Franck', 'Adeline P. Guthrie'] | 2023-05-05 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 4.35978293e-01 2.17838779e-01 -4.88316387e-01 -5.23300231e-01
-6.80415273e-01 -5.16208351e-01 4.21129316e-01 2.11842731e-01
-4.70410854e-01 9.24705863e-01 1.63502797e-01 -8.56141448e-01
-4.89214361e-01 -7.17588544e-01 -4.90544677e-01 -3.99673522e-01
3.35731834e-01 3.78797203e-01 1.63185045e-01 1.77914634... | [8.441324234008789, 4.967484951019287] |
173452bf-841e-4a51-95d1-b6b40ebfaaa6 | defocus-blur-detection-via-salient-region | 2011.09677 | null | https://arxiv.org/abs/2011.09677v1 | https://arxiv.org/pdf/2011.09677v1.pdf | Defocus Blur Detection via Salient Region Detection Prior | Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision. Current works for defocus b... | ['Liguo Weng', 'Zhiwei Wang', 'Chunyi Sun', 'Min Xia', 'Ming Qian'] | 2020-11-19 | null | null | null | null | ['defocus-blur-detection'] | ['computer-vision'] | [ 3.58124197e-01 -5.64781070e-01 5.65658472e-02 -4.28159416e-01
9.54913795e-02 -2.94815779e-01 2.13374868e-01 -7.31190979e-01
-3.09687972e-01 7.16178775e-01 5.97914100e-01 -6.13514483e-02
-1.10504478e-01 -4.05303180e-01 -5.59257209e-01 -7.57185817e-01
5.13855040e-01 -5.13447881e-01 3.90174329e-01 6.19439110... | [11.401311874389648, -2.7469160556793213] |
a2b3153b-bc48-45b5-bcf2-d66736b81d3e | matte-anything-interactive-natural-image | 2306.04121 | null | https://arxiv.org/abs/2306.04121v1 | https://arxiv.org/pdf/2306.04121v1.pdf | Matte Anything: Interactive Natural Image Matting with Segment Anything Models | Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimaps often requires significant labor, which limits the widespread application of matting algorithms on a large scale. To address the issue, we propose Matte Anything model (MatAny)... | ['Wenyu Liu', 'Lang Ye', 'Xinggang Wang', 'Jingfeng Yao'] | 2023-06-07 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.32170182e-01 4.34311241e-01 2.55403876e-01 -2.51038611e-01
-3.15853804e-01 -3.75940293e-01 6.27370894e-01 -4.99135047e-01
1.05457112e-01 -1.44953812e-02 -3.38022299e-02 -2.30067626e-01
6.04016483e-01 -7.56868243e-01 -1.10409009e+00 -4.33501363e-01
5.39908648e-01 6.86760664e-01 4.19646174e-01 -2.12195039... | [10.646543502807617, -0.8727659583091736] |
c96bf3b0-03ec-4967-893d-0512292cb558 | partitioning-distributed-compute-jobs-with | 2301.13799 | null | https://arxiv.org/abs/2301.13799v1 | https://arxiv.org/pdf/2301.13799v1.pdf | Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks | From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and n... | ['Georgios Zervas', 'Alessandro Ottino', 'Zacharaya Shabka', 'Christopher W. F. Parsonson'] | 2023-01-31 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 2.88283467e-01 7.70371184e-02 -1.65669486e-01 -4.80346233e-01
-3.46966624e-01 -3.63871366e-01 2.76159436e-01 3.51589322e-01
-5.41002810e-01 4.68449920e-01 -1.80596530e-01 -6.91605985e-01
-5.29646516e-01 -8.70191038e-01 -7.45910585e-01 -8.68224919e-01
-5.28880000e-01 1.29924619e+00 -9.25090462e-02 1.21618465... | [5.301111698150635, 3.0475873947143555] |
e36b19a2-10eb-4f5d-b056-cc213508d266 | modelling-the-development-of-counting-with | 2105.10577 | null | https://arxiv.org/abs/2105.10577v1 | https://arxiv.org/pdf/2105.10577v1.pdf | Modelling the development of counting with memory-augmented neural networks | Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinf... | ['Jonathan Cohen', 'Taylor Webb', 'Zack Dulberg'] | 2021-05-21 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 3.26246142e-01 1.52645379e-01 1.84781644e-02 -3.29934627e-01
5.02580285e-01 -5.95150113e-01 4.64435011e-01 5.89671373e-01
-1.01157260e+00 6.57864749e-01 -3.08801651e-01 -4.79893863e-01
-2.19465688e-01 -1.13745260e+00 -8.07429910e-01 -2.38751799e-01
-6.30403697e-01 5.20055354e-01 3.48989815e-01 -3.19866151... | [10.16303539276123, 8.59407901763916] |
