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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d3c65d4c-a853-4767-a1e2-a27eae5bde5b | select-and-trade-towards-unified-pair-trading | 2301.10724 | null | https://arxiv.org/abs/2301.10724v2 | https://arxiv.org/pdf/2301.10724v2.pdf | Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning | Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information p... | ['Jimin Huang', 'Yanzhao Lai', 'Min Peng', 'Qianqian Xie', 'Boyi Zhang', 'Weiguang Han'] | 2023-01-25 | null | null | null | null | ['hierarchical-reinforcement-learning', 'pair-trading'] | ['methodology', 'time-series'] | [-2.28773504e-01 8.87054503e-02 -2.79130310e-01 -3.12552243e-01
-8.92760038e-01 -7.48824120e-01 7.58302271e-01 -1.48909852e-01
-4.65893149e-01 9.86640155e-01 -1.94616869e-01 -5.65997362e-01
-5.80402054e-02 -9.45615590e-01 -6.82424545e-01 -5.74534178e-01
-1.16022810e-01 7.69249022e-01 5.71833789e-01 -1.11277863... | [4.435232639312744, 3.9004290103912354] |
6af29234-a960-4bf6-9947-fbb89a09c3f9 | multiple-object-forecasting-predicting-future | 1909.11944 | null | https://arxiv.org/abs/1909.11944v2 | https://arxiv.org/pdf/1909.11944v2.pdf | Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments | This paper introduces the problem of multiple object forecasting (MOF), in which the goal is to predict future bounding boxes of tracked objects. In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-leve... | ['Victor Sanchez', 'Tanaya Guha', 'Olly Styles'] | 2019-09-26 | null | null | null | null | ['multiple-object-forecasting'] | ['computer-vision'] | [-2.34453291e-01 -4.12058890e-01 -2.63288051e-01 -5.22249281e-01
-7.98130095e-01 -6.19723916e-01 7.65156806e-01 1.34327095e-02
-1.51240021e-01 3.73542219e-01 6.67936265e-01 -1.29033938e-01
1.96540002e-02 -6.35891616e-01 -8.90688717e-01 -4.35803294e-01
-3.73205155e-01 2.74747431e-01 7.90984988e-01 -1.61400408... | [6.139135837554932, 0.6245556473731995] |
7119f3ac-e306-410c-a5d0-1b4fbd19ec18 | bridging-the-gap-between-multi-step-and-one | 2306.03367 | null | https://arxiv.org/abs/2306.03367v1 | https://arxiv.org/pdf/2306.03367v1.pdf | Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision | Accurate vehicle trajectory prediction is an unsolved problem in autonomous driving with various open research questions. State-of-the-art approaches regress trajectories either in a one-shot or step-wise manner. Although one-shot approaches are usually preferred for their simplicity, they relinquish powerful self-supe... | ['J. Marius Zöllner', 'Maxim Dolgov', 'Max Keller', 'Faris Janjoš'] | 2023-06-06 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 4.63373326e-02 3.56652707e-01 -8.53305399e-01 -1.01951289e+00
-7.33383119e-01 -2.01428980e-01 9.54961360e-01 1.54288858e-01
-3.10627639e-01 9.59019959e-01 1.37384787e-01 -5.58722615e-01
-1.56213090e-01 -7.79965699e-01 -9.24242795e-01 -6.66339040e-01
-1.35228187e-01 5.30540764e-01 7.19513595e-01 -4.22634780... | [5.965817451477051, 0.970575213432312] |
49237467-5a6f-4676-beca-2349f29ff8ea | rethinking-movie-genre-classification-with | 2012.02639 | null | https://arxiv.org/abs/2012.02639v3 | https://arxiv.org/pdf/2012.02639v3.pdf | Rethinking movie genre classification with fine-grained semantic clustering | Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies within a single genre definition. We expand these 'coarse' genre labels by identifying 'fine-grained' semantic information within the multi-modal... | ['Andrew Gilbert', 'Jon Weinbren', 'Edward Fish'] | 2020-12-04 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.19052508e-01 -5.65384090e-01 -2.64971793e-01 -4.53448057e-01
-8.29374135e-01 -1.09974539e+00 7.26483285e-01 5.00915051e-01
-3.93375725e-01 3.86065781e-01 6.26426458e-01 2.67828107e-01
-4.71993744e-01 -4.63435739e-01 -4.55088496e-01 -5.75146437e-01
-1.67400941e-01 2.26849675e-01 2.43317023e-01 -1.04068585... | [15.505935668945312, 5.032244682312012] |
8db49586-efcb-4a0d-bf7e-140338ba50cd | learning-unsupervised-word-translations | null | null | https://aclanthology.org/D18-1063 | https://aclanthology.org/D18-1063.pdf | Learning Unsupervised Word Translations Without Adversaries | Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks. Recent research has shown that word translation can be achieved in an unsupervised manner, without parallel seed dictionaries or aligned corpora. However, state of the art methods un... | ['Timothy Hospedales', 'Makoto Yamada', 'Tanmoy Mukherjee'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['multilingual-word-embeddings'] | ['methodology'] | [ 2.61147946e-01 -4.45792004e-02 -5.66568553e-01 -1.07596509e-01
-9.91072536e-01 -1.15196621e+00 9.99890327e-01 1.40842259e-01
-4.66630936e-01 1.17691529e+00 2.30395392e-01 -9.19942796e-01
3.03316832e-01 -7.20799387e-01 -7.61956632e-01 -7.00606346e-01
1.83267400e-01 1.06329787e+00 -1.86667591e-01 -7.85420299... | [11.110664367675781, 10.112458229064941] |
e218b185-faa0-47a9-8892-0382e8eb0d56 | greed-is-good-near-optimal-submodular | 1704.01652 | null | http://arxiv.org/abs/1704.01652v1 | http://arxiv.org/pdf/1704.01652v1.pdf | Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization | It is known that greedy methods perform well for maximizing monotone
submodular functions. At the same time, such methods perform poorly in the face
of non-monotonicity. In this paper, we show - arguably, surprisingly - that
invoking the classical greedy algorithm $O(\sqrt{k})$-times leads to the
(currently) fastest de... | ['Amin Karbasi', 'Moran Feldman', 'Christopher Harshaw'] | 2017-04-05 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.00072220e-01 2.61297584e-01 -3.49805325e-01 -1.42996192e-01
-1.00195706e+00 -1.07814515e+00 -3.88028830e-01 4.51318808e-02
-4.02463824e-01 7.25693762e-01 4.50413860e-02 -5.17324448e-01
-6.27007067e-01 -9.64482486e-01 -9.82113540e-01 -7.50281096e-01
-5.04019499e-01 5.82906187e-01 -2.38419548e-01 -3.91667813... | [6.56952428817749, 4.845683574676514] |
20765d8f-2652-43f9-9a37-1b64a15150e2 | boosting-multi-modal-e-commerce-attribute | 2207.07278 | null | https://arxiv.org/abs/2207.07278v2 | https://arxiv.org/pdf/2207.07278v2.pdf | Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization | With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such diversified data, especially via extracting the attribute-value pairs in text sequences with the aid of helpful image regions. Although a seri... | ['Xu-Cheng Yin', 'Wei Liu', 'Hongfa Wang', 'Weibo Gu', 'Hongyu Gao', 'Chao Zhu', 'Mengyin Liu'] | 2022-07-15 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 3.71736139e-01 -3.08747947e-01 -5.67200363e-01 -6.85390651e-01
-9.16079402e-01 -5.76398194e-01 5.01358390e-01 1.66681275e-01
-3.73581290e-01 4.17697459e-01 1.32850826e-01 2.72694062e-02
-3.21288735e-01 -9.03605640e-01 -7.10569382e-01 -8.38495135e-01
1.14116229e-01 5.66899896e-01 -1.11444719e-01 -4.86642778... | [10.636398315429688, 1.7333823442459106] |
f7689f2c-36fc-4d48-863a-802e830b01f7 | self-aware-and-cross-sample-prototypical | 2305.16214 | null | https://arxiv.org/abs/2305.16214v1 | https://arxiv.org/pdf/2305.16214v1.pdf | Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation | Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stabi... | ['Zhicheng Jiao', 'Fan Yang', 'Xin Li', 'Heng Zhou', 'Chunna Tian', 'Ran Ran', 'Zhenxi Zhang'] | 2023-05-25 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.02305852e-01 2.83429702e-03 -8.13707709e-01 -6.83764219e-01
-9.45801973e-01 -1.22151375e-01 1.71278298e-01 2.31139123e-01
-4.34429765e-01 9.03825223e-01 -7.60761499e-02 -1.40155271e-01
-3.62028897e-01 -4.09338981e-01 -4.26760077e-01 -1.07157958e+00
4.19086635e-01 4.87717599e-01 5.13234317e-01 1.32611975... | [14.804557800292969, -2.0380966663360596] |
751ad82e-42a4-4434-8786-fa634a9b80ae | a-knowledge-distillation-framework-for | 2211.02638 | null | https://arxiv.org/abs/2211.02638v1 | https://arxiv.org/pdf/2211.02638v1.pdf | A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data | Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG i... | ['Anjula C. De Silva', 'Chamira U. S. Edussooriya', 'Simon L. Kappel', 'Jathurshan Pradeepkumar', 'Mithunjha Anandakumar'] | 2022-10-27 | null | null | null | null | ['sleep-quality-prediction', 'sleep-staging', 'eeg-based-sleep-staging'] | ['medical', 'medical', 'time-series'] | [-3.45721513e-01 2.70818900e-02 -6.95984438e-02 -3.73906523e-01
-4.21411395e-01 -2.14198202e-01 -2.05388457e-01 6.11372106e-02
-7.21742213e-01 1.11350524e+00 1.65256053e-01 -4.38895402e-03
-1.62032261e-01 -4.56900477e-01 7.60269985e-02 -5.85051775e-01
2.11227193e-01 1.71874419e-01 2.06577435e-01 1.86294485... | [13.523958206176758, 3.466531991958618] |
751cc477-a687-4c4f-a568-b42380302e82 | automatic-acute-ischemic-stroke-lesion | 1908.03735 | null | https://arxiv.org/abs/1908.03735v3 | https://arxiv.org/pdf/1908.03735v3.pdf | Automatic acute ischemic stroke lesion segmentation using semi-supervised learning | Ischemic stroke is a common disease in the elderly population, which can cause long-term disability and even death. However, the time window for treatment of ischemic stroke in its acute stage is very short. To fast localize and quantitively evaluate the acute ischemic stroke (AIS) lesions, many deep-learning-based les... | ['Shuxue Ding', 'Hong Wu', 'Chen Cao', 'Bin Zhao', 'Zhiyang Liu', 'Song Jin', 'Guohua Liu'] | 2019-08-10 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [-2.24578064e-02 -1.34767637e-01 -2.65261233e-01 -5.14467120e-01
-8.68255258e-01 -4.23945844e-01 3.35165352e-01 2.74014354e-01
-8.19140255e-01 8.45622420e-01 6.45898804e-02 -1.76892102e-01
-1.81492418e-02 -7.40901232e-01 -1.70572504e-01 -8.33861947e-01
-2.81335384e-01 6.48532569e-01 8.36528003e-01 2.48947248... | [14.292842864990234, -2.092604398727417] |
c8c792d0-5f17-425f-91ba-12e99b379428 | coresets-for-time-series-clustering | 2110.15263 | null | https://arxiv.org/abs/2110.15263v1 | https://arxiv.org/pdf/2110.15263v1.pdf | Coresets for Time Series Clustering | We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and economics due to the proliferation of sensors facilitating real-time measurement and rapid drop in storage costs. In particular, we consider... | ['Nisheeth K. Vishnoi', 'K. Sudhir', 'Lingxiao Huang'] | 2021-10-28 | null | http://proceedings.neurips.cc/paper/2021/hash/c115ba9e04ab27fbbb664f932112246d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c115ba9e04ab27fbbb664f932112246d-Paper.pdf | neurips-2021-12 | ['time-series-clustering'] | ['time-series'] | [-2.11689830e-01 -9.69615802e-02 8.77529010e-02 -1.09681942e-01
-4.27839965e-01 -5.77651918e-01 5.22227883e-02 4.14160609e-01
-5.65635562e-01 4.92226660e-01 -3.80674899e-01 -1.22288354e-01
-6.67717695e-01 -8.02813172e-01 -7.93147206e-01 -8.74142826e-01
-7.47641921e-01 8.26346874e-01 -8.93715769e-02 4.32368368... | [6.730195045471191, 4.68437385559082] |
6f681d2c-7c4c-4c94-a3db-e4a4fd00c5ef | speech-separation-based-on-contrastive | 2305.10652 | null | https://arxiv.org/abs/2305.10652v1 | https://arxiv.org/pdf/2305.10652v1.pdf | Speech Separation based on Contrastive Learning and Deep Modularization | The current monaural state of the art tools for speech separation relies on supervised learning. This means that they must deal with permutation problem, they are impacted by the mismatch on the number of speakers used in training and inference. Moreover, their performance heavily relies on the presence of high-quality... | ['Peter Ochieng'] | 2023-05-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.98073864e-01 2.09427476e-01 1.60946518e-01 -1.02051072e-01
-8.39495182e-01 -4.04141039e-01 5.92038929e-01 1.45898223e-01
-2.75327176e-01 6.80454254e-01 3.78098696e-01 -1.13336161e-01
-2.95712918e-01 -7.68459886e-02 -4.31684941e-01 -1.09198189e+00
-1.28214657e-02 2.56723613e-01 1.75184309e-01 -3.36296260... | [14.930232048034668, 5.794812202453613] |
