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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a31b9505-688f-4a96-9007-1fa242a5b069 | uncovering-and-quantifying-social-biases-in | 2305.15377 | null | https://arxiv.org/abs/2305.15377v1 | https://arxiv.org/pdf/2305.15377v1.pdf | Uncovering and Quantifying Social Biases in Code Generation | With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the social bias problem in pre-trained code generation models. We propose a new paradigm to construct code prompts and successfully uncover social bi... | ['Tsung-Yi Ho', 'Pin-Yu Chen', 'Jian-Guang Lou', 'Daoguang Zan', 'Fengji Zhang', 'Zhe Su', 'Yan Gao', 'Xiaokang Chen', 'Yan Liu'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-3.05576678e-02 4.28153247e-01 -2.31215537e-01 -4.02819067e-01
-6.36328906e-02 -6.08948648e-01 6.72960162e-01 5.63781023e-01
5.81295006e-02 5.07642865e-01 7.94326186e-01 -5.79691172e-01
6.29315004e-02 -6.53849781e-01 -3.64188701e-01 -2.90169865e-01
-1.64919928e-01 -2.28150144e-01 -3.67658645e-01 -4.33387309... | [9.01158618927002, 10.19959545135498] |
19f1bd45-c1ef-4f7d-8811-63da2b275996 | tinycd-a-not-so-deep-learning-model-for | 2207.13159 | null | https://arxiv.org/abs/2207.13159v2 | https://arxiv.org/pdf/2207.13159v2.pdf | TINYCD: A (Not So) Deep Learning Model For Change Detection | In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current state-of-the-art change detection models due to industrial needs. Despite being from 13 to 140 times smaller than the compared change detection models, and expo... | ['Alessandro Ferrari', 'Gabriele Lombardi', 'Andrea Codegoni'] | 2022-07-26 | null | null | null | null | ['change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing'] | ['miscellaneous', 'miscellaneous'] | [-2.01649934e-01 -2.81621426e-01 -1.95937872e-01 -1.06081985e-01
-5.03791630e-01 -3.77573162e-01 7.16029823e-01 6.28536120e-02
-6.85367882e-01 3.64356726e-01 1.69180214e-01 8.63321349e-02
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-2.66899198e-01 1.24149114e-01 4.66688395e-01 -4.10227358... | [9.611506462097168, -0.9982553124427795] |
2574d070-f656-479c-ab38-c10d86897cb9 | video-frame-interpolation-with-densely | 2304.13596 | null | https://arxiv.org/abs/2304.13596v1 | https://arxiv.org/pdf/2304.13596v1.pdf | Video Frame Interpolation with Densely Queried Bilateral Correlation | Video Frame Interpolation (VFI) aims to synthesize non-existent intermediate frames between existent frames. Flow-based VFI algorithms estimate intermediate motion fields to warp the existent frames. Real-world motions' complexity and the reference frame's absence make motion estimation challenging. Many state-of-the-a... | ['Gangshan Wu', 'Jie Tang', 'Jie Liu', 'Chang Zhou'] | 2023-04-26 | null | null | null | null | ['video-frame-interpolation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [-2.54993320e-01 -4.78251427e-01 -3.26741725e-01 -1.61420688e-01
-3.62352222e-01 -2.10194200e-01 5.06370902e-01 -4.65234160e-01
-3.31382900e-01 8.33261490e-01 4.54120219e-01 1.35324016e-01
2.29109943e-01 -8.16354573e-01 -7.48306274e-01 -6.51881576e-01
-1.60659283e-01 -1.57282203e-01 7.15478182e-01 -1.96426675... | [10.723173141479492, -1.446092963218689] |
ea1f1623-0f63-4731-9196-7f9c909f008a | an-evaluation-of-deep-cnn-baselines-for-scene | 1805.06086 | null | http://arxiv.org/abs/1805.06086v1 | http://arxiv.org/pdf/1805.06086v1.pdf | An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification | In recent years, a variety of proposed methods based on deep convolutional
neural networks (CNNs) have improved the state of the art for large-scale
person re-identification (ReID). While a large number of optimizations and
network improvements have been proposed, there has been relatively little
evaluation of the infl... | ['Michael Jamieson', 'Paul Marchwica', 'Parthipan Siva'] | 2018-05-16 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-1.42574206e-01 -3.13471884e-01 1.31118655e-01 -8.07978630e-01
-3.66165459e-01 -6.47150218e-01 7.32158899e-01 1.40925512e-01
-9.88854408e-01 7.17756033e-01 4.74551141e-01 1.38495073e-01
-4.57688197e-02 -4.84394670e-01 -6.19975507e-01 -3.38068753e-01
-2.76624523e-02 6.26324177e-01 -2.16450930e-01 -1.12867929... | [14.64822769165039, 0.9984248280525208] |
b8ed4673-6ee6-4cde-82a4-b26adc884541 | community-detection-and-portfolio | 2112.13383 | null | https://arxiv.org/abs/2112.13383v1 | https://arxiv.org/pdf/2112.13383v1.pdf | Community detection and portfolio optimization | Community detection methods can be used to explore the structure of complex systems. The well-known modular configurations in complex financial systems indicate the existence of community structures. Here we analyze the community properties of correlation-based networks in worldwide stock markets and use community info... | ['Lin Chen', 'H. Eugene Stanley', 'Gang-Jin Wang', 'Chao Wang', 'Longfeng Zhao'] | 2021-12-26 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.10476279e-01 -1.86332345e-01 2.41257697e-01 3.74844670e-01
4.13951159e-01 -1.19563520e+00 6.24233186e-01 4.24557418e-01
-4.25269920e-03 5.90745091e-01 1.07755519e-01 -6.46789670e-01
-5.22934556e-01 -1.31437743e+00 7.32626021e-02 -5.11149168e-01
-9.70007479e-01 6.35433435e-01 6.34027779e-01 -6.02583170... | [6.798125267028809, 5.198350429534912] |
3317d9fa-7884-4344-af7d-964fbb6dc9f7 | instance-wise-depth-and-motion-learning-from | 1912.09351 | null | https://arxiv.org/abs/1912.09351v2 | https://arxiv.org/pdf/1912.09351v2.pdf | Instance-wise Depth and Motion Learning from Monocular Videos | We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision. Our technical contributions are three-fold. First, we propose a differentiable forward rigid projection module that plays a key role in... | ['Seokju Lee', 'Stephen Lin', 'In So Kweon', 'Sunghoon Im'] | 2019-12-19 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-1.87664516e-02 -1.75704956e-01 -2.52584875e-01 -4.61340934e-01
-8.58134627e-01 -7.38990963e-01 5.87646008e-01 -7.23203242e-01
-2.96700537e-01 4.62289214e-01 -1.00640748e-02 -6.40746504e-02
2.97327101e-01 -3.52130830e-01 -9.07634795e-01 -5.82457364e-01
1.78170025e-01 3.39969486e-01 4.58091617e-01 3.73014212... | [8.520462989807129, -1.9590915441513062] |
3ca7cb7b-08d5-471c-a24e-67e56d2dc8a3 | i3cl-intra-and-inter-instance-collaborative | 2108.01343 | null | https://arxiv.org/abs/2108.01343v3 | https://arxiv.org/pdf/2108.01343v3.pdf | I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection | Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i.e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context. To address these issues, we propose a novel method named Intra- a... | ['Jian Ye', 'Bo Du', 'DaCheng Tao', 'Juhua Liu', 'Jing Zhang'] | 2021-08-03 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.10618673e-01 -4.17678118e-01 2.84168171e-03 -2.72600502e-01
-1.20630217e+00 -5.72633266e-01 5.27527392e-01 -1.13209315e-01
-2.68419474e-01 2.48268038e-01 8.39692950e-02 -1.19193763e-01
2.29160979e-01 -6.21996701e-01 -6.61636651e-01 -8.90976846e-01
4.55409527e-01 3.97366107e-01 5.15023768e-01 -9.72649232... | [11.996685981750488, 2.245739459991455] |
7e6f0043-9534-408c-89ac-315ccbc08148 | validating-weak-form-market-efficiency-in | 1909.05151 | null | https://arxiv.org/abs/1909.05151v1 | https://arxiv.org/pdf/1909.05151v1.pdf | Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning | The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price h... | ['Jeffrey Gropp', 'Samuel Showalter'] | 2019-09-11 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-6.17080569e-01 -2.06837848e-01 -7.84981191e-01 1.57201234e-02
-5.48342288e-01 -8.59385848e-01 6.98082626e-01 -1.14616016e-02
-4.28935945e-01 5.82073092e-01 2.42520258e-01 -1.12167990e+00