8d530700-bea4-4f08-8bed-7e3ee2afb474 | benchmark-for-uncertainty-robustness-in-self | 2212.12411 | null | https://arxiv.org/abs/2212.12411v1 | https://arxiv.org/pdf/2212.12411v1.pdf | Benchmark for Uncertainty & Robustness in Self-Supervised Learning | Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertain... | ['Iliana Maifeld-Carucci', 'Ha Manh Bui'] | 2022-12-23 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-4.85953800e-02 1.72895819e-01 -3.91451418e-01 -8.24008822e-01
-1.26621163e+00 -6.17613077e-01 6.26221240e-01 9.49479491e-02
-5.31365931e-01 1.09235895e+00 1.78408492e-02 -4.70528454e-01
-3.01801730e-02 -4.30786431e-01 -1.05719912e+00 -7.21218586e-01
2.36952722e-01 6.98734522e-01 7.87465367e-03 1.10669881... | [9.62000560760498, 3.2858726978302] |
6d722cd6-14d4-4910-a1d4-daa1c723149c | combining-compressions-for-multiplicative | 2208.09684 | null | https://arxiv.org/abs/2208.09684v1 | https://arxiv.org/pdf/2208.09684v1.pdf | Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. For each of the e... | ['Chris DuBois', 'Ajay Gupta', 'Shayne Longpre', 'Jinhao Lei', 'Rajiv Movva'] | 2022-08-20 | null | https://aclanthology.org/2022.coling-1.252 | https://aclanthology.org/2022.coling-1.252.pdf | coling-2022-10 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.31988922e-01 1.25684023e-01 -4.47160006e-01 -4.82396394e-01
-8.46216619e-01 -6.26335502e-01 4.94983792e-01 4.23193067e-01
-7.06580698e-01 9.77981389e-01 2.80079544e-01 -5.95109999e-01
-4.42676961e-01 -8.49557877e-01 -7.42227674e-01 -4.79229987e-01
1.97509304e-01 5.22301495e-01 -9.23727453e-03 1.59987122... | [8.556049346923828, 3.358154296875] |
c88f4d9c-6ffe-4dfb-8600-29be64d7ada9 | fast-bi-layer-neural-synthesis-of-one-shot | 2008.10174 | null | https://arxiv.org/abs/2008.10174v1 | https://arxiv.org/pdf/2008.10174v1.pdf | Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars | We propose a neural rendering-based system that creates head avatars from a single photograph. Our approach models a person's appearance by decomposing it into two layers. The first layer is a pose-dependent coarse image that is synthesized by a small neural network. The second layer is defined by a pose-independent te... | ['Aliaksandra Shysheya', 'Egor Zakharov', 'Victor Lempitsky', 'Aleksei Ivakhnenko'] | 2020-08-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1637_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570511.pdf | eccv-2020-8 | ['talking-head-generation'] | ['computer-vision'] | [ 3.04676145e-01 5.31779587e-01 5.99080741e-01 -6.16457999e-01
-6.81643307e-01 -1.61313023e-02 5.61343610e-01 -4.68755692e-01
-1.36942953e-01 5.93309045e-01 4.73450392e-01 1.14014000e-01
5.89721560e-01 -8.74856114e-01 -9.66709375e-01 -4.18799907e-01
1.58606023e-01 6.49113297e-01 3.73175144e-01 -1.74028516... | [12.747259140014648, -0.41416463255882263] |
3accc0d9-7f78-4325-8cc3-c7765bbe5d51 | deep-semi-supervised-and-self-supervised | 2208.02408 | null | https://arxiv.org/abs/2208.02408v1 | https://arxiv.org/pdf/2208.02408v1.pdf | Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection | Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, ... | ['Fabio A. González', 'Oscar Perdómo', 'Jose Miguel Arrieta Ramos'] | 2022-08-04 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.60300431e-02 3.17199767e-01 -1.79923490e-01 -7.97913015e-01
-4.68978435e-01 -4.81989413e-01 1.94826081e-01 -1.62703842e-01
-5.10691941e-01 9.58807528e-01 1.95358455e-01 -2.78984696e-01
4.81355265e-02 -5.38698435e-01 -2.48240888e-01 -5.57955682e-01
2.23791376e-01 3.98403198e-01 2.27570429e-01 1.60157025... | [15.839672088623047, -4.00199031829834] |