c82f8f7b-2870-43a6-85dc-57c07676f70d | definition-modelling-for-appropriate | null | null | https://aclanthology.org/2021.emnlp-main.194 | https://aclanthology.org/2021.emnlp-main.194.pdf | Definition Modelling for Appropriate Specificity | Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific d... | ['Yuki Arase', 'Tomoyuki Kajiwara', 'Han Huang'] | null | null | null | null | emnlp-2021-11 | ['definition-modelling'] | ['natural-language-processing'] | [ 9.17511344e-01 1.72340423e-01 -3.49148065e-01 -3.92554939e-01
-9.37592685e-01 -5.35662889e-01 8.21869195e-01 -5.88671900e-02
-5.08467495e-01 1.00189018e+00 2.60821909e-01 -3.13604951e-01
5.08680642e-02 -8.10950518e-01 -5.01536310e-01 -2.43390605e-01
5.98782003e-01 2.47696415e-01 1.78564504e-01 -4.66596961... | [11.530105590820312, 9.377727508544922] |
36a59909-5736-49a9-89d8-daced6b71c1e | contextual-causal-bayesian-optimisation | 2301.12412 | null | https://arxiv.org/abs/2301.12412v1 | https://arxiv.org/pdf/2301.12412v1.pdf | Contextual Causal Bayesian Optimisation | Causal Bayesian optimisation (CaBO) combines causality with Bayesian optimisation (BO) and shows that there are situations where the optimal reward is not achievable if causal knowledge is ignored. While CaBO exploits causal relations to determine the set of controllable variables to intervene on, it does not exploit p... | ['Haitham Bou-Ammar', 'Antoine Grosnit', 'Vahan Arsenyan'] | 2023-01-29 | null | null | null | null | ['bayesian-optimisation', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 5.10659337e-01 3.62241596e-01 -6.72624052e-01 -1.39087349e-01
-7.04320073e-01 -6.50002301e-01 7.57109344e-01 1.67731076e-01
-6.35384619e-01 1.18497121e+00 4.35527533e-01 -7.52712011e-01
-1.33762336e+00 -7.16116250e-01 -7.28978634e-01 -1.06590366e+00
-2.36236438e-01 6.11360013e-01 2.66285241e-02 2.20862135... | [7.75745964050293, 5.227025032043457] |
85943013-0c43-4717-b706-a069733453e8 | the-segment-anything-model-sam-for-remote | 2306.16623 | null | https://arxiv.org/abs/2306.16623v1 | https://arxiv.org/pdf/2306.16623v1.pdf | The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot | Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-s... | ['José Marcato Junior', 'Jonathan Li', 'Ana Paula Marques Ramos', 'Wesley Nunes Gonçalves', 'Eduardo Lopes de Lemos', 'Qiusheng Wu', 'Lucas Prado Osco'] | 2023-06-29 | null | null | null | null | ['zero-shot-learning'] | ['methodology'] | [ 6.59010470e-01 1.56968459e-02 -1.06609315e-01 -4.42472368e-01
-7.21150815e-01 -6.96145177e-01 5.47481596e-01 1.18544333e-01
-5.88763595e-01 3.95608932e-01 7.58539066e-02 -6.11577272e-01
-6.73955619e-01 -9.89024460e-01 -1.71999052e-01 -5.02785921e-01
-3.46139312e-01 3.94173980e-01 1.94616452e-01 -2.46056288... | [9.443699836730957, -1.45437490940094] |
16492d4e-0531-4a4a-8460-a9590be9daa7 | va-gcn-a-vector-attention-graph-convolution | 2106.00227 | null | https://arxiv.org/abs/2106.00227v1 | https://arxiv.org/pdf/2106.00227v1.pdf | VA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds | Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models. However, existing local aggregation operators in the current literature fail to attach decent importance to the local information of the point cloud, which limits the power of the mod... | ['Huixiao Le', 'Fanyi Wang', 'Haotian Hu'] | 2021-06-01 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-3.63421291e-01 -3.65916580e-01 2.05782324e-01 -2.17245266e-01
-2.45964110e-01 -2.99976408e-01 3.73696953e-01 1.69963524e-01
-1.16185188e-01 5.23236617e-02 3.28111611e-02 -2.94912606e-01
-2.04900056e-01 -1.13940275e+00 -7.98304379e-01 -5.28774023e-01
-1.22757912e-01 9.35158953e-02 3.50043476e-01 -1.54680103... | [7.939726829528809, -3.5721893310546875] |
ebf76e8a-c693-4a9f-83e8-449340ce71a6 | learnable-weight-initialization-for | 2306.09320 | null | https://arxiv.org/abs/2306.09320v3 | https://arxiv.org/pdf/2306.09320v3.pdf | Learnable Weight Initialization for Volumetric Medical Image Segmentation | Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention. While mainly focusing on architectural modifications, most existing hybrid approaches still use conventional data-independent weight initialization sche... | ['Fahad Shahbaz Khan', 'Salman Khan', 'Muzammal Naseer', 'Abdelrahman Shaker', 'Shahina Kunhimon'] | 2023-06-15 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 7.65864775e-02 1.62124887e-01 -4.01502281e-01 -4.94638741e-01
-9.31227386e-01 -2.37345248e-01 3.20564538e-01 4.27878469e-01
-6.23003900e-01 5.63353121e-01 4.20824662e-02 -2.66247183e-01
-2.18987949e-02 -7.01298714e-01 -4.79361117e-01 -7.87823796e-01
1.80235401e-01 5.72621286e-01 4.71942663e-01 3.51493172... | [14.69597053527832, -2.441546678543091] |
8fd70fc9-a7cc-46a4-8253-dde1b0685804 | probabilistic-robustness-analysis-for-dnns | 2101.10102 | null | https://arxiv.org/abs/2101.10102v2 | https://arxiv.org/pdf/2101.10102v2.pdf | Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning | To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. F... | ['Lijun Zhang', 'Youcheng Sun', 'Bai Xue', 'Cheng-Chao Huang', 'Pengfei Yang', 'Renjue Li'] | 2021-01-25 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 7.95211494e-02 3.06161195e-01 -3.71364564e-01 -1.47622988e-01
-8.02042902e-01 -8.87985289e-01 7.59350836e-01 -1.45967960e-01
-3.78019840e-01 7.97232449e-01 -1.11227319e-01 -8.16737950e-01
-3.03878158e-01 -5.92480481e-01 -1.36336184e+00 -8.95415366e-01
-2.46118799e-01 3.41196686e-01 3.57996702e-01 -7.78978169... | [5.811282157897949, 7.6856489181518555] |
63a0b016-718d-4dd3-9fbb-d61677365196 | a-rule-based-approach-to-aspect-extraction | null | null | https://aclanthology.org/W14-5905 | https://aclanthology.org/W14-5905.pdf | A Rule-Based Approach to Aspect Extraction from Product Reviews | null | ['er', 'Lun-Wei Ku', 'Soujanya Poria', 'Chen Gui', 'Alex Gelbukh', 'Erik Cambria'] | 2014-08-01 | null | null | null | ws-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... | [-7.492306709289551, 3.829979181289673] |
3fa750c5-427c-4539-88f4-7a2482a8f1c4 | quality-assessment-of-image-matchers-for-dsm | 2108.08369 | null | https://arxiv.org/abs/2108.08369v1 | https://arxiv.org/pdf/2108.08369v1.pdf | Quality assessment of image matchers for DSM generation -- a comparative study based on UAV images | Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public softw... | ['Cive Fraser', 'Armin Gruen', 'Rongjun Qin'] | 2021-08-18 | null | null | null | null | ['3d-surface-generation'] | ['computer-vision'] | [ 6.12405717e-01 -3.25347751e-01 4.27127957e-01 -4.87320930e-01
-5.77148080e-01 -5.16043186e-01 8.55503321e-01 -1.06518483e-02
-2.07934961e-01 9.77355182e-01 -3.32745850e-01 -5.01352131e-01
-2.68056411e-02 -1.23202538e+00 -1.09335184e-01 -4.88227159e-01
-3.11541297e-02 1.02401221e+00 4.68291640e-01 -5.61211824... | [8.446907043457031, -2.539177656173706] |
7e4efed4-d271-4120-87b1-bc22707860d5 | unified-loss-of-pair-similarity-optimization | 2209.13869 | null | https://arxiv.org/abs/2209.13869v2 | https://arxiv.org/pdf/2209.13869v2.pdf | Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval | There are two popular loss functions used for vision-language retrieval, i.e., triplet loss and contrastive learning loss, both of them essentially minimize the difference between the similarities of negative pairs and positive pairs. More specifically, Triplet loss with Hard Negative mining (Triplet-HN), which is wide... | ['Zhongtian Du', 'Jenq-Neng Hwang', 'Zerun Feng', 'Xin Wang', 'Caili Guo', 'Zheng Li'] | 2022-09-28 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-2.78102398e-01 -7.41065502e-01 -6.24774098e-01 -3.25829327e-01
-1.22820973e+00 -2.10964456e-01 6.15600944e-01 1.50061086e-01
-5.89909434e-01 3.84807199e-01 -7.38633126e-02 -6.64088055e-02
-2.38729700e-01 -5.82184613e-01 -4.84389782e-01 -7.73561358e-01
3.22589487e-01 2.35571474e-01 3.69738936e-01 -4.57050353... | [10.76323127746582, 1.2839003801345825] |
b82a7bfa-2bbb-41f9-97f9-179020cf3c34 | multiple-hankel-matrix-rank-minimization-for | 2303.18023 | null | https://arxiv.org/abs/2303.18023v1 | https://arxiv.org/pdf/2303.18023v1.pdf | Multiple Hankel matrix rank minimization for audio inpainting | Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structured matrices constructed from time-domain signal blocks. We adapt their approach to solve the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modificat... | ['Ondřej Mokrý', 'Pavel Rajmic', 'Pavel Záviška'] | 2023-03-31 | null | null | null | null | ['audio-declipping', 'audio-inpainting'] | ['audio', 'audio'] | [ 5.10805011e-01 2.70190328e-01 4.80817296e-02 1.46484703e-01
-1.11900723e+00 -7.73488641e-01 3.47306579e-01 -1.03553832e-01
-1.27336457e-01 7.03031540e-01 4.68563169e-01 -1.64665189e-02
-4.15013164e-01 -1.78383023e-01 -6.67409003e-01 -7.26156652e-01
-2.52432466e-01 2.41236594e-02 5.52898599e-03 -2.98241377... | [15.489969253540039, 5.567897796630859] |
e33d113e-2c2a-45aa-871b-3c7fb72b4cec | ae-red-a-hyperspectral-unmixing-framework | 2307.00269 | null | https://arxiv.org/abs/2307.00269v1 | https://arxiv.org/pdf/2307.00269v1.pdf | AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising | Spectral unmixing has been extensively studied with a variety of methods and used in many applications. Recently, data-driven techniques with deep learning methods have obtained great attention to spectral unmixing for its superior learning ability to automatically learn the structure information. In particular, autoen... | ['Nicolas Dobigeon', 'Jie Chen', 'Min Zhao'] | 2023-07-01 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [-1.55942261e-01 -3.02332729e-01 -3.03410720e-02 -3.20542119e-02
-5.43535769e-01 -2.54962683e-01 7.42441356e-01 -5.81461191e-01
-1.02772959e-01 4.33322608e-01 5.56011140e-01 8.62481669e-02
-1.55598968e-01 -4.97332096e-01 -6.47666037e-01 -1.21011007e+00
5.37083149e-01 3.06682318e-01 -4.46268708e-01 -2.22311556... | [10.133695602416992, -2.090115547180176] |
4dd26f0b-b71a-4135-9837-f62080ec19bd | tram-a-token-level-retrieval-augmented | 2305.11074 | null | https://arxiv.org/abs/2305.11074v1 | https://arxiv.org/pdf/2305.11074v1.pdf | Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization | Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although Neural Language Models achieve significant performance in this field, an emerging trend is combining neural models with external knowledge. Most previous approaches rely on the sen... | ['Shouling Ji', 'Wenhai Wang', 'Peiyu Liu', 'Yangkai Du', 'Xuhong Zhang', 'Tengfei Ma', 'Lingfei Wu', 'Tong Ye'] | 2023-05-18 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.58216822e-01 1.97745889e-01 -4.15600985e-01 -2.10808694e-01
-1.30747759e+00 -3.31394464e-01 4.71524209e-01 6.89840436e-01
-1.57659084e-01 4.76844907e-01 8.75844777e-01 -3.12870622e-01
2.87771523e-01 -7.69355834e-01 -8.29598904e-01 -1.82650108e-02
1.75777882e-01 -1.60728976e-01 2.25450844e-01 -2.41831347... | [7.619733810424805, 7.93511438369751] |
5d8f72ec-4b79-4748-a330-583217501508 | residual-pattern-learning-for-pixel-wise-out | 2211.14512 | null | https://arxiv.org/abs/2211.14512v1 | https://arxiv.org/pdf/2211.14512v1.pdf | Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation | Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on their ability not only to classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD dete... | ['Gustavo Carneiro', 'Ian Reid', 'Vasileios Belagiannis', 'Guansong Pang', 'Yu Tian', 'Choubo Ding', 'Yuyuan Liu'] | 2022-11-26 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.84716448e-01 1.51346549e-01 -2.42144298e-02 -2.09636062e-01
-7.50998497e-01 -7.35643387e-01 5.85918009e-01 1.91949517e-01
-3.80344808e-01 4.46621507e-01 -2.11406425e-01 -3.17889273e-01
3.83993387e-01 -8.56131256e-01 -8.67397487e-01 -5.77654839e-01
1.54826418e-01 3.80305916e-01 9.29378688e-01 2.67318673... | [9.140945434570312, -0.6083329916000366] |