-3.57172340e-01 -7.78497696e-01 -2.64629662e-01 -2.38490924e-01
-1.76842898e-01 4.23413366e-01 -2.53338844e-01 -9.20618996... | [4.5543975830078125, 4.213082313537598] |
2e0081de-4927-479c-a9c0-6eb894963e59 | unsupervised-change-point-detection-for | 2305.11976 | null | https://arxiv.org/abs/2305.11976v1 | https://arxiv.org/pdf/2305.11976v1.pdf | Unsupervised Change Point Detection for heterogeneous sensor signals | Change point detection is a crucial aspect of analyzing time series data, as the presence of a change point indicates an abrupt and significant change in the process generating the data. While many algorithms for the problem of change point detection have been developed over time, it can be challenging to select the ap... | ['Mario Krause'] | 2023-05-19 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [ 3.59691173e-01 -6.14487350e-01 -9.29648653e-02 -2.15591207e-01
-2.31032774e-01 -9.05803859e-01 7.78789520e-01 7.22603202e-01
-2.26312160e-01 5.53357303e-01 -2.71375030e-01 -3.29836130e-01
-4.75598574e-01 -6.14019692e-01 -1.44140124e-01 -7.37352669e-01
-3.24979931e-01 3.36612225e-01 3.13624948e-01 -2.58500367... | [7.240373134613037, 3.3325536251068115] |
c177ef8a-af6b-490d-a444-4762212a2c29 | template-based-automatic-search-of-compact | 1904.02365 | null | https://arxiv.org/abs/1904.02365v2 | https://arxiv.org/pdf/1904.02365v2.pdf | Template-Based Automatic Search of Compact Semantic Segmentation Architectures | Automatic search of neural architectures for various vision and natural language tasks is becoming a prominent tool as it allows to discover high-performing structures on any dataset of interest. Nevertheless, on more difficult domains, such as dense per-pixel classification, current automatic approaches are limited in... | ['Chunhua Shen', 'Vladimir Nekrasov', 'Ian Reid'] | 2019-04-04 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 2.42292210e-01 6.93102553e-02 6.28582537e-02 -4.33058619e-01
-5.78055263e-01 -4.99186516e-01 5.59629083e-01 2.50489879e-02
-7.59417534e-01 5.63403726e-01 -2.47774482e-01 -3.58834565e-01
-1.59714997e-01 -6.03296161e-01 -8.10464025e-01 -7.54916191e-01
-1.04422178e-02 7.14618206e-01 4.83712554e-01 -1.79357186... | [8.71527099609375, 2.984646797180176] |
35a53318-ab5e-4754-9145-67b50b589372 | utilizing-bert-for-aspect-based-sentiment | 1903.09588 | null | http://arxiv.org/abs/1903.09588v1 | http://arxiv.org/pdf/1903.09588v1.pdf | Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence | Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained
opinion polarity towards a specific aspect, is a challenging subtask of
sentiment analysis (SA). In this paper, we construct an auxiliary sentence from
the aspect and convert ABSA to a sentence-pair classification task, such as
question answeri... | ['Luyao Huang', 'Xipeng Qiu', 'Chi Sun'] | 2019-03-22 | utilizing-bert-for-aspect-based-sentiment-1 | https://aclanthology.org/N19-1035 | https://aclanthology.org/N19-1035.pdf | naacl-2019-6 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 3.12369823e-01 1.27445206e-01 1.01714700e-01 -1.08474040e+00
-1.09366274e+00 -7.43414700e-01 7.34413743e-01 4.22707617e-01
-2.98336238e-01 6.05650783e-01 5.01869977e-01 -4.85719860e-01
4.30600494e-01 -9.40617204e-01 -5.82489789e-01 -2.41769224e-01
4.03502345e-01 6.26798987e-01 -1.13505475e-01 -9.43871319... | [11.483524322509766, 6.675646781921387] |
f21d3853-a860-4b0a-8c91-f167dde9e211 | sources-of-hallucination-by-large-language | 2305.14552 | null | https://arxiv.org/abs/2305.14552v1 | https://arxiv.org/pdf/2305.14552v1.pdf | Sources of Hallucination by Large Language Models on Inference Tasks | Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization, yet this capability is under-explored. We present a series of behavioral studies on several LLM families (LLaMA, GPT-3.5, and PaLM) which probe their behavio... | ['Mark Steedman', 'Mark Johnson', 'Mohammad Javad Hosseini', 'Liang Cheng', 'Tianyi Li', 'Nick McKenna'] | 2023-05-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.99809462e-01 6.18377268e-01 -1.97402105e-01 -1.90113351e-01
-8.53055179e-01 -6.53100908e-01 9.37068641e-01 3.83119822e-01
-4.60459471e-01 8.97658408e-01 5.12763917e-01 -7.57108331e-01
-1.19795784e-01 -8.17174971e-01 -7.00213492e-01 -3.56740147e-01
6.71502799e-02 7.12898195e-01 1.91689134e-01 -1.61635086... | [11.663224220275879, 9.09545612335205] |
8e4defcd-3598-4e68-be43-8bc44779127d | self-paced-kernel-estimation-for-robust-blind | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Gong_Self-Paced_Kernel_Estimation_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Gong_Self-Paced_Kernel_Estimation_ICCV_2017_paper.pdf | Self-Paced Kernel Estimation for Robust Blind Image Deblurring | The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model... | ['Anton Van Den Hengel', 'Yanning Zhang', 'Dong Gong', 'Qinfeng Shi', 'Mingkui Tan'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.81880016e-02 -5.82011104e-01 2.38765091e-01 -1.57950267e-01
-6.64191127e-01 -4.89597023e-01 2.96768278e-01 -1.74194336e-01
-4.20714408e-01 6.45779073e-01 3.13248008e-01 1.95479169e-01
-2.98774481e-01 -4.52053500e-03 -6.18478417e-01 -9.08640623e-01
-3.86184896e-03 -2.70010028e-02 1.75238878e-01 3.64384472... | [11.568583488464355, -2.7031612396240234] |
6cf949b1-4680-4f08-8475-efe49bd13ce1 | lcpformer-towards-effective-3d-point-cloud | 2210.12755 | null | https://arxiv.org/abs/2210.12755v2 | https://arxiv.org/pdf/2210.12755v2.pdf | LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers | Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, separated local regions from the general sampling architecture corrupt the structural information of the instances, and the inherent relationsh... | ['Jungong Han', 'Banghuai Li', 'Zhiyou Zhao', 'Zhuoxu Huang'] | 2022-10-23 | null | null | null | null | ['3d-shape-retrieval', '3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 1.12680100e-01 2.78173368e-02 -1.96030989e-01 -5.58067858e-01
-3.81156713e-01 -4.56195921e-01 5.35158038e-01 4.45580602e-01
-1.26174912e-01 3.32913220e-01 8.67187232e-02 -1.25987694e-01
-2.16762736e-01 -8.92171919e-01 -9.64058697e-01 -8.59572589e-01
-2.65652180e-01 6.47982776e-01 5.42122126e-01 -2.17870399... | [7.952996253967285, -3.480642318725586] |
a99675e0-2619-44aa-b0f6-ea621bb0f504 | superpixel-based-refinement-for-object | 2101.04574 | null | https://arxiv.org/abs/2101.04574v1 | https://arxiv.org/pdf/2101.04574v1.pdf | Superpixel-based Refinement for Object Proposal Generation | Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually generate segmentations of objects based on coarse feature maps, due to the inherent downsampling in CNNs. This leads to segmentation boundaries no... | ['Simone Frintrop', 'Christian Wilms'] | 2021-01-12 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 4.60795969e-01 6.71388566e-01 6.44568652e-02 -4.05939460e-01
-9.25593555e-01 -2.03012973e-01 5.25697708e-01 3.92939776e-01
-5.26397467e-01 7.66288757e-01 -1.56840563e-01 2.49685869e-01
6.99689835e-02 -9.95188653e-01 -9.79179323e-01 -5.28807163e-01
5.78365624e-01 8.72901738e-01 8.65945518e-01 1.38212413... | [9.526917457580566, 0.47979679703712463] |
5b38dee5-2a71-4997-91ae-39e3a7681c26 | patch-mix-contrastive-learning-with-audio | 2305.14032 | null | https://arxiv.org/abs/2305.14032v2 | https://arxiv.org/pdf/2305.14032v2.pdf | Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification | Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; howev... | ['Se-Young Yun', 'Sungnyun Kim', 'Kyongpil Tae', 'Changwan Ha', 'Byungjo Lee', 'Soyoun Son', 'Hyerim Baek', 'Won-Yang Cho', 'June-Woo Kim', 'Sangmin Bae'] | 2023-05-23 | null | null | null | null | ['audio-classification', 'sound-classification'] | ['audio', 'audio'] | [ 2.58067459e-01 -3.88912588e-01 9.03248787e-03 1.10106222e-01