f601f579-5b3d-4078-ac67-63f7ce6be42e | color-image-steganography-using-deep | 2211.09409 | null | https://arxiv.org/abs/2211.09409v1 | https://arxiv.org/pdf/2211.09409v1.pdf | Color Image steganography using Deep convolutional Autoencoders based on ResNet architecture | In this paper, a deep learning color image steganography scheme combining convolutional autoencoders and ResNet architecture is proposed. Traditional steganography methods suffer from some critical defects such as low capacity, security, and robustness. In recent decades, image hiding and image extraction were realized... | ['Saeed Khorashadizadeh', 'Mohammad-Hassan Majidi', 'Seyed Hesam Odin Hashemi'] | 2022-11-17 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 3.30019772e-01 -4.90260571e-02 3.55992407e-01 2.98628271e-01
3.01195592e-01 -1.40960664e-01 3.36109132e-01 -3.81925374e-01
-6.28622174e-01 6.66088164e-01 -4.69951965e-02 -2.03342319e-01
2.72517055e-01 -1.04341376e+00 -4.99112070e-01 -1.08171463e+00
-2.41778523e-01 -6.24794126e-01 2.98189461e-01 -4.67462003... | [4.294556617736816, 8.053814888000488] |
a3fbd58f-d57e-4f57-9052-832753bafff7 | 2003-13328 | 2003.13328 | null | https://arxiv.org/abs/2003.13328v1 | https://arxiv.org/pdf/2003.13328v1.pdf | Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling s... | ['Ming-Ming Cheng', 'Li Zhang', 'Jiashi Feng', 'Qibin Hou'] | 2020-03-30 | strip-pooling-rethinking-spatial-pooling-for | http://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-parsing'] | ['computer-vision'] | [ 1.20071448e-01 1.81491688e-01 -2.47442216e-01 -5.62014520e-01
-6.04863822e-01 -4.53817278e-01 4.44937527e-01 -3.96350995e-02
-5.68470716e-01 5.30858040e-01 4.24974650e-01 -5.87573767e-01
-5.34811281e-02 -9.47038293e-01 -9.98985946e-01 -5.80229163e-01
1.18731763e-02 -4.68182981e-01 8.83468866e-01 -6.73175380... | [9.595792770385742, 0.26466840505599976] |
604ebf0b-e0b8-44a0-a5f4-49c49ad1765c | aerial-monocular-3d-object-detection | 2208.03974 | null | https://arxiv.org/abs/2208.03974v1 | https://arxiv.org/pdf/2208.03974v1.pdf | Aerial Monocular 3D Object Detection | Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, exis... | ['Siheng Chen', 'Weidi Xie', 'Shaoheng Fang', 'Yue Hu'] | 2022-08-08 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-1.14193216e-01 -3.27742040e-01 1.93704039e-01 -7.54016489e-02
-1.10395007e-01 -9.32795942e-01 4.98273849e-01 -7.76755929e-01
-2.49402955e-01 2.56525427e-01 -2.96988755e-01 -3.61762702e-01
2.47600779e-01 -7.74440765e-01 -8.11219990e-01 -4.58523899e-01
6.25191703e-02 3.63076478e-01 6.95002556e-01 -5.93595326... | [7.904501914978027, -1.8160465955734253] |
c8a51ec3-1acd-466b-8f70-d1b7c3375f54 | visinger-variational-inference-with | 2110.08813 | null | https://arxiv.org/abs/2110.08813v2 | https://arxiv.org/pdf/2110.08813v2.pdf | VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis | In this paper, we propose VISinger, a complete end-to-end high-quality singing voice synthesis (SVS) system that directly generates audio waveform from lyrics and musical score. Our approach is inspired by VITS, which adopts VAE-based posterior encoder augmented with normalizing flow-based prior encoder and adversarial... | ['Mengxiao Bi', 'Pengcheng Zhu', 'Lei Xie', 'Heyang Xue', 'Jian Cong', 'Yongmao Zhang'] | 2021-10-17 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.51036203e-01 -1.04585417e-01 1.18647799e-01 -1.18208982e-01
-1.17636418e+00 -6.58385038e-01 2.16619685e-01 -6.31789923e-01
2.42057238e-02 5.01970112e-01 6.33456349e-01 3.81937600e-03
2.62650460e-01 -3.74194294e-01 -6.25044167e-01 -7.51308143e-01
2.47828364e-01 -1.75055996e-01 -1.99514790e-03 -2.79118299... | [15.530017852783203, 6.163241863250732] |
3595c779-a8ce-464d-868b-be7fce17058c | grounded-language-learning-in-a-simulated-3d | 1706.06551 | null | http://arxiv.org/abs/1706.06551v2 | http://arxiv.org/pdf/1706.06551v2.pdf | Grounded Language Learning in a Simulated 3D World | We are increasingly surrounded by artificially intelligent technology that
takes decisions and executes actions on our behalf. This creates a pressing
need for general means to communicate with, instruct and guide artificial
agents, with human language the most compelling means for such communication.