bb9b9391-05ec-41d9-86e3-f6ed37f562d2 | neto-neural-reconstruction-of-transparent | 2303.11219 | null | https://arxiv.org/abs/2303.11219v1 | https://arxiv.org/pdf/2303.11219v1.pdf | NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing | We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although exi... | ['Chunxia Xiao', 'Fei Luo', 'Wenping Wang', 'Tuo Cao', 'Yusen Wang', 'Xiaoxiao Long', 'Zongcheng Li'] | 2023-03-20 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 2.90305138e-01 -5.13966680e-02 2.38965943e-01 -1.70498371e-01
-5.54520845e-01 -2.99616247e-01 3.56853575e-01 -2.48909548e-01
1.38781369e-01 6.92972064e-01 -4.78899218e-02 -2.58903295e-01
2.20814273e-01 -1.05683637e+00 -5.08672357e-01 -6.19591177e-01
-1.25141507e-02 5.43715775e-01 6.63220704e-01 -1.27435282... | [9.414554595947266, -3.1451117992401123] |
c1d2719b-eb8d-40e7-922d-8810a968002a | epvt-environment-aware-prompt-vision | 2304.01508 | null | https://arxiv.org/abs/2304.01508v3 | https://arxiv.org/pdf/2304.01508v3.pdf | EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition | Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e., dark c... | ['ZongYuan Ge', 'Peter Soyer', 'Monika Janda', 'Victoria Mar', 'Dwarikanath Mahapatrainst', 'Lie Ju', 'Zhen Yu', 'Chi Liu', 'Siyuan Yan'] | 2023-04-04 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 3.82077396e-01 -2.11523607e-01 -2.32241571e-01 -4.21827316e-01
-7.74613976e-01 -6.15626991e-01 4.58003998e-01 -4.00357731e-02
-1.81867585e-01 6.38337076e-01 3.72034073e-01 1.16480114e-02
-3.71693850e-01 -4.99583870e-01 -3.79057050e-01 -9.51418281e-01
3.72417629e-01 -4.55107001e-05 1.85495511e-01 2.38402877... | [14.633111953735352, -1.9767301082611084] |
11f2d94f-bee8-4ddf-b98c-c91f1779b4a0 | snider-single-noisy-image-denoising-and | 1910.03876 | null | https://arxiv.org/abs/1910.03876v1 | https://arxiv.org/pdf/1910.03876v1.pdf | SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition | In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately a... | ['Younkwan Lee', 'Moongu Jeon', 'Juhyun Lee', 'Hoyeon Ahn'] | 2019-10-09 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.24522668e-01 -4.15399909e-01 1.70440748e-01 -3.80760521e-01
-1.39196420e+00 -3.11218292e-01 2.89563477e-01 -9.51998413e-01
-3.98685426e-01 4.57407922e-01 1.00089535e-01 2.33533466e-03
-2.04358831e-01 -2.80938029e-01 -9.62476909e-01 -8.01891327e-01
7.40490317e-01 8.51593316e-02 4.96171787e-02 -2.88634926... | [11.140545845031738, -2.270021677017212] |
3016b9df-0bf8-4f2c-9e0a-40b5189c4bd8 | a-deep-complex-network-with-multi-frame | 2202.01630 | null | https://arxiv.org/abs/2202.01630v2 | https://arxiv.org/pdf/2202.01630v2.pdf | A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation | In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end... | ['Renhua Peng', 'Yuquan Wu', 'XiaoDong Li', 'Andong Li', 'Chengshi Zheng', 'Linjuan Cheng'] | 2022-02-03 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.54903704e-01 -4.14203346e-01 6.06796026e-01 -7.64354914e-02
-7.83452332e-01 -3.83652508e-01 1.67717755e-01 -2.41806492e-01
-3.84413034e-01 3.93082976e-01 5.67457020e-01 -1.86985642e-01
-1.19133823e-01 -4.79438543e-01 -2.22538769e-01 -8.79081428e-01
2.82555878e-01 -4.88036186e-01 -6.74142241e-02 -2.73620367... | [14.975893020629883, 5.944431304931641] |
f3ba9f51-7cd5-4a2d-9b8f-d8a506ee2009 | fractal-measures-of-image-local-features-an | 2108.12491 | null | https://arxiv.org/abs/2108.12491v1 | https://arxiv.org/pdf/2108.12491v1.pdf | Fractal measures of image local features: an application to texture recognition | Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature ... | ['Joao B. Florindo', 'Pedro M. Silva'] | 2021-08-27 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.53633559e-01 -4.01087373e-01 -1.10852025e-01 3.17519642e-02
-3.94334018e-01 -8.26252759e-01 8.55244160e-01 4.99981016e-01
-1.37077704e-01 5.00510812e-01 -1.32677495e-01 -8.39268118e-02
-8.38258147e-01 -1.02093983e+00 1.44877836e-01 -1.05194271e+00
-4.99439240e-01 3.66364747e-01 3.38630915e-01 -2.72906959... | [10.306479454040527, -0.40694117546081543] |
94ce66a6-f7db-4840-bda4-960e1f23fcfe | dymslam4d-dynamic-scene-reconstruction-based | 2003.04569 | null | https://arxiv.org/abs/2003.04569v1 | https://arxiv.org/pdf/2003.04569v1.pdf | DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation | Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynami... | ['Xin Su', 'Lu Yin', 'Chengyuan Li', 'Yajun Wang', 'Bin Luo', 'Yun Zhang', 'Qing Zhao', 'Wei Wang', 'Chenjie Wang'] | 2020-03-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-1.00132428e-01 -4.63601023e-01 1.55003816e-01 -5.52322119e-02
-1.57173857e-01 -5.80343544e-01 6.56769574e-01 -1.59241512e-01
-5.85328102e-01 5.11566997e-01 -3.75667363e-01 1.64110556e-01
-1.03598749e-02 -7.14473307e-01 -6.65467858e-01 -8.68046165e-01
2.01602019e-02 1.21367562e+00 9.21106696e-01 -7.09976703... | [7.313623905181885, -2.278855562210083] |
c438285d-33dd-472d-a6d8-3a37d3c959be | online-clustering-based-multi-camera-vehicle | 2102.04091 | null | https://arxiv.org/abs/2102.04091v1 | https://arxiv.org/pdf/2102.04091v1.pdf | Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs | Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high ... | ['Marcos Escudero-Viñolo', 'Jose M. Martínez', 'Juan C. SanMiguel', 'Elena Luna'] | 2021-02-08 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 7.27279037e-02 -7.24181235e-01 -8.32941234e-02 -2.25410610e-01
-8.01631987e-01 -6.44039392e-01 6.99490249e-01 1.98850095e-01
-5.63779891e-01 4.79657799e-01 -4.79580641e-01 -3.78611088e-01
8.62405524e-02 -5.79067588e-01 -6.71251655e-01 -8.93142939e-01
8.36844742e-02 5.11083663e-01 1.12042594e+00 1.65730059... | [6.617086410522461, -2.056678295135498] |
e987888c-443e-4287-b6c9-b00f368dc24d | learning-from-extreme-bandit-feedback | 2009.12947 | null | https://arxiv.org/abs/2009.12947v2 | https://arxiv.org/pdf/2009.12947v2.pdf | Learning from eXtreme Bandit Feedback | We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions are made over sets consisting of millions of choices in a single day, yielding massive observational ... | ['Inderjit S. Dhillon', 'Michael. I. Jordan', 'Romain Lopez'] | 2020-09-27 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 2.92412072e-01 -7.49948844e-02 -8.91571403e-01 -3.96982789e-01
-1.13930607e+00 -3.64699870e-01 6.02175772e-01 -9.50729381e-03
-5.97767711e-01 1.17543638e+00 4.15105999e-01 -6.07552886e-01
-4.76815641e-01 -5.05941629e-01 -1.06669557e+00 -8.04876864e-01
1.46457091e-01 7.17880547e-01 -3.24024409e-02 6.92980811... | [4.463441371917725, 3.114252805709839] |
e1909332-b23e-4acb-9050-7e1f6d01f42c | metal-inpainting-in-cbct-projections-using | 2209.09733 | null | https://arxiv.org/abs/2209.09733v1 | https://arxiv.org/pdf/2209.09733v1.pdf | Metal Inpainting in CBCT Projections Using Score-based Generative Model | During orthopaedic surgery, the inserting of metallic implants or screws are often performed under mobile C-arm systems. Due to the high attenuation of metals, severe metal artifacts occur in 3D reconstructions, which degrade the image quality greatly. To reduce the artifacts, many metal artifact reduction algorithms h... | ['Andreas Maier', 'Fuxin Fan', 'Siyuan Mei'] | 2022-09-20 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.51235545e-01 1.30531833e-01 3.74907196e-01 1.30346283e-01
-7.13229358e-01 1.91263676e-01 9.70552117e-02 -4.92211998e-01
-1.62269548e-01 8.68535161e-01 5.06280601e-01 1.97478995e-01
1.62814092e-02 -7.45902240e-01 -7.30591834e-01 -7.78993785e-01
4.88958716e-01 3.19510669e-01 3.99712026e-01 -4.15600184... | [13.50485610961914, -2.546243906021118] |
58cc08d6-46b1-4665-bc06-a714fc35c8cb | a-two-stage-deep-network-for-high-dynamic | 2104.09386 | null | https://arxiv.org/abs/2104.09386v1 | https://arxiv.org/pdf/2104.09386v1.pdf | A Two-stage Deep Network for High Dynamic Range Image Reconstruction | Mapping a single exposure low dynamic range (LDR) image into a high dynamic range (HDR) is considered among the most strenuous image to image translation tasks due to exposure-related missing information. This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network. No... | ['Kim Sungjun', 'Mithun Biswas', 'Rizwan Ali Naqvi', 'SMA Sharif'] | 2021-04-19 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.51110512e-01 -2.94970930e-01 2.79722452e-01 -3.25038821e-01
-8.21103930e-01 -2.91918188e-01 3.77895266e-01 -6.44674122e-01
-1.94915459e-01 6.30151033e-01 2.22644046e-01 -1.33920312e-01
1.92975048e-02 -6.73606813e-01 -9.91409063e-01 -6.62998855e-01
4.06280339e-01 -2.73007572e-01 -1.56040102e-01 -3.87401372... | [10.875387191772461, -2.216691255569458] |
a8837677-8157-4f35-be69-bb6dc11a4012 | patched-diffusion-models-for-unsupervised | 2303.03758 | null | https://arxiv.org/abs/2303.03758v1 | https://arxiv.org/pdf/2303.03758v1.pdf | Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI | The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a r... | ['Alexander Schlaefer', 'Roland Opfer', 'Julia Krüger', 'Debayan Bhattacharya', 'Finn Behrendt'] | 2023-03-07 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 5.50520182e-01 3.99010092e-01 4.04958278e-01 -6.07266724e-01
-9.62896705e-01 -4.09681141e-01 6.21495187e-01 1.97086453e-01
-3.08972567e-01 4.32967484e-01 3.56191367e-01 -2.03069627e-01
5.23429960e-02 -4.78333920e-01 -3.60615939e-01 -7.46235251e-01
-2.07819045e-01 8.49014461e-01 4.49450970e-01 1.43476263... | [14.204392433166504, -2.233954668045044] |
7710169b-101f-444f-acdb-4bd68c76f7c2 | neural-quality-estimation-with-multiple | 2105.04443 | null | https://arxiv.org/abs/2105.04443v1 | https://arxiv.org/pdf/2105.04443v1.pdf | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction | Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid m... | ['Tat-Seng Chua', 'Liner Yang', 'Maosong Sun', 'Xiaoyuan Yi', 'Zhenghao Liu'] | 2021-05-10 | null | https://aclanthology.org/2021.naacl-main.429 | https://aclanthology.org/2021.naacl-main.429.pdf | naacl-2021-4 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-5.32774962e-02 1.84717163e-01 -3.98347937e-02 -5.83899200e-01
-7.23028183e-01 -4.76886109e-02 1.68110386e-01 5.41643560e-01
-5.29295206e-01 9.30693030e-01 2.66570717e-01 -4.63472158e-01
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5.13937831e-01 4.40635413e-01 2.73142844e-01 -5.61317265... | [11.05243968963623, 10.774455070495605] |
c646694c-250c-4aa8-9487-9124abd1a92e | first-order-penalty-methods-for-bilevel | 2301.01716 | null | https://arxiv.org/abs/2301.01716v1 | https://arxiv.org/pdf/2301.01716v1.pdf | First-order penalty methods for bilevel optimization | In this paper we study a class of unconstrained and constrained bilevel optimization problems in which the lower-level part is a convex optimization problem, while the upper-level part is possibly a nonconvex optimization problem. In particular, we propose penalty methods for solving them, whose subproblems turn out to... | ['Sanyou Mei', 'Zhaosong Lu'] | 2023-01-04 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [ 2.48622820e-01 3.33480984e-01 -1.66141525e-01 -9.95688662e-02
-6.82196200e-01 -3.57621700e-01 -2.04197094e-01 2.45680079e-01
-5.67320406e-01 1.05231178e+00 -4.19500321e-01 -3.74556690e-01
-8.32630038e-01 -5.41932106e-01 -7.00504482e-01 -8.51456761e-01
-4.20106530e-01 2.19392672e-01 -6.72386363e-02 -2.82788545... | [6.507540702819824, 4.411956787109375] |
a370bc7a-a68f-4648-ae34-5c386979717a | multiearth-2022-multimodal-learning-for-earth | 2204.07649 | null | https://arxiv.org/abs/2204.07649v3 | https://arxiv.org/pdf/2204.07649v3.pdf | MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge | The Multimodal Learning for Earth and Environment Challenge (MultiEarth 2022) will be the first competition aimed at the monitoring and analysis of deforestation in the Amazon rainforest at any time and in any weather conditions. The goal of the Challenge is to provide a common benchmark for multimodal information proc... | ['Jean Piou', 'Brandon Swenson', 'Yen-Chen Lin', 'Bill Freeman', 'Taylor Perron', 'Phillip Isola', 'Armando Cabrera', 'Sam Goldberg', 'Mark Hamilton', 'Gregory Angelides', 'Morgan Schmidt', 'Kuan Wei Huang', 'Miriam Cha'] | 2022-04-15 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.40845704e-01 -4.38197821e-01 -1.71443418e-01 -3.61574113e-01
-1.02437592e+00 -8.12584162e-01 9.87148404e-01 1.40291825e-01
-3.55305046e-01 5.07693708e-01 4.94833350e-01 -4.78994310e-01
2.18337044e-01 -6.61366820e-01 -3.35432351e-01 -8.60011816e-01
-4.60679710e-01 4.93552983e-01 -7.84604311e-01 -3.39034706... | [9.821402549743652, -1.7057514190673828] |
12eb7105-3812-4775-aabb-c3c206370276 | flow-based-density-of-states-for-complex | 2203.01243 | null | https://arxiv.org/abs/2203.01243v1 | https://arxiv.org/pdf/2203.01243v1.pdf | Flow-based density of states for complex actions | Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be used to compute thermodynamic quantities which are difficult to access with traditional methods. This suggests that they are also applicable t... | ['Julian M. Urban', 'Jan M. Pawlowski'] | 2022-03-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 3.71570766e-01 -5.23882434e-02 -4.92640175e-02 8.05139989e-02
-4.72692341e-01 -4.46683526e-01 6.90195739e-01 1.02761500e-01
-6.13991618e-01 1.22281146e+00 -2.77815938e-01 -2.46509731e-01
-2.20685601e-01 -1.00409758e+00 -3.00051063e-01 -1.32294822e+00
1.05322357e-02 5.62420964e-01 6.07039966e-02 -4.47262436... | [5.6149821281433105, 4.894018173217773] |
a9893f01-8a69-49ac-9e19-9edc3c0fa7ed | low-resource-compositional-semantic-parsing | 2301.09809 | null | https://arxiv.org/abs/2301.09809v2 | https://arxiv.org/pdf/2301.09809v2.pdf | Low-Resource Compositional Semantic Parsing with Concept Pretraining | Semantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve the capabilities of a voice assistant by adding a new domain, the underlying semantic parsing model needs to be retrained usi... | ['Mukund Sridhar', 'Andrew McCallum', 'Wael Hamza', 'Konstantine Arkoudas', 'Haidar Khan', 'Subendhu Rongali'] | 2023-01-24 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.47994143e-01 6.43818676e-01 -1.18528329e-01 -7.56179452e-01
-1.06633663e+00 -7.72094369e-01 3.85449529e-01 -1.17022008e-01
-7.28462517e-01 7.85836637e-01 6.73019946e-01 -2.94969827e-01
2.04154357e-01 -6.77064598e-01 -7.53764689e-01 1.03956074e-01
3.77407581e-01 1.29448938e+00 5.80226958e-01 -5.19136667... | [10.841257095336914, 8.921285629272461] |
5ddb6b02-8953-4dd6-aefa-c29c511bab6b | end-to-end-out-of-distribution-detection-with | 2307.00519 | null | https://arxiv.org/abs/2307.00519v1 | https://arxiv.org/pdf/2307.00519v1.pdf | End-to-End Out-of-distribution Detection with Self-supervised Sampling | Out-of-distribution (OOD) detection empowers the model trained on the closed set to identify unknown data in the open world. Though many prior techniques have yielded considerable improvements, two crucial obstacles still remain. Firstly, a unified perspective has yet to be presented to view the developed arts with ind... | ['Xun Wang', 'Xinglong Wu', 'Qi Chen', 'Peng Qin', 'Jiaxi Sun', 'Sen Pei'] | 2023-07-02 | null | null | null | null | ['out-of-distribution-detection', 'ood-detection'] | ['computer-vision', 'computer-vision'] | [ 4.22173031e-02 -1.21993862e-01 -5.28750360e-01 -1.90498292e-01
-9.14822102e-01 -5.27478874e-01 5.37325263e-01 -1.92331925e-01
5.36350086e-02 3.54053855e-01 1.40616313e-01 -2.24596485e-01
-6.01448491e-02 -6.38444483e-01 -6.18535876e-01 -7.86806464e-01
-9.27286744e-02 1.74462974e-01 1.38009071e-01 2.23853678... | [9.313443183898926, 1.6331632137298584] |
cc3c3447-7e03-420d-b97c-84381391c054 | exploiting-unlabeled-data-for-target-oriented | 2208.08280 | null | https://arxiv.org/abs/2208.08280v1 | https://arxiv.org/pdf/2208.08280v1.pdf | Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction | Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the sentence. Recently, deep learning approaches have made remarkable progress on this task. Nevertheless, the TOWE task still suffers from the sc... | ['Yue Zhang', 'Manabu Okumura', 'Takahiro Shinozaki', 'Jindong Wang', 'Zhen Wu', 'Wenxin Hou', 'Ao Liu', 'Hao Wu', 'Yidong Wang'] | 2022-08-17 | null | https://aclanthology.org/2022.coling-1.617 | https://aclanthology.org/2022.coling-1.617.pdf | coling-2022-10 | ['target-oriented-opinion-words-extraction'] | ['natural-language-processing'] | [-7.16685876e-02 -3.72247517e-01 -2.27746256e-02 -7.42685795e-01
-1.11411476e+00 -5.13896406e-01 3.91099215e-01 1.82697684e-01
-4.27514732e-01 7.27390707e-01 2.75314778e-01 -1.95809931e-01
-8.36685486e-03 -5.60029268e-01 -6.11820996e-01 -7.66713798e-01
4.87979650e-01 1.97110742e-01 6.66659093e-03 -2.62027800... | [11.395617485046387, 6.649773120880127] |
44419536-0f9e-4090-811d-e0965573e73d | artiboost-boosting-articulated-3d-hand-object | 2109.05488 | null | https://arxiv.org/abs/2109.05488v2 | https://arxiv.org/pdf/2109.05488v2.pdf | ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis | Estimating the articulated 3D hand-object pose from a single RGB image is a highly ambiguous and challenging problem, requiring large-scale datasets that contain diverse hand poses, object types, and camera viewpoints. Most real-world datasets lack these diversities. In contrast, data synthesis can easily ensure those ... | ['Cewu Lu', 'Jiefeng Li', 'Wenqiang Xu', 'Jun Lv', 'Xinyu Zhan', 'Lixin Yang', 'Kailin Li'] | 2021-09-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_ArtiBoost_Boosting_Articulated_3D_Hand-Object_Pose_Estimation_via_Online_Exploration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['hand-object-pose'] | ['computer-vision'] | [-1.64089158e-01 -3.12162697e-01 -3.04691523e-01 -3.15395117e-01
-1.08268523e+00 -6.68617129e-01 2.85067052e-01 -4.52198297e-01
-2.02340320e-01 6.29821777e-01 4.11573887e-01 1.47661880e-01
-1.18235633e-01 -1.69646367e-01 -8.45242500e-01 -6.80237353e-01
1.72801182e-01 1.11284959e+00 3.03413659e-01 9.45875142... | [6.55222225189209, -1.0510857105255127] |
009cb4a3-1f55-46fc-806e-1297763765e4 | active-speakers-in-context | 2005.09812 | null | https://arxiv.org/abs/2005.09812v1 | https://arxiv.org/pdf/2005.09812v1.pdf | Active Speakers in Context | Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. Although this strategy can be enough for addressing single-speaker scenarios, it prevents accurate detection when the task is to identify who of many candidate speakers are talking. This paper intro... | ['Pablo Arbelaez', 'Joon-Young Lee', 'Long Mai', 'Juan Leon Alcazar', 'Federico Perazzi', 'Fabian Caba Heilbron', 'Bernard Ghanem'] | 2020-05-20 | active-speakers-in-context-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Alcazar_Active_Speakers_in_Context_CVPR_2020_paper.pdf | cvpr-2020-6 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.95349425e-01 -7.21989712e-03 -9.03693810e-02 -5.02005935e-01
-1.64241695e+00 -6.64843917e-01 8.74270499e-01 2.17348039e-01
-1.79189786e-01 2.39739433e-01 6.69659555e-01 9.32484195e-02
-1.19671516e-01 -1.75806999e-01 -4.06355232e-01 -8.72007430e-01
-4.89754558e-01 3.87529790e-01 2.55773246e-01 -1.85568884... | [14.453512191772461, 5.154694557189941] |
c466df3f-7b5e-4c7e-b70c-028c4f9acfda | 3dqd-generalized-deep-3d-shape-prior-via-part | 2303.10406 | null | https://arxiv.org/abs/2303.10406v1 | https://arxiv.org/pdf/2303.10406v1.pdf | 3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process | We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE... | ['Fuzhen Wang', 'Yutian Liu', 'Yilin Sun', 'Bingbing Ni', 'Xuanhong Chen', 'Yishun Dou', 'Yuhan Li'] | 2023-03-18 | null | null | null | null | ['point-cloud-completion', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [-1.25313312e-01 -2.43634842e-02 2.27856353e-01 -1.63396716e-01
-1.05612135e+00 -4.79800820e-01 8.63620043e-01 2.12063938e-02
3.14942986e-01 3.71546179e-01 3.32730561e-01 2.03118458e-01
-1.71557292e-01 -9.56009388e-01 -6.16770446e-01 -8.83459628e-01
4.11568433e-01 5.08649170e-01 -1.27644718e-01 -2.87522316... | [8.780757904052734, -3.6625258922576904] |
46d60808-7b33-4be9-aaf1-1b05893f120d | gafar-graph-attention-feature-augmentation | 2307.02339 | null | https://arxiv.org/abs/2307.02339v1 | https://arxiv.org/pdf/2307.02339v1.pdf | GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm | Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitab... | ['Friedrich Fraundorfer', 'Ismail Geles', 'Ludwig Mohr'] | 2023-07-05 | null | null | null | null | ['3d-scene-reconstruction', 'graph-attention'] | ['computer-vision', 'graphs'] | [ 4.40518185e-02 -1.59352094e-01 1.40362322e-01 -3.79040480e-01
-5.66197813e-01 -4.06220376e-01 7.58376896e-01 3.33973080e-01
-5.98367691e-01 2.65538454e-01 -2.48732954e-01 3.97460312e-02
-5.42424917e-01 -6.66617453e-01 -9.22550619e-01 -6.10899627e-01
-2.94634402e-01 1.01600575e+00 1.48708597e-01 -3.23109120... | [7.667191982269287, -2.9751083850860596] |
6508760b-96cf-4a22-9e9f-ae9f35d5abc7 | multi-channel-time-series-person-and-soft | 2304.01585 | null | https://arxiv.org/abs/2304.01585v1 | https://arxiv.org/pdf/2304.01585v1.pdf | Multi-Channel Time-Series Person and Soft-Biometric Identification | Multi-channel time-series datasets are popular in the context of human activity recognition (HAR). On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e.g., in industrial settings. Contradictory, the gait ... | ['Gernot A. Fink', 'Christopher Reining', 'Fernando Moya Rueda', 'Nilah Ravi Nair'] | 2023-04-04 | null | null | null | null | ['person-identification', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 2.87063211e-01 -2.49759004e-01 -1.53588101e-01 -3.26903105e-01
-3.24114621e-01 -2.95403272e-01 7.29790390e-01 1.46955580e-01
-4.17212009e-01 5.16773522e-01 2.47573033e-01 3.07828188e-01
-2.46643916e-01 -6.73551142e-01 -2.77059138e-01 -1.03423440e+00
-4.45378907e-02 3.10733229e-01 -2.61046797e-01 -2.20487788... | [13.979778289794922, 1.592843770980835] |
8d092b71-3231-475a-a2cd-366771b075ec | a-general-mobile-manipulator-automation | 2302.04486 | null | https://arxiv.org/abs/2302.04486v1 | https://arxiv.org/pdf/2302.04486v1.pdf | A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments | To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh indu... | ['Robert B. Fisher', 'Jinnian Pu', 'Chuanyu Yang', 'Can Pu'] | 2023-02-09 | null | null | null | null | ['point-cloud-registration', 'marketing'] | ['computer-vision', 'miscellaneous'] | [-1.38938636e-01 1.16401829e-01 2.62705714e-01 -5.90039864e-02
-7.35273659e-02 -7.65779793e-01 2.63156861e-01 -1.94749504e-01
-2.81928211e-01 5.67799151e-01 -9.42663252e-01 -4.57586646e-01
-6.64603174e-01 -5.82787991e-01 -9.14425731e-01 -8.38532448e-01
2.07892209e-01 8.35928023e-01 2.08200872e-01 -4.30586606... | [4.855081081390381, 1.3061952590942383] |
2a04758d-c797-4818-bbe1-2f698e03e43b | optimizing-transformer-for-low-resource | 2011.02266 | null | https://arxiv.org/abs/2011.02266v1 | https://arxiv.org/pdf/2011.02266v1.pdf | Optimizing Transformer for Low-Resource Neural Machine Translation | Language pairs with limited amounts of parallel data, also known as low-resource languages, remain a challenge for neural machine translation. While the Transformer model has achieved significant improvements for many language pairs and has become the de facto mainstream architecture, its capability under low-resource ... | ['Christof Monz', 'Ali Araabi'] | 2020-11-04 | null | https://aclanthology.org/2020.coling-main.304 | https://aclanthology.org/2020.coling-main.304.pdf | coling-2020-8 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 5.91751095e-03 -5.24560928e-01 -6.19499624e-01 -2.24812716e-01
-1.20153952e+00 -7.34195828e-01 8.39843988e-01 -9.45949778e-02
-7.17135251e-01 8.87328386e-01 2.26548687e-01 -6.96947992e-01
3.31587940e-01 -4.83247966e-01 -7.30697453e-01 -3.01379442e-01
2.43939534e-01 9.93753731e-01 4.22257408e-02 -6.97937071... | [11.59305477142334, 10.264487266540527] |