-1.20353079e+00 -3.97603095e-01 3.91975850e-01 6.29556254e-02
-1.60092503e-01 3.46847326e-01 3.02887410e-01 -3.55474293e-01
3.36694233e-02 -5.42887032e-01 -4.79657412e-01 -8.29393983e-01
2.74121404e-01 2.67374843e-01 1.46042973e-01 2.86439270... | [14.568245887756348, 3.900416374206543] |
9ac04e26-3636-4c0d-8d92-494cd5fd536d | auto-encoder-based-co-training-multi-view | 2201.02978 | null | https://arxiv.org/abs/2201.02978v1 | https://arxiv.org/pdf/2201.02978v1.pdf | Auto-Encoder based Co-Training Multi-View Representation Learning | Multi-view learning is a learning problem that utilizes the various representations of an object to mine valuable knowledge and improve the performance of learning algorithm, and one of the significant directions of multi-view learning is sub-space learning. As we known, auto-encoder is a method of deep learning, which... | ['Xin Zuo', 'Hao-jie Xie', 'Yuan-Fang Wang', 'Jian-wei Liu', 'Run-kun Lu'] | 2022-01-09 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-2.81242073e-01 -1.04548559e-01 -3.38751376e-01 -4.51342434e-01
-5.53609550e-01 -2.21765116e-01 5.27786255e-01 -5.39006233e-01
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-6.94093779e-02 -7.70537853e-01 -6.80610120e-01 -6.87291205e-01
2.33020127e-01 2.56153554e-01 1.77961186e-01 1.65443625... | [8.458002090454102, 4.557337284088135] |
54491e36-a913-4dbf-b978-039cbdf8ed5f | suggestion-miner-at-semeval-2019-task-9 | null | null | https://aclanthology.org/S19-2218 | https://aclanthology.org/S19-2218.pdf | Suggestion Miner at SemEval-2019 Task 9: Suggestion Detection in Online Forum using Word Graph | This paper describes the suggestion miner system that participates in SemEval 2019 Task 9 - SubTask A - Suggestion Mining from Online Reviews and Forums. The system participated in the subtasks A. This paper discusses the results of our system in the development, evaluation and post evaluation. Each class in the datase... | ['Luqman Ahmed', 'Syed Jawad Hussain', 'Humera Liaquat', 'Usman Ahmed'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [ 3.94245470e-03 7.51374185e-01 -2.35681504e-01 -7.16673493e-01
-4.89293039e-01 -4.63803291e-01 8.71326804e-01 5.84155679e-01
-5.90519369e-01 7.84408689e-01 -1.85075011e-02 -6.25162601e-01
-2.61218220e-01 -6.52251959e-01 -2.21331879e-01 -3.23915601e-01
-1.67159408e-01 9.13669109e-01 3.28543812e-01 -3.78045678... | [10.92527961730957, 7.449750900268555] |
4b0ed255-3171-480d-878e-6f247e8be46a | cross-document-non-fiction-narrative | null | null | https://aclanthology.org/W15-4509 | https://aclanthology.org/W15-4509.pdf | Cross-Document Non-Fiction Narrative Alignment | null | ['Shakthidhar Gopavaram', 'Ben Miller', 'Ayush Shrestha', 'Jennifer Olive'] | 2015-07-01 | null | null | null | ws-2015-7 | ['graph-similarity'] | ['graphs'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.531668186187744, 3.55029296875] |
8a9f93b1-472c-45e7-8ff9-982cd33fe40d | beamformed-fingerprint-learning-for-accurate | 1804.04112 | null | http://arxiv.org/abs/1804.04112v1 | http://arxiv.org/pdf/1804.04112v1.pdf | Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning | With millimeter wave wireless communications, the resulting radiation
reflects on most visible objects, creating rich multipath environments, namely
in urban scenarios. The radiation captured by a listening device is thus shaped
by the obstacles encountered, which carry latent information regarding their
relative posit... | ['Gabriel Falcão', 'João Gante', 'Leonel Sousa'] | 2018-04-11 | null | null | null | null | ['outdoor-positioning'] | ['miscellaneous'] | [ 4.97700647e-02 2.47065783e-01 2.79438823e-01 -1.07693270e-01
-4.41581994e-01 -5.50526917e-01 3.68872046e-01 -1.21750861e-01
-3.09072405e-01 7.33851552e-01 4.21043336e-01 -3.38379800e-01
-3.37900519e-01 -1.25773859e+00 -3.30546230e-01 -1.21368408e+00
-4.71161485e-01 1.32571563e-01 -1.02297924e-01 -9.73903015... | [6.302882671356201, 1.113948941230774] |
9cc9588f-510e-4f30-a259-64631dc3f63a | disambiguating-confusion-sets-as-an-aid-for | null | null | https://aclanthology.org/2020.readi-1.1 | https://aclanthology.org/2020.readi-1.1.pdf | Disambiguating Confusion Sets as an Aid for Dyslexic Spelling | Spell checkers and other proofreading software are crucial tools for people with dyslexia and other reading disabilities. Most spell checkers automatically detect spelling mistakes by looking up individual words and seeing if they exist in the vocabulary. However, one of the biggest challenges of automatic spelling cor... | ['Anton Karl Ingason', "Steinunn Rut Fri{\\dh}riksd{\\'o}ttir"] | 2020-05-01 | null | null | null | lrec-2020-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.44182068e-01 -3.17659616e-01 2.53326952e-01 -1.21148974e-01
-6.20568514e-01 -7.06151664e-01 2.96703964e-01 7.91192830e-01
-9.35652137e-01 8.30190897e-01 3.65081459e-01 -7.62420237e-01
-1.49000436e-01 -6.49637818e-01 -5.02915680e-01 -4.87563640e-01
6.00783467e-01 5.84400058e-01 2.96015114e-01 -5.33201158... | [10.970078468322754, 10.627943992614746] |
6474fa98-05d4-43c8-8a76-02f44c38610c | innovation-pursuit-a-new-approach-to-subspace | 1512.00907 | null | http://arxiv.org/abs/1512.00907v5 | http://arxiv.org/pdf/1512.00907v5.pdf | Innovation Pursuit: A New Approach to Subspace Clustering | In subspace clustering, a group of data points belonging to a union of
subspaces are assigned membership to their respective subspaces. This paper
presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of
subspace clustering using a new geometrical idea whereby subspaces are
identified based on the... | ['Mostafa Rahmani', 'George Atia'] | 2015-12-02 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 5.53188752e-03 -1.00080207e-01 -2.15147976e-02 1.96794271e-01
-6.45579815e-01 -7.90819824e-01 3.78485680e-01 -1.42918602e-01
-6.87029660e-02 3.45761180e-01 4.04209793e-02 -1.94802850e-01
-6.27408326e-01 -3.12389225e-01 -3.60022664e-01 -1.24026418e+00
-1.81206107e-01 5.60044408e-01 8.00579414e-02 9.63840708... | [7.7387800216674805, 4.4289164543151855] |
0ae10564-2a8c-4724-8b48-b8f4a059507b | certification-of-semantic-perturbations-via | 2002.12463 | null | https://arxiv.org/abs/2002.12463v4 | https://arxiv.org/pdf/2002.12463v4.pdf | Certified Defense to Image Transformations via Randomized Smoothing | We extend randomized smoothing to cover parameterized transformations (e.g., rotations, translations) and certify robustness in the parameter space (e.g., rotation angle). This is particularly challenging as interpolation and rounding effects mean that image transformations do not compose, in turn preventing direct cer... | ['Martin Vechev', 'Marc Fischer', 'Maximilian Baader'] | 2020-02-27 | null | http://proceedings.neurips.cc/paper/2020/hash/5fb37d5bbdbbae16dea2f3104d7f9439-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/5fb37d5bbdbbae16dea2f3104d7f9439-Paper.pdf | neurips-2020-12 | ['provable-adversarial-defense'] | ['adversarial'] | [ 1.25956908e-01 8.55424255e-02 4.76350263e-02 2.29609162e-02
-1.42620444e+00 -1.36663413e+00 3.50742847e-01 2.95827910e-02
-3.88057321e-01 7.00449586e-01 -1.13390356e-01 -5.85853755e-01
3.70202512e-02 -5.89452982e-01 -1.27871680e+00 -8.46635044e-01
-2.65099585e-01 -1.48534484e-03 7.61213154e-02 -3.00872959... | [5.871531963348389, 7.341946125030518] |
6cc8fa1b-e61c-45be-9572-d459881eaa77 | learning-deep-structured-multi-scale-features | 1801.00524 | null | http://arxiv.org/abs/1801.00524v1 | http://arxiv.org/pdf/1801.00524v1.pdf | Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction | Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-s... | ['Xavier Alameda-Pineda', 'Elisa Ricci', 'Wanli Ouyang', 'Xiaogang Wang', 'Nicu Sebe', 'Dan Xu'] | 2018-01-01 | learning-deep-structured-multi-scale-features-1 | http://papers.nips.cc/paper/6985-learning-deep-structured-multi-scale-features-using-attention-gated-crfs-for-contour-prediction | http://papers.nips.cc/paper/6985-learning-deep-structured-multi-scale-features-using-attention-gated-crfs-for-contour-prediction.pdf | neurips-2017-12 | ['contour-detection'] | ['computer-vision'] | [ 8.90269727e-02 1.44013569e-01 -1.19998582e-01 -4.98493105e-01
-9.82551277e-01 -4.25603509e-01 6.25316501e-01 3.03474426e-01
-2.89386690e-01 6.23525798e-01 3.36670339e-01 5.27893715e-02
1.27008870e-01 -1.02155232e+00 -6.50012791e-01 -6.12259150e-01
-9.68988165e-02 1.34710476e-01 4.82182592e-01 -2.55009383... | [9.60216999053955, 0.16193534433841705] |
3c2fc354-cf60-4ea9-9fde-bc0d2b3c9c21 | reward-shaping-with-subgoals-for-social | 2104.06410 | null | https://arxiv.org/abs/2104.06410v1 | https://arxiv.org/pdf/2104.06410v1.pdf | Reward Shaping with Subgoals for Social Navigation | Social navigation has been gaining attentions with the growth in machine intelligence. Since reinforcement learning can select an action in the prediction phase at a low computational cost, it has been formulated in a social navigation tasks. However, reinforcement learning takes an enormous number of iterations until ... | ['Seiji Yamada', 'Takato Okudo'] | 2021-04-13 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 6.11788742e-02 2.96462387e-01 1.33503079e-01 -2.48657480e-01
-6.03040047e-02 -4.34558541e-02 4.20582771e-01 1.57137826e-01
-1.05032170e+00 1.16420114e+00 -2.03593925e-01 -1.13472424e-01
-1.96737796e-01 -1.06278634e+00 -5.70916355e-01 -8.45852196e-01
-3.80453736e-01 5.21078646e-01 7.92289436e-01 -7.35721707... | [4.022695064544678, 1.727403998374939] |
e85e3169-4526-478a-9264-a7f4dfb42623 | glyphdraw-learning-to-draw-chinese-characters | 2303.17870 | null | https://arxiv.org/abs/2303.17870v2 | https://arxiv.org/pdf/2303.17870v2.pdf | GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation | Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions.Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insuff... | ['Xiaodong Lin', 'Haonan Lu', 'Di Niu', 'Ruichen Wang', 'Chen Chen', 'Mingjun Zhao', 'Jian Ma'] | 2023-03-31 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 4.22108680e-01 1.78995192e-01 -1.99053641e-02 -1.44910336e-01
-6.18482590e-01 -5.82988679e-01 7.32354224e-01 -3.05825144e-01
-7.53135756e-02 7.21462548e-01 3.59544069e-01 -4.03212339e-01
2.48643219e-01 -9.49447393e-01 -7.77900338e-01 -5.25038183e-01
3.91242176e-01 1.38954848e-01 -3.37480903e-02 -2.72584498... | [11.394576072692871, -0.25990059971809387] |
7ce1d85f-870b-4a6a-a047-a90c90a4adc8 | keyphrase-generation-with-cross-document | 2004.09800 | null | https://arxiv.org/abs/2004.09800v2 | https://arxiv.org/pdf/2004.09800v2.pdf | Keyphrase Generation with Cross-Document Attention | Keyphrase generation aims to produce a set of phrases summarizing the essentials of a given document. Conventional methods normally apply an encoder-decoder architecture to generate the output keyphrases for an input document, where they are designed to focus on each current document so they inevitably omit crucial cor... | ['Yan Song', 'Shizhe Diao', 'Tong Zhang'] | 2020-04-21 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [-9.70185734e-03 7.87281338e-03 -2.95131236e-01 1.02432452e-01
-1.06630695e+00 -5.67924500e-01 9.89282966e-01 1.15414545e-01
-1.39230996e-01 8.12406659e-01 8.72675419e-01 -2.42491841e-01
4.57666814e-03 -7.92679429e-01 -7.22643971e-01 -6.15816653e-01
4.91497934e-01 3.33779454e-01 1.69644549e-01 -4.42460716... | [12.335434913635254, 8.948945999145508] |
40b03131-31f5-4eca-a82f-f7dad842a0fa | small-noisy-and-perspective-face-detection | 2010.16164 | null | https://arxiv.org/abs/2010.16164v1 | https://arxiv.org/pdf/2010.16164v1.pdf | Small Noisy and Perspective Face Detection using Deformable Symmetric Gabor Wavelet Network | Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization. Moreover, facial expression gives additional distortion on this small and noisy face. In this paper, we propose deformable symmetric Gabor wavelet network face model for ... | ['Seungkyu Lee', 'Sherzod Salokhiddinov'] | 2020-10-30 | null | null | null | null | ['face-model'] | ['computer-vision'] | [-8.43908861e-02 -2.96212763e-01 -1.32975042e-01 -4.12820429e-01
-7.09481835e-02 -4.99649853e-01 2.44639724e-01 -9.29569602e-01
-3.36658955e-01 5.91366768e-01 -1.82388350e-02 4.80786264e-01
-1.03548005e-01 -7.03920901e-01 -2.70802885e-01 -8.34730864e-01
-1.78045943e-01 6.34871200e-02 1.63008600e-01 -3.38920988... | [13.235306739807129, 0.6572482585906982] |
95e98473-cd57-41dc-82ce-d74357d432b3 | pessimistic-bootstrapping-for-uncertainty-1 | 2202.11566 | null | https://arxiv.org/abs/2202.11566v1 | https://arxiv.org/pdf/2202.11566v1.pdf | Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning | Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problem by pen... | ['Zhaoran Wang', 'Peng Liu', 'Animesh Garg', 'Zhihong Deng', 'Zhuoran Yang', 'Lingxiao Wang', 'Chenjia Bai'] | 2022-02-23 | pessimistic-bootstrapping-for-uncertainty | https://openreview.net/forum?id=Y4cs1Z3HnqL | https://openreview.net/pdf?id=Y4cs1Z3HnqL | iclr-2022-4 | ['d4rl'] | ['robots'] | [-2.17143446e-01 4.09405261e-01 -6.33012295e-01 -2.15826273e-01
-9.52772439e-01 -6.96305990e-01 5.39184034e-01 3.22775692e-01
-5.42539835e-01 1.36630940e+00 -2.33903170e-01 -5.87198436e-01
-2.79823840e-01 -7.82213271e-01 -1.10970426e+00 -7.47580826e-01
-2.84057826e-01 6.46038294e-01 1.65984854e-01 1.62566811... | [4.219857215881348, 2.4509871006011963] |
1f3186cb-7908-44a8-b5b3-f6bdcba7ac4f | a-brief-review-of-hypernetworks-in-deep | 2306.06955 | null | https://arxiv.org/abs/2306.06955v1 | https://arxiv.org/pdf/2306.06955v1.pdf | A Brief Review of Hypernetworks in Deep Learning | Hypernetworks, or hypernets in short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, faster training, information sharing, and model compression etc. Hypernets h... | ['David A. Clifton', 'Soheila Molaei', 'Ping Lu', 'Jiandong Zhou', 'Vinod Kumar Chauhan'] | 2023-06-12 | null | null | null | null | ['causal-inference', 'model-compression', 'causal-inference'] | ['knowledge-base', 'methodology', 'miscellaneous'] | [-4.93654460e-02 4.61028904e-01 -2.27225140e-01 -3.91411424e-01
2.46406108e-01 -1.98152333e-01 5.06650269e-01 -2.20233724e-01
-4.76197332e-01 9.94590342e-01 1.01098932e-01 -1.99830696e-01
-6.81946278e-01 -1.05754173e+00 -6.70908511e-01 -8.74285281e-01
-2.65616208e-01 4.01101917e-01 3.20230514e-01 -6.52157441... | [8.586774826049805, 3.2307162284851074] |
32bb771e-36bc-4f1a-922a-dcd6dc5439c1 | learning-functional-distributional-semantics-1 | 2204.10624 | null | https://arxiv.org/abs/2204.10624v1 | https://arxiv.org/pdf/2204.10624v1.pdf | Learning Functional Distributional Semantics with Visual Data | Functional Distributional Semantics is a recently proposed framework for learning distributional semantics that provides linguistic interpretability. It models the meaning of a word as a binary classifier rather than a numerical vector. In this work, we propose a method to train a Functional Distributional Semantics mo... | ['Guy Emerson', 'Yinhong Liu'] | 2022-04-22 | null | https://aclanthology.org/2022.acl-long.275 | https://aclanthology.org/2022.acl-long.275.pdf | acl-2022-5 | ['language-acquisition'] | ['natural-language-processing'] | [ 3.62019956e-01 3.13676566e-01 -4.18540627e-01 -8.15457523e-01
-1.38819188e-01 -9.21167850e-01 7.16116369e-01 8.16851735e-01
-5.37840366e-01 5.20435750e-01 8.15681100e-01 -6.12185001e-01
1.01934165e-01 -6.56295776e-01 -7.54890978e-01 -2.54749745e-01