To achieve this i... | ['Chris Apps', 'Marcus Wainwright', 'David Szepesvari', 'Wojciech Marian Czarnecki', 'Ryan Faulkner', 'Fumin Wang', 'Felix Hill', 'Denis Teplyashin', 'Hubert Soyer', 'Simon Green', 'Karl Moritz Hermann', 'Demis Hassabis', 'Phil Blunsom', 'Max Jaderberg'] | 2017-06-20 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.52732265e-01 2.97290444e-01 1.58917531e-01 -3.16239893e-01
-8.04062709e-02 -8.33707273e-01 9.39766765e-01 2.72431731e-01
-4.58102763e-01 9.15247738e-01 1.87088758e-01 -2.83429801e-01
7.75693133e-02 -9.55211878e-01 -5.30280948e-01 -4.19915169e-01
-9.35727209e-02 6.48446858e-01 1.96999498e-02 -6.19781733... | [4.298269271850586, 1.2046788930892944] |
8dd5b330-f2a6-47c5-9bdc-30d43bf89b88 | stereotypical-bias-removal-for-hate-speech | 2001.05495 | null | https://arxiv.org/abs/2001.05495v1 | https://arxiv.org/pdf/2001.05495v1.pdf | Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations | With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to proactively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been... | ['Vasudeva Varma', 'Manish Gupta', 'Pinkesh Badjatiya'] | 2020-01-15 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 1.01429485e-01 2.66307089e-02 -6.03508234e-01 -2.77527213e-01
-3.95658076e-01 -6.80306017e-01 4.97463763e-01 3.79836768e-01
-5.47962606e-01 8.44836473e-01 4.30602044e-01 -3.64289373e-01
-1.61687702e-01 -7.54950523e-01 -5.76551080e-01 -4.97084439e-01
-2.51644522e-01 4.64645475e-02 2.32972488e-01 -3.94428313... | [8.766624450683594, 10.41285514831543] |
acb40ed5-91c1-4fe7-9c45-de60691930a5 | generalizing-natural-language-analysis-1 | 1911.03822 | null | https://arxiv.org/abs/1911.03822v2 | https://arxiv.org/pdf/1911.03822v2.pdf | Generalizing Natural Language Analysis through Span-relation Representations | Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format c... | ['Wei Xu', 'Zhengbao Jiang', 'Jun Araki', 'Graham Neubig'] | 2019-11-10 | generalizing-natural-language-analysis-2 | https://aclanthology.org/2020.acl-main.192 | https://aclanthology.org/2020.acl-main.192.pdf | acl-2020-6 | ['constituency-parsing', 'semantic-role-labeling-predicted-predicates'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.13711017e-01 3.65571827e-01 -4.82639432e-01 -8.27316046e-01
-8.17762792e-01 -9.14234698e-01 7.75154531e-01 7.91301012e-01
-2.37210512e-01 6.60271704e-01 5.79290032e-01 -3.50987256e-01
1.20034955e-01 -6.26040339e-01 -4.81158286e-01 -3.20209950e-01
-9.73773226e-02 6.29196525e-01 3.66462946e-01 -4.32738453... | [11.36967658996582, 6.876171112060547] |
693e30be-88c2-43dd-8ef3-4d133653b3eb | hierarchical-semi-supervised-contrastive | 2207.11789 | null | https://arxiv.org/abs/2207.11789v1 | https://arxiv.org/pdf/2207.11789v1.pdf | Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection | Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. However, when contaminated with unlabeled abnormal samples in training set under semi-supervised settin... | ['Klara Nahrstedt', 'Mingli Song', 'Xinchao Wang', 'Yibing Zhan', 'Gaoang Wang'] | 2022-07-24 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.15939835e-01 -4.06514972e-01 -3.96530479e-02 -4.73011732e-01
-1.14091969e+00 -4.17166322e-01 6.28512263e-01 3.95114362e-01
-1.10065937e-01 3.77968460e-01 -1.05445877e-01 -3.01185757e-01
-2.24231094e-01 -5.48392713e-01 -3.21580768e-01 -8.18884134e-01
-2.04223096e-01 3.04737896e-01 1.44942850e-01 2.34297384... | [7.629715442657471, 2.319509983062744] |
f0d72633-494b-4963-b289-6886d00bc3ae | detecting-localising-and-classifying-polyps | 2101.03285 | null | https://arxiv.org/abs/2101.03285v1 | https://arxiv.org/pdf/2101.03285v1.pdf | Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning | In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of ... | ['Gustavo Carneiro', 'Rajvinder Singh', 'Seon Ho Shin', 'Alastair D. Burt', 'Johan W. Verjans', 'Gabriel Maicas', 'Yuyuan Liu', 'Leonardo Zorron Cheng Tao Pu', 'Yu Tian'] | 2021-01-09 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 5.69492996e-01 3.04725021e-01 1.50391772e-01 -2.84761041e-01
-5.90982020e-01 -6.02667987e-01 5.07057309e-01 1.03932214e+00
-3.54839593e-01 4.40751255e-01 1.47474065e-01 -3.27269435e-01
-2.29971021e-01 -5.62124133e-01 -6.12388849e-01 -9.67885733e-01
-3.19694638e-01 2.81138927e-01 7.03698218e-01 3.48260939... | [14.037428855895996, -3.162731170654297] |
3712c2e1-6f16-4c8a-9acf-5c28311f6d99 | student-t-networks-for-melody-estimation | 2110.07419 | null | https://arxiv.org/abs/2110.07419v2 | https://arxiv.org/pdf/2110.07419v2.pdf | Student-t Networks for Melody Estimation | Melody estimation or melody extraction refers to the extraction of the primary or fundamental dominant frequency in a melody. This sequence of frequencies obtained represents the pitch of the dominant melodic line from recorded music audio signals. The music signal may be monophonic or polyphonic. The melody extraction... | ['Avi', 'Bhavesh Jain', 'Udhav Gupta'] | 2021-10-14 | null | null | null | null | ['melody-extraction'] | ['music'] | [ 4.00745749e-01 -6.41532004e-01 9.51002017e-02 4.38215554e-01
-6.80203736e-01 -7.84515679e-01 2.80718774e-01 2.71062553e-01
-1.10967427e-01 8.08813930e-01 4.70502675e-01 1.79003313e-01
-3.28849792e-01 -3.04924309e-01 -2.59244535e-02 -6.33659422e-01
-1.82537004e-01 -3.45548540e-01 1.45244345e-01 -3.91395576... | [15.917218208312988, 5.277510166168213] |
aa4521e8-0b62-42f6-b543-835d7feafa2c | towards-a-comprehensive-taxonomy-and-large | null | null | https://aclanthology.org/2020.alw-1.17 | https://aclanthology.org/2020.alw-1.17.pdf | Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage | Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, ev... | ['Derek Ruths', 'Haji Mohammad Saleem', 'Jana Kurrek'] | null | null | null | null | emnlp-alw-2020-11 | ['abusive-language'] | ['natural-language-processing'] | [-1.35538056e-01 1.41585112e-01 -4.86533821e-01 -4.21627074e-01
-7.87553549e-01 -9.49807465e-01 5.71308374e-01 7.24103630e-01
-5.98442078e-01 6.65790498e-01 8.78097653e-01 -2.06447542e-01
4.20634478e-01 -3.77907366e-01 -1.00721382e-01 -3.14362735e-01
1.16881996e-01 -4.96368576e-03 -1.99646816e-01 -5.75261712... | [8.711308479309082, 10.444212913513184] |
68186653-3437-4055-babc-0152c0bfdbab | using-partial-monotonicity-in-submodular | 2202.03051 | null | https://arxiv.org/abs/2202.03051v2 | https://arxiv.org/pdf/2202.03051v2.pdf | Using Partial Monotonicity in Submodular Maximization | Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications. Traditionally, the study of submodular functions was based on binary function properties. However, such properties have an inherit weakness, namely, if an algorithm... | ['Moran Feldman', 'Loay Mualem'] | 2022-02-07 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.24559039e-01 4.16914672e-01 -4.07108873e-01 -2.63394028e-01
-2.20643625e-01 -8.17864776e-01 1.89599425e-01 2.90571094e-01
-2.06622198e-01 1.01137078e+00 -5.47130480e-02 -1.40955299e-01
-6.19036674e-01 -9.08459783e-01 -7.31726050e-01 -9.11802948e-01
-2.31828362e-01 5.07095456e-01 9.13178623e-02 -5.61736047... | [6.693417549133301, 4.616962432861328] |
0c011544-5d97-4cc7-b85c-4138414836de | ice-monitoring-in-swiss-lakes-from-optical | 2010.14300 | null | https://arxiv.org/abs/2010.14300v1 | https://arxiv.org/pdf/2010.14300v1.pdf | Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning | Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set u... | ['Konrad Schindler', 'Laura Leal-Taixe', 'Emmanuel Baltsavias', 'Tianyu Wu', 'Rajanie Prabha', 'Manu Tom'] | 2020-10-27 | null | null | null | null | ['lake-ice-detection', 'lake-ice-detection'] | ['computer-vision', 'miscellaneous'] | [ 4.63816486e-02 -3.15325141e-01 1.59429640e-01 -5.17826557e-01
-7.93271005e-01 -9.34302747e-01 5.89144588e-01 3.09241205e-01
-7.48447120e-01 7.29754865e-01 -2.29668081e-01 -2.89918929e-01
1.38109043e-01 -9.93161321e-01 -6.64503098e-01 -8.83844018e-01
-2.58718729e-01 4.33640629e-01 2.53291875e-01 -2.66268760... | [9.490689277648926, -1.6306923627853394] |
d5cc8128-1ade-46fe-b270-2554379256a5 | identification-of-novel-diagnostic | 2305.18841 | null | https://arxiv.org/abs/2305.18841v1 | https://arxiv.org/pdf/2305.18841v1.pdf | Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis | Autism Spectrum Disorder is one of the leading neurodevelopmental disorders in our world, present in over 1% of the population and rapidly increasing in prevalence, yet the condition lacks a robust, objective, and efficient diagnostic. Clinical diagnostic criteria rely on subjective behavioral assessments, which are pr... | ['Annie Adhikary'] | 2023-05-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.33553633e-01 -3.70885502e-03 2.70665027e-02 -3.07181507e-01
-1.62531640e-02 -2.33512685e-01 1.74148187e-01 5.85708857e-01
-5.98516524e-01 4.80040044e-01 7.15815648e-02 6.61990270e-02
-6.43706143e-01 -3.27718705e-01 -2.20030934e-01 -5.25139570e-01
-6.11013353e-01 4.90671426e-01 -2.20528379e-01 -9.84623358... | [12.6722993850708, 3.08431077003479] |
2de9c9af-3f24-4a54-bab0-3b2a15dc32ae | neural-graph-embedding-methods-for-natural | 1911.03042 | null | https://arxiv.org/abs/1911.03042v3 | https://arxiv.org/pdf/1911.03042v3.pdf | Neural Graph Embedding Methods for Natural Language Processing | Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs. However, all of them tend to be sparse with very few facts per entity. In the first part of the thesis... | ['Shikhar Vashishth'] | 2019-11-08 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-2.01694041e-01 4.28740919e-01 -2.55055517e-01 -1.94301248e-01
2.09548548e-01 -4.75641102e-01 5.25301874e-01 8.12064052e-01
-2.95465708e-01 6.48275256e-01 2.30442867e-01 -2.78921455e-01
-3.28459501e-01 -1.44546950e+00 -5.64242601e-01 -4.67833281e-01
-4.16806370e-01 4.40313607e-01 1.92368031e-01 -4.18029457... | [8.8206148147583, 7.834240436553955] |
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