b72f6f06-7c92-40ff-9425-9cdc2e1975c2 | mafw-a-large-scale-multi-modal-compound | 2208.00847 | null | https://arxiv.org/abs/2208.00847v1 | https://arxiv.org/pdf/2208.00847v1.pdf | MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild | Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accur... | ['Shiguang Shan', 'Jiabei Zeng', 'Guanghao Yin', 'Wenbin Wang', 'Chuanxu Feng', 'Wei Dai', 'Yuanyuan Liu'] | 2022-08-01 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-1.06267877e-01 -3.04106176e-01 -1.48275092e-01 -7.06221163e-01
-6.51824474e-01 -4.84652102e-01 1.36639550e-01 -6.04077540e-02
-1.19896330e-01 6.52436197e-01 1.92554116e-01 5.58969021e-01
1.61034331e-01 -3.20112646e-01 -2.67697632e-01 -7.86170125e-01
-1.15923032e-01 -7.61995763e-02 -3.95552367e-01 -2.71689415... | [13.572757720947266, 2.1737592220306396] |
443d8689-612d-4ac9-8d94-2b517981dd62 | dub-discrete-unit-back-translation-for-speech | 2305.11411 | null | https://arxiv.org/abs/2305.11411v1 | https://arxiv.org/pdf/2305.11411v1.pdf | DUB: Discrete Unit Back-translation for Speech Translation | How can speech-to-text translation (ST) perform as well as machine translation (MT)? The key point is to bridge the modality gap between speech and text so that useful MT techniques can be applied to ST. Recently, the approach of representing speech with unsupervised discrete units yields a new way to ease the modality... | ['Yaqian Zhou', 'Mingxuan Wang', 'Tom Ko', 'Rong Ye', 'Dong Zhang'] | 2023-05-19 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.89292747e-01 3.63208503e-01 -2.52238184e-01 -2.50674814e-01
-1.56205404e+00 -7.34732926e-01 9.55761492e-01 -3.00274312e-01
-2.18949810e-01 8.85710657e-01 6.46655500e-01 -6.08334720e-01
6.40643120e-01 -4.90173459e-01 -7.52436638e-01 -4.29480404e-01
6.57947898e-01 6.60164237e-01 1.40551403e-01 -5.78714788... | [14.454512596130371, 7.213380336761475] |
e70c0655-9157-4549-9eb7-4f9081c8a37d | amt-all-pairs-multi-field-transforms-for | 2304.09790 | null | https://arxiv.org/abs/2304.09790v1 | https://arxiv.org/pdf/2304.09790v1.pdf | AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation | We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional correlation volumes for all pairs of pixels, and use the predicted bilateral flows to retrieve correlations for updating both flows and the int... | ['Ming-Ming Cheng', 'Chun-Le Guo', 'Qibin Hou', 'Ling-Hao Han', 'Zuo-Liang Zhu', 'Zhen Li'] | 2023-04-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_AMT_All-Pairs_Multi-Field_Transforms_for_Efficient_Frame_Interpolation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [-2.73126394e-01 -4.67037797e-01 -2.29145408e-01 -2.51106918e-01
-4.57269400e-01 -2.94873923e-01 5.97142577e-01 -4.18552369e-01
-1.18487738e-01 8.99456084e-01 5.64741850e-01 -1.75679713e-01
1.91755950e-01 -9.92294133e-01 -6.69214725e-01 -3.98930252e-01
-2.03165039e-01 -1.08884061e-02 5.32494962e-01 -2.64177173... | [10.714204788208008, -1.4435256719589233] |
f94823a1-8515-4206-b90d-2571f7a4b46c | lemmas-generation-selection-application | 2303.05854 | null | https://arxiv.org/abs/2303.05854v1 | https://arxiv.org/pdf/2303.05854v1.pdf | Lemmas: Generation, Selection, Application | Noting that lemmas are a key feature of mathematics, we engage in an investigation of the role of lemmas in automated theorem proving. The paper describes experiments with a combined system involving learning technology that generates useful lemmas for automated theorem provers, demonstrating improvement for several re... | ['Wolfgang Bibel', 'Zsolt Zombori', 'Christoph Wernhard', 'Michael Rawson'] | 2023-03-10 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 3.53033934e-03 6.00561559e-01 -3.00789356e-01 1.39541805e-01
-7.44391024e-01 -9.55353916e-01 5.51961005e-01 4.05925274e-01
5.62076531e-02 1.12115300e+00 -2.37347469e-01 -1.52299404e+00
-3.02270383e-01 -9.58368719e-01 -9.02306378e-01 -1.54999390e-01
-6.14267766e-01 2.89051920e-01 2.76143998e-01 -2.46870995... | [8.890029907226562, 6.978947162628174] |
b57372d8-c3ab-40fc-b303-ad072552a7a3 | collaborative-diffusion-for-multi-modal-face | 2304.10530 | null | https://arxiv.org/abs/2304.10530v1 | https://arxiv.org/pdf/2304.10530v1.pdf | Collaborative Diffusion for Multi-Modal Face Generation and Editing | Diffusion models arise as a powerful generative tool recently. Despite the great progress, existing diffusion models mainly focus on uni-modal control, i.e., the diffusion process is driven by only one modality of condition. To further unleash the users' creativity, it is desirable for the model to be controllable by m... | ['Ziwei Liu', 'Yuming Jiang', 'Kelvin C. K. Chan', 'Ziqi Huang'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Collaborative_Diffusion_for_Multi-Modal_Face_Generation_and_Editing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-generation'] | ['computer-vision'] | [ 5.83866760e-02 8.87862369e-02 8.04547444e-02 -2.19707116e-01
-3.34106743e-01 -6.22107327e-01 8.25793684e-01 -6.28070772e-01
2.11375803e-01 4.02146935e-01 4.22191978e-01 2.94166952e-01
-1.52639002e-01 -8.37859690e-01 -4.34466720e-01 -9.22050416e-01
4.75518525e-01 1.43739581e-01 -2.37986743e-01 -3.26940656... | [12.142297744750977, -0.37747958302497864] |
162e0bbe-7777-4572-a9ff-781fe3c86dd2 | asking-clarification-questions-to-handle | 2305.13808 | null | https://arxiv.org/abs/2305.13808v1 | https://arxiv.org/pdf/2305.13808v1.pdf | Asking Clarification Questions to Handle Ambiguity in Open-Domain QA | Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, ... | ['Kyomin Jung', 'Sang-Woo Lee', 'Joonsuk Park', 'Hwanhee Lee', 'Minwoo Lee', 'Segwang Kim', 'Dongryeol Lee'] | 2023-05-23 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 3.38939726e-01 5.85290670e-01 2.44072109e-01 -5.82078755e-01
-1.68355846e+00 -1.21476567e+00 5.43203354e-01 3.06707621e-01
-4.60554659e-01 1.08911502e+00 6.34002090e-01 -7.58482873e-01
-1.81770578e-01 -4.59644258e-01 -5.02044976e-01 2.27229390e-02
4.98470843e-01 1.02030790e+00 4.70011353e-01 -6.59554064... | [11.625113487243652, 8.004964828491211] |
4f8eedca-4572-4875-805e-7f58f565c642 | latent-coincidence-analysis-a-hidden-variable | null | null | http://papers.nips.cc/paper/4634-latent-coincidence-analysis-a-hidden-variable-model-for-distance-metric-learning | http://papers.nips.cc/paper/4634-latent-coincidence-analysis-a-hidden-variable-model-for-distance-metric-learning.pdf | Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning | We describe a latent variable model for supervised dimensionality reduction and distance metric learning. The model discovers linear projections of high dimensional data that shrink the distance between similarly labeled inputs and expand the distance between differently labeled ones. The model’s continuous latent vari... | ['Matthew Der', 'Lawrence K. Saul'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 8.84717703e-03 1.71780944e-01 -2.80932099e-01 -4.82029766e-01
-7.88638651e-01 -6.74779832e-01 7.33896494e-01 -1.04478247e-01
-4.94988829e-01 4.91723269e-01 3.23588371e-01 -2.95088559e-01
-7.52321064e-01 -4.17462885e-01 -3.19751859e-01 -8.95167470e-01
-8.72180834e-02 9.95533824e-01 -1.78674519e-01 5.03333569... | [7.865699768066406, 4.179345607757568] |
24eb539f-31b7-4e4d-a633-9027b533f238 | a-study-of-semantic-augmentation-of-word | null | null | https://aclanthology.org/W19-8909 | https://aclanthology.org/W19-8909.pdf | A study of semantic augmentation of word embeddings for extractive summarization | In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensiona... | ['Vangelis Karkaletsis', 'Nikiforos Pittaras'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 3.45288604e-01 5.80512404e-01 -2.79363215e-01 -1.69213474e-01
-7.68474340e-01 -4.11315262e-01 9.46421325e-01 1.00907493e+00
-9.02695477e-01 7.40512490e-01 1.38551748e+00 -6.12354204e-02
-4.22872573e-01 -7.86290348e-01 -2.06890941e-01 -2.40128502e-01
-2.01050565e-01 4.04243469e-01 -3.54105741e-01 -7.41696298... | [12.45615005493164, 9.509936332702637] |
dc5a1b03-6453-461c-8ae4-76f6e6581809 | on-the-expected-size-of-conformal-prediction | 2306.07254 | null | https://arxiv.org/abs/2306.07254v1 | https://arxiv.org/pdf/2306.07254v1.pdf | On the Expected Size of Conformal Prediction Sets | While conformal predictors reap the benefits of rigorous statistical guarantees for their error frequency, the size of their corresponding prediction sets is critical to their practical utility. Unfortunately, there is currently a lack of finite-sample analysis and guarantees for their prediction set sizes. To address ... | ['Tom Rainforth', 'George Deligiannidis', 'Guneet S. Dhillon'] | 2023-06-12 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [ 3.48774850e-01 2.28058800e-01 -3.83931786e-01 -4.26678687e-01
-1.26375258e+00 -5.38483262e-01 5.21634877e-01 2.36684650e-01
2.18335651e-02 9.30477619e-01 -4.29663733e-02 -5.71555197e-01
-6.35351360e-01 -7.39814997e-01 -5.99460125e-01 -7.46297836e-01
-2.88126171e-01 4.40476507e-01 1.50942013e-01 2.97463179... | [7.811441898345947, 4.470707416534424] |
194426b8-6927-4ca4-b091-10e01394343b | on-stopwords-filtering-and-data-sparsity-for | null | null | https://aclanthology.org/L14-1265 | https://aclanthology.org/L14-1265.pdf | On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter | Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, t... | ['Fern', 'Yulan He', 'Miriam ez', 'Hassan Saif', 'Harith Alani'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.97650373e-01 -2.74543256e-01 -9.13269669e-02 -3.45421463e-01
-5.34895360e-01 -7.71561027e-01 7.89144099e-01 9.32743669e-01
-7.72552371e-01 5.31578124e-01 4.28320438e-01 -2.86425829e-01
-2.39793994e-02 -8.06792736e-01 -3.20780843e-01 -7.20195115e-01
2.08809316e-01 -1.77487612e-01 1.36264588e-03 -5.79933584... | [10.945183753967285, 6.9961395263671875] |
b7cfedd7-8300-4f9a-b787-c4d57436b18a | unsupervised-person-re-identification-by-deep | 1901.10177 | null | http://arxiv.org/abs/1901.10177v1 | http://arxiv.org/pdf/1901.10177v1.pdf | Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding | Person re-identification (Re-ID) aims to match identities across
non-overlapping camera views. Researchers have proposed many supervised Re-ID
models which require quantities of cross-view pairwise labelled data. This
limits their scalabilities to many applications where a large amount of data
from multiple disjoint ca... | ['An-Cong Wu', 'Wei-Shi Zheng', 'Hong-Xing Yu'] | 2019-01-29 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.17050670e-01 -4.45294768e-01 -3.26509848e-02 -7.40067780e-01
-4.63578165e-01 -5.64558506e-01 6.61646366e-01 -3.01954329e-01
-3.27268839e-01 3.07699621e-01 3.41917932e-01 2.90538043e-01
-2.42044598e-01 -5.43312073e-01 -5.23608506e-01 -8.10212314e-01
2.84914106e-01 5.68235755e-01 -2.15452552e-01 4.34071347... | [14.744454383850098, 1.0196088552474976] |
d4c8df18-9ab9-418c-a676-e49f86a3e2cc | a-survey-of-decision-making-in-adversarial | 2207.07971 | null | https://arxiv.org/abs/2207.07971v1 | https://arxiv.org/pdf/2207.07971v1.pdf | A Survey of Decision Making in Adversarial Games | Game theory has by now found numerous applications in various fields, including economics, industry, jurisprudence, and artificial intelligence, where each player only cares about its own interest in a noncooperative or cooperative manner, but without obvious malice to other players. However, in many practical applicat... | ['Jie Chen', 'Yiguang Hong', 'Min Meng', 'Xiuxian Li'] | 2022-07-16 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 2.19099354e-02 3.66172940e-01 1.17126003e-01 5.56690395e-01
-3.17607485e-02 -1.10299170e+00 -4.64566285e-04 2.38838792e-02
-6.90232933e-01 1.23390543e+00 -4.85923469e-01 -6.09756291e-01
-6.94436729e-01 -1.04467630e+00 -3.56592499e-02 -9.83176827e-01
-4.02173221e-01 5.44737577e-01 3.06521785e-02 -8.17790449... | [4.140823841094971, 2.65437912940979] |
0bcdec03-02c9-47dd-a2a7-2afb8009c506 | can-the-state-of-relevant-neurons-in-a-deep | 2010.15974 | null | https://arxiv.org/abs/2010.15974v1 | https://arxiv.org/pdf/2010.15974v1.pdf | Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks? | We present a method for adversarial attack detection based on the inspection of a sparse set of neurons. We follow the hypothesis that adversarial attacks introduce imperceptible perturbations in the input and that these perturbations change the state of neurons relevant for the concepts modelled by the attacked model.... | ['Jose Oramas', 'Tinne Tuytelaars', 'Roger Granda'] | 2020-10-29 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 4.06482279e-01 2.09574506e-01 2.75868475e-01 1.33862972e-01
-2.91388094e-01 -1.14878654e+00 1.10193145e+00 2.07044393e-01
-1.03842922e-01 3.53030354e-01 -4.79850322e-02 -9.35170949e-02
3.86000216e-01 -8.31038296e-01 -9.31184709e-01 -7.33406186e-01
-2.60967970e-01 2.87282150e-02 4.87500757e-01 -3.95149052... | [5.691901206970215, 7.801114082336426] |
0406259e-a618-419a-90e9-cb8efdbbdf25 | few-shot-dialogue-generation-without | 1908.05854 | null | https://arxiv.org/abs/1908.05854v1 | https://arxiv.org/pdf/1908.05854v1.pdf | Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach | Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, eff... | ['Sungjin Lee', 'Arash Eshghi', 'Igor Shalyminov', 'Oliver Lemon'] | 2019-08-16 | few-shot-dialogue-generation-without-1 | https://aclanthology.org/W19-5904 | https://aclanthology.org/W19-5904.pdf | ws-2019-9 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 1.76873747e-02 5.17043889e-01 -6.02988377e-02 -5.13905287e-01
-1.08088350e+00 -6.46584988e-01 9.70591366e-01 2.57121056e-01
-4.90912586e-01 1.10525525e+00 3.77985299e-01 -1.87890187e-01
1.06962375e-01 -6.08891845e-01 -8.33316073e-02 -1.33706048e-01
2.16784000e-01 1.17669785e+00 4.25288349e-01 -1.17359257... | [12.79814624786377, 7.9952850341796875] |
29432de9-4fa1-446a-aa41-27056226fb52 | semeval-2023-task-2-fine-grained-multilingual | 2305.06586 | null | https://arxiv.org/abs/2305.06586v2 | https://arxiv.org/pdf/2305.06586v2.pdf | SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2) | We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scena... | ['Shervin Malmasi', 'Oleg Rokhlenko', 'Zhiyu Chen', 'Sudipta Kar', 'Besnik Fetahu'] | 2023-05-11 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-6.72664165e-01 -2.31089592e-01 -2.00479664e-02 -1.31063670e-01
-1.18956280e+00 -1.12201881e+00 8.14798057e-01 1.51332781e-01
-1.07719374e+00 1.23339069e+00 5.61758816e-01 -3.75891253e-02
-4.08682004e-02 -5.10293603e-01 -7.53901839e-01 -1.57742724e-01
1.93450421e-01 6.22277081e-01 4.14354764e-02 -4.34581101... | [9.692137718200684, 9.69176197052002] |
111cad2d-af90-464f-82f3-ac173383b144 | good-is-bad-causality-inspired-cloth | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Good_Is_Bad_Causality_Inspired_Cloth-Debiasing_for_Cloth-Changing_Person_Re-Identification_CVPR_2023_paper.pdf | Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification | Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In... | ['Zheng Wang', 'Yu Wu', 'Xian Zhong', 'Meng Lin', 'Zhengwei Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 1.95781395e-01 -1.86958313e-01 -4.19284739e-02 -4.33218747e-01
-9.61147323e-02 -5.39661646e-01 7.03247786e-01 -3.16346914e-01
-3.05978775e-01 8.03217411e-01 6.08205736e-01 -2.62400825e-02
-2.27249369e-01 -4.60601062e-01 -8.23992252e-01 -6.74244702e-01
1.70766696e-01 1.37048569e-02 -3.93348873e-01 3.04826116... | [14.712698936462402, 0.9919779896736145] |
d61a56c0-57bd-4fd3-8531-b4c85989fb8d | position-wise-optimizer-a-nature-inspired | 2204.05312 | null | https://arxiv.org/abs/2204.05312v1 | https://arxiv.org/pdf/2204.05312v1.pdf | Position-wise optimizer: A nature-inspired optimization algorithm | The human nervous system utilizes synaptic plasticity to solve optimization problems. Previous studies have tried to add the plasticity factor to the training process of artificial neural networks, but most of those models require complex external control over the network or complex novel rules. In this manuscript, a n... | ['Amir Valizadeh'] | 2022-04-11 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [ 1.46028146e-01 -2.91792750e-01 -1.01865031e-01 -2.08653122e-01
8.93701553e-01 2.30025593e-02 2.42065176e-01 -3.61221641e-01
-8.83477628e-01 1.27379501e+00 -5.16866744e-01 2.99637895e-02
-2.02482879e-01 -7.86348164e-01 -8.02219391e-01 -6.73038244e-01
2.57710308e-01 9.26406309e-02 5.32312632e-01 -4.73858863... | [8.097557067871094, 2.9013280868530273] |
34ff4d45-bc56-4e9b-bb35-ca00f7854aed | dermoscopic-image-classification-with-neural | 2105.07592 | null | https://arxiv.org/abs/2105.07592v2 | https://arxiv.org/pdf/2105.07592v2.pdf | Dermoscopic Image Classification with Neural Style Transfer | Skin cancer, the most commonly found human malignancy, is primarily diagnosed visually via dermoscopic analysis, biopsy, and histopathological examination. However, unlike other types of cancer, automated image classification of skin lesions is deemed more challenging due to the irregularity and variability in the lesi... | ['Mike Yeh', 'Annie Qu', 'Ruoqing Zhu', 'Yutong Li'] | 2021-05-17 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.68817163e-01 -9.10587683e-02 -1.91591591e-01 -1.59319028e-01
-5.78983903e-01 -7.72814929e-01 6.02539182e-01 1.89621732e-01
-2.15296596e-01 1.82595953e-01 1.11516647e-01 -1.17681533e-01
-4.18459326e-02 -6.91044331e-01 -4.65412915e-01 -1.04772711e+00
6.62204921e-02 4.84581525e-03 6.65724054e-02 -1.23877995... | [15.53854751586914, -2.8476781845092773] |
617ac35c-5fcd-475d-9922-614847ebc75b | ghost-handwritten-digit-recognition-based-on | 2004.02068 | null | https://arxiv.org/abs/2004.02068v2 | https://arxiv.org/pdf/2004.02068v2.pdf | Ghost Handwritten Digit Recognition based on Deep Learning | We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed dee... | ['Le Wang', 'Shengmei Zhao', 'Xing He'] | 2020-04-05 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-4.84165363e-03 -5.68109155e-01 2.43829459e-01 -1.96367994e-01
2.40361691e-02 -1.13585964e-01 4.57293361e-01 -6.35690570e-01
-5.34517169e-01 6.44266725e-01 -4.98879887e-03 -3.26298177e-02
-1.51137948e-01 -1.13002622e+00 -2.30899051e-01 -1.36653733e+00
1.37713969e-01 4.80278917e-02 2.46490523e-01 1.18481748... | [10.776891708374023, -2.2022101879119873] |
14d7fae2-b0c6-4eeb-9ec2-605b7bc26d14 | reinforcement-learning-based-graph-to | 1908.04942 | null | https://arxiv.org/abs/1908.04942v4 | https://arxiv.org/pdf/1908.04942v4.pdf | Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation | Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail t... | ['Lingfei Wu', 'Yu Chen', 'Mohammed J. Zaki'] | 2019-08-14 | null | https://openreview.net/forum?id=HygnDhEtvr | https://openreview.net/pdf?id=HygnDhEtvr | iclr-2020-1 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 3.98335546e-01 4.75799829e-01 2.76583582e-01 -2.30785578e-01
-1.49997115e+00 -8.28471005e-01 5.02940953e-01 1.29835844e-01
-2.23164499e-01 8.65171552e-01 5.95767796e-01 -5.30531883e-01
2.35996619e-01 -1.00693953e+00 -7.86227584e-01 -2.13000193e-01
2.43078768e-01 5.21512508e-01 2.12586090e-01 -4.91289079... | [11.43330192565918, 8.206619262695312] |
6c4b45c5-41b8-44ce-ab62-c7ca0a70caa7 | efficient-nonlinear-manifold-reduced-order | 2011.07727 | null | https://arxiv.org/abs/2011.07727v1 | https://arxiv.org/pdf/2011.07727v1.pdf | Efficient nonlinear manifold reduced order model | Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, such as advection-dominat... | ['Tarek Zohdi', 'David Widemann', 'Youngsoo Choi', 'Youngkyu Kim'] | 2020-11-13 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-2.29757950e-01 -2.62547642e-01 2.36010566e-01 3.15311283e-01
-3.10959309e-01 -7.00744987e-02 6.50641680e-01 -2.77225763e-01
-2.86433190e-01 7.83928692e-01 4.04229611e-02 -3.57525736e-01
-3.59166294e-01 -7.62199342e-01 -5.60559809e-01 -9.78408098e-01
-2.17485890e-01 6.52100921e-01 -1.35893017e-01 -5.33841670... | [6.4916839599609375, 3.470029354095459] |
866c0afb-3b4c-4222-adb8-e5c1168423e8 | video-text-modeling-with-zero-shot-transfer | 2212.04979 | null | https://arxiv.org/abs/2212.04979v3 | https://arxiv.org/pdf/2212.04979v3.pdf | VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners | We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion ... | ['Jiahui Yu', 'Yonghui Wu', 'Soham Ghosh', 'Mi Zhang', 'Yuan Cao', 'ZiRui Wang', 'Tao Zhu', 'Shen Yan'] | 2022-12-09 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification', 'video-question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.67811829e-01 -4.73496430e-02 -2.48699814e-01 -2.86867112e-01
-8.79342198e-01 -3.60147834e-01 8.06479812e-01 -2.79601187e-01
-4.94244665e-01 2.61404455e-01 5.73593855e-01 -1.37639403e-01
2.29862735e-01 -3.12700629e-01 -1.18911266e+00 -4.18239504e-01
8.10543746e-02 2.53896981e-01 4.74518359e-01 -1.21425517... | [10.363106727600098, 0.9240260124206543] |
ded27bc6-5176-4463-a0bc-1829071598b8 | separating-flows-in-encrypted-tunnel-traffic | null | null | https://doi.org/10.1109/ICMLA55696.2022.00094 | https://alexhartl.eu/papers/separating.pdf | Separating Flows in Encrypted Tunnel Traffic | In many scenarios like wireless Internet access or encrypted VPN tunnels, encryption is performed on a per-packet basis. While this encryption approach effectively protects the confidentiality of the transmitted payload, it leaves traffic patterns involving inter-arrival times and packet lengths observable, e.g., to ea... | ['Tanja Zseby', 'Joachim Fabini', 'Alexander Hartl'] | 2022-12-12 | null | null | null | ieee-international-conference-on-machine | ['network-intrusion-detection'] | ['miscellaneous'] | [ 4.15672421e-01 -2.15562388e-01 1.27427563e-01 -1.56460822e-01
-1.82969093e-01 -9.18675065e-01 3.70807528e-01 3.12084138e-01
-3.87620151e-01 5.85360348e-01 -5.58424413e-01 -1.04664338e+00
-2.83711135e-01 -1.12011170e+00 -4.80610520e-01 -7.05045819e-01
-5.91055870e-01 3.80847782e-01 2.94905722e-01 -1.32258505... | [5.195478916168213, 7.259975910186768] |
71c6bcfb-b2e2-4186-ba30-844bf0727de2 | evaluating-neural-model-robustness-for | null | null | https://aclanthology.org/2021.eacl-main.210 | https://aclanthology.org/2021.eacl-main.210.pdf | Evaluating Neural Model Robustness for Machine Comprehension | We evaluate neural model robustness to adversarial attacks using different types of linguistic unit perturbations {--} character and word, and propose a new method for strategic sentence-level perturbations. We experiment with different amounts of perturbations to examine model confidence and misclassification rate, an... | ['Svitlana Volkova', 'Dustin Arendt', 'Winston Wu'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['triviaqa'] | ['miscellaneous'] | [ 1.28151208e-01 -9.30742994e-02 2.93102980e-01 -3.07169735e-01
-7.20791399e-01 -1.09033489e+00 4.20867562e-01 3.19032907e-01
-7.34490693e-01 6.64371729e-01 3.69997561e-01 -7.77372122e-01
9.87806097e-02 -9.27499533e-01 -9.70750988e-01 -2.22542077e-01
-2.66805314e-03 2.90594190e-01 1.74437016e-01 -7.95347214... | [6.143889904022217, 8.174858093261719] |
5dd5bec7-8542-4cc0-8ea5-c557663d4c0c | efficient-and-flexible-topic-modeling-using | 2302.03106 | null | https://arxiv.org/abs/2302.03106v2 | https://arxiv.org/pdf/2302.03106v2.pdf | Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences | Pre-trained language models have led to a new state-of-the-art in many NLP tasks. However, for topic modeling, statistical generative models such as LDA are still prevalent, which do not easily allow incorporating contextual word vectors. They might yield topics that do not align very well with human judgment. In this ... | ['Johannes Schneider'] | 2023-02-06 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.94071436e-01 1.82151690e-01 -3.34385842e-01 -4.89321738e-01
-8.19435060e-01 -4.31229442e-01 1.05833459e+00 3.24453086e-01
-3.43493044e-01 5.73208213e-01 4.11741495e-01 -4.36264038e-01
-9.18078702e-03 -9.91813779e-01 -5.21453559e-01 -6.25057757e-01
1.92279711e-01 6.92755699e-01 2.51008958e-01 -5.43541601... | [10.386733055114746, 7.023213863372803] |
a8df547a-978a-4625-b298-ddfe277c584a | anomaly-detection-in-networks-via-score-based | 2306.15324 | null | https://arxiv.org/abs/2306.15324v1 | https://arxiv.org/pdf/2306.15324v1.pdf | Anomaly Detection in Networks via Score-Based Generative Models | Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art results of score-based models in graph generative modeling, we propose to incorporate them into the aforementioned problem. Our method achie... | ['Evgeny Burnaev', 'Dmitrii Gavrilev'] | 2023-06-27 | null | null | null | null | ['anomaly-detection', 'outlier-detection'] | ['methodology', 'methodology'] | [-1.66922271e-01 4.27964032e-01 2.46280823e-02 -2.95315772e-01
-7.44671226e-01 -2.28891715e-01 5.13643920e-01 2.59146899e-01
1.10635400e-01 4.82966453e-01 1.00630477e-01 -3.86528939e-01
-2.41884947e-01 -1.07178640e+00 -6.29664421e-01 -3.69030476e-01
-3.97577316e-01 9.16200101e-01 5.35414159e-01 -2.39049625... | [6.956419944763184, 6.02780818939209] |
f316922d-b2f3-402f-9b8b-0c051cd82f74 | mipi-2022-challenge-on-rgb-tof-depth | 2209.07057 | null | https://arxiv.org/abs/2209.07057v1 | https://arxiv.org/pdf/2209.07057v1.pdf | MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report | Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and a... | ['Jinwei Gu', 'Chen Change Loy', 'Qingyu Yang', 'Jun Jiang', 'Shangchen Zhou', 'Ruicheng Feng', 'Chongyi Li', 'Qingpeng Zhu', 'Wenxiu Sun'] | 2022-09-15 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 5.37985981e-01 -2.83620358e-01 1.66521430e-01 -6.21267080e-01
-6.94525599e-01 -3.65400970e-01 2.31287852e-01 -5.18538713e-01
-6.24449193e-01 5.40594935e-01 -1.97048187e-01 -5.74818291e-02
-3.14717321e-03 -5.19811153e-01 -7.24167705e-01 -6.27891600e-01
3.47295702e-01 9.72498655e-02 2.06898078e-01 8.12558308... | [9.192266464233398, -2.3160483837127686] |
bcdb9f92-57ec-4db8-902e-1deed371c729 | deep-learning-enabled-sleep-staging-from | 2306.03711 | null | https://arxiv.org/abs/2306.03711v1 | https://arxiv.org/pdf/2306.03711v1.pdf | Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera | Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of th... | ['Lionel Tarassenko', 'Oliver Gibson', 'Bindia Venugopal', 'João Jorge', 'Jonathan Carter'] | 2023-06-06 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 3.39999139e-01 -3.12803276e-02 -3.56634319e-01 -5.77040553e-01
-4.80140060e-01 -1.76414326e-01 -1.37631416e-01 2.54571944e-01
-7.07987249e-01 6.93445802e-01 6.12280183e-02 -1.78404078e-01
1.59776554e-01 -4.69882697e-01 2.87407767e-02 -6.67912960e-01
-2.77628396e-02 8.67289305e-02 5.88139370e-02 1.88942045... | [13.588335037231445, 3.421602487564087] |
eab04586-ffeb-443d-85e9-57e6eb75bf57 | application-of-particle-swarm-optimization-to | 1403.2842 | null | http://arxiv.org/abs/1403.2842v1 | http://arxiv.org/pdf/1403.2842v1.pdf | Application of Particle Swarm Optimization to Microwave Tapered Microstrip Lines | Application of metaheuristic algorithms has been of continued interest in the
field of electrical engineering because of their powerful features. In this
work special design is done for a tapered transmission line used for matching
an arbitrary real load to a 50{\Omega} line. The problem at hand is to match
this arbitr... | ['Ezgi Deniz Ulker', 'Sadik Ulker'] | 2014-03-12 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.37180710e-01 -1.32263750e-01 2.77717143e-01 -9.51612219e-02
-2.15708539e-01 -3.20064902e-01 -1.10933244e-01 1.11853138e-01
-3.54622662e-01 1.16756463e+00 -5.11503160e-01 -2.03845456e-01
-1.23336303e+00 -8.17714691e-01 1.66195676e-01 -9.68441784e-01
3.86532880e-02 1.05003023e+00 -7.62515962e-02 -7.72372067... | [5.708830833435059, 3.42673397064209] |
8a46316e-4989-4207-a840-b39836bf448d | delexicalised-multilingual-discourse | null | null | https://aclanthology.org/2021.disrpt-1.4 | https://aclanthology.org/2021.disrpt-1.4.pdf | Delexicalised Multilingual Discourse Segmentation for DISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 Languages | This paper describes our participating system for the Shared Task on Discourse Segmentation and Connective Identification across Formalisms and Languages. Key features of the presented approach are the formulation as a clause-level classification task, a language-independent feature inventory based on Universal Depende... | ['Tillmann Dönicke'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-segmentation'] | ['natural-language-processing'] | [-1.73210219e-01 3.74706596e-01 -5.19911110e-01 -5.55727720e-01
-8.61647069e-01 -9.48111176e-01 6.68500066e-01 5.95833361e-01
-3.93211097e-01 1.06731021e+00 4.19810444e-01 -6.01667762e-01
-1.36095081e-02 -3.95520836e-01 2.24044502e-01 -3.81554335e-01
-2.44483411e-01 7.12953150e-01 5.07375658e-01 -6.34822726... | [10.687566757202148, 9.663330078125] |
96923aac-e0bd-4969-bf7f-9f3ff79a3039 | an-offline-technique-for-localization-of | 1003.1072 | null | http://arxiv.org/abs/1003.1072v2 | http://arxiv.org/pdf/1003.1072v2.pdf | An Offline Technique for Localization of License Plates for Indian Commercial Vehicles | Automatic License Plate Recognition (ALPR) is a challenging area of research
due to its importance to variety of commercial applications. The overall
problem may be subdivided into two key modules, firstly, localization of
license plates from vehicle images, and secondly, optical character recognition
of extracted lice... | ['Subhadip Basu', 'Mita Nasipuri', 'Dipak Kumar Basu', 'Satadal Saha'] | 2010-03-04 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 1.58323735e-01 -6.48647070e-01 9.20419097e-02 -1.20053440e-01
-1.06125379e+00 -1.17305422e+00 5.44641376e-01 -5.42387426e-01
-2.71627933e-01 6.18463397e-01 -4.92882371e-01 -4.73339021e-01
2.75980234e-01 -6.05799019e-01 -5.82277417e-01 -6.72432899e-01
4.21911299e-01 6.63548410e-01 8.03322256e-01 -8.09140056... | [9.81038761138916, -4.980533123016357] |
1f1e74ad-ccd7-4806-8df0-410d457c14e0 | siamese-labels-auxiliary-network-silanet | 2103.00200 | null | https://arxiv.org/abs/2103.00200v3 | https://arxiv.org/pdf/2103.00200v3.pdf | Siamese Labels Auxiliary Learning | In deep learning, auxiliary training has been widely used to assist the training of models. During the training phase, using auxiliary modules to assist training can improve the performance of the model. During the testing phase, auxiliary modules can be removed, so the test parameters are not increased. In this paper,... | ['Tong Zhang', 'C. L. Philip Chen', 'Zhulin Liu', 'Wenrui Gan'] | 2021-02-27 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-1.96115971e-01 -5.37317917e-02 -3.44284266e-01 -2.80504435e-01
3.37405838e-02 -2.89178610e-01 3.67264956e-01 -8.08756575e-02
-5.01381159e-01 9.15920794e-01 -3.99135619e-01 -4.87981200e-01
-3.41929436e-01 -9.01855946e-01 -5.96831441e-01 -8.90588105e-01
-3.63356508e-02 4.55647647e-01 3.83520693e-01 -2.90874302... | [9.390814781188965, 3.1665284633636475] |
47d0d808-1b37-4eb7-9084-87bd2a73be2f | deep-learning-applied-to-chest-x-rays | 2009.10132 | null | https://arxiv.org/abs/2009.10132v1 | https://arxiv.org/pdf/2009.10132v1.pdf | Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts | While deep learning has shown promise in improving the automated diagnosis of disease based on chest X-rays, deep networks may exhibit undesirable behavior related to shortcuts. This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcom... | ['Ella Kazerooni', 'Sarah Jabbour', 'Jenna Wiens', 'Michael W. Sjoding', 'David Fouhey'] | 2020-09-21 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 2.97200769e-01 4.95055318e-01 -4.79297131e-01 -7.98657477e-01
-6.82289898e-01 -3.29924643e-01 1.87180012e-01 5.21761835e-01
-3.38551521e-01 1.08925831e+00 -1.16892740e-01 -8.03125739e-01
-4.54922736e-01 -9.74737406e-01 -6.43072844e-01 -8.26569140e-01
-2.31706023e-01 8.85411382e-01 -3.28489214e-01 2.74970412... | [15.016472816467285, -2.004750967025757] |
185f5cbe-db2f-4103-9491-49eba47ae248 | towards-comprehensive-representation | null | null | https://link.springer.com/chapter/10.1007/978-3-031-19769-7_18 | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610299.pdf | Towards Comprehensive Representation Enhancement in Semantics-guided Self-supervised Monocular Depth Estimation | Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics. However, since depth estimation and
semantic segmentation are fundamentally two types of tasks: one is regression while the other is classification, the distributio... | ['ShiLiang Pu', 'Xian Zhao', 'Nan Liu', 'Xiangyu Lei', 'Jingyuan Ma'] | 2022-10-23 | null | null | null | eccv-2022-10 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.03560457e-01 6.13014549e-02 -4.86465633e-01 -8.42187524e-01
-5.22757292e-01 -1.41046137e-01 4.76956576e-01 -1.61654964e-01
-3.48754853e-01 6.36656225e-01 4.01577860e-01 3.19879949e-01
-1.11806110e-01 -9.24202025e-01 -2.97089487e-01 -7.18986452e-01
4.39188153e-01 8.29738975e-02 3.79745662e-01 1.36668205... | [9.644466400146484, -0.8750687837600708] |
2bb4be69-3f37-4097-b281-ec4858417f8c | efficient-joint-noise-removal-and-multi | 2112.03701 | null | https://arxiv.org/abs/2112.03701v1 | https://arxiv.org/pdf/2112.03701v1.pdf | Efficient joint noise removal and multi exposure fusion | Multi-exposure fusion (MEF) is a technique for combining different images of the same scene acquired with different exposure settings into a single image. All the proposed MEF algorithms combine the set of images, somehow choosing from each one the part with better exposure. We propose a novel multi-exposure image fusi... | ['O. Martorell', 'J. L Lisani', 'A. Buades'] | 2021-12-04 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 5.92348814e-01 -6.09729946e-01 5.79170763e-01 -1.52945906e-01
-8.28663707e-01 -4.15731698e-01 5.95098674e-01 3.38216007e-01
-7.77844131e-01 5.24084628e-01 9.65896472e-02 1.34681731e-01
-6.91609502e-01 -1.00598359e+00 -4.52760905e-01 -1.14645052e+00
3.90135348e-01 -1.61114801e-02 3.61075997e-01 -3.18225056... | [10.822032928466797, -2.488813877105713] |
dddc918d-d5f9-4b84-9f36-6f965b4b95ae | idol-net-an-interactive-dual-domain-parallel | 2104.01405 | null | https://arxiv.org/abs/2104.01405v1 | https://arxiv.org/pdf/2104.01405v1.pdf | IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction | Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network models have been proposed for metal artifact reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid information from both ... | ['Yi Zhang', 'Jiliu Zhou', 'Hu Chen', 'Yan Liu', 'Huaiqiang Sun', 'Zexin Lu', 'Wenjun Xia', 'Tao Wang'] | 2021-04-03 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.29561731e-01 1.29115984e-01 -3.18977162e-02 -1.09960361e-04
-7.21575499e-01 1.97534673e-02 3.34780455e-01 -1.69046253e-01
-2.01748461e-01 6.66773021e-01 2.48509243e-01 -1.43382892e-01
-3.75792563e-01 -7.92746127e-01 -4.79174137e-01 -8.94209623e-01
2.67086208e-01 4.86552805e-01 5.65496683e-01 -6.42758980... | [13.518719673156738, -2.537931203842163] |
8060c064-b088-4418-a94e-69378fee2b68 | roadnet-rt-high-throughput-cnn-architecture | 2006.07644 | null | https://arxiv.org/abs/2006.07644v2 | https://arxiv.org/pdf/2006.07644v2.pdf | RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation | In recent years, convolutional neural network has gained popularity in many engineering applications especially for computer vision. In order to achieve better performance, often more complex structures and advanced operations are incorporated into the neural networks, which results very long inference time. For time-c... | ['Yecheng Lyu', 'Xinming Huang', 'Lin Bai'] | 2020-06-13 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.40592247e-01 -4.50348519e-02 6.84982985e-02 -4.93623435e-01
1.65111810e-01 -1.23316534e-01 1.06549330e-01 3.20269652e-02
-9.66187000e-01 3.68039608e-01 -6.13913238e-01 -8.49014103e-01
3.84992696e-02 -9.43185329e-01 -4.96882290e-01 -5.62400699e-01
1.92464486e-01 1.45548554e-02 5.91502845e-01 -6.39164895... | [9.078721046447754, -0.5741207003593445] |
89517fca-67f5-4440-848d-94cc86961c03 | kvasir-seg-a-segmented-polyp-dataset | 1911.07069 | null | https://arxiv.org/abs/1911.07069v1 | https://arxiv.org/pdf/1911.07069v1.pdf | Kvasir-SEG: A Segmented Polyp Dataset | Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, man... | ['Håvard D. Johansen', 'Pål Halvorsen', 'Thomas de Lange', 'Michael A. Riegler', 'Dag Johansen', 'Debesh Jha', 'Pia H. Smedsrud'] | 2019-11-16 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [ 4.67630357e-01 3.48343670e-01 1.23335958e-01 -2.11397603e-01
-4.69527900e-01 -7.46270537e-01 -2.39812359e-02 8.04990828e-01
-6.13401771e-01 2.11500704e-01 -2.23427415e-01 -7.54440129e-01
2.38582864e-01 -8.55341077e-01 -8.10191393e-01 -3.66555244e-01
-2.77238429e-01 4.25852776e-01 6.09313667e-01 -9.16900635... | [14.341938972473145, -2.999859094619751] |
57d3f6f0-8e73-4892-bed7-967340a5be6b | few-shot-classification-in-unseen-domains-by | 2112.13539 | null | https://arxiv.org/abs/2112.13539v1 | https://arxiv.org/pdf/2112.13539v1.pdf | Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains | Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-c... | ['Yu-Chiang Frank Wang', 'Fu-En Yang', 'Ci-Siang Lin', 'Yuan-Chia Cheng'] | 2021-12-27 | null | null | null | null | ['generalized-few-shot-classification'] | ['computer-vision'] | [ 4.77162987e-01 5.04786009e-03 -6.00247502e-01 -6.26462400e-01
-1.08424318e+00 -4.05324608e-01 7.07811296e-01 4.32459921e-01
-3.22115630e-01 8.16393077e-01 1.89427938e-02 3.33920270e-01
-3.62556159e-01 -1.01592910e+00 -4.95755941e-01 -4.20664459e-01
-7.11858124e-02 6.56754553e-01 7.36560643e-01 -2.93963194... | [10.031939506530762, 3.0713350772857666] |
f5588c49-f990-428e-af39-82ac15bfb99d | rid-noise-towards-robust-inverse-design-under | 2112.03912 | null | https://arxiv.org/abs/2112.03912v1 | https://arxiv.org/pdf/2112.03912v1.pdf | RID-Noise: Towards Robust Inverse Design under Noisy Environments | From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single... | ['De-Chuan Zhan', 'Hao Ma', 'Ke-Bin Fan', 'Jia-Qi Yang'] | 2021-12-07 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.73265234e-01 -2.35311702e-01 -2.10891776e-02 -3.28632265e-01
-7.03736782e-01 -3.11025769e-01 1.94046602e-01 -3.98746699e-01
4.16507088e-02 4.65401530e-01 2.29653686e-01 -2.51217008e-01
-5.12679338e-01 -7.09873617e-01 -8.85722995e-01 -9.25407708e-01
2.46675983e-01 -7.20775202e-02 -6.00896366e-02 -2.84632951... | [5.8892340660095215, 3.2466938495635986] |
c18ada7d-ed00-4ff0-bf7d-91f0a7a1469e | segmentation-and-classification-of-emg-time | 2104.09627 | null | https://arxiv.org/abs/2104.09627v3 | https://arxiv.org/pdf/2104.09627v3.pdf | Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement | Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements. Previous studies mainly implemented steady-state EMG classification w... | ['Sezen Yagmur Gunay', 'Deniz Erdogmus', 'Gunar Schirner', 'Mehrshad Zandigohar', 'Mo Han'] | 2021-04-19 | null | null | null | null | ['motion-detection', 'electromyography-emg'] | ['computer-vision', 'medical'] | [ 4.75902706e-01 -3.50463957e-01 -5.79221070e-01 -2.29371175e-01
-5.40277541e-01 -6.77310169e-01 3.91578883e-01 -2.73566961e-01
-5.09819984e-01 4.27199036e-01 1.63053632e-01 1.70477316e-01
-5.03014863e-01 -1.24401130e-01 -4.47632045e-01 -7.50275552e-01
-6.25398934e-01 5.22332609e-01 1.82741731e-01 -1.85028255... | [6.8489251136779785, 0.23033101856708527] |
d26eb40b-c6c7-4f76-baa5-27b7fb089182 | proximal-causal-learning-of-heterogeneous | 2301.10913 | null | https://arxiv.org/abs/2301.10913v2 | https://arxiv.org/pdf/2301.10913v2.pdf | Proximal Causal Learning of Conditional Average Treatment Effects | Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available. These, however, typically rely on the assumption... | ['Yifan Cui', 'Erik Sverdrup'] | 2023-01-26 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.90200293e-01 2.95380592e-01 -1.05644715e+00 -5.66768169e-01
-1.21122086e+00 -3.28111291e-01 1.33117214e-01 3.92814726e-01
-5.08597434e-01 1.09973085e+00 2.86122441e-01 -4.65902716e-01
-5.22929668e-01 -6.69007361e-01 -9.25093591e-01 -6.46583676e-01
-3.13917160e-01 4.84822154e-01 -2.74246395e-01 5.44617772... | [7.98095178604126, 5.24630880355835] |
59f240d5-bd77-4639-9a95-311000aadff0 | automatic-interaction-and-activity | 2304.09789 | null | https://arxiv.org/abs/2304.09789v2 | https://arxiv.org/pdf/2304.09789v2.pdf | Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection | This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion pa... | ['Arash Ajoudani', 'Edoardo Lamon', 'Marta Lagomarsino', 'Elena Merlo'] | 2023-04-19 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 4.67298120e-01 -5.98004580e-01 -2.34520987e-01 -9.89290550e-02
-1.87325150e-01 -6.77612901e-01 8.22946548e-01 2.99209744e-01
-4.76951033e-01 5.69912374e-01 8.58317390e-02 1.41858727e-01
-4.83396620e-01 -1.79010138e-01 -3.03010970e-01 -5.37155509e-01
-4.94724959e-01 3.99773598e-01 9.25405502e-01 3.43136579... | [8.27098274230957, 0.36531755328178406] |
167f9cf9-5dc4-4bb9-aaa8-33b4fb2e349a | the-challenge-of-non-technical-loss-detection | 1606.00626 | null | http://arxiv.org/abs/1606.00626v3 | http://arxiv.org/pdf/1606.00626v3.pdf | The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey | Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity ... | ['Radu State', 'Jorge Augusto Meira', 'Petko Valtchev', 'Patrick Glauner', 'Franck Bettinger'] | 2016-06-02 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-2.46147588e-01 -1.29351139e-01 5.63108884e-02 -2.58319259e-01
-4.72506396e-02 -5.05007684e-01 2.79732883e-01 4.46099669e-01
9.07637924e-02 9.62628901e-01 -4.30623919e-01 -4.34061646e-01
-4.46655363e-01 -1.17879069e+00 -8.81915241e-02 -8.42581987e-01
-3.09793651e-01 5.85724175e-01 -3.88201714e-01 -4.87068631... | [6.06097412109375, 2.601689577102661] |
18b46fbf-3bb3-46f1-9dc6-d39d4923cfae | fiba-frequency-injection-based-backdoor | 2112.01148 | null | https://arxiv.org/abs/2112.01148v2 | https://arxiv.org/pdf/2112.01148v2.pdf | FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis | In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a u... | ['DaCheng Tao', 'Yong Xia', 'Shanshan Zhao', 'Jing Zhang', 'Benteng Ma', 'Yu Feng'] | 2021-12-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Feng_FIBA_Frequency-Injection_Based_Backdoor_Attack_in_Medical_Image_Analysis_CVPR_2022_paper.pdf | cvpr-2022-1 | ['skin-lesion-classification'] | ['medical'] | [ 4.09708589e-01 -1.99018463e-01 -1.14578173e-01 1.88160688e-01
-8.92322302e-01 -8.74620914e-01 3.43360394e-01 3.74150090e-02
-3.93214151e-02 1.26959085e-01 -1.79418579e-01 -6.67602599e-01
2.00244695e-01 -8.55022132e-01 -8.32245469e-01 -1.00634062e+00
-3.04652125e-01 -2.73942232e-01 2.99119771e-01 -1.46329682... | [5.655803203582764, 7.768509864807129] |
580fa100-f1f1-4b43-8d04-5b8d6f070e61 | siamthn-siamese-target-highlight-network-for | 2303.12304 | null | https://arxiv.org/abs/2303.12304v1 | https://arxiv.org/pdf/2303.12304v1.pdf | SiamTHN: Siamese Target Highlight Network for Visual Tracking | Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and henc... | ['Menglong Yan', 'Wenhui Diao', 'Liangjin Zhao', 'Xian Sun', 'Kaiqiang Chen', 'Jiahao Bao'] | 2023-03-22 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.69574484e-01 -1.94522247e-01 -3.42479318e-01 -1.44717664e-01
-1.72948256e-01 -5.40961146e-01 3.63543957e-01 -1.89759806e-01
-5.35996199e-01 5.30526519e-01 -9.39115584e-02 5.80788180e-02
2.10237533e-01 -3.14018607e-01 -7.34066129e-01 -8.37195158e-01
5.69678321e-02 -2.71436898e-03 9.95967865e-01 1.28419042... | [6.330306529998779, -2.1380362510681152] |
6e6bfe41-c2a8-4f7e-ac33-619886b73d9a | robust-variable-speed-variable-pitch-power | 2210.08928 | null | https://arxiv.org/abs/2210.08928v4 | https://arxiv.org/pdf/2210.08928v4.pdf | Variable-Pitch Power Regulation of Tethered-Wing Systems Based on Robust Gain-Scheduling H-infinity Control | In this paper, we deal with the power regulation of tethered-wing systems and demonstrate advantages of variable-pitch control in mitigating the dynamic mechanical loads and power fluctuations. The proposed scheme is based on a strategy that maximizes the energy capture during low-speed wind and prevents overloads duri... | ['Amin Nikoobin', 'Mani Kakavand'] | 2022-10-17 | null | null | null | null | ['pitch-control'] | ['audio'] | [-1.38817504e-01 2.35624969e-01 -1.11996472e-01 5.85768342e-01
4.81570899e-01 -1.06102633e+00 3.91084075e-01 -4.20359045e-01
2.17848316e-01 1.01001048e+00 -1.17209546e-01 -2.66376585e-02
-9.37486947e-01 -7.16574371e-01 -1.67651623e-01 -8.97435606e-01
-2.62083203e-01 -2.49543749e-02 -2.41897300e-01 -6.82675719... | [5.393289089202881, 2.4589288234710693] |
d23dce2b-c9f1-4f8f-9554-d448b94026d8 | clover-towards-a-unified-video-language | 2207.07885 | null | https://arxiv.org/abs/2207.07885v3 | https://arxiv.org/pdf/2207.07885v3.pdf | Clover: Towards A Unified Video-Language Alignment and Fusion Model | Building a universal Video-Language model for solving various video understanding tasks (\emph{e.g.}, text-video retrieval, video question answering) is an open challenge to the machine learning field. Towards this goal, most recent works build the model by stacking uni-modal and cross-modal feature encoders and train ... | ['Rongrong Ji', 'Xiaoshuai Sun', 'Xinglong Wu', 'Jiashi Feng', 'Yinan Li', 'Jingjia Huang'] | 2022-07-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Clover_Towards_a_Unified_Video-Language_Alignment_and_Fusion_Model_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 3.44806641e-01 -2.40769044e-01 -1.56151071e-01 -4.88381565e-01
-1.49711096e+00 -6.47073567e-01 6.08838260e-01 -3.03407848e-01
-5.40708303e-01 2.40691081e-01 3.27913314e-01 -1.71918035e-01
-2.72415280e-01 -2.90488660e-01 -9.38036203e-01 -5.29372334e-01
2.75813669e-01 3.61626089e-01 4.73847985e-02 -3.23914886... | [10.350907325744629, 1.0454764366149902] |
3c6fa0ef-a9dc-42d9-8d25-d310a4fb85d7 | citesum-citation-text-guided-scientific | 2205.06207 | null | https://arxiv.org/abs/2205.06207v2 | https://arxiv.org/pdf/2205.06207v2.pdf | CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision | Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the heavy human annotation and domain expertise required. In this paper, we propose a simple yet effective approach to automatically extracting... | ['Jiawei Han', 'Ming Zhong', 'Yuning Mao'] | 2022-05-12 | null | null | null | null | ['headline-generation', 'extreme-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.17269969e-02 2.40043312e-01 -4.36325341e-01 1.02372617e-01
-1.70627606e+00 -6.63671196e-01 9.24750388e-01 4.08656061e-01
-2.75850117e-01 1.14488542e+00 6.89829648e-01 -2.60889679e-01
-1.84747651e-01 -4.81675118e-01 -7.27081120e-01 -4.67551559e-01
2.61521250e-01 4.63888377e-01 7.25516230e-02 7.53546655... | [12.403359413146973, 9.542767524719238] |
d59e830f-6ac0-4a39-bbab-f5d2b7b6d193 | density-aware-feature-embedding-for-face | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.pdf | Density-Aware Feature Embedding for Face Clustering | Clustering has many applications in research and industry. However, traditional clustering methods, such as K-means, DBSCAN and HAC, impose oversimplifying assumptions and thus are not well-suited to face clustering. To adapt to the distribution of realistic problems, a natural approach is to use Graph Convolutional Ne... | [' Rui Zhao', ' Xiaogang Wang', ' Chao Zhang', ' Dapeng Chen', ' Jing Xu', 'Senhui Guo'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['face-clustering'] | ['computer-vision'] | [-4.11292553e-01 -3.02911997e-01 -6.45217970e-02 -6.49902523e-01
-3.07727575e-01 -2.33985841e-01 5.11841178e-01 -1.14178598e-01
-9.99570265e-02 2.60002553e-01 1.42178059e-01 1.25085622e-01
-1.97579950e-01 -9.92775857e-01 -5.41407585e-01 -1.04446900e+00
-1.45483062e-01 4.87047076e-01 -5.00682816e-02 2.87697732... | [13.365599632263184, 1.1177064180374146] |
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