1.20457329e-01 5.95980763e-01 1.12949554e-02 -2.41946205... | [10.571187019348145, 2.2932794094085693] |
bb4a6f60-12bc-4b25-b5b5-f5e1713f75c4 | fast-and-high-quality-singing-voice-synthesis | 1910.11690 | null | https://arxiv.org/abs/1910.11690v2 | https://arxiv.org/pdf/1910.11690v2.pdf | Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks | The present paper describes singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. As singing voices represent a rich form of expression, a p... | ['Keiichi Tokuda', 'Yoshihiko Nankaku', 'Keiichiro Oura', 'Kei Hashimoto', 'Kazuhiro Nakamura', 'Shinji Takaki'] | 2019-10-24 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.45313561e-01 -3.52310181e-01 8.05506632e-02 -2.10178327e-02
-2.78065890e-01 -4.66843992e-01 1.04359657e-01 -8.43508542e-01
-9.59764943e-02 4.43614542e-01 2.15489626e-01 6.70827627e-02
1.73078135e-01 -7.30295658e-01 -5.50923169e-01 -7.26727486e-01
2.03973223e-02 -9.60941166e-02 2.17795577e-02 -3.85988057... | [15.543153762817383, 6.185049057006836] |
21079fbf-459a-4574-9dda-938112f57cff | a-lightweight-instrument-agnostic-model-for | 2203.09893 | null | https://arxiv.org/abs/2203.09893v2 | https://arxiv.org/pdf/2203.09893v2.pdf | A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation | Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results over instrument-agnost... | ['Sebastian Ewert', 'Gabriel Meseguer-Brocal', 'David Rubinstein', 'Juan José Bosch', 'Rachel M. Bittner'] | 2022-03-18 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.04372692e-01 -3.95671666e-01 -1.81067392e-01 -6.19333461e-02
-1.25368416e+00 -8.33597481e-01 2.18221724e-01 -1.84613734e-01
-2.42070019e-01 2.99548507e-01 1.25811696e-01 1.09970868e-01
8.71959925e-02 -2.17416272e-01 -3.79142642e-01 -5.54403007e-01
7.42959278e-03 -3.28079164e-02 -1.18921988e-03 -5.42973690... | [15.78536605834961, 5.376358985900879] |
2aeb2598-4b9c-4ec3-878c-c0e0ef95ea77 | real-time-human-centric-segmentation-for | 2108.07199 | null | https://arxiv.org/abs/2108.07199v1 | https://arxiv.org/pdf/2108.07199v1.pdf | Real-time Human-Centric Segmentation for Complex Video Scenes | Most existing video tasks related to "human" focus on the segmentation of salient humans, ignoring the unspecified others in the video. Few studies have focused on segmenting and tracking all humans in a complex video, including pedestrians and humans of other states (e.g., seated, riding, or occluded). In this paper, ... | ['Yujiu Yang', 'Haoqian Wang', 'Xinyuan Zhao', 'Weihao Xia', 'Chenyu Tian', 'Ran Yu'] | 2021-08-16 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-2.29643732e-02 -1.49918497e-01 -1.76765040e-01 -3.00883174e-01
-3.09037745e-01 -2.93286115e-01 3.57756108e-01 -8.95743594e-02
-7.09274411e-01 6.94294035e-01 -1.56452522e-01 3.98505367e-02
4.08201993e-01 -5.99409461e-01 -8.85500073e-01 -6.05483234e-01
1.11427672e-01 4.71585006e-01 8.53422463e-01 3.43973823... | [8.251976013183594, -0.42179787158966064] |
c5f3e1c2-3cb8-47e9-835a-bb56da9785f0 | instant-one-shot-word-learning-for-context | 2107.02268 | null | https://arxiv.org/abs/2107.02268v1 | https://arxiv.org/pdf/2107.02268v1.pdf | Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition | Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded characters, these systems are in principal open vocabulary systems. In practice, however, they often fail to recognize words not seen during tra... | ['Alexander Waibel', 'Sebastian Stüker', 'Juan Hussain', 'Christian Huber'] | 2021-07-05 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 4.63405907e-01 -5.01501001e-02 3.15209366e-02 -2.66085267e-01
-1.06123948e+00 -7.85618424e-01 5.84753096e-01 4.22573328e-01
-8.27360034e-01 6.50140226e-01 1.68005899e-01 -9.01116133e-01
6.12878382e-01 -5.62804103e-01 -7.05405653e-01 -3.49940568e-01
2.28914723e-01 5.47668636e-01 2.33413652e-01 -4.90141124... | [14.284862518310547, 6.835782051086426] |
a1231e75-93c3-4548-8c81-90173cec8273 | flycap-markerless-motion-capture-using | 1610.09534 | null | http://arxiv.org/abs/1610.09534v3 | http://arxiv.org/pdf/1610.09534v3.pdf | FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras | Aiming at automatic, convenient and non-instrusive motion capture, this paper
presents a new generation markerless motion capture technique, the FlyCap
system, to capture surface motions of moving characters using multiple
autonomous flying cameras (autonomous unmanned aerial vehicles(UAV) each
integrated with an RGBD ... | ['Wei Cheng', 'Lu Fang', 'Kaiwen Guo', 'Lan Xu', 'Guyue Zhou', 'Yebin Liu', 'Qionghai Dai'] | 2016-10-29 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 2.11733043e-01 -3.53582621e-01 5.34989275e-02 1.77530020e-01
-4.08240825e-01 -1.09527314e+00 4.28806484e-01 -6.92546844e-01
-5.88818789e-01 3.03024441e-01 -4.01161760e-01 3.48073065e-01
8.93019512e-02 -2.60313064e-01 -7.17043161e-01 -4.63739574e-01
1.46842688e-01 3.89879286e-01 7.42925525e-01 -4.35917191... | [7.297806262969971, -1.374818205833435] |
308d49a0-87b0-4e2f-9e12-a0260ed5c2d9 | llmscore-unveiling-the-power-of-large | 2305.11116 | null | https://arxiv.org/abs/2305.11116v1 | https://arxiv.org/pdf/2305.11116v1.pdf | LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation | Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor correlation with human judgments. In this work, we propose LLMScore, a new framework that offers evaluation scores with multi-granularity c... | ['William Yang Wang', 'Xin Eric Wang', 'Xiujun Li', 'Xianjun Yang', 'Yujie Lu'] | 2023-05-18 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 5.00533283e-01 -1.78632453e-01 -1.82898238e-01 -3.37236017e-01
-9.67689395e-01 -6.31024837e-01 9.07966912e-01 2.37081528e-01
-1.51640236e-01 2.02510700e-01 1.69260949e-01 -1.85657144e-01
1.14648826e-01 -5.98379076e-01 -6.92972898e-01 -3.01687270e-01
4.72498238e-01 2.43122831e-01 4.26613718e-01 -1.67558957... | [11.132686614990234, 0.9965345859527588] |
9b849a7e-50de-4ec1-9981-6b92ab3334a2 | prior-knowledge-and-memory-enriched | null | null | https://aclanthology.org/2022.findings-acl.297 | https://aclanthology.org/2022.findings-acl.297.pdf | Prior Knowledge and Memory Enriched Transformer for Sign Language Translation | This paper attacks the challenging problem of sign language translation (SLT), which involves not only visual and textual understanding but also additional prior knowledge learning (i.e. performing style, syntax). However, the majority of existing methods with vanilla encoder-decoder structures fail to sufficiently exp... | ['Xingshan Zeng', 'Meng Zhang', 'Zhou Zhao', 'Tao Jin'] | null | null | null | null | findings-acl-2022-5 | ['sign-language-translation'] | ['computer-vision'] | [ 4.25092131e-01 -4.51116979e-01 -3.49974990e-01 -3.60634506e-01
-9.12780404e-01 -5.53798914e-01 5.49204409e-01 -6.73126996e-01
-3.75619024e-01 4.97239143e-01 9.45315719e-01 -2.54163414e-01
4.00864244e-01 -4.02113199e-01 -7.39038348e-01 -6.22883558e-01
2.97513574e-01 -2.21400559e-02 -2.09637910e-01 -2.78548837... | [9.210939407348633, -6.512601375579834] |
03712ae6-903c-4f9c-9ae4-d49e082e8dce | audio-visual-speech-recognition-using-deep | 1611.02879 | null | http://arxiv.org/abs/1611.02879v1 | http://arxiv.org/pdf/1611.02879v1.pdf | Audio Visual Speech Recognition using Deep Recurrent Neural Networks | In this work, we propose a training algorithm for an audio-visual automatic
speech recognition (AV-ASR) system using deep recurrent neural network
(RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal
Classification (CTC) objective function. The frame labels obtained from the
acoustic model are ... | ['Abhinav Thanda', 'Shankar M Venkatesan'] | 2016-11-09 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 3.65423471e-01 -2.55188107e-01 3.58234048e-01 -3.72442335e-01
-1.10840499e+00 -3.23293388e-01 7.52309799e-01 -1.49763748e-01
-6.60858393e-01 4.09391224e-01 4.61484224e-01 -3.89347434e-01
3.66831034e-01 -1.28186718e-01 -4.91805464e-01 -8.53387654e-01
3.66173297e-01 9.00856629e-02 -1.12927154e-01 -1.45100296... | [14.358404159545898, 5.176866054534912] |
9c9a1ee2-1fc0-43c2-9b6f-1279ea39b529 | methods-for-the-frugal-labeler-multi-class | null | null | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0263656 | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0263656&type=printable | Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels | Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical i... | ['Markus Reischl', 'Christian Pylatiuk', 'Luca Rettenberger', 'Mark Schutera'] | 2022-02-08 | null | null | null | plos-one-2022-2 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 7.28612661e-01 1.72828272e-01 -2.75024980e-01 -5.63124478e-01
-8.83221567e-01 -5.78882277e-01 -1.14626102e-02 3.81031781e-01
-7.82282591e-01 7.58119047e-01 -4.33543444e-01 -2.91512161e-02
5.77842109e-02 -6.84434772e-01 -6.70309424e-01 -9.52413023e-01
2.25376651e-01 9.92747188e-01 -5.67409918e-02 3.92425925... | [14.70792007446289, -2.3366856575012207] |
67653c00-677f-44f5-8b32-aad26062f138 | hdpv-slam-hybrid-depth-augmented-panoramic | 2301.11823 | null | https://arxiv.org/abs/2301.11823v3 | https://arxiv.org/pdf/2301.11823v3.pdf | HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera | This paper proposes a novel visual simultaneous localization and mapping (SLAM) system called Hybrid Depth-augmented Panoramic Visual SLAM (HDPV-SLAM), that employs a panoramic camera and a tilted multi-beam LiDAR scanner to generate accurate and metrically-scaled trajectories. RGB-D SLAM was the design basis for HDPV-... | ['Yujia Zhang', 'Gunho Sohn', 'Mohammad Moein Sheikholeslami', 'Zahra Arjmandi', 'Amin Alizadeh Naeini', 'Mostafa Ahmadi'] | 2023-01-27 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-8.19171667e-02 -3.44812900e-01 -3.13614495e-02 -3.33819002e-01
-7.16334164e-01 -1.98499739e-01 5.66451907e-01 -1.85700595e-01
-7.12112904e-01 8.30322623e-01 -1.85597286e-01 -1.71413541e-01
-1.77743912e-01 -8.65365744e-01 -5.24224043e-01 -4.77295756e-01
6.60067275e-02 7.87660718e-01 2.58925825e-01 -1.74952537... | [7.519136428833008, -2.247694969177246] |
bd00fd62-2c7d-4b8b-b730-40a0b20c0fe1 | towards-goal-feasibility-and-diversity | 2206.07170 | null | https://arxiv.org/abs/2206.07170v1 | https://arxiv.org/pdf/2206.07170v1.pdf | Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design | Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimize statistical divergence between the distribution over generated data and distribution over the data... | ['Faez Ahmed', 'Lyle Regenwetter'] | 2022-06-14 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 7.14957789e-02 1.41841725e-01 -1.70187056e-01 -5.33384025e-01
-8.08726370e-01 -4.55077916e-01 5.87622583e-01 -1.81963608e-01
3.79999250e-01 8.31473470e-01 2.14678437e-01 -2.05711387e-02
-3.64973545e-01 -9.20434952e-01 -6.46300316e-01 -6.31131709e-01
4.05441433e-01 8.44900191e-01 -5.71259201e-01 -2.90670961... | [5.813911437988281, 3.305405855178833] |
f99ab37a-708b-47d2-9dae-57d4d4a57f8d | identity-aware-multi-sentence-video | 2008.09791 | null | https://arxiv.org/abs/2008.09791v1 | https://arxiv.org/pdf/2008.09791v1.pdf | Identity-Aware Multi-Sentence Video Description | Standard video and movie description tasks abstract away from person identities, thus failing to link identities across sentences. We propose a multi-sentence Identity-Aware Video Description task, which overcomes this limitation and requires to re-identify persons locally within a set of consecutive clips. We introduc... | ['Jae Sung Park', 'Anna Rohrbach', 'Trevor Darrell'] | 2020-08-22 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3739_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660358.pdf | eccv-2020-8 | ['video-description', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [ 1.42182216e-01 -1.43627360e-01 -8.69259238e-02 -6.99114561e-01
-1.09132588e+00 -7.48967767e-01 9.72356081e-01 1.94102779e-01
-3.50535452e-01 6.52310491e-01 5.78267753e-01 5.27161598e-01
1.92849800e-01 -4.13201600e-01 -6.67192578e-01 -3.38411450e-01
1.21360011e-01 7.73457587e-01 1.00715578e-01 -5.84910028... | [14.505143165588379, 0.9507742524147034] |
d3685ea8-780b-4fca-a727-8500c1e35c4c | rethinking-and-designing-a-high-performing | 2011.14936 | null | https://arxiv.org/abs/2011.14936v2 | https://arxiv.org/pdf/2011.14936v2.pdf | Rethinking and Designing a High-performing Automatic License Plate Recognition Approach | In this paper, we propose a real-time and accurate automatic license plate recognition (ALPR) approach. Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should... | ['Lap-Pui Chau', 'Yunhao Zhou', 'Zhen-Peng Bian', 'Yi Wang'] | 2020-11-30 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [ 1.54294744e-01 -6.09792113e-01 -8.53484794e-02 -2.61337936e-01
-7.56385028e-01 -5.78561246e-01 1.26543447e-01 -5.08474886e-01
-3.44473839e-01 3.74699980e-01 -4.07507181e-01 -4.09803033e-01
1.15162194e-01 -1.01188457e+00 -9.65662539e-01 -5.62826991e-01
5.27650833e-01 1.34617344e-01 6.17266655e-01 -3.33475888... | [9.849594116210938, -4.92580509185791] |
261151b1-7d76-4499-8b16-035b4000f9c3 | images-speak-in-images-a-generalist-painter | 2212.02499 | null | https://arxiv.org/abs/2212.02499v2 | https://arxiv.org/pdf/2212.02499v2.pdf | Images Speak in Images: A Generalist Painter for In-Context Visual Learning | In-context learning, as a new paradigm in NLP, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. But in computer vision, the difficulties for in-context learning lie in that tasks vary significantly in the output representations, thus it is unclear how to define the general... | ['Tiejun Huang', 'Chunhua Shen', 'Yue Cao', 'Wen Wang', 'Xinlong Wang'] | 2022-12-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Images_Speak_in_Images_A_Generalist_Painter_for_In-Context_Visual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Images_Speak_in_Images_A_Generalist_Painter_for_In-Context_Visual_CVPR_2023_paper.pdf | cvpr-2023-1 | ['keypoint-detection', 'personalized-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.39018655e-01 -7.35483319e-02 4.33182865e-02 -4.21201676e-01
-4.25124943e-01 -6.42992258e-01 1.01311553e+00 -2.08245099e-01
-5.37632048e-01 4.63550597e-01 -1.90870315e-01 -4.06726092e-01
3.77893671e-02 -5.00934422e-01 -9.57520962e-01 -7.90034294e-01
5.16003907e-01 3.14268202e-01 1.95257545e-01 -5.98274171... | [10.229183197021484, 1.6889493465423584] |
731f55c6-5e9b-46cb-9104-1ec3dd60e0a5 | justdeep-at-nlp4if-2019-task-1-propaganda | null | null | https://aclanthology.org/D19-5016 | https://aclanthology.org/D19-5016.pdf | JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models | The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining {``}fake news{''} is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead ... | ['Hani Al-Omari', 'Samira Shaikh', 'Ola AlTiti', 'Malak Abdullah'] | 2019-11-01 | null | null | null | ws-2019-11 | ['logical-fallacies', 'propaganda-detection'] | ['miscellaneous', 'natural-language-processing'] | [-9.94720832e-02 2.69228876e-01 -4.22496438e-01 -1.06974654e-01
-5.32154620e-01 -4.05043542e-01 1.05499208e+00 4.70526695e-01
-2.55606800e-01 1.00934863e+00 7.11588442e-01 -5.71211994e-01
5.60632348e-01 -8.02424848e-01 -9.23170686e-01 -4.31261778e-01
5.25603235e-01 2.30654955e-01 -8.72398093e-02 -5.73286414... | [8.247309684753418, 10.343926429748535] |
7508ae93-0ae9-476f-a318-e8f3037e505a | poet-a-self-learning-framework-for-profinet | 2305.03175 | null | https://arxiv.org/abs/2305.03175v1 | https://arxiv.org/pdf/2305.03175v1.pdf | POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour | Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological ... | ['Jürgen Beyerer', 'Christian Haas', 'Markus Karch', 'Ankush Meshram'] | 2023-04-29 | null | null | null | null | ['network-intrusion-detection', 'self-learning'] | ['miscellaneous', 'natural-language-processing'] | [ 6.34982407e-01 2.04890408e-02 3.09419185e-02 2.07418859e-01
2.30998993e-01 -1.05386031e+00 6.22336268e-01 2.33458742e-01
2.89964497e-01 2.79559076e-01 -9.33528066e-01 -1.13876081e+00
-5.71301401e-01 -9.81606960e-01 -3.93476903e-01 -3.60226899e-01
-4.06069875e-01 4.21446830e-01 5.87117910e-01 3.29866409... | [5.34089469909668, 7.137630462646484] |
7d7368e2-1b8f-4722-b47e-af48530f26ba | 6d-dynamic-camera-relocalization-from-single | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Feng_6D_Dynamic_Camera_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Feng_6D_Dynamic_Camera_CVPR_2016_paper.pdf | 6D Dynamic Camera Relocalization From Single Reference Image | Dynamic relocalization of 6D camera pose from single reference image is a costly and challenging task that requires delicate hand-eye calibration and precision positioning platform to do 3D mechanical rotation and translation. In this paper, we show that high-quality camera relocalization can be achieved in a much less... | ['Qian Zhang', 'Fei-Peng Tian', 'Jizhou Sun', 'Wei Feng'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['camera-relocalization'] | ['computer-vision'] | [ 1.56140849e-01 -3.75515455e-03 -2.39107460e-01 3.77205983e-02
-5.05104780e-01 -9.59456086e-01 2.50806063e-01 -5.90928137e-01
-5.44159055e-01 5.84131718e-01 -3.09158683e-01 -2.78219432e-01
-2.18131185e-01 1.26733676e-01 -9.37618911e-01 -7.95707107e-01
7.07943439e-01 2.49829277e-01 1.76018223e-01 2.35038847... | [7.903773307800293, -2.2010228633880615] |
c3bfbfc6-13e7-4be6-aa1f-b30cca690c88 | towards-interactive-language-modeling-1 | null | null | https://openreview.net/forum?id=vD5JzgHTt9Q | https://openreview.net/pdf?id=vD5JzgHTt9Q | Towards Interactive Language Modeling | Interaction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction plays little to no role in artificial language modeling---which also targets the acquisition of human language, yet by artificial models. Mo... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['language-acquisition'] | ['natural-language-processing'] | [ 5.88560477e-02 1.06842160e+00 -5.12971468e-02 -5.47899127e-01
-1.65986598e-01 -3.70005012e-01 7.29357421e-01 4.48737949e-01
-3.09498012e-01 4.73276883e-01 3.37323368e-01 -7.13921726e-01
7.45044053e-02 -7.77677417e-01 -4.74055022e-01 2.51574162e-02
-7.89984912e-02 5.31147718e-01 1.14966318e-01 -2.95871556... | [10.390406608581543, 8.772350311279297] |
5e49aca9-f879-42c5-a354-d9db789439de | single-image-dehazing-via-combining-the-prior | 2111.05701 | null | https://arxiv.org/abs/2111.05701v2 | https://arxiv.org/pdf/2111.05701v2.pdf | Single image dehazing via combining the prior knowledge and CNNs | Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge an... | ['Wangming Xu', 'Shiqian Wu', 'Chaobing Zheng', 'Yuwen Li'] | 2021-11-10 | null | null | null | null | ['image-dehazing', 'single-image-haze-removal'] | ['computer-vision', 'computer-vision'] | [ 3.04795653e-01 -2.34898061e-01 7.03582287e-01 -2.72609830e-01
-2.61965871e-01 1.53102249e-01 -1.02271236e-01 -4.97601300e-01
-2.11131454e-01 4.85909700e-01 1.74987718e-01 -7.75609724e-03
-3.57696302e-02 -1.02014744e+00 -4.93730187e-01 -1.39082587e+00
2.60147959e-01 -2.96532899e-01 5.22928536e-01 -3.29996198... | [10.885265350341797, -3.1652872562408447] |
907c7e1c-418d-4185-90b9-2b85335a4d6a | multimodality-multi-lead-ecg-arrhythmia | 2210.06297 | null | https://arxiv.org/abs/2210.06297v1 | https://arxiv.org/pdf/2210.06297v1.pdf | Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning | Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploi... | ['Ngan Le', 'Morten Olgaard Jensen', 'Jingxian Wu', 'Donald Adjeroh', 'Patel Brijesh', 'Duc Le', 'Thinh Phan'] | 2022-09-30 | null | null | null | null | ['self-knowledge-distillation', 'ecg-classification'] | ['computer-vision', 'medical'] | [ 4.25625771e-01 -1.25653833e-01 -3.88378315e-02 -4.91740972e-01
-8.54462445e-01 -4.29112613e-01 3.40690196e-01 5.40995836e-01
-2.51622021e-01 7.97231734e-01 1.62015989e-01 -3.35425317e-01
-5.06741345e-01 -5.81274867e-01 -1.55820489e-01 -7.19888985e-01
-2.13022381e-01 1.12688541e-03 -3.85841012e-01 -6.69749230... | [14.234299659729004, 3.2613325119018555] |
0944894a-6286-422b-8daa-7e7fad576700 | end-to-end-neural-sentence-ordering-using | 1611.04953 | null | http://arxiv.org/abs/1611.04953v2 | http://arxiv.org/pdf/1611.04953v2.pdf | End-to-End Neural Sentence Ordering Using Pointer Network | Sentence ordering is one of important tasks in NLP. Previous works mainly
focused on improving its performance by using pair-wise strategy. However, it
is nontrivial for pair-wise models to incorporate the contextual sentence
information. In addition, error prorogation could be introduced by using the
pipeline strategy... | ['Xinchi Chen', 'Xuanjing Huang', 'Xipeng Qiu', 'Jingjing Gong'] | 2016-11-15 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.01454565e-01 5.52786700e-02 2.09955111e-01 -8.44785810e-01
-6.81600511e-01 -4.14752185e-01 1.62315313e-02 2.54540414e-01
-6.11743033e-01 6.34472847e-01 5.21127462e-01 -4.11205173e-01
-1.07860319e-01 -6.28354847e-01 -5.74056387e-01 -1.83812425e-01
1.43161923e-01 2.17497632e-01 3.36237252e-01 -3.26083511... | [10.827478408813477, 8.979944229125977] |
5a56916e-67b7-4704-bfa8-3b5e9d9588a6 | do-vision-language-pretrained-models-learn | 2203.17271 | null | https://arxiv.org/abs/2203.17271v3 | https://arxiv.org/pdf/2203.17271v3.pdf | Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? | Vision-language (VL) pretrained models have achieved impressive performance on multimodal reasoning and zero-shot recognition tasks. Many of these VL models are pretrained on unlabeled image and caption pairs from the internet. In this paper, we study whether representations of primitive concepts--such as colors, shape... | ['Chen Sun', 'Ellie Pavlick', 'Usha Bhalla', 'Tian Yun'] | 2022-03-31 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.60698736e-01 2.10976839e-01 -1.43303066e-01 -4.89437580e-01
-2.94569165e-01 -8.28708470e-01 9.61897552e-01 7.13156611e-02
-3.68329763e-01 4.99549299e-01 1.73550725e-01 -3.56625468e-01
5.20511940e-02 -7.68238425e-01 -1.04428577e+00 -5.04967391e-01
8.03012326e-02 7.72990823e-01 -9.16598961e-02 -2.62614489... | [10.347854614257812, 1.9953994750976562] |
ed290824-5a95-411f-b6a1-1b8b87c9c075 | out-of-the-box-embodied-navigation-in-the | 2105.05873 | null | https://arxiv.org/abs/2105.05873v1 | https://arxiv.org/pdf/2105.05873v1.pdf | Out of the Box: Embodied Navigation in the Real World | The research field of Embodied AI has witnessed substantial progress in visual navigation and exploration thanks to powerful simulating platforms and the availability of 3D data of indoor and photorealistic environments. These two factors have opened the doors to a new generation of intelligent agents capable of achiev... | ['Rita Cucchiara', 'Lorenzo Baraldi', 'Silvia Cascianelli', 'Marcella Cornia', 'Federico Landi', 'Roberto Bigazzi'] | 2021-05-12 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-3.01765770e-01 2.22431913e-01 3.47766161e-01 -2.65845537e-01
-1.74537057e-03 -7.99930334e-01 8.01264346e-01 -2.42814094e-01
-8.33807588e-01 8.09890330e-01 -1.19620219e-01 -4.53700781e-01
1.35345116e-01 -8.55634093e-01 -9.11688328e-01 -5.99566996e-01
-4.64418292e-01 7.58791447e-01 4.85155284e-01 -6.65810227... | [4.675886631011963, 0.6931507587432861] |
2f949901-6230-4b61-b3f9-2ad15c415788 | masked-video-distillation-rethinking-masked | 2212.04500 | null | https://arxiv.org/abs/2212.04500v2 | https://arxiv.org/pdf/2212.04500v2.pdf | Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning | Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations from scratch through reconstructing low-level features like raw pixel RGB values. In this paper, we propose masked video distillation (MVD),... | ['Yu-Gang Jiang', 'Lu Yuan', 'Mengchen Liu', 'Xiyang Dai', 'Yinpeng Chen', 'Zuxuan Wu', 'Dongdong Chen', 'Rui Wang'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Masked_Video_Distillation_Rethinking_Masked_Feature_Modeling_for_Self-Supervised_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Masked_Video_Distillation_Rethinking_Masked_Feature_Modeling_for_Self-Supervised_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.04097696e-02 5.28951846e-02 -3.93385977e-01 -3.07143748e-01
-1.01592493e+00 -4.30872202e-01 5.56802273e-01 -1.36341542e-01
-2.66902715e-01 3.44862670e-01 1.71170816e-01 -2.49798745e-01
7.25793689e-02 -4.97117758e-01 -1.30769944e+00 -7.76072383e-01
-1.89444143e-02 -7.13432133e-02 3.66649359e-01 -1.05205335... | [9.354334831237793, 0.8955991864204407] |
5cb7cd3e-1900-4b48-925d-d98a8fd52d7b | outpainting-by-queries | 2207.05312 | null | https://arxiv.org/abs/2207.05312v1 | https://arxiv.org/pdf/2207.05312v1.pdf | Outpainting by Queries | Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent inductive biases to achieve effective sample learning, which may degrade the performance ceiling. In this paper, motivated by the flexible ... | ['Rui Zhang', 'Jie Sun', 'Kaizhu Huang', 'Xi Yang', 'Penglei Gao', 'Kai Yao'] | 2022-07-12 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 5.92172563e-01 2.51683623e-01 -9.82315913e-02 -2.48152375e-01
-8.33273172e-01 -2.65007224e-02 4.55291688e-01 -3.66981775e-01
-2.01895088e-01 6.42364442e-01 2.14702800e-01 -1.13303833e-01
1.38783738e-01 -8.57584178e-01 -1.28210223e+00 -5.51628709e-01
5.29090166e-01 -1.02205679e-01 1.55474544e-01 -1.81851447... | [11.294840812683105, -1.128976583480835] |
ddd11eda-9de6-4670-87c1-1e9c32e6f8be | transformers-meet-directed-graphs | 2302.00049 | null | https://arxiv.org/abs/2302.00049v2 | https://arxiv.org/pdf/2302.00049v2.pdf | Transformers Meet Directed Graphs | Transformers were originally proposed as a sequence-to-sequence model for text but have become vital for a wide range of modalities, including images, audio, video, and undirected graphs. However, transformers for directed graphs are a surprisingly underexplored topic, despite their applicability to ubiquitous domains,... | ['Cosmin Paduraru', 'Stephan Günnemann', 'Ali Taylan Cemgil', 'Daniel Mankowitz', 'Yujia Li', 'Simon Geisler'] | 2023-01-31 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 5.45140505e-01 1.67854801e-01 -4.89616990e-01 -1.96918696e-01
-3.61618638e-01 -1.01002681e+00 3.52740705e-01 5.25145233e-01
3.21446359e-01 4.62678462e-01 3.39214861e-01 -1.06177938e+00
-3.22244108e-01 -9.36923444e-01 -9.05441284e-01 -4.34053987e-01
-5.30455351e-01 3.65615487e-01 5.42504072e-01 -3.39857072... | [6.94898796081543, 6.211045265197754] |
2c2ccbe9-3d58-4bbf-a11e-ada4bebe83d4 | humans-can-decipher-adversarial-images | 1809.04120 | null | http://arxiv.org/abs/1809.04120v3 | http://arxiv.org/pdf/1809.04120v3.pdf | Humans can decipher adversarial images | How similar is the human mind to the sophisticated machine-learning systems
that mirror its performance? Models of object categorization based on
convolutional neural networks (CNNs) have achieved human-level benchmarks in
assigning known labels to novel images. These advances promise to support
transformative technolo... | ['Chaz Firestone', 'Zhenglong Zhou'] | 2018-09-11 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.86548072e-01 2.85535157e-01 1.13069698e-01 -5.00536561e-01
-1.57101810e-01 -1.05453205e+00 8.52003634e-01 -7.09170103e-02
-7.89028645e-01 4.36755329e-01 -1.50284529e-01 -5.50418794e-01
2.81785488e-01 -6.11781418e-01 -8.24211001e-01 -6.01544559e-01
1.78178668e-01 4.65990186e-01 1.61093056e-01 -2.42767990... | [10.032305717468262, 2.3425228595733643] |
8356560a-b081-41a9-8709-69203ab81b7c | self-supervised-video-centralised-transformer | 2203.13166 | null | https://arxiv.org/abs/2203.13166v4 | https://arxiv.org/pdf/2203.13166v4.pdf | Self-supervised Video-centralised Transformer for Video Face Clustering | This paper presents a novel method for face clustering in videos using a video-centralised transformer. Previous works often employed contrastive learning to learn frame-level representation and used average pooling to aggregate the features along the temporal dimension. This approach may not fully capture the complica... | ['Maja Pantic', 'Stavros Petridis', 'Pingchuan Ma', 'Yiming Lin', 'Yiming Luo', 'Jie Shen', 'Mingzhi Dong', 'Yujiang Wang'] | 2022-03-24 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-2.01530173e-01 -3.86204273e-01 1.41275860e-02 -5.21090329e-01
-4.64226097e-01 -1.89493895e-01 6.69380844e-01 -6.07191443e-01
-1.17787592e-01 2.72145808e-01 2.63042122e-01 3.72447789e-01
-3.91343683e-01 -3.59417021e-01 -8.58674228e-01 -1.02214956e+00
-5.30430317e-01 3.67060304e-01 4.06145081e-02 -4.15542312... | [13.338793754577637, 1.171366572380066] |
3eb05c1b-565f-41a7-906e-56291440ef8f | generalization-and-robustness-implications-in | 2107.00637 | null | https://arxiv.org/abs/2107.00637v3 | https://arxiv.org/pdf/2107.00637v3.pdf | Generalization and Robustness Implications in Object-Centric Learning | The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be injected into neural networks to potentially improve systematic generalization and performance of downs... | ['Francesco Locatello', 'Ole Winther', 'Bernhard Schölkopf', 'Michele De Vita', 'Samuele Papa', 'Andrea Dittadi'] | 2021-07-01 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 6.01607442e-01 -1.91595733e-01 -1.05030172e-01 -5.45008779e-01
-3.53144407e-01 -8.88283908e-01 6.39266551e-01 3.30965638e-01
-3.82933348e-01 4.36476380e-01 2.15758666e-01 -1.05376847e-01
-2.82736659e-01 -7.21171141e-01 -1.12122035e+00 -9.06557322e-01
1.09684743e-01 5.35156727e-01 4.36557263e-01 -7.14028999... | [9.625937461853027, 1.8888870477676392] |
3b5aeab2-cd78-49f7-953a-e60ad1d0730d | synthetic-pseudo-anomalies-for-unsupervised | 2303.05112 | null | https://arxiv.org/abs/2303.05112v1 | https://arxiv.org/pdf/2303.05112v1.pdf | Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder | Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well wh... | ['Zhiqiang Wu', 'Lvdong Chen', 'Chenxing Gao', 'Caidan Zhao', 'Xiangyu Huang'] | 2023-03-09 | null | null | null | null | ['video-anomaly-detection', 'one-class-classification'] | ['computer-vision', 'miscellaneous'] | [ 3.97107005e-01 -2.52761722e-01 -9.20276567e-02 -8.65371674e-02
-2.22411558e-01 -3.16766441e-01 4.24397975e-01 -3.39974910e-02
-1.71699479e-01 3.87586474e-01 8.88418853e-02 -9.96108502e-02
2.43340731e-01 -7.27642059e-01 -8.47682238e-01 -9.48285878e-01
-1.81613445e-01 -2.41588160e-01 -1.56723894e-02 -2.25471389... | [7.666881561279297, 2.0637166500091553] |
f48711fd-c122-4a80-9a0a-f453be3616c7 | valhalla-visual-hallucination-for-machine | 2206.00100 | null | https://arxiv.org/abs/2206.00100v1 | https://arxiv.org/pdf/2206.00100v1.pdf | VALHALLA: Visual Hallucination for Machine Translation | Designing better machine translation systems by considering auxiliary inputs such as images has attracted much attention in recent years. While existing methods show promising performance over the conventional text-only translation systems, they typically require paired text and image as input during inference, which l... | ['Nuno Vasconcelos', 'David Cox', 'Rogerio Feris', 'Chen', 'Chun-Fu', 'Yoon Kim', 'Rameswar Panda', