paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d7d6f83d-880d-4ea6-9930-2a1422f1eae3 | zusammenqa-data-augmentation-with-specialized | 2205.14981 | null | https://arxiv.org/abs/2205.14981v1 | https://arxiv.org/pdf/2205.14981v1.pdf | ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System | This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA). In this challenging scenario, given an input question the system has to gather evidence documents from a multilingual pool and generate from them an answer in the language of the question. We dev... | ['Simone Paolo Ponzetto', 'Goran Glavaš', 'Marco Bombieri', 'Sotaro Takeshita', 'Tornike Tsereteli', 'Robert Litschko', 'Tommaso Green', 'Chia-Chien Hung'] | 2022-05-30 | null | https://aclanthology.org/2022.mia-1.8 | https://aclanthology.org/2022.mia-1.8.pdf | naacl-mia-2022-7 | ['passage-retrieval'] | ['natural-language-processing'] | [-4.80222479e-02 7.51418024e-02 1.42049477e-01 -2.32942164e-01
-1.95739245e+00 -9.43027198e-01 8.10394526e-01 4.42341447e-01
-1.07161570e+00 1.01895738e+00 3.73638749e-01 -4.27132279e-01
-5.64488843e-02 -5.47078252e-01 -8.49940240e-01 -2.06745237e-01
2.12190315e-01 9.31442022e-01 2.94688284e-01 -7.82561243... | [11.366140365600586, 8.264545440673828] |
cbf2ec9a-86d1-460e-bd49-a13d9166d067 | interleaved-multitask-learning-for-audio | 1908.05182 | null | https://arxiv.org/abs/1908.05182v1 | https://arxiv.org/pdf/1908.05182v1.pdf | Interleaved Multitask Learning for Audio Source Separation with Independent Databases | Deep Neural Network-based source separation methods usually train independent models to optimize for the separation of individual sources. Although this can lead to good performance for well-defined targets, it can also be computationally expensive. The multitask alternative of a single network jointly optimizing for a... | ['Olumide Okubadejo', 'Clement S. J. Doire'] | 2019-08-14 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.98075199e-01 3.03633325e-03 -1.92566454e-01 -5.42306483e-01
-1.34358358e+00 -4.53041703e-01 4.73292261e-01 1.37460023e-01
-3.94895554e-01 9.09208596e-01 6.64126873e-02 1.33275315e-01
-2.19132125e-01 -4.87140805e-01 -9.48311865e-01 -9.95784879e-01
-1.62265107e-01 7.38262236e-01 7.31783062e-02 2.90488482... | [15.303679466247559, 5.61536169052124] |
9e47d28b-4ada-489c-bf56-1a151e3b945c | can-eye-movement-data-be-used-as-ground-truth | 1804.08749 | null | http://arxiv.org/abs/1804.08749v1 | http://arxiv.org/pdf/1804.08749v1.pdf | Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation? | In recent years a certain success in the task of modeling lexical semantics
was obtained with distributional semantic models. Nevertheless, the scientific
community is still unaware what is the most reliable evaluation method for
these models. Some researchers argue that the only possible gold standard could
be obtaine... | ['Amir Bakarov'] | 2018-04-23 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [-1.79035634e-01 6.76237270e-02 -4.39171419e-02 -3.73950362e-01
1.46465935e-02 -4.76446301e-01 9.15177643e-01 6.33257449e-01
-1.17859483e+00 5.18900335e-01 4.03538853e-01 -5.53689361e-01
-2.81077445e-01 -7.06540167e-01 -1.96411833e-01 -2.74277449e-01
3.63847226e-01 4.42648977e-01 4.77038682e-01 -3.71107429... | [10.530095100402832, 9.304298400878906] |
31c88216-66fd-48bc-9bd1-4e708ad7de62 | adversarial-embedding-a-robust-and-elusive | 1912.01487 | null | https://arxiv.org/abs/1912.01487v1 | https://arxiv.org/pdf/1912.01487v1.pdf | Adversarial Embedding: A robust and elusive Steganography and Watermarking technique | We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encodi... | ['Salah Ghamizi', 'Mike Papadakis', 'Yves Le Traon', 'Maxime Cordy'] | 2019-11-14 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.06611538e-01 5.05438745e-01 7.52262771e-02 1.66191593e-01
-5.17964900e-01 -1.04447782e+00 7.11539924e-01 -1.46469548e-01
-4.65069294e-01 3.98444861e-01 3.58440094e-02 -6.50718570e-01
5.21032393e-01 -1.05068624e+00 -1.16167819e+00 -7.52158344e-01
-7.59179115e-01 -4.55047280e-01 2.70270109e-01 -3.51194561... | [4.481238842010498, 8.001784324645996] |
8161dc44-440f-4a75-944a-ccab66041e5d | exploring-multimodal-sentiment-analysis-via | 2303.14708 | null | https://arxiv.org/abs/2303.14708v1 | https://arxiv.org/pdf/2303.14708v1.pdf | Exploring Multimodal Sentiment Analysis via CBAM Attention and Double-layer BiLSTM Architecture | Because multimodal data contains more modal information, multimodal sentiment analysis has become a recent research hotspot. However, redundant information is easily involved in feature fusion after feature extraction, which has a certain impact on the feature representation after fusion. Therefore, in this papaer, we ... | ['chunming Ma', 'Dan Yang', 'Zenyu Ren', 'Xiuhong Li', 'Huiru Wang'] | 2023-03-26 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 1.45580634e-01 -3.49752069e-01 1.02547169e-01 -6.46526337e-01
-6.89118147e-01 -2.01973811e-01 4.05843318e-01 -4.31848429e-02
-7.52658188e-01 3.79290730e-01 4.99601722e-01 2.45367885e-01
3.48045111e-01 -4.77069348e-01 -4.52026308e-01 -8.55380714e-01
5.05220115e-01 -3.67033005e-01 1.70502067e-01 -4.55693364... | [13.133956909179688, 5.016683101654053] |
22aaaa74-e222-4448-bd31-95d2baa6695a | on-the-importance-of-sign-labeling-the | 2302.10768 | null | https://arxiv.org/abs/2302.10768v2 | https://arxiv.org/pdf/2302.10768v2.pdf | On the Importance of Sign Labeling: The Hamburg Sign Language Notation System Case Study | Labeling is the cornerstone of supervised machine learning, which has been exploited in a plethora of various applications, with sign language recognition being one of them. However, such algorithms must be fed with a huge amount of consistently labeled data during the training process to elaborate a well-generalizing ... | ['Jakub Nalepa', 'Milena Olech', 'Agnieszka Mikołajczyk-Bareła', 'Alicja Kwaśniwska', 'Marta Plantykow', 'Sylwia Majchrowska', 'Maria Ferlin'] | 2023-01-19 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.63661346e-01 -3.33310604e-01 -4.86915171e-01 -5.40099859e-01
-5.59917688e-01 -7.38052905e-01 5.21747231e-01 -5.16738057e-01
-5.14477074e-01 5.15723467e-01 4.33149815e-01 -1.87810495e-01
-1.29207864e-01 -2.31703475e-01 -2.46708721e-01 -6.43354714e-01
3.19050848e-01 4.17379618e-01 1.65193424e-01 -1.61804736... | [9.124751091003418, -6.426894187927246] |
1ef430ed-3932-4eee-8edc-352f03ae4b46 | a-digital-swedish-yiddish-yiddish-swedish | null | null | https://aclanthology.org/2022.eurali-1.14 | https://aclanthology.org/2022.eurali-1.14.pdf | A Digital Swedish-Yiddish/Yiddish-Swedish Dictionary: A Web-Based Dictionary that is also Available Offline | Yiddish is one of the national minority languages of Sweden, and one of the languages for which the Swedish Institute for Language and Folklore is responsible for developing useful language resources. We here describe the web-based version of a Swedish-Yiddish/Yiddish-Swedish dictionary. The single search field of the ... | ['Rickard Domeij', 'Maria Skeppstedt', 'Gunnar Eriksson', 'Jean Hessel', 'Magnus Ahltorp'] | null | null | null | null | eurali-lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [-1.02473944e-01 -3.53524864e-01 -6.99583828e-01 2.16762871e-02
-4.77130651e-01 -1.04413748e+00 4.85796094e-01 1.72423333e-01
-9.15938377e-01 9.00663674e-01 4.83089417e-01 -1.09052408e+00
-8.35171267e-02 -7.65917182e-01 1.76684260e-01 -4.46289212e-01
5.32363296e-01 6.24890804e-01 3.03933144e-01 -5.69151044... | [10.346799850463867, 10.287995338439941] |
6dd3a6c5-a30d-4511-8332-1ecbe7571dea | parsing-to-noncrossing-dependency-graphs | null | null | https://aclanthology.org/Q15-1040 | https://aclanthology.org/Q15-1040.pdf | Parsing to Noncrossing Dependency Graphs | We study the generalization of maximum spanning tree dependency parsing to maximum acyclic subgraphs. Because the underlying optimization problem is intractable even under an arc-factored model, we consider the restriction to noncrossing dependency graphs. Our main contribution is a cubic-time exact inference algorithm... | ['Peter Jonsson', 'Marco Kuhlmann'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.29492486e-01 7.44363844e-01 -3.66858184e-01 -5.90771139e-01
-9.53421593e-01 -9.74968791e-01 -1.08803310e-01 2.02830061e-01
-2.90025055e-01 9.10248280e-01 -1.28370672e-01 -8.95281613e-01
-2.76343137e-01 -1.06605387e+00 -7.80205250e-01 -3.92173439e-01
-4.98053104e-01 7.42915213e-01 6.09629869e-01 -1.48183927... | [10.312551498413086, 9.604239463806152] |
e67a8878-5ccb-4430-b80f-62feb86eb34d | projective-urban-texturing | 2201.10938 | null | https://arxiv.org/abs/2201.10938v2 | https://arxiv.org/pdf/2201.10938v2.pdf | Projective Urban Texturing | This paper proposes a method for automatic generation of textures for 3D city meshes in immersive urban environments. Many recent pipelines capture or synthesize large quantities of city geometry using scanners or procedural modeling pipelines. Such geometry is intricate and realistic, however the generation of photo-r... | ['Evangelos Kalogerakis', 'Tom Kelly', 'Melinos Averkiou', 'Yiangos Georgiou'] | 2022-01-25 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 7.41123259e-01 2.85141706e-01 5.65480709e-01 -1.46506101e-01
-6.36852682e-01 -7.17969358e-01 8.54285419e-01 -3.92089635e-01
3.04326355e-01 5.71297348e-01 1.46960104e-02 -5.64277582e-02
3.30590278e-01 -1.46163964e+00 -1.26311576e+00 -4.91178572e-01
2.41787851e-01 8.95596504e-01 3.23874623e-01 -4.87630188... | [9.216387748718262, -3.341254711151123] |
042a9288-2d58-443a-bd26-7b460aff4a03 | nilc_usp-aspect-extraction-using-semantic | null | null | https://aclanthology.org/S14-2075 | https://aclanthology.org/S14-2075.pdf | NILC\_USP: Aspect Extraction using Semantic Labels | null | ['Pedro Balage Filho', 'Thiago Pardo'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['aspect-extraction'] | ['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... | [-7.323763847351074, 3.8498995304107666] |
abf998e7-7f1e-4ed1-a5fd-927d35200cef | collaborative-video-object-segmentation-by-1 | 2010.06349 | null | https://arxiv.org/abs/2010.06349v2 | https://arxiv.org/pdf/2010.06349v2.pdf | Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration | This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Unlike previous practices that focus on exploring the embedding learning of foreground object (s), we consider background should be equally treated. Thus, we propose a Collaborative video ob... | ['Yi Yang', 'Yunchao Wei', 'Zongxin Yang'] | 2020-10-13 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.38017982e-01 -1.80335119e-01 -3.12087238e-01 -1.72297031e-01
-5.62370837e-01 -3.96273226e-01 3.66809189e-01 -7.80845582e-02
-3.63801420e-01 3.67996216e-01 3.25879455e-02 -7.94074386e-02
2.48175085e-01 -6.11192882e-01 -7.30074525e-01 -9.23091650e-01
9.53976437e-02 4.36798520e-02 8.29548359e-01 2.84163773... | [9.294143676757812, -0.13594751060009003] |
0f95a327-1eef-467a-a6d2-86845dc1af34 | preventing-zero-shot-transfer-degradation-in | 2303.06628 | null | https://arxiv.org/abs/2303.06628v1 | https://arxiv.org/pdf/2303.06628v1.pdf | Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models | Continual learning (CL) can help pre-trained vision-language models efficiently adapt to new or under-trained data distributions without re-training. Nevertheless, during the continual training of the Contrastive Language-Image Pre-training (CLIP) model, we observe that the model's zero-shot transfer ability significan... | ['Yang You', 'Xiangyu Yue', 'Ziheng Qin', 'Kai Wang', 'Mingyuan Ma', 'Zangwei Zheng'] | 2023-03-12 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 2.43353352e-01 -2.43514761e-01 -1.44783139e-01 -3.01505774e-01
-7.92997360e-01 -4.74612683e-01 8.02360117e-01 -2.05602020e-01
-7.42663622e-01 7.57456601e-01 -1.26529694e-01 -1.38384283e-01
2.83010483e-01 -3.00344795e-01 -1.08573496e+00 -7.19015300e-01
4.35445637e-01 4.14316654e-01 6.69553936e-01 5.02168685... | [9.932376861572266, 3.220952033996582] |
19e28185-2738-4b35-8022-9430c12cb4a7 | industrial-scene-change-detection-using-deep | 2212.14278 | null | https://arxiv.org/abs/2212.14278v1 | https://arxiv.org/pdf/2212.14278v1.pdf | Industrial Scene Change Detection using Deep Convolutional Neural Networks | Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance. This is mainly due to the fact that addition or removal of important objects for some environment... | ['Hassan Shahbazi', 'Kiavash azimi', 'Ehsan Rahnama', 'Ali Atghaei'] | 2022-12-29 | null | null | null | null | ['scene-change-detection', 'change-detection'] | ['computer-vision', 'computer-vision'] | [ 4.70925868e-01 -5.39237380e-01 5.88741481e-01 -4.63569850e-01
3.20396096e-01 -3.43369335e-01 4.64886069e-01 2.55339444e-01
-2.58962721e-01 5.44649482e-01 -2.16317236e-01 -6.55533820e-02
-2.98083723e-01 -8.14543366e-01 -5.09409785e-01 -7.35979140e-01
1.53796300e-01 1.00366540e-01 3.95727277e-01 -4.06263798... | [9.764660835266113, -1.9996412992477417] |
158fe6fa-25a1-4039-912a-e0cd550890ff | searching-for-robust-neural-architectures-via | 2203.03128 | null | https://arxiv.org/abs/2203.03128v2 | https://arxiv.org/pdf/2203.03128v2.pdf | $A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks | The robustness of deep neural networks (DNN) models has attracted increasing attention due to the urgent need for security in many applications. Numerous existing open-sourced tools or platforms are developed to evaluate the robustness of DNN models by ensembling the majority of adversarial attack or defense algorithms... | ['Xiaoqian Chen', 'Wen Yao', 'Chao Li', 'Tingsong Jiang', 'Jialiang Sun'] | 2022-03-07 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-1.94794476e-01 -4.84353125e-01 5.10194123e-01 -1.02781370e-01
-3.23849201e-01 -1.10662293e+00 4.56608534e-01 -4.17390287e-01
-3.36404353e-01 3.83141071e-01 -1.31381437e-01 -4.61490363e-01
-3.30609143e-01 -9.22181487e-01 -6.18538380e-01 -9.22696650e-01
1.74939111e-02 -5.44091836e-02 2.12105885e-01 -5.70421219... | [5.524845600128174, 7.941572666168213] |
9e6af9df-5a05-4bff-b8db-e241c8ca4d2b | shiro-soft-hierarchical-reinforcement | 2212.12786 | null | https://arxiv.org/abs/2212.12786v1 | https://arxiv.org/pdf/2212.12786v1.pdf | SHIRO: Soft Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning (HRL) algorithms have been demonstrated to perform well on high-dimensional decision making and robotic control tasks. However, because they solely optimize for rewards, the agent tends to search the same space redundantly. This problem reduces the speed of learning and achieved rewa... | ['Omer Eldar', 'Mathew Strong', 'Kandai Watanabe'] | 2022-12-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.28511023e-02 2.81596035e-01 -4.26865041e-01 9.32790190e-02
-4.91720796e-01 -5.22480190e-01 6.39154613e-01 2.92670727e-01
-9.32133794e-01 1.20525956e+00 -6.88758492e-02 -2.30172291e-01
-3.45793456e-01 -7.09451199e-01 -7.42438138e-01 -1.07858610e+00
-5.01922190e-01 3.46539348e-01 2.16358975e-01 -3.37277293... | [4.166051864624023, 2.0854110717773438] |
fa8591a7-aa66-4201-906d-9cb298d5a612 | boosting-breast-ultrasound-video | 2306.06877 | null | https://arxiv.org/abs/2306.06877v1 | https://arxiv.org/pdf/2306.06877v1.pdf | Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers | Breast ultrasound videos contain richer information than ultrasound images, therefore it is more meaningful to develop video models for this diagnosis task. However, the collection of ultrasound video datasets is much harder. In this paper, we explore the feasibility of enhancing the performance of ultrasound video cla... | ['LiWei Wang', 'Dong Wang', 'Yuting Dai', 'Meng Lei', 'Zhao Zhang', 'AnLan Sun'] | 2023-06-12 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 2.55921632e-01 2.81734049e-01 -2.78339088e-01 -5.61893642e-01
-6.95341527e-01 -1.54912114e-01 2.73814410e-01 -3.58059794e-01
-1.03924036e-01 3.70778620e-01 3.44070256e-01 -4.22182173e-01
-1.72842279e-01 -4.67052013e-01 -1.04131854e+00 -7.99016178e-01
-4.08519566e-01 -9.37890559e-02 1.49757698e-01 1.28590062... | [8.945693969726562, 0.3117155134677887] |
41702ffa-f7c4-4165-aff4-8ea1c0953f4e | active-semantic-localization-with-graph | 2305.06141 | null | https://arxiv.org/abs/2305.06141v3 | https://arxiv.org/pdf/2305.06141v3.pdf | Active Semantic Localization with Graph Neural Embedding | Semantic localization, i.e., robot self-localization with semantic image modality, is critical in recently emerging embodied AI applications such as point-goal navigation, object-goal navigation and vision language navigation. However, most existing works on semantic localization focus on passive vision tasks without v... | ['Daiki Iwata', 'Ryogo Yamamoto', 'Kanji Tanaka', 'Mitsuki Yoshida'] | 2023-05-10 | null | null | null | null | ['vision-language-navigation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 2.52252907e-01 4.44006063e-02 -1.76846221e-01 -2.96295494e-01
-4.24039572e-01 -5.22947729e-01 5.62589109e-01 2.42351711e-01
-5.35843551e-01 5.44001102e-01 3.13448869e-02 -4.03796509e-02
1.45708872e-02 -7.26208746e-01 -8.33892643e-01 -7.99222589e-01
-5.08760884e-02 3.00389528e-01 4.76452500e-01 -2.32974529... | [7.474506855010986, -1.9533249139785767] |
94fb5380-26a8-4ddb-b7b2-9866246438b2 | dynamic-clustering-and-cluster-contrastive | 2303.06810 | null | https://arxiv.org/abs/2303.06810v1 | https://arxiv.org/pdf/2303.06810v1.pdf | Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification | Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the relationship between module parameters of Re-ID framework and feature distributions, which may lead to feature misalignment and hinder the model performance. To address this prob... | ['Fei Su', 'Zhicheng Zhao', 'Yunhao Du', 'Mengjia Xue', 'Ziqi He'] | 2023-03-13 | null | null | null | null | ['person-re-identification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [-2.58014619e-01 -3.97152722e-01 -3.30257386e-01 -8.17070723e-01
-6.18495643e-01 -3.53069752e-01 5.93293905e-01 2.74079084e-01
-4.51676607e-01 2.63686091e-01 1.56863004e-01 2.46750310e-01
-3.43073994e-01 -4.51837689e-01 -9.21848565e-02 -1.06229687e+00
7.10231811e-02 4.18760061e-01 2.25996375e-01 4.07189280... | [14.860363006591797, 1.220374584197998] |
8622021e-1c64-468d-9ee2-e3682a3cbd1b | finding-lookalike-customers-for-e-commerce | 2301.03147 | null | https://arxiv.org/abs/2301.03147v2 | https://arxiv.org/pdf/2301.03147v2.pdf | Finding Lookalike Customers for E-Commerce Marketing | Customer-centric marketing campaigns generate a large portion of e-commerce website traffic for Walmart. As the scale of customer data grows larger, expanding the marketing audience to reach more customers is becoming more critical for e-commerce companies to drive business growth and bring more value to customers. In ... | ['Wei Shen', 'Changzheng Liu', 'Yang Peng'] | 2023-01-09 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-7.50886917e-01 -1.73110381e-01 -4.23680484e-01 -1.05760658e+00
-7.71901488e-01 -4.58153069e-01 1.97546825e-01 6.01300299e-01
-2.24294186e-01 1.23532534e-01 3.69748384e-01 -3.49195868e-01
-1.44889727e-01 -1.35805821e+00 -2.74662673e-01 -1.33497849e-01
-9.03532431e-02 1.03105187e+00 -1.50533214e-01 -7.91953504... | [9.968194007873535, 6.013302326202393] |
ca36329e-2e96-49a0-b6f3-4fdcbe405ead | local-and-global-point-cloud-reconstruction | 2112.06389 | null | https://arxiv.org/abs/2112.06389v1 | https://arxiv.org/pdf/2112.06389v1.pdf | Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation | This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image. To that end, we present a novel pipeline for local and global point cloud reconstruction using a 3D hand template while learning a latent representation for pose estimation. To demonstrate our method... | ['Angela Yao', 'Shicheng Chen', 'Linlin Yang', 'Ziwei Yu'] | 2021-12-13 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction', '3d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.46850002e-01 -5.14488995e-01 -1.80620909e-01 -1.03501290e-01
-8.59080076e-01 -8.53061974e-01 1.99732363e-01 -7.24873126e-01
-2.48049483e-01 1.07283182e-01 1.41991541e-01 -4.63667372e-03
1.32725090e-01 -2.80513525e-01 -6.83892429e-01 -4.40118283e-01
3.03183436e-01 1.42307317e+00 2.57068247e-01 -9.70072001... | [6.538787841796875, -0.8778291344642639] |
44a17003-29ff-4719-8426-522f6d7ee0c5 | controlling-styles-in-neural-machine | 2212.08909 | null | https://arxiv.org/abs/2212.08909v2 | https://arxiv.org/pdf/2212.08909v2.pdf | Controlling Styles in Neural Machine Translation with Activation Prompt | Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience. Earlier studies on this topic typically concentrate on regulating the level of formality and achieve some progress in this area. However, they still encounter two major challenges. The fi... | ['Mingxuan Wang', 'Weiguo Zheng', 'Shanbo Cheng', 'Zewei Sun', 'Yifan Wang'] | 2022-12-17 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.46680522e-01 -4.50052649e-01 -2.66753167e-01 -5.31274080e-01
-8.40920091e-01 -9.20259595e-01 7.12444365e-01 -1.70377746e-01
-4.83142823e-01 7.73003757e-01 1.71187714e-01 -4.61006463e-01
3.05092752e-01 -5.28887093e-01 -4.20163661e-01 -5.27527153e-01
6.79873645e-01 4.94763821e-01 4.13321741e-02 -6.03013813... | [11.641361236572266, 10.048783302307129] |
92a7dccb-2893-475e-9ee0-6f01e2ab86bc | supercon-supervised-contrastive-learning-for | 2202.05685 | null | https://arxiv.org/abs/2202.05685v1 | https://arxiv.org/pdf/2202.05685v1.pdf | SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification | Convolutional neural networks (CNNs) have achieved great success in skin lesion classification. A balanced dataset is required to train a good model. However, due to the appearance of different skin lesions in practice, severe or even deadliest skin lesion types (e.g., melanoma) naturally have quite small amount repres... | ['J. Morris Chang', 'Di Zhuang', 'Keyu Chen'] | 2022-02-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.62448740e-01 -2.30675623e-01 -5.18255472e-01 -5.54223418e-01
-6.31081164e-01 -3.51328254e-02 2.13475779e-01 3.59976590e-01
-3.27336699e-01 7.20219493e-01 -1.01644963e-01 -1.82585001e-01
-3.28114897e-01 -9.55016613e-01 -2.25627750e-01 -9.27825212e-01
1.93470702e-01 5.13834395e-02 1.82128400e-01 -1.05664946... | [15.575234413146973, -2.908388137817383] |
285e6738-0533-4ca7-bced-48fceeb9269e | dynamic-few-shot-visual-learning-without | 1804.09458 | null | http://arxiv.org/abs/1804.09458v1 | http://arxiv.org/pdf/1804.09458v1.pdf | Dynamic Few-Shot Visual Learning without Forgetting | The human visual system has the remarkably ability to be able to effortlessly
learn novel concepts from only a few examples. Mimicking the same behavior on
machine learning vision systems is an interesting and very challenging research
problem with many practical advantages on real world vision applications. In
this co... | ['Spyros Gidaris', 'Nikos Komodakis'] | 2018-04-25 | dynamic-few-shot-visual-learning-without-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Gidaris_Dynamic_Few-Shot_Visual_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Gidaris_Dynamic_Few-Shot_Visual_CVPR_2018_paper.pdf | cvpr-2018-6 | ['novel-concepts'] | ['reasoning'] | [ 1.86810315e-01 -9.67711657e-02 1.67538628e-01 -3.30640852e-01
-3.16469550e-01 -3.59529644e-01 9.54432905e-01 1.43571664e-02
-6.14830196e-01 5.05388618e-01 -2.44778410e-01 -2.60940902e-02
-1.31631293e-03 -7.78286695e-01 -6.92628086e-01 -7.03052402e-01
1.20417468e-01 2.37409100e-01 6.08907342e-01 -3.22646081... | [9.932404518127441, 2.7950448989868164] |
79dba9a9-ff59-4b35-a5d0-874fb3774fc5 | technical-outlier-detection-via-convolutional | 2305.12068 | null | https://arxiv.org/abs/2305.12068v1 | https://arxiv.org/pdf/2305.12068v1.pdf | Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset | The ADMANI datasets (annotated digital mammograms and associated non-image datasets) from the Transforming Breast Cancer Screening with AI programme (BRAIx) run by BreastScreen Victoria in Australia are multi-centre, large scale, clinically curated, real-world databases. The datasets are expected to aid in the developm... | ['Davis J. McCarthy', 'Susan Wei', 'Carlos A. Pena Solorzano', 'Hui Li'] | 2023-05-20 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'outlier-detection'] | ['knowledge-base', 'medical', 'methodology'] | [ 3.39671135e-01 3.93838495e-01 1.13030292e-01 -4.26681936e-01
-1.04388082e+00 -2.49010697e-01 1.53293326e-01 6.08235896e-01
-3.71067584e-01 1.27383590e-01 4.36718166e-01 -6.85188472e-01
-5.13171136e-01 -6.36861265e-01 -8.54659081e-01 -5.66370666e-01
-1.56822845e-01 7.46875465e-01 1.92842647e-01 2.70037264... | [15.246638298034668, -2.4819483757019043] |
24cfab08-0ab5-4f04-b872-5c61edba70e1 | measuring-and-improving-compositional-1 | 2205.02054 | null | https://arxiv.org/abs/2205.02054v1 | https://arxiv.org/pdf/2205.02054v1.pdf | Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment | In text-to-SQL tasks -- as in much of NLP -- compositional generalization is a major challenge: neural networks struggle with compositional generalization where training and test distributions differ. However, most recent attempts to improve this are based on word-level synthetic data or specific dataset splits to gene... | ['Matthew Purver', 'Qiuping Huang', 'Xinyun Chen', 'Yujian Gan'] | 2022-05-04 | null | https://aclanthology.org/2022.findings-naacl.62 | https://aclanthology.org/2022.findings-naacl.62.pdf | findings-naacl-2022-7 | ['text-to-sql'] | ['computer-code'] | [ 6.96201503e-01 3.19279075e-01 -5.19896112e-02 -7.76011407e-01
-9.19967055e-01 -8.40537429e-01 4.93921310e-01 6.15189262e-02
-2.68924683e-01 8.92996728e-01 1.32597148e-01 -5.74699342e-01
3.86927962e-01 -8.73106480e-01 -1.01960647e+00 -3.57536107e-01
8.55855867e-02 9.18733776e-01 5.39364278e-01 -3.82489353... | [11.071906089782715, 8.96293830871582] |
49a24163-0cb3-428d-9e68-ecfc933d57f0 | visithers-visible-thermal-infrared-stereo | 2304.11291 | null | https://arxiv.org/abs/2304.11291v1 | https://arxiv.org/pdf/2304.11291v1.pdf | VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette | This paper presents a novel approach for visible-thermal infrared stereoscopy, focusing on the estimation of disparities of human silhouettes. Visible-thermal infrared stereo poses several challenges, including occlusions and differently textured matching regions in both spectra. Finding matches between two spectra wit... | ['Wassim Bouachir', 'Guillaume-Alexandre Bilodeau', 'Philippe Duplessis-Guindon', 'Noreen Anwar'] | 2023-04-22 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 5.08551419e-01 -3.96170169e-01 -4.34803106e-02 -1.87901482e-01
-8.62977147e-01 -4.24597353e-01 2.38306940e-01 5.00126556e-03
-2.70252883e-01 4.12891626e-01 1.98571280e-01 -6.48726150e-02
-8.51464923e-03 -8.33050072e-01 -5.06537557e-01 -8.33601534e-01
1.29843310e-01 -2.53716350e-01 1.10381424e-01 -1.47334486... | [10.150654792785645, -2.457443952560425] |
b8c969e5-0500-402b-919d-08a32b6f2a0e | high-dynamic-range-imaging-with-context-aware | 2304.04416 | null | https://arxiv.org/abs/2304.04416v4 | https://arxiv.org/pdf/2304.04416v4.pdf | High Dynamic Range Imaging with Context-aware Transformer | Avoiding the introduction of ghosts when synthesising LDR images as high dynamic range (HDR) images is a challenging task. Convolutional neural networks (CNNs) are effective for HDR ghost removal in general, but are challenging to deal with the LDR images if there are large movements or oversaturation/undersaturation. ... | ['Zhenming Fu', 'Dan Zhang', 'Fangfang Zhou'] | 2023-04-10 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.21395960e-01 -1.50439948e-01 8.81092623e-02 1.93866089e-01
-5.22648752e-01 -2.36266702e-01 3.38468999e-01 -6.26026928e-01
4.71317507e-02 7.23293602e-01 3.13307971e-01 6.74911961e-02
2.69778907e-01 -6.93776608e-01 -5.48080385e-01 -1.21962845e+00
9.87372026e-02 -1.67174116e-01 6.01579487e-01 -4.91794646... | [10.90865421295166, -2.1398561000823975] |
66ae0abc-adf2-40ec-83fa-3c915fe084f6 | human-trajectory-prediction-using-spatially | 1705.09436 | null | http://arxiv.org/abs/1705.09436v1 | http://arxiv.org/pdf/1705.09436v1.pdf | Human Trajectory Prediction using Spatially aware Deep Attention Models | Trajectory Prediction of dynamic objects is a widely studied topic in the
field of artificial intelligence. Thanks to a large number of applications like
predicting abnormal events, navigation system for the blind, etc. there have
been many approaches to attempt learning patterns of motion directly from data
using a wi... | ['G. Srinivasaraghavan', 'Daksh Varshneya'] | 2017-05-26 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-1.13708358e-02 -3.31917971e-01 1.42312184e-01 -4.90662575e-01
-5.47974885e-01 -3.59569490e-01 1.12990975e+00 6.74729422e-02
-6.74551129e-01 5.66837013e-01 3.55007589e-01 -2.93208957e-01
-3.62302721e-01 -7.44474232e-01 -7.59106457e-01 -7.49537766e-01
-3.03868562e-01 3.15185159e-01 9.23846066e-01 -4.13355678... | [6.488801956176758, 0.5494167804718018] |
2c82b09e-5e43-4097-9ccd-17ea0bdb2180 | robust-3d-scene-segmentation-through | 2111.08434 | null | https://arxiv.org/abs/2111.08434v1 | https://arxiv.org/pdf/2111.08434v1.pdf | Robust 3D Scene Segmentation through Hierarchical and Learnable Part-Fusion | 3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from the part misclassification problem, wherein parts of the same object are labelled incorrectly. Previous ... | ['Sreenivas Subramoney', 'Om J Omer', 'Prashant Laddha', 'Benjamin Ummenhofer', 'Anirud Thyagharajan'] | 2021-11-16 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.10770881e-01 7.53426790e-01 -2.40049794e-01 -7.84363091e-01
-6.60872161e-01 -3.95818889e-01 3.77439886e-01 4.91060436e-01
-1.22729465e-01 3.40509266e-01 -2.98934877e-01 -2.44119272e-01
-3.46342593e-01 -8.32077026e-01 -7.52522051e-01 -4.97506022e-01
1.46086693e-01 8.81232917e-01 8.50074053e-01 -2.08298430... | [8.154186248779297, -2.9281740188598633] |
5117af7a-a4b2-41a5-848d-b04cbdba47fc | makeup-extraction-of-3d-representation-via | 2302.13279 | null | https://arxiv.org/abs/2302.13279v1 | https://arxiv.org/pdf/2302.13279v1.pdf | Makeup Extraction of 3D Representation via Illumination-Aware Image Decomposition | Facial makeup enriches the beauty of not only real humans but also virtual characters; therefore, makeup for 3D facial models is highly in demand in productions. However, painting directly on 3D faces and capturing real-world makeup are costly, and extracting makeup from 2D images often struggles with shading effects a... | ['Yoshihiro Kanamori', 'Takafumi Taketomi', 'Xingchao Yang'] | 2023-02-26 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 4.43067610e-01 5.39049171e-02 6.32953048e-02 -4.39103037e-01
-3.30451101e-01 -4.93625224e-01 4.66668934e-01 -6.48973048e-01
3.90899539e-01 5.83814144e-01 -5.65610491e-02 2.85762906e-01
3.60360742e-01 -1.02637529e+00 -6.59443796e-01 -7.84125209e-01
4.09889489e-01 3.42302561e-01 -1.84018314e-01 -3.06353897... | [12.752662658691406, -0.3261476755142212] |
5075f5b4-10df-4b89-a32c-f045ebf58390 | understanding-pure-clip-guidance-for-voxel | 2209.15172 | null | https://arxiv.org/abs/2209.15172v1 | https://arxiv.org/pdf/2209.15172v1.pdf | Understanding Pure CLIP Guidance for Voxel Grid NeRF Models | We explore the task of text to 3D object generation using CLIP. Specifically, we use CLIP for guidance without access to any datasets, a setting we refer to as pure CLIP guidance. While prior work has adopted this setting, there is no systematic study of mechanics for preventing adversarial generations within CLIP. We ... | ['Angel X. Chang', 'Han-Hung Lee'] | 2022-09-30 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 3.31754714e-01 4.58414048e-01 3.38011056e-01 6.00032061e-02
-8.23110461e-01 -8.44532490e-01 8.10352981e-01 -2.59629846e-01
-4.61824611e-02 6.04012609e-01 4.01126981e-01 -1.69257477e-01
2.53613412e-01 -8.94000769e-01 -1.12900567e+00 -4.83604252e-01
-8.54729414e-02 3.32911789e-01 2.42849976e-01 -2.12325633... | [11.316457748413086, -0.4792816936969757] |
539a82b7-bdea-40b9-ae5a-3786bdc932a8 | s3lam-structured-scene-slam | 2109.07339 | null | https://arxiv.org/abs/2109.07339v2 | https://arxiv.org/pdf/2109.07339v2.pdf | S3LAM: Structured Scene SLAM | We propose a new SLAM system that uses the semantic segmentation of objects and structures in the scene. Semantic information is relevant as it contains high level information which may make SLAM more accurate and robust. Our contribution is twofold: i) A new SLAM system based on ORB-SLAM2 that creates a semantic map m... | ['Jérôme Royan', 'Amine Kacete', 'Eric Marchand', 'Mathieu Gonzalez'] | 2021-09-15 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 7.26785064e-02 -1.08530104e-01 5.45320846e-02 -5.51051259e-01
-4.72985655e-01 -7.56597698e-01 7.05560684e-01 2.18832627e-01
-3.72635543e-01 3.64933789e-01 6.90224990e-02 1.52237996e-01
-1.76747650e-01 -7.58019209e-01 -9.56663966e-01 -2.98358738e-01
2.52679497e-01 1.01408803e+00 7.73492157e-01 -1.80488363... | [7.348358154296875, -2.2714362144470215] |
f568405f-350d-4f57-a3fe-3586fa1f504b | active-detection-and-localization-of | 1603.07022 | null | http://arxiv.org/abs/1603.07022v1 | http://arxiv.org/pdf/1603.07022v1.pdf | Active Detection and Localization of Textureless Objects in Cluttered Environments | This paper introduces an active object detection and localization framework
that combines a robust untextured object detection and 3D pose estimation
algorithm with a novel next-best-view selection strategy. We address the
detection and localization problems by proposing an edge-based registration
algorithm that refine... | ['Alberto Pretto', 'Marco Imperoli'] | 2016-03-22 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 2.21765548e-01 -1.26236096e-01 1.61078006e-01 -9.15410072e-02
-6.75225317e-01 -4.93951261e-01 6.21274889e-01 1.79738566e-01
-8.14505160e-01 3.80015761e-01 -2.14654356e-01 3.78723472e-01
-4.61170465e-01 -5.58595479e-01 -8.05437803e-01 -1.08957303e+00
1.94022909e-01 1.03474808e+00 5.19569337e-01 2.50865519... | [7.023499488830566, -2.2542011737823486] |
045942b8-fea3-4c81-826d-e26f5023c322 | adaptive-fusion-for-rgb-d-salient-object | 1901.01369 | null | http://arxiv.org/abs/1901.01369v2 | http://arxiv.org/pdf/1901.01369v2.pdf | Adaptive Fusion for RGB-D Salient Object Detection | RGB-D salient object detection aims to identify the most visually distinctive
objects in a pair of color and depth images. Based upon an observation that
most of the salient objects may stand out at least in one modality, this paper
proposes an adaptive fusion scheme to fuse saliency predictions generated from
two moda... | ['Ningning Wang', 'Xiaojin Gong'] | 2019-01-05 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.84715843e-01 9.52980220e-02 -1.30497038e-01 -5.48137009e-01
-6.12212777e-01 -2.64800042e-02 4.10672486e-01 1.10864937e-01
-3.12921286e-01 4.27840739e-01 2.06601337e-01 4.35489565e-02
1.11127250e-01 -4.61994410e-01 -8.20601404e-01 -5.98798931e-01
8.12506899e-02 -3.02982718e-01 8.21617723e-01 -1.27529338... | [9.760245323181152, -0.6138543486595154] |
535fff05-9c47-499e-b41c-84880b44eaf2 | merging-classification-predictions-with | 2210.00834 | null | https://arxiv.org/abs/2210.00834v1 | https://arxiv.org/pdf/2210.00834v1.pdf | Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments | Low-overhead visual place recognition (VPR) is a highly active research topic. Mobile robotics applications often operate under low-end hardware, and even more hardware capable systems can still benefit from freeing up onboard system resources for other navigation tasks. This work addresses lightweight VPR by proposing... | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Bruno Arcanjo'] | 2022-10-03 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.20232612e-01 -4.04444225e-02 -2.99466670e-01 -3.47437441e-01
-3.96268576e-01 -5.06999671e-01 6.71766043e-01 1.36790663e-01
-1.02408516e+00 4.38094854e-01 -2.43798167e-01 -6.33613408e-01
-1.19252943e-01 -7.82411814e-01 -8.20221663e-01 -4.84636605e-01
-2.84999460e-01 1.81876585e-01 7.25058496e-01 -3.01000029... | [7.646856307983398, -1.859878659248352] |
d5ffc57b-9a8b-4d47-8b68-b975e727f745 | transfer-dynamics-in-emergent-evolutionary | 2203.10941 | null | https://arxiv.org/abs/2203.10941v1 | https://arxiv.org/pdf/2203.10941v1.pdf | Transfer Dynamics in Emergent Evolutionary Curricula | PINSKY is a system for open-ended learning through neuroevolution in game-based domains. It builds on the Paired Open-Ended Trailblazer (POET) system, which originally explored learning and environment generation for bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI) system. Previous work show... | ['L. B. Soros', 'Julian Togelius', 'Amy K Hoover', 'Aaron Dharna'] | 2022-03-03 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 1.98690742e-02 9.52689871e-02 8.84859264e-02 4.52749580e-01
2.83639848e-01 -6.53618872e-01 5.45738935e-01 5.42949922e-02
-7.39400327e-01 1.38035345e+00 -1.62700787e-01 -4.70307559e-01
-5.79498410e-01 -1.05370235e+00 -7.40430057e-01 -9.93137717e-01
-5.24417400e-01 4.42436159e-01 4.66972798e-01 -1.19394159... | [5.548349857330322, 3.9319703578948975] |
dea3e681-25e7-46e6-9b47-c2f1bc853ce1 | policy-gradient-methods-in-the-presence-of | 2305.05666 | null | https://arxiv.org/abs/2305.05666v1 | https://arxiv.org/pdf/2305.05666v1.pdf | Policy Gradient Methods in the Presence of Symmetries and State Abstractions | Reinforcement learning on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the continuous-control setting, and extend the definition of MDP homomorphisms to the setting of continuous state and action spaces. We derive a policy... | ['Doina Precup', 'David Meger', 'Rosie Zhao', 'Sahand Rezaei-Shoshtari', 'Prakash Panangaden'] | 2023-05-09 | null | null | null | null | ['policy-gradient-methods', 'continuous-control'] | ['methodology', 'playing-games'] | [-1.86234787e-01 1.84258789e-01 -4.91455913e-01 1.64163157e-01
-4.38337266e-01 -7.94352472e-01 9.98447001e-01 -1.48095816e-01
-1.93028301e-01 7.38557100e-01 6.57011688e-01 -4.44325417e-01
-2.28075668e-01 -4.39869761e-01 -7.44935751e-01 -6.65524840e-01
-5.51800907e-01 2.46191531e-01 -1.38643980e-01 -2.62837976... | [4.126101016998291, 1.893982172012329] |
e5e60996-cbd6-4970-a120-acebb2c82d24 | multiple-instance-learning-via-iterative-self | 2210.09452 | null | https://arxiv.org/abs/2210.09452v1 | https://arxiv.org/pdf/2210.09452v1.pdf | Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning | Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two differen... | ['Carlos Fernandez-Granda', 'Krzysztof J. Geras', 'Narges Razavian', 'Sheng Liu', 'Yiqiu Shen', 'Weicheng Zhu', 'Kangning Liu'] | 2022-10-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Multiple_Instance_Learning_via_Iterative_Self-Paced_Supervised_Contrastive_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Multiple_Instance_Learning_via_Iterative_Self-Paced_Supervised_Contrastive_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multiple-instance-learning'] | ['methodology'] | [ 6.65453196e-01 3.28435451e-02 -8.14127684e-01 -5.51568031e-01
-1.31313813e+00 -3.31876367e-01 3.97615343e-01 7.04108894e-01
-2.32288435e-01 1.06567097e+00 4.36965078e-02 1.39844745e-01
-3.43624353e-01 -8.27598333e-01 -6.51646495e-01 -8.54349196e-01
-5.91956638e-02 7.04706967e-01 -1.16959445e-01 9.63795558... | [9.525864601135254, 3.4253644943237305] |
b23dc5a2-6a8c-48bb-8325-ab74aff9ac91 | balanced-districting-on-grid-graphs-with | 2102.05028 | null | https://arxiv.org/abs/2102.05028v1 | https://arxiv.org/pdf/2102.05028v1.pdf | Balanced Districting on Grid Graphs with Provable Compactness and Contiguity | Given a graph $G = (V,E)$ with vertex weights $w(v)$ and a desired number of parts $k$, the goal in graph partitioning problems is to partition the vertex set V into parts $V_1,\ldots,V_k$. Metrics for compactness, contiguity, and balance of the parts $V_i$ are frequent objectives, with much existing literature focusin... | ['Yao Xie', 'Swati Gupta', 'Shixiang Zhu', 'Cyrus Hettle'] | 2021-02-09 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.41133356e-01 3.00542802e-01 -4.31942195e-01 -3.71958539e-02
-2.92089611e-01 -7.20880151e-01 -6.14582479e-01 4.71859008e-01
-2.96567883e-02 8.49037826e-01 -3.26130778e-01 -7.09792554e-01
-8.66914153e-01 -1.10511518e+00 -4.45683807e-01 -2.70278245e-01
-8.10557306e-01 1.21022213e+00 3.23190898e-01 1.47130108... | [6.903982162475586, 5.1224822998046875] |
bcd3319e-da6f-4d2b-8172-09539b03a767 | semi-uformer-semi-supervised-uncertainty | 2210.16057 | null | https://arxiv.org/abs/2210.16057v1 | https://arxiv.org/pdf/2210.16057v1.pdf | Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing | Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy scenarios. Besides, they commonly give deterministic dehazed images while neglecting to mine their uncertainty. To bridge the domain gap and en... | ['Mingqiang Wei', 'Xuefeng Yan', 'Peng Cui', 'Yongzhen Wang', 'Ming Tong'] | 2022-10-28 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.50385454e-01 1.48786724e-01 9.10175890e-02 -5.85692644e-01
-6.80840313e-01 -1.40652731e-01 3.71539026e-01 -2.69162714e-01
-7.92075098e-02 8.09354961e-01 -3.29040028e-02 -2.44200021e-01
-9.75704491e-02 -1.03679109e+00 -1.01098633e+00 -9.17024076e-01
5.17140388e-01 2.15688929e-01 3.51144820e-01 -1.59984127... | [10.938675880432129, -3.1439783573150635] |
7c11e5b7-398a-43d0-ad60-0b6900afc3f4 | local-group-invariant-representations-via | 1612.01988 | null | http://arxiv.org/abs/1612.01988v2 | http://arxiv.org/pdf/1612.01988v2.pdf | Local Group Invariant Representations via Orbit Embeddings | Invariance to nuisance transformations is one of the desirable properties of
effective representations. We consider transformations that form a \emph{group}
and propose an approach based on kernel methods to derive local group invariant
representations. Locality is achieved by defining a suitable probability
distributi... | ['Bernhard Schölkopf', 'P. Thomas Fletcher', 'Anant Raj', 'Youssef Mroueh', 'Abhishek Kumar'] | 2016-12-06 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [-6.27965033e-02 1.44388154e-01 -1.33598760e-01 -5.40073633e-01
-9.91263151e-01 -6.73648000e-01 8.08050692e-01 -1.94196120e-01
-6.58218682e-01 5.66668093e-01 4.46654022e-01 -5.16074784e-02
-4.86275136e-01 -7.05115795e-01 -9.83256280e-01 -9.60518181e-01
-2.88543195e-01 2.64074385e-01 1.35674447e-01 1.48290411... | [8.948630332946777, 2.555530309677124] |
c7f48eb3-ad6c-4d89-8445-268a627019bb | cd-tools-condensed-detachment-and-structure | 2207.08453 | null | https://arxiv.org/abs/2207.08453v1 | https://arxiv.org/pdf/2207.08453v1.pdf | CD Tools -- Condensed Detachment and Structure Generating Theorem Proving (System Description) | CD Tools is a Prolog library for experimenting with condensed detachment in first-order ATP, which puts a recent formal view centered around proof structures into practice. From the viewpoint of first-order ATP, condensed detachment offers a setting that is relatively simple but with essential features and serious appl... | ['Christoph Wernhard'] | 2022-07-18 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.60338625e-01 1.00376737e+00 -1.93686098e-01 2.39989936e-01
-5.97278059e-01 -9.28221166e-01 1.03230727e+00 3.83550644e-01
-8.13688114e-02 1.31856894e+00 -2.75506467e-01 -1.03650558e+00
-6.13402128e-01 -1.05015349e+00 -6.51132643e-01 -5.65110445e-01
-4.14022803e-01 9.16657865e-01 6.39741361e-01 -6.16713226... | [8.753703117370605, 6.891692638397217] |
f84238cc-dc93-4ba0-96f4-9b5c5dd8b81b | a-survey-on-facial-image-deblurring | 2302.05017 | null | https://arxiv.org/abs/2302.05017v2 | https://arxiv.org/pdf/2302.05017v2.pdf | A survey on facial image deblurring | When a facial image is blurred, it significantly affects high-level vision tasks such as face recognition. The purpose of facial image deblurring is to recover a clear image from a blurry input image, which can improve the recognition accuracy, etc. However, general deblurring methods do not perform well on facial imag... | ['Quan Zheng', 'Fanjiang Xu', 'Bingnan Wang'] | 2023-02-10 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.55851662e-01 -4.37583089e-01 -8.16807970e-02 -4.93383706e-01
-2.35897258e-01 -4.66070212e-02 3.48048925e-01 -9.17922139e-01
-1.41368397e-02 7.65594661e-01 4.88095790e-01 2.44337022e-01
-2.86668800e-02 -1.18763879e-01 -5.12191415e-01 -1.05202353e+00
2.50273794e-01 -2.88999379e-01 -4.37211365e-01 2.42807552... | [11.643357276916504, -2.6472318172454834] |
bb8180f0-309a-4165-a590-d7d571bb618d | agmi-attention-guided-multi-omics-integration | 2112.08366 | null | https://arxiv.org/abs/2112.08366v2 | https://arxiv.org/pdf/2112.08366v2.pdf | AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks | Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents a novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multi-edge Graph (MeG) for each cell line, and then aggregates multi-omics features to predict drug resp... | ['Jian Wu', 'Ji Cao', 'Danny Z. Chen', 'Minshan Lai', 'Yufeng Xie', 'Ruiwei Feng'] | 2021-12-15 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 7.85357431e-02 1.16598804e-03 -6.76285625e-01 -6.89152302e-03
-6.47128880e-01 -2.97364622e-01 2.59778172e-01 6.26734078e-01
3.03119212e-01 9.53263402e-01 2.55251497e-01 -3.97120714e-01
-5.87052643e-01 -1.07912040e+00 -4.25518304e-01 -6.84375763e-01
7.07760230e-02 8.00275803e-01 3.44885588e-02 -1.70045540... | [5.758615016937256, 5.738428592681885] |
e458c0f2-e3dd-428f-8015-3d0d9312adc9 | repere-premiers-resultats-dun-defi-autour-de | null | null | https://aclanthology.org/F12-1063 | https://aclanthology.org/F12-1063.pdf | REPERE : premiers r\'esultats d'un d\'efi autour de la reconnaissance multimodale des personnes (REPERE : preliminary results of a multimodal person recognition challenge) [in French] | null | ["Matthieu Carr{\\'e}", 'Ludovic Quintard', 'Juliette Kahn', 'Olivier Galibert', 'Aude Giraudel'] | 2012-06-01 | repere-premiers-resultats-dun-defi-autour-de-1 | https://aclanthology.org/F12-1063 | https://aclanthology.org/F12-1063.pdf | jeptalnrecital-2012-6 | ['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
-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... | [-7.418349742889404, 3.6206932067871094] |
dc1b4b51-9425-45f7-8326-63d9517d9135 | usr-unsupervised-separated-3d-garment-and | 2302.10518 | null | https://arxiv.org/abs/2302.10518v3 | https://arxiv.org/pdf/2302.10518v3.pdf | USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency | Dressed people reconstruction from images is a popular task with promising applications in the creative media and game industry. However, most existing methods reconstruct the human body and garments as a whole with the supervision of 3D models, which hinders the downstream interaction tasks and requires hard-to-obtain... | ['Jingyi Chai', 'Wenjun Zhang', 'Bingbing Ni', 'Yuxuan Xiong', 'Yue Shi'] | 2023-02-21 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 5.43927066e-02 7.44437873e-02 3.17086279e-01 -3.02037865e-01
-1.94139406e-01 -3.61142546e-01 1.18059188e-01 -5.95254123e-01
1.22397058e-01 2.06204280e-01 1.03622206e-01 4.80678052e-01
1.49723470e-01 -9.26416636e-01 -8.86396587e-01 -6.21503949e-01
4.86585051e-01 6.17201507e-01 4.37935621e-01 -4.68089789... | [7.2929840087890625, -1.3059332370758057] |
e63fc1ff-412d-48c4-a00b-aa4b4edb731e | secure-data-sharing-with-flow-model | 2009.11762 | null | https://arxiv.org/abs/2009.11762v1 | https://arxiv.org/pdf/2009.11762v1.pdf | Secure Data Sharing With Flow Model | In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine learning training purposes, but the data are also encrypted so that they cannot be recovered by othe... | ['Chenwei Wu', 'Yang Yuan', 'Chenzhuang Du'] | 2020-09-24 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 2.03621045e-01 9.94082242e-02 -1.97464347e-01 -1.89069703e-01
-1.07566035e+00 -1.49464393e+00 6.34563267e-01 -2.02088699e-01
-3.12987417e-01 7.99657643e-01 -1.81147084e-02 -6.33691967e-01
1.17620997e-01 -1.17209828e+00 -8.34778607e-01 -1.16664743e+00
-3.52212526e-02 6.71870887e-01 -3.81814837e-01 1.80334389... | [5.836306571960449, 6.857388496398926] |
b631c7cb-6920-4c83-8ed8-4f5bbb7a23db | the-hardware-impact-of-quantization-and | 2302.04174 | null | https://arxiv.org/abs/2302.04174v1 | https://arxiv.org/pdf/2302.04174v1.pdf | The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks | Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the biological brain. Efficient implementations of SNNs on modern digital hardware are... | ['Siddharth Joshi', 'Mark Horeni', 'Pooria Taheri', 'Clemens JS Schaefer'] | 2023-02-08 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 5.88891268e-01 5.69521263e-02 -3.72576411e-03 -1.49236664e-01
-8.31978321e-02 -2.83107460e-01 6.50312424e-01 -1.17402487e-02
-9.77026939e-01 3.36158454e-01 -2.12897882e-01 -3.76924068e-01
-1.03878275e-01 -8.40167761e-01 -5.98290801e-01 -8.95255268e-01
-9.84017998e-02 2.50834346e-01 6.39474571e-01 -1.95892411... | [8.317597389221191, 2.5746407508850098] |
22c25b79-88ef-4067-8635-e89af62ffe63 | medfuse-multi-modal-fusion-with-clinical-time | 2207.07027 | null | https://arxiv.org/abs/2207.07027v2 | https://arxiv.org/pdf/2207.07027v2.pdf | MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images | Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is often collected asynchronously. Hence, requiring the presence of all modalities for a given sample is... | ['Farah E. Shamout', 'Krzysztof J. Geras', 'Nasir Hayat'] | 2022-07-14 | null | null | null | null | ['phenotype-classification'] | ['medical'] | [ 3.04211706e-01 -1.94556087e-01 -7.28939474e-02 -4.03599054e-01
-1.69759572e+00 -4.53697234e-01 4.33029324e-01 5.64589739e-01
-4.71743077e-01 7.93325245e-01 5.73326170e-01 -3.76685917e-01
-3.31703782e-01 -4.24686074e-01 -4.62860525e-01 -6.79363489e-01
-1.57384679e-01 5.06664813e-01 -2.34921306e-01 2.78145343... | [15.021893501281738, -1.806209921836853] |
05b53574-f325-4341-b2ad-bd5f411e011c | learning-rich-features-for-image-manipulation | 1805.04953 | null | http://arxiv.org/abs/1805.04953v1 | http://arxiv.org/pdf/1805.04953v1.pdf | Learning Rich Features for Image Manipulation Detection | Image manipulation detection is different from traditional semantic object
detection because it pays more attention to tampering artifacts than to image
content, which suggests that richer features need to be learned. We propose a
two-stream Faster R-CNN network and train it endto- end to detect the tampered
regions gi... | ['Peng Zhou', 'Xintong Han', 'Larry S. Davis', 'Vlad I. Morariu'] | 2018-05-13 | learning-rich-features-for-image-manipulation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Learning_Rich_Features_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Learning_Rich_Features_CVPR_2018_paper.pdf | cvpr-2018-6 | ['image-manipulation-detection', 'steganalysis'] | ['computer-vision', 'computer-vision'] | [ 1.07377005e+00 -3.55124652e-01 -8.23296607e-02 -1.54627666e-01
-9.20876622e-01 -3.62421840e-01 4.59335834e-01 -5.45512363e-02
-2.88562089e-01 -8.98310244e-02 1.14265606e-01 -8.52368400e-02
4.31152463e-01 -8.14685404e-01 -9.89912927e-01 -6.94246948e-01
-1.03730805e-01 -5.97712040e-01 4.30520803e-01 -3.18908751... | [12.281764030456543, 0.927366316318512] |
ab021239-4c50-43ca-b36f-9feccc35aa71 | towards-open-set-text-recognition-via-label | 2203.05179 | null | https://arxiv.org/abs/2203.05179v3 | https://arxiv.org/pdf/2203.05179v3.pdf | Towards Open-Set Text Recognition via Label-to-Prototype Learning | Scene text recognition is a popular topic and extensively used in the industry. Although many methods have achieved satisfactory performance for the close-set text recognition challenges, these methods lose feasibility in open-set scenarios, where collecting data or retraining models for novel characters could yield a ... | ['Cheng-Lin Liu', 'Xu-Cheng Yin', 'Xiaobin Zhu', 'Hai-Bo Qin', 'Chun Yang', 'Chang Liu'] | 2022-03-10 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.56669235e-01 -4.74773467e-01 -1.98855370e-01 -7.53698409e-01
-8.68052840e-01 -5.61604559e-01 6.68112040e-01 2.27580398e-01
-4.68036979e-01 3.03228527e-01 -2.82658041e-01 -5.80007806e-02
2.04154506e-01 -5.36360741e-01 -4.63327289e-01 -6.88045144e-01
3.50000769e-01 8.70358944e-01 3.46813023e-01 -1.65304095... | [10.219867706298828, 3.432596445083618] |
67fe9bd0-f495-41e6-9de4-5b84656d3256 | recurrent-color-constancy | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Qian_Recurrent_Color_Constancy_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Qian_Recurrent_Color_Constancy_ICCV_2017_paper.pdf | Recurrent Color Constancy | We introduce a novel formulation of temporal color constancy which considers multiple frames preceding the frame for which illumination is estimated. We propose an end-to-end trainable recurrent color constancy network -- the RCC-Net -- which exploits convolutional LSTMs and a simulated sequence to learn compositional ... | ['Joni-Kristian Kamarainen', 'Yanlin Qian', 'Ke Chen', 'Jiri Matas', 'Jarno Nikkanen'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['color-constancy'] | ['computer-vision'] | [ 7.15970919e-02 -6.68920815e-01 -1.28010795e-01 -4.56062824e-01
-6.45676792e-01 -4.36664194e-01 6.29948974e-01 -6.72697127e-01
-6.34107769e-01 6.55662298e-01 -2.51371376e-02 -3.58858138e-01
4.92778182e-01 -2.64139920e-01 -9.82938945e-01 -8.63966167e-01
-1.99946329e-01 -3.88176918e-01 4.48383182e-01 -1.87227920... | [10.801830291748047, -1.5128265619277954] |
94d23bea-db56-4dce-bf40-b29be5b70e42 | leveraging-modality-specific-representations | 2212.05301 | null | https://arxiv.org/abs/2212.05301v2 | https://arxiv.org/pdf/2212.05301v2.pdf | Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning | Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating the noise-robustness of speech recognition. Mainstream methods focus on fusing audio and visual inputs to obtain modality-invariant representations. However, such representations are prone to over-reliance on audio modality as it is m... | ['Eng Siong Chng', 'Beier Zhu', 'Heqing Zou', 'Qiang Zhang', 'Yuchen Hu', 'Chen Chen'] | 2022-12-10 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.93796307e-01 -4.25340980e-01 -3.33016887e-02 -1.64488167e-01
-1.19436526e+00 -3.53039950e-01 6.16182327e-01 -3.25690895e-01
-4.13481176e-01 4.40044075e-01 4.34404194e-01 -3.53272319e-01
1.15992635e-01 -2.83895284e-01 -6.83756709e-01 -9.60306466e-01
4.01326984e-01 -3.39361221e-01 -5.22961430e-02 -2.00986579... | [14.288662910461426, 5.188167095184326] |
9c46a9b1-4f8a-41ef-84a6-341ca3e9d6f4 | an-adaptive-cm-array-preconditioner-for-blind | 1807.09692 | null | http://arxiv.org/abs/1807.09692v1 | http://arxiv.org/pdf/1807.09692v1.pdf | An Adaptive CM Array Preconditioner for Blind Multi-User Separation | The family of constant-modulus algorithms is widely used in wireless
communication systems and in radar. The classical constant-modulus adaptive
(CMA) algorithm, however, fails to lock onto a single mode when used in
conjunction with an antenna array. Instead, it equalizes the entire spatial
spectrum. In this paper, we... | [] | 2018-07-25 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 5.92423022e-01 -1.01875611e-01 3.70635629e-01 1.23640738e-01
-4.58979309e-01 -6.79703057e-01 3.65419269e-01 1.33020068e-02
-4.31912839e-01 4.60127294e-01 -3.28973114e-01 -6.36326373e-01
-7.07800984e-01 -7.77336478e-01 -1.99002296e-01 -1.07492065e+00
-3.79868895e-01 7.11659193e-02 -1.40635923e-01 -1.67877719... | [6.4805707931518555, 1.334424614906311] |
b15ddcb0-3757-4d37-82f1-5dba0b023c6e | losdd-leave-out-support-vector-data | 2212.13626 | null | https://arxiv.org/abs/2212.13626v1 | https://arxiv.org/pdf/2212.13626v1.pdf | LOSDD: Leave-Out Support Vector Data Description for Outlier Detection | Support Vector Machines have been successfully used for one-class classification (OCSVM, SVDD) when trained on clean data, but they work much worse on dirty data: outliers present in the training data tend to become support vectors, and are hence considered "normal". In this article, we improve the effectiveness to det... | ['Erich Schubert', 'Thomas Liebig', 'Daniel Boiar'] | 2022-12-27 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.12459235e-01 -1.14012875e-01 -1.30505264e-02 -3.96159381e-01
-3.79799873e-01 -4.58988130e-01 2.00804085e-01 5.45804977e-01
-5.18194556e-01 7.18479156e-01 -4.00712550e-01 -4.41467673e-01
-3.82559584e-03 -4.78183389e-01 -4.49434608e-01 -9.39183474e-01
-2.09641680e-01 4.13325071e-01 6.62485421e-01 -1.75676316... | [7.70510721206665, 2.6490941047668457] |
d5f89c14-4eaf-4ac3-8deb-6db41c6538cd | unsupervised-video-analysis-based-on-a | 1503.06917 | null | http://arxiv.org/abs/1503.06917v1 | http://arxiv.org/pdf/1503.06917v1.pdf | Unsupervised Video Analysis Based on a Spatiotemporal Saliency Detector | Visual saliency, which predicts regions in the field of view that draw the
most visual attention, has attracted a lot of interest from researchers. It has
already been used in several vision tasks, e.g., image classification, object
detection, foreground segmentation. Recently, the spectrum analysis based
visual salien... | ['Yilin Wang', 'Qiang Zhang', 'Baoxin Li'] | 2015-03-24 | null | null | null | null | ['interest-point-detection', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.45308530e-01 -4.45298076e-01 -3.07972968e-01 -8.16604570e-02
-1.85395285e-01 -1.03395492e-01 4.68158156e-01 4.69780356e-01
-2.88169235e-01 6.42955422e-01 1.13202278e-02 8.96730796e-02
-1.78453967e-01 -3.87114942e-01 -4.88551736e-01 -7.86683381e-01
6.91154748e-02 -3.30438972e-01 1.28249407e+00 5.02470769... | [9.769124031066895, -0.46672648191452026] |
a8cbaa56-b26b-4372-a2f4-416dcc18d17d | chaotic-variational-auto-encoder-based-one | 2212.07802 | null | https://arxiv.org/abs/2212.07802v1 | https://arxiv.org/pdf/2212.07802v1.pdf | Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection | Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insur... | ['Vadlamani Ravi', 'Yelleti Vivek', 'B. Akhil Kumar', 'K. S. N. V. K. Gangadhar'] | 2022-12-15 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [-3.70405436e-01 3.39204329e-03 7.68847540e-02 -1.81075603e-01
-5.11832535e-01 -2.18281433e-01 3.71629715e-01 -1.37647197e-01
-3.70647758e-01 8.96982789e-01 2.19392285e-01 -4.15163696e-01
1.24852382e-01 -9.39446211e-01 -7.20668137e-01 -6.00949168e-01
2.93468926e-02 2.05473796e-01 -2.68106610e-01 -4.49219882... | [7.802088737487793, 5.184650897979736] |
4412e11a-88d7-42b2-8d0d-f12a7de8a07a | unsupervised-domain-adaptation-for-point | 2208.04510 | null | https://arxiv.org/abs/2208.04510v1 | https://arxiv.org/pdf/2208.04510v1.pdf | Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching | Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data. Most of existing methods use global-level feature alignment to transfer the knowledge from the source domain to the target domain, which may cause the semantic amb... | ['Jin Xie', 'Jianjun Qian', 'Le Hui', 'Yikai Bian'] | 2022-08-09 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 8.09401423e-02 -8.93690437e-02 -1.75291210e-01 -7.03301847e-01
-6.80536985e-01 -4.80023354e-01 1.62212685e-01 1.37243673e-01
-1.83942422e-01 3.78593862e-01 -2.23904043e-01 2.45266691e-01
-2.50870079e-01 -9.19338048e-01 -5.93695045e-01 -7.57777393e-01
3.17749828e-01 7.57100523e-01 5.88580012e-01 -6.09792769... | [9.662360191345215, 1.4967410564422607] |
1cd6ad0e-156d-4c6c-8431-ebf42d96d427 | consensus-based-phase-connectivity | 2301.03938 | null | https://arxiv.org/abs/2301.03938v1 | https://arxiv.org/pdf/2301.03938v1.pdf | Consensus based phase connectivity identification for distribution network with limited observability | The mitigation of distribution network (DN) unbalance and the use of single-phase flexibility for congestion mitigation requires accurate phase connection information, which is often not available. For a large DN, the naive phase identification proposed in the majority of the prior works using a single voltage referenc... | ['Dirk Van Hertem', 'Arpan Koirala', 'Rickard Lundholm', 'David Brummund', 'Md Umar Hashmi'] | 2023-01-10 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.03353196e-01 -2.95889564e-02 -6.26530945e-02 2.08333060e-02
-6.40511930e-01 -9.69391763e-01 2.59428054e-01 6.21739030e-01
2.06203878e-01 1.05949402e+00 -1.14159800e-01 -3.06451499e-01
-7.15553403e-01 -9.94192123e-01 -9.72639546e-02 -9.20425534e-01
-3.78272563e-01 6.11425519e-01 6.93173409e-02 -3.61429542... | [5.882043838500977, 2.5610387325286865] |
b4a7eef0-eac0-4aa9-8244-3f3b271a557f | a-low-shot-object-counting-network-with | 2211.08217 | null | https://arxiv.org/abs/2211.08217v1 | https://arxiv.org/pdf/2211.08217v1.pdf | A Low-Shot Object Counting Network With Iterative Prototype Adaptation | We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries from exemplars and matching them with image features to infer the object counts. Existing methods e... | ['Matej Kristan', 'Vitjan Zavrtanik', 'Alan Lukezic', 'Nikola Djukic'] | 2022-11-15 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.9036461e-01 -3.0108559e-01 -9.0323187e-02 -3.7161541e-01
-8.5602784e-01 -5.6296355e-01 7.4192548e-01 4.3017662e-01
-7.6277548e-01 3.6406386e-01 -2.6544085e-01 3.9982179e-01
9.1452308e-02 -7.7739364e-01 -9.0430099e-01 -4.5575652e-01
1.9891690e-01 6.3699394e-01 9.4774395e-01 1.7830627e-01
4.5038944e-01... | [9.00898265838623, 0.5478692650794983] |
53394e68-280b-42f5-be92-44079c29d69e | generating-pertinent-and-diversified-comments | 2005.04396 | null | https://arxiv.org/abs/2005.04396v1 | https://arxiv.org/pdf/2005.04396v1.pdf | Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks | Comment generation, a new and challenging task in Natural Language Generation (NLG), attracts a lot of attention in recent years. However, comments generated by previous work tend to lack pertinence and diversity. In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN),... | ['Kang Xu', 'Junheng Huang', 'Weihua Peng', 'Lu Pan', 'Fayuan Li'] | 2020-05-09 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [-5.67040555e-02 3.36707830e-01 -3.81768733e-01 -1.62879050e-01
-8.34430695e-01 -1.81872129e-01 9.51448143e-01 -3.01108453e-02
1.46294639e-01 1.26380551e+00 1.04231381e+00 -2.47983202e-01
4.68371809e-01 -9.81756985e-01 -4.45831209e-01 -3.83134693e-01
4.08557564e-01 2.92734057e-01 2.34805495e-01 -5.55839539... | [12.150935173034668, 9.01282024383545] |
bdc8f0f4-2cd0-4345-8f55-ac9801a4d801 | a-signed-subgraph-encoding-approach-via | 2305.09869 | null | https://arxiv.org/abs/2305.09869v1 | https://arxiv.org/pdf/2305.09869v1.pdf | A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction | In this paper, we consider the problem of inferring the sign of a link based on limited sign data in signed networks. Regarding this link sign prediction problem, SDGNN (Signed Directed Graph Neural Networks) provides the best prediction performance currently to the best of our knowledge. In this paper, we propose a di... | ['Yaonan Wang', 'Shaolin Tan', 'Zhihong Fang'] | 2023-05-17 | null | null | null | null | ['link-sign-prediction'] | ['graphs'] | [ 1.70114249e-01 5.85247815e-01 -6.54728353e-01 -6.42770886e-01
6.14324026e-02 -2.98509926e-01 6.05487585e-01 2.56838696e-03
4.81949486e-02 7.17783868e-01 1.41042277e-01 -2.38789126e-01
-7.85804451e-01 -8.56861889e-01 -4.35793310e-01 -2.33440876e-01
-7.49118984e-01 4.40151900e-01 5.49580753e-01 -1.46059811... | [7.170010566711426, 6.267354965209961] |
0d20a148-1a32-479a-bbfd-ebd8c6dedaac | multi-view-gait-recognition-based-on-siamese | 2210.10421 | null | https://arxiv.org/abs/2210.10421v1 | https://arxiv.org/pdf/2210.10421v1.pdf | Multi-view Gait Recognition based on Siamese Vision Transformer | While the Vision Transformer has been used in gait recognition, its application in multi-view gait recognition is still limited. Different views significantly affect the extraction and identification accuracy of the characteristics of gait contour. To address this, this paper proposes a Siamese Mobile Vision Transforme... | ['Feiyan Cheng', 'Ruoyu Li', 'Lijun Yun', 'Yanchen Yang'] | 2022-10-19 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-3.24833304e-01 -7.38353014e-01 -2.62925714e-01 4.73372936e-02
-5.90013862e-01 -3.04951500e-02 3.51520509e-01 -9.04565811e-01
-1.22258335e-01 5.75743139e-01 2.85100639e-01 4.92240161e-01
2.16710702e-01 -8.12439322e-01 6.10287301e-02 -9.95006382e-01
-7.61773363e-02 4.42090631e-01 2.13862941e-01 -5.12103796... | [14.262801170349121, 1.4392391443252563] |
77dfae19-f61a-4ff5-aedb-c1726e68c376 | kosmos-2-grounding-multimodal-large-language | 2306.14824 | null | https://arxiv.org/abs/2306.14824v2 | https://arxiv.org/pdf/2306.14824v2.pdf | Kosmos-2: Grounding Multimodal Large Language Models to the World | We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world. Specifically, we represent refer expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where object descriptions ... | ['Furu Wei', 'Shuming Ma', 'Shaohan Huang', 'Yaru Hao', 'Li Dong', 'Wenhui Wang', 'Zhiliang Peng'] | 2023-06-26 | null | null | null | null | ['referring-expression-generation', 'visual-grounding', 'referring-expression', 'image-captioning', 'phrase-grounding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.78550982e-01 2.17589185e-01 -1.56649977e-01 -3.40951145e-01
-8.17983091e-01 -8.25290024e-01 9.36388791e-01 1.98633775e-01
-2.45662734e-01 2.83709556e-01 8.02098095e-01 -4.06401038e-01
3.51818830e-01 -8.94153535e-01 -9.33589935e-01 -3.78600895e-01
1.46165833e-01 3.47601533e-01 -3.44919294e-01 -4.91272122... | [10.773225784301758, 1.6455872058868408] |
6424a281-bdd9-402b-bc4f-c0f1451d04e1 | segdensenet-iris-segmentation-for-pre-and | 1801.10100 | null | http://arxiv.org/abs/1801.10100v2 | http://arxiv.org/pdf/1801.10100v2.pdf | SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery | Cataract is caused due to various factors such as age, trauma, genetics,
smoking and substance consumption, and radiation. It is one of the major common
ophthalmic diseases worldwide which can potentially affect iris-based biometric
systems. India, which hosts the largest biometrics project in the world, has
about 8 mi... | ['Mayank Vatsa', 'Rohit Keshari', 'Pavani Tripathi', 'Richa Singh', 'Aditya Lakra'] | 2018-01-30 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-1.55819237e-01 -2.28974134e-01 -1.07282229e-01 -1.81771070e-01
2.38493811e-02 -3.40996653e-01 1.70432478e-01 -9.74072292e-02
-5.24093568e-01 4.87499148e-01 3.70710939e-01 -3.93565029e-01
-1.28593773e-01 -4.67546642e-01 -4.15004462e-01 -6.25719666e-01
4.96070832e-02 4.07488555e-01 -3.00234199e-01 3.39563221... | [3.74393367767334, -3.6304168701171875] |
fd02f543-43e0-43ca-87f6-f20ebb5d367b | raid-g-robust-estimation-of-approximate | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_RAID-G_Robust_Estimation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_RAID-G_Robust_Estimation_CVPR_2016_paper.pdf | RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition | Infinite dimensional covariance descriptors can provide richer and more discriminative information than their low dimensional counterparts. In this paper, we propose a novel image descriptor, namely, robust approximate infinite dimensional Gaussian (RAID-G). The challenges of RAID-G mainly lie on two aspects: (1) descr... | ['WangMeng Zuo', 'Peihua Li', 'Lei Zhang', 'Qilong Wang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['material-recognition'] | ['computer-vision'] | [-2.09289387e-01 -2.58123070e-01 -7.54754692e-02 -1.12239495e-01
-8.57567132e-01 -2.64657557e-01 5.92481256e-01 -3.74302387e-01
-1.58643067e-01 4.48368549e-01 2.21774966e-01 1.98322773e-01
-4.79119003e-01 -7.25197911e-01 -7.08265543e-01 -9.49212193e-01
-2.74020672e-01 2.88985848e-01 1.27235323e-01 -2.63069179... | [8.928760528564453, 2.0362493991851807] |
7481ec9a-b40b-4343-9844-1872729d16a4 | learning-to-compile-smartly-for-program-size | 2301.05104 | null | https://arxiv.org/abs/2301.05104v2 | https://arxiv.org/pdf/2301.05104v2.pdf | Learning Compiler Pass Orders using Coreset and Normalized Value Prediction | Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency. Prior works on compilation pass ordering have two major drawbacks. They either require an excessive budget (in terms of compilation steps) at compile time or fail to generalize ... | ['Yuandong Tian', 'Hugh Leather', 'Xiaomeng Yang', 'Pengtao Xie', 'Benoit Steiner', 'Jiadong Guo', 'Mostafa Elhoushi', 'Chris Cummins', 'Ali Shameli', 'Kevin Stone', 'Youwei Liang'] | 2023-01-09 | null | null | null | null | ['compiler-optimization', 'value-prediction'] | ['computer-code', 'computer-code'] | [ 5.92779629e-02 -1.87926486e-01 -5.80393493e-01 -2.53803670e-01
-9.83815908e-01 -9.65905786e-01 3.15177031e-02 1.78941697e-01
-1.61873087e-01 6.62752867e-01 1.42865032e-01 -6.19979620e-01
-4.87968698e-02 -8.85412455e-01 -1.22677636e+00 -2.41389453e-01
-3.24932277e-01 3.81342173e-01 2.54744917e-01 -3.85539889... | [7.878328800201416, 7.615054607391357] |
c8e2adee-1e45-4011-ab0d-1da717ef8e6a | multiresolution-deep-implicit-functions-for | 2109.05591 | null | https://arxiv.org/abs/2109.05591v2 | https://arxiv.org/pdf/2109.05591v2.pdf | Multiresolution Deep Implicit Functions for 3D Shape Representation | We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical representation that can recover fine geometry detail, while being able to perform global operations such as shape completion. Our model represents a complex 3D shape with a hierarchy of latent grids, which can be decoded into different levels o... | ['Danhang Tang', 'Thomas Funkhouser', 'Cem Keskin', 'Ruofei Du', 'Christian Haene', 'Sofien Bouaziz', 'Sean Fanello', 'Kyle Genova', 'yinda zhang', 'Zhang Chen'] | 2021-09-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_Multiresolution_Deep_Implicit_Functions_for_3D_Shape_Representation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_Multiresolution_Deep_Implicit_Functions_for_3D_Shape_Representation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-representation'] | ['computer-vision'] | [ 6.80951104e-02 3.01166266e-01 4.31724899e-02 -1.22948162e-01
-1.26834273e+00 -6.44543946e-01 5.95755756e-01 -1.10567071e-01
2.95303226e-01 4.20161188e-01 6.49622262e-01 -2.14261100e-01
3.70962560e-01 -9.82983470e-01 -8.19995463e-01 -3.81735504e-01
1.40696950e-02 9.47416961e-01 2.46330649e-02 -3.16430442... | [8.984600067138672, -3.499633312225342] |
72edddfc-7f01-47ec-a74e-4facd223e298 | m2fnet-multi-modal-fusion-network-for-emotion | 2206.02187 | null | https://arxiv.org/abs/2206.02187v1 | https://arxiv.org/pdf/2206.02187v1.pdf | M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation | Emotion Recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent characteristics of these modalities, multi-modal ERC has always been consid... | ['Naoyuki Onoe', 'Pankaj Wasnik', 'Nirmesh Shah', 'Ashish Gudmalwar', 'Purbayan Kar', 'Vishal Chudasama'] | 2022-06-05 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.17139423e-01 -3.50603878e-01 8.32327455e-02 -3.69980872e-01
-1.20128548e+00 -1.92082182e-01 5.38575232e-01 -6.58023804e-02
-4.83836144e-01 5.53590059e-01 5.20688951e-01 3.05658996e-01
-3.10759321e-02 -5.58813252e-02 -3.56051773e-01 -7.33331680e-01
1.80031881e-01 2.26902161e-02 -3.70310158e-01 -1.28451943... | [13.262347221374512, 5.127523899078369] |
2fe79d72-627d-42b2-992f-c1329f79c8cd | open-set-rf-fingerprinting-via-improved | 2306.13895 | null | https://arxiv.org/abs/2306.13895v1 | https://arxiv.org/pdf/2306.13895v1.pdf | Open-Set RF Fingerprinting via Improved Prototype Learning | Deep learning has been widely used in radio frequency (RF) fingerprinting. Despite its excellent performance, most existing methods only consider a closed-set assumption, which cannot effectively tackle signals emitted from those unknown devices that have never been seen during training. In this letter, we exploit prot... | ['Lu Gan', 'Hongshu Liao', 'Weidong Wang'] | 2023-06-24 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.81187069e-01 -2.60899097e-01 -6.24523103e-01 -9.07268286e-01
-6.78520381e-01 -6.35103285e-01 2.55131155e-01 -3.78692925e-01
-2.19547004e-01 7.90242910e-01 -1.72628880e-01 -5.14692426e-01
-7.13411510e-01 -9.16879952e-01 -8.82225454e-01 -5.03234625e-01
-2.34813318e-01 1.37941778e-01 -3.89046699e-01 3.66083831... | [6.502023220062256, 0.9273337125778198] |
0c9a2932-4e38-45da-b0f7-18f0f1a24278 | meta-curriculum-learning-for-domain | 2103.02262 | null | https://arxiv.org/abs/2103.02262v1 | https://arxiv.org/pdf/2103.02262v1.pdf | Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation | Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-c... | ['Lidia S. Chao', 'Derek F. Wong', 'Xuebo Liu', 'Runzhe Zhan'] | 2021-03-03 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.23173007e-01 -8.34950283e-02 -6.03511274e-01 -3.54308605e-01
-1.06176925e+00 -7.34147906e-01 4.33386683e-01 -2.34761894e-01
-3.68886411e-01 1.03717792e+00 5.70864901e-02 -4.89070892e-01
1.94705620e-01 -4.69411820e-01 -1.07801044e+00 -5.34212649e-01
3.87293369e-01 8.61594975e-01 -1.07418820e-01 -5.24433076... | [11.640252113342285, 10.278436660766602] |
b0bb8a61-e2de-4140-965b-5ae0dcfb1c04 | invariant-3d-shape-recognition-using | 2005.11558 | null | https://arxiv.org/abs/2005.11558v1 | https://arxiv.org/pdf/2005.11558v1.pdf | Invariant 3D Shape Recognition using Predictive Modular Neural Networks | In this paper PREMONN (PREdictive MOdular Neural Networks) model/architecture is generalized to functions of two variables and to non-Euclidean spaces. It is presented in the context of 3D invariant shape recognition and texture recognition. PREMONN uses local relation, it is modular and exhibits incremental learning. ... | ['Vasileios Petridis'] | 2020-05-23 | null | null | null | null | ['3d-shape-recognition', 'dynamic-texture-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.11261678e-01 -1.63946897e-01 -3.29701960e-01 -1.63199306e-01
1.83276415e-01 -2.75427073e-01 6.95333064e-01 -1.37314528e-01
-3.18013608e-01 4.96669710e-01 -3.12711895e-01 -2.09284350e-01
-5.34701347e-01 -9.12991464e-01 -3.64327133e-01 -9.09219146e-01
-5.92575148e-02 6.96905494e-01 5.84191024e-01 -1.55757263... | [10.147196769714355, -0.515471339225769] |
5e98aa00-0e0d-4546-a2bf-b57d261871da | solving-seismic-wave-equations-on-variable | 2209.12340 | null | https://arxiv.org/abs/2209.12340v3 | https://arxiv.org/pdf/2209.12340v3.pdf | Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator | In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial differential equations, including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. Th... | ['Youzuo Lin', 'Xiu Yang', 'Hanchen Wang', 'Bian Li'] | 2022-09-25 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 4.87725884e-02 -3.37755263e-01 3.43013644e-01 -3.50393355e-02
-8.61113727e-01 -1.81990325e-01 -1.01396674e-02 -3.57335120e-01
-3.12549442e-01 5.05036414e-01 -1.15425386e-01 -3.57890874e-01
-6.61501944e-01 -1.02142453e+00 -8.52109492e-01 -1.02423525e+00
-6.09071076e-01 2.44750947e-01 1.29715592e-01 -2.93890208... | [6.875374794006348, 2.6072983741760254] |
febcb26e-8721-4575-ab39-edc11692e73b | learning-multiple-stock-trading-patterns-with | 2106.12950 | null | https://arxiv.org/abs/2106.12950v2 | https://arxiv.org/pdf/2106.12950v2.pdf | Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport | Successful quantitative investment usually relies on precise predictions of the future movement of the stock price. Recently, machine learning based solutions have shown their capacity to give more accurate stock prediction and become indispensable components in modern quantitative investment systems. However, the i.i.... | ['Jiang Bian', 'Weiqing Liu', 'Dong Zhou', 'Hengxu Lin'] | 2021-06-24 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-4.48137373e-01 -4.80898887e-01 -5.60767651e-01 -5.16757786e-01
-6.77920997e-01 -5.82396567e-01 5.35624921e-01 2.29111444e-02
-3.86924654e-01 8.61547291e-01 -3.11156898e-03 -5.14829695e-01
-2.09770367e-01 -9.65410829e-01 -8.60839725e-01 -5.79028368e-01
-2.94889361e-02 6.02296114e-01 5.07085681e-01 -3.63318771... | [4.406030178070068, 4.2534894943237305] |
3e23d0e1-0025-4801-bfa7-bac5e1753ffc | cracking-the-black-box-distilling-deep-sports | 2006.04551 | null | https://arxiv.org/abs/2006.04551v4 | https://arxiv.org/pdf/2006.04551v4.pdf | Cracking the Black Box: Distilling Deep Sports Analytics | This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights fro... | ['Xiangyu Sun', 'Oliver Schulte', 'Jack Davis', 'Guiliang Liu'] | 2020-06-04 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-2.37696916e-01 8.28849018e-01 -8.21435153e-01 -5.72587669e-01
-2.65698016e-01 -5.81662118e-01 1.99734896e-01 1.51486889e-01
-5.72756119e-02 6.20823026e-01 5.76921940e-01 -4.55129296e-01
-3.28897476e-01 -9.56299126e-01 -1.18318725e+00 1.73538309e-02
-2.29073286e-01 7.27027118e-01 -1.96561351e-01 -1.89576641... | [8.979409217834473, 6.14611291885376] |
865f6356-8a52-46a1-82d7-3116afb58b41 | activity-detection-for-grant-free-noma-in | 2301.01274 | null | https://arxiv.org/abs/2301.01274v1 | https://arxiv.org/pdf/2301.01274v1.pdf | Activity Detection for Grant-Free NOMA in Massive IoT Networks | Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame ... | ['Masoud Ardakani', 'Mostafa Mohammadkarimi', 'Mehrtash Mehrabi'] | 2022-12-23 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 2.90802777e-01 1.10392787e-01 -5.41670263e-01 -2.10498497e-01
-3.46185833e-01 -2.92825729e-01 2.42342371e-02 -1.44270569e-01
-3.97345841e-01 9.28941250e-01 -1.41998846e-02 -5.45647800e-01
-3.25358838e-01 -9.26668465e-01 -5.49090683e-01 -1.01508260e+00
-2.80976534e-01 3.16203028e-01 3.99055421e-01 6.19790733... | [6.190345764160156, 1.4026297330856323] |
6515a5c9-0a1b-4779-b214-25538bb05936 | physq-a-physics-informed-reinforcement | 2211.11830 | null | https://arxiv.org/abs/2211.11830v1 | https://arxiv.org/pdf/2211.11830v1.pdf | PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control | Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of total energy consumption in EU, unlocking its flexibility is a key step in the energy transition process. This paper focuses specifically on ... | ['Chris Develder', 'Bert Claessens', 'Gargya Gokhale'] | 2022-11-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.53927374e-01 3.64556104e-01 -6.83996379e-01 1.69879440e-02
-6.66907310e-01 -5.90215802e-01 5.33649981e-01 3.13725889e-01
1.63020134e-01 1.03703976e+00 1.98442653e-01 -5.43321073e-01
-4.62093830e-01 -1.37361598e+00 -9.52223420e-01 -8.21115077e-01
-8.41564238e-02 5.31350315e-01 -1.51562005e-01 -4.99421239... | [5.289761543273926, 2.3849120140075684] |
a2e66619-597a-44d2-8f21-663a38fb30fb | one-shot-object-detection-without-fine-tuning | 2005.03819 | null | https://arxiv.org/abs/2005.03819v1 | https://arxiv.org/pdf/2005.03819v1.pdf | One-Shot Object Detection without Fine-Tuning | Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich such categories by addressing the one-shot object detection problem, where the number of annotated training examples for learning an unseen c... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Xiang Li', 'Yau Pun Chen', 'Lin Zhang'] | 2020-05-08 | null | null | null | null | ['one-shot-object-detection'] | ['computer-vision'] | [ 2.27893084e-01 2.35235199e-01 -3.05737793e-01 -3.34423095e-01
-1.03900278e+00 -3.78816307e-01 5.47882378e-01 2.45363086e-01
-6.89781070e-01 3.39148223e-01 -1.20575204e-01 2.31734477e-02
-3.20769921e-02 -7.72214353e-01 -5.95095456e-01 -3.21894288e-01
9.33862105e-02 5.62838316e-01 9.58999813e-01 -7.49829561... | [9.26494026184082, 1.182039737701416] |
1d29c548-f9e8-426e-aa32-47b0b5a7ea65 | neurodavis-a-neural-network-model-for-data | 2304.01222 | null | https://arxiv.org/abs/2304.01222v1 | https://arxiv.org/pdf/2304.01222v1.pdf | NeuroDAVIS: A neural network model for data visualization | The task of dimensionality reduction and visualization of high-dimensional datasets remains a challenging problem since long. Modern high-throughput technologies produce newer high-dimensional datasets having multiple views with relatively new data types. Visualization of these datasets require proper methodology that ... | ['Rajat K. De', 'Dibyendu B. Seal', 'Chayan Maitra'] | 2023-04-01 | null | null | null | null | ['data-visualization', 'data-visualization'] | ['methodology', 'miscellaneous'] | [-3.65799338e-01 -1.87323630e-01 2.32078776e-01 3.18976976e-02
5.97707704e-02 -5.31436563e-01 8.93023431e-01 4.00119483e-01
-4.80545163e-01 7.02672064e-01 3.81899655e-01 -4.05245394e-01
-6.69066072e-01 -7.91408658e-01 -2.78833598e-01 -1.07486200e+00
-5.00774741e-01 5.50269902e-01 1.45500407e-01 -1.92818210... | [8.000448226928711, 4.446290493011475] |
012fa89a-1cfb-443c-a7b4-db97cd67fae5 | enhancing-vision-language-pre-training-with | 2305.11769 | null | https://arxiv.org/abs/2305.11769v1 | https://arxiv.org/pdf/2305.11769v1.pdf | Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner | Large pre-trained multimodal models have demonstrated significant success in a range of downstream tasks, including image captioning, image-text retrieval, visual question answering (VQA), etc. However, many of these methods rely on image-text pairs collected from the web as pre-training data and unfortunately overlook... | ['Jing Liu', 'Xingjian He', 'Handong Li', 'Longteng Guo', 'Sihan Chen', 'Zikang Liu'] | 2023-05-19 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 3.52283001e-01 -6.00052625e-02 7.21034929e-02 -5.09438217e-01
-1.47117829e+00 -8.08089197e-01 7.98147559e-01 -1.34537201e-02
-4.60899770e-01 4.52007860e-01 2.96572030e-01 -2.86327124e-01
4.50727701e-01 -4.04937476e-01 -9.99548793e-01 -3.52695197e-01
5.80269158e-01 6.29663825e-01 2.15652376e-01 -2.62837023... | [10.934112548828125, 1.3757244348526] |
98ae153d-3b3b-4eb5-ba68-0bfe0195fed4 | mead-a-large-scale-audio-visual-dataset-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3837_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660698.pdf | MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation | The synthesis of natural emotional reactions is an essentialcriteria in vivid talking-face video generation. This criteria is nevertheless seldom taken into consideration in previous works due to the absence of a large-scale, high-quality emotional audio-visual dataset. To address this issue, we build the Multi-view Em... | ['Kaisiyuan Wang Qianyi Wu Linsen Song Zhuoqian Yang Wayne Wu Chen Qian Ran He Yu Qiao Chen Change Loy'] | null | null | null | null | eccv-2020-8 | ['talking-head-generation', 'talking-face-generation'] | ['computer-vision', 'computer-vision'] | [ 2.03685075e-01 4.71724314e-04 6.31869137e-02 -6.46334767e-01
-7.76821077e-01 -6.74855530e-01 6.89251900e-01 -4.18080032e-01
1.32638454e-01 5.04994988e-01 5.38486540e-01 3.64810228e-01
2.63460636e-01 -5.02066076e-01 -4.46249038e-01 -7.71919310e-01
6.21542819e-02 4.88451123e-02 -1.51575685e-01 -2.23775283... | [13.255362510681152, -0.3889140784740448] |
990cc00e-bc0a-454f-9add-f4dcbc0199ad | phrase-localization-and-visual-relationship | 1611.06641 | null | http://arxiv.org/abs/1611.06641v4 | http://arxiv.org/pdf/1611.06641v4.pdf | Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues | This paper presents a framework for localization or grounding of phrases in
images using a large collection of linguistic and visual cues. We model the
appearance, size, and position of entity bounding boxes, adjectives that
contain attribute information, and spatial relationships between pairs of
entities connected by... | ['Julia Hockenmaier', 'Christopher M. Cervantes', 'Svetlana Lazebnik', 'Bryan A. Plummer', 'Arun Mallya'] | 2016-11-21 | phrase-localization-and-visual-relationship-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Plummer_Phrase_Localization_and_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Plummer_Phrase_Localization_and_ICCV_2017_paper.pdf | iccv-2017-10 | ['visual-relationship-detection'] | ['computer-vision'] | [-1.94975108e-01 -4.08011638e-02 -1.66443005e-01 -6.64932907e-01
-5.41027069e-01 -9.15995479e-01 6.22249484e-01 8.84636045e-01
-5.72602808e-01 6.03411973e-01 4.12161767e-01 5.53591885e-02
1.93059444e-01 -6.94792747e-01 -7.52296805e-01 -3.34429502e-01
-3.88375580e-01 5.27588785e-01 3.07488233e-01 9.64900479... | [10.452692985534668, 1.5000805854797363] |
e9b165bd-a1d0-4e4e-8469-7eb48db68668 | security-and-privacy-preserving-deep-learning | 2006.12698 | null | https://arxiv.org/abs/2006.12698v2 | https://arxiv.org/pdf/2006.12698v2.pdf | Security and Privacy Preserving Deep Learning | Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users perso... | ['Saisree Miriyala', 'Saichethan Miriyala Reddy'] | 2020-06-23 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.31657049e-01 1.30146638e-01 -7.40852803e-02 -6.05818748e-01
-2.96242177e-01 -7.35594690e-01 2.90786624e-01 5.07004499e-01
-7.90528238e-01 9.81948555e-01 -9.55463201e-03 -2.57442623e-01
-1.72981456e-01 -1.24094474e+00 -4.71596450e-01 -7.82141805e-01
6.78418875e-02 2.68973768e-01 -7.18800072e-03 -2.42197558... | [6.056069850921631, 6.97682523727417] |
d8d33ef8-7297-4e4b-9aa5-41c6e28095c1 | deep-multi-task-learning-with-low-level-tasks | null | null | https://aclanthology.org/P16-2038 | https://aclanthology.org/P16-2038.pdf | Deep multi-task learning with low level tasks supervised at lower layers | null | ['Anders S{\\o}gaard', 'Yoav Goldberg'] | 2016-08-01 | null | null | null | acl-2016-8 | ['ccg-supertagging'] | ['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... | [-7.404977798461914, 3.7628939151763916] |
e92cb0b4-6b40-4b50-b970-33ec23663edb | deep-quaternion-networks | 1712.04604 | null | http://arxiv.org/abs/1712.04604v3 | http://arxiv.org/pdf/1712.04604v3.pdf | Deep Quaternion Networks | The field of deep learning has seen significant advancement in recent years.
However, much of the existing work has been focused on real-valued numbers.
Recent work has shown that a deep learning system using the complex numbers can
be deeper for a fixed parameter budget compared to its real-valued counterpart.
In this... | ['Chase Gaudet', 'Anthony Maida'] | 2017-12-13 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [-2.83337265e-01 2.72019804e-01 9.91775542e-02 -7.25242972e-01
-3.27797085e-01 -2.25042313e-01 3.80501807e-01 -7.97274336e-02
-1.37713552e+00 6.57409370e-01 -3.70752007e-01 -4.65403557e-01
2.95969188e-01 -6.91649497e-01 -7.32163489e-01 -4.50242460e-01
-5.22517204e-01 4.05834019e-01 9.97513300e-04 -6.07072413... | [8.859745025634766, 2.765063524246216] |
3e224ad0-18f0-4ea9-83e5-186dc6defee3 | cascaded-classification-models-combining | null | null | http://papers.nips.cc/paper/3472-cascaded-classification-models-combining-models-for-holistic-scene-understanding | http://papers.nips.cc/paper/3472-cascaded-classification-models-combining-models-for-holistic-scene-understanding.pdf | Cascaded Classification Models: Combining Models for Holistic Scene Understanding | One of the original goals of computer vision was to fully understand a natural scene. This requires solving several problems simultaneously, including object detection, labeling of meaningful regions, and 3d reconstruction. While great progress has been made in tackling each of these problems in isolation, only recentl... | ['Geremy Heitz', 'Daphne Koller', 'Stephen Gould', 'Ashutosh Saxena'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.64833617e-01 -1.35592185e-02 -9.24690813e-02 -6.21910453e-01
-5.73796213e-01 -7.37971663e-01 7.38642216e-01 1.69366658e-01
-4.31588948e-01 2.18394682e-01 -3.27472359e-01 -4.52449381e-01
4.70496006e-02 -6.17397606e-01 -5.69343448e-01 -6.45497680e-01
1.65679961e-01 5.63907743e-01 6.35571897e-01 -2.21697241... | [9.537595748901367, 0.4075257480144501] |
5a2c0f60-4406-4e53-924e-9409deae57a2 | boosting-object-representation-learning-via | 2211.09771 | null | https://arxiv.org/abs/2211.09771v1 | https://arxiv.org/pdf/2211.09771v1.pdf | Boosting Object Representation Learning via Motion and Object Continuity | Recent unsupervised multi-object detection models have shown impressive performance improvements, largely attributed to novel architectural inductive biases. Unfortunately, they may produce suboptimal object encodings for downstream tasks. To overcome this, we propose to exploit object motion and continuity, i.e., obje... | ['Kristian Kersting', 'Dwarak Vittal', 'Thomas Rothenbacher', 'Wolfgang Stammer', 'Quentin Delfosse'] | 2022-11-16 | null | null | null | null | ['object-discovery', 'atari-games'] | ['computer-vision', 'playing-games'] | [ 1.15530275e-01 1.39516862e-02 -4.43478078e-02 -1.30369022e-01
-5.48009396e-01 -6.32401168e-01 8.20587575e-01 2.43961737e-01
-5.32279670e-01 3.17573339e-01 2.31539294e-01 8.14224854e-02
-1.56774819e-01 -5.78826070e-01 -9.67136860e-01 -5.69379747e-01
-1.01448067e-01 4.76128906e-01 6.44687355e-01 1.20933503... | [9.494319915771484, 0.32302579283714294] |
0d504c20-f38d-4894-9b2a-da9f8cfd7f81 | subverting-machines-fluctuating-identities-re | 2205.13740 | null | https://arxiv.org/abs/2205.13740v1 | https://arxiv.org/pdf/2205.13740v1.pdf | Subverting machines, fluctuating identities: Re-learning human categorization | Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as mall... | ['Kevin R. McKee', 'Jackie Kay', 'Christina Lu'] | 2022-05-27 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 4.88209426e-02 8.36170495e-01 -1.49079695e-01 -1.99346945e-01
4.17585582e-01 -5.42639911e-01 1.40050101e+00 3.60139087e-02
-1.89759329e-01 5.52680552e-01 7.15400457e-01 -3.63883972e-01
-3.21418732e-01 -9.37185049e-01 -2.16828614e-01 -6.83998227e-01
1.33368373e-01 5.12181699e-01 -5.65767407e-01 -8.07870507... | [9.096700668334961, 6.32020378112793] |
c3b7ef6e-2bb0-4dcd-8249-e501e5de9fce | few-shot-generalization-for-single-image-3d | 1909.01205 | null | https://arxiv.org/abs/1909.01205v1 | https://arxiv.org/pdf/1909.01205v1.pdf | Few-Shot Generalization for Single-Image 3D Reconstruction via Priors | Recent work on single-view 3D reconstruction shows impressive results, but has been restricted to a few fixed categories where extensive training data is available. The problem of generalizing these models to new classes with limited training data is largely open. To address this problem, we present a new model archite... | ['Bram Wallace', 'Bharath Hariharan'] | 2019-09-03 | few-shot-generalization-for-single-image-3d-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wallace_Few-Shot_Generalization_for_Single-Image_3D_Reconstruction_via_Priors_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wallace_Few-Shot_Generalization_for_Single-Image_3D_Reconstruction_via_Priors_ICCV_2019_paper.pdf | iccv-2019-10 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.27180356e-01 3.40770870e-01 -1.95009246e-01 -5.63597322e-01
-8.51978719e-01 -9.27150726e-01 8.88558567e-01 -5.55702507e-01
-2.36309707e-01 4.00275618e-01 4.92265105e-01 -3.50702852e-02
2.63806999e-01 -5.03596187e-01 -1.13813138e+00 -4.79265988e-01
3.99002224e-01 9.45643663e-01 4.02609080e-01 -2.37188698... | [8.41846752166748, -3.038545608520508] |
87d57028-95ad-4169-a202-12dd1fe71fb4 | learning-6-dof-object-poses-to-grasp-category | 2205.04028 | null | https://arxiv.org/abs/2205.04028v1 | https://arxiv.org/pdf/2205.04028v1.pdf | Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions | This paper studies the task of any objects grasping from the known categories by free-form language instructions. This task demands the technique in computer vision, natural language processing, and robotics. We bring these disciplines together on this open challenge, which is essential to human-robot interaction. Crit... | ['xiangyang xue', 'Yanwei Fu', 'Haitao Lin', 'Chilam Cheang'] | 2022-05-09 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 1.39963003e-02 -3.71355861e-02 -9.15849730e-02 -4.18773443e-01
-5.53290963e-01 -6.29143119e-01 2.27023423e-01 -5.04335277e-02
-2.38364294e-01 2.70420104e-01 -2.79696822e-01 -3.69819347e-04
-2.64054507e-01 -6.38832092e-01 -1.00251913e+00 -6.86635375e-01
2.35892758e-02 8.45669091e-01 2.14154497e-01 -1.33054003... | [5.851699352264404, -0.8962050676345825] |
c2638808-77a5-4b6f-88d7-91d4a14a7ca6 | samaug-point-prompt-augmentation-for-segment | 2307.01187 | null | https://arxiv.org/abs/2307.01187v1 | https://arxiv.org/pdf/2307.01187v1.pdf | SAMAug: Point Prompt Augmentation for Segment Anything Model | This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information to SAM. From the initial point prompt, SAM produces the initial mask, which is then f... | ['Xiang Li', 'Tianming Liu', 'Quanzheng Li', 'Wei Liu', 'Dajiang Zhu', 'Zihao Wu', 'Lin Zhao', 'Xiaozheng Wei', 'Peng Shu', 'Yiwei Li', 'Zhengliang Liu', 'Chong Ma', 'Haixing Dai'] | 2023-07-03 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 5.97737789e-01 3.15062314e-01 -3.69541913e-01 -1.97648600e-01
-6.92552626e-01 -6.46062553e-01 1.37721315e-01 2.72300094e-01
-1.07709289e-01 3.33299816e-01 8.14431533e-02 -4.99163181e-01
4.45080251e-01 -4.54413772e-01 -5.20570219e-01 -3.90472621e-01
2.01462820e-01 2.11895525e-01 7.34665096e-01 -1.51227146... | [9.558037757873535, -0.18225005269050598] |
fcc6518a-63d0-4949-a957-ba1a94cf6ebb | neumap-neural-coordinate-mapping-by-auto | 2211.11177 | null | https://arxiv.org/abs/2211.11177v2 | https://arxiv.org/pdf/2211.11177v2.pdf | NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization | This paper presents an end-to-end neural mapping method for camera localization, dubbed NeuMap, encoding a whole scene into a grid of latent codes, with which a Transformer-based auto-decoder regresses 3D coordinates of query pixels. State-of-the-art feature matching methods require each scene to be stored as a 3D poin... | ['Yasutaka Furukawa', 'Ping Tan', 'Andrea Tagliasacchi', 'Sicong Tang', 'Shitao Tang'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_NeuMap_Neural_Coordinate_Mapping_by_Auto-Transdecoder_for_Camera_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_NeuMap_Neural_Coordinate_Mapping_by_Auto-Transdecoder_for_Camera_Localization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-localization'] | ['computer-vision'] | [ 2.23585851e-02 -2.91457415e-01 -2.04698026e-01 -5.15229642e-01
-1.20984709e+00 -5.34762204e-01 4.65600580e-01 -2.68192627e-02
-6.00683928e-01 7.82007799e-02 1.59945324e-01 -3.50132063e-02
5.62107675e-02 -8.00207257e-01 -1.14686966e+00 -5.89869618e-01
4.70161065e-02 4.68341827e-01 1.84492469e-01 2.70768970... | [7.742676734924316, -2.177781820297241] |
bbc3f4d4-3255-41ea-aff4-5aa0bfda3e36 | ultra-fine-entity-typing-with-indirect | 2202.06167 | null | https://arxiv.org/abs/2202.06167v1 | https://arxiv.org/pdf/2202.06167v1.pdf | Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference | The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for this task lies in the large amount of types and the scarcity of annotated data per type. Existing systems formulate the task as ... | ['Muhao Chen', 'Wenpeng Yin', 'Bangzheng Li'] | 2022-02-12 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 1.23409830e-01 1.95759118e-01 -5.39827287e-01 -3.89036536e-01
-5.48010826e-01 -7.42531300e-01 6.46220982e-01 3.77181977e-01
-6.32098436e-01 1.10831428e+00 1.14494145e-01 -4.22894955e-01
2.13231929e-02 -9.32880521e-01 -8.87932777e-01 -2.55064726e-01
-5.46200108e-03 8.22951913e-01 1.47225261e-01 -3.52074951... | [9.66506290435791, 8.784217834472656] |
11a03b6a-9552-434b-a9a4-066cf8092597 | mind-your-clever-neighbours-unsupervised | 2112.01839 | null | https://arxiv.org/abs/2112.01839v2 | https://arxiv.org/pdf/2112.01839v2.pdf | Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling | Unsupervised person re-identification (Re-ID) attracts increasing attention due to its potential to resolve the scalability problem of supervised Re-ID models. Most existing unsupervised methods adopt an iterative clustering mechanism, where the network was trained based on pseudo labels generated by unsupervised clust... | ['Pingping Zhang', 'Yinjie Lei', 'Tianyu Yan', 'Xiehao Ye', 'Chenyang Yu', 'Lianjie Jia'] | 2021-12-03 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-8.01716000e-02 -1.22379452e-01 -2.69608647e-01 -6.40949070e-01
-2.40108863e-01 -1.55535638e-01 5.18565834e-01 1.04625545e-01
-6.37728155e-01 6.33855999e-01 1.99583635e-01 1.09088883e-01
-2.02655092e-01 -6.59488380e-01 -2.29630098e-01 -6.04020715e-01
5.12442961e-02 7.90922523e-01 5.48475347e-02 2.96065450... | [14.867118835449219, 1.1406325101852417] |
576acef7-0430-4aba-9823-f36b6ecc18f5 | polarimetric-imaging-for-perception | 2305.14787 | null | https://arxiv.org/abs/2305.14787v1 | https://arxiv.org/pdf/2305.14787v1.pdf | Polarimetric Imaging for Perception | Autonomous driving and advanced driver-assistance systems rely on a set of sensors and algorithms to perform the appropriate actions and provide alerts as a function of the driving scene. Typically, the sensors include color cameras, radar, lidar and ultrasonic sensors. Strikingly however, although light polarization i... | ['Dan Levi', "Tomer Pe'er", 'Michael Baltaxe'] | 2023-05-24 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 3.48452181e-01 2.11326387e-02 1.21860117e-01 -7.34967172e-01
-4.43482220e-01 -7.23620713e-01 5.94796240e-01 -1.68615401e-01
-8.01909685e-01 5.09237945e-01 -3.59471947e-01 -6.20998442e-01
-9.46702287e-02 -9.29338396e-01 -5.40228307e-01 -8.95812929e-01
2.82666951e-01 4.98447329e-01 3.40526760e-01 -6.80826068... | [8.06129264831543, -1.8463847637176514] |
64de504b-bc3a-447f-b2b7-a2c26404a958 | auto-encoding-score-distribution-regression | 2111.11029 | null | https://arxiv.org/abs/2111.11029v2 | https://arxiv.org/pdf/2111.11029v2.pdf | Auto-Encoding Score Distribution Regression for Action Quality Assessment | The action quality assessment (AQA) of videos is a challenging vision task since the relation between videos and action scores is difficult to model. Thus, AQA has been widely studied in the literature. Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scor... | ['Xin Geng', 'Xu Yang', 'HUI ZHANG', 'Yinfei Xu', 'Jiayuan Chen', 'Boyu Zhang'] | 2021-11-22 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [-2.64021218e-01 -2.65927970e-01 -1.42884240e-01 -5.44325173e-01
-1.22626424e+00 -4.00502712e-01 4.09459352e-01 -6.44752026e-01
-1.18106052e-01 5.81184030e-01 4.34524238e-01 1.38402075e-01
-1.21744253e-01 -5.98605931e-01 -1.01233208e+00 -8.26503038e-01
1.36332646e-01 1.69561446e-01 7.05078542e-02 2.76739120... | [8.494704246520996, 0.7608508467674255] |
1987d4d5-9859-423a-acd8-55b8c6a2b17e | visual-relationship-detection-with-low-rank | 1911.09895 | null | https://arxiv.org/abs/1911.09895v1 | https://arxiv.org/pdf/1911.09895v1.pdf | Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition | We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object). We observe that given a pair of bounding box proposals, objects often participate in multiple relations implying the distribution of tr... | ['Zhen Zhang', 'Mohammed Haroon Dupty', 'Wee Sun Lee'] | 2019-11-22 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-2.14094520e-02 -9.51701775e-02 -2.11320654e-01 -2.56103069e-01
-6.07061148e-01 -9.06861842e-01 8.63882661e-01 3.72195661e-01
-1.20678842e-01 3.23209673e-01 3.60639915e-02 -2.40601182e-01
-2.76956767e-01 -6.42773867e-01 -8.90871108e-01 -8.10508847e-01
-2.08777532e-01 1.24370646e+00 2.32575804e-01 8.51890296... | [10.298783302307129, 1.634637713432312] |
af38b7eb-14cf-4961-9e82-86e02010c84f | object-detection-with-pixel-intensity | 1305.4537 | null | http://arxiv.org/abs/1305.4537v5 | http://arxiv.org/pdf/1305.4537v5.pdf | Object Detection with Pixel Intensity Comparisons Organized in Decision Trees | We describe a method for visual object detection based on an ensemble of
optimized decision trees organized in a cascade of rejectors. The trees use
pixel intensity comparisons in their internal nodes and this makes them able to
process image regions very fast. Experimental analysis is provided through a
face detection... | ['Robert Forchheimer', 'Jörgen Ahlberg', 'Igor S. Pandžić', 'Miroslav Frljak', 'Nenad Markuš'] | 2013-05-20 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 2.08404630e-01 -3.37019823e-02 -2.46961236e-01 -2.75454491e-01
-3.71678621e-01 -5.36580980e-01 4.99422431e-01 -1.22322261e-01
-3.83407682e-01 3.45447570e-01 -3.68715763e-01 -2.45635778e-01
3.30228925e-01 -5.78925073e-01 -3.04382205e-01 -1.02013588e+00
-1.44222707e-01 2.96693236e-01 7.86152959e-01 9.66109894... | [8.632831573486328, -0.5333139300346375] |
5ff750e3-9fd4-4358-b661-6baf6ac5870b | deep-learning-on-lie-groups-for-skeleton | 1612.05877 | null | http://arxiv.org/abs/1612.05877v2 | http://arxiv.org/pdf/1612.05877v2.pdf | Deep Learning on Lie Groups for Skeleton-based Action Recognition | In recent years, skeleton-based action recognition has become a popular 3D
classification problem. State-of-the-art methods typically first represent each
motion sequence as a high-dimensional trajectory on a Lie group with an
additional dynamic time warping, and then shallowly learn favorable Lie group
features. In th... | ['Luc van Gool', 'Zhiwu Huang', 'Chengde Wan', 'Thomas Probst'] | 2016-12-18 | deep-learning-on-lie-groups-for-skeleton-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Deep_Learning_on_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Deep_Learning_on_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-classification', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.15243001e-02 -1.21070035e-01 -4.94202793e-01 -4.87785071e-01
-3.12239498e-01 1.60822365e-02 7.42460847e-01 -5.39376378e-01
-4.26797688e-01 1.38787240e-01 5.75882971e-01 9.52085108e-03
-4.37345915e-02 -5.39096236e-01 -5.88672161e-01 -7.28167355e-01
-2.99024403e-01 1.94944575e-01 2.41005719e-01 -6.05387762... | [7.786080360412598, 0.3653239607810974] |
e631b7a1-d2aa-45f4-bed2-4a2ae9a36c09 | self-adapter-at-semeval-2021-task-10-entropy | null | null | https://aclanthology.org/2021.semeval-1.55 | https://aclanthology.org/2021.semeval-1.55.pdf | Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation | Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment. We study the domain adaption in the sequence labeling problem where the model trained on the source domain data is given. We propose two methods: Self-Adapter and Selective Clas... | ['Kyomin Jung', 'Yanghoon Kim', 'Sangwon Yoon'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.91356635e-01 1.81956828e-01 -4.43261594e-01 -8.99042189e-01
-5.44045269e-01 -7.04633176e-01 7.27393985e-01 2.76755273e-01
-9.38592255e-01 1.11504602e+00 1.82402313e-01 -1.38662890e-01
2.66052246e-01 -6.37758911e-01 -6.43021584e-01 -4.99844730e-01
1.24132410e-01 8.15703452e-01 3.65321606e-01 -3.63224447... | [10.827225685119629, 7.884324550628662] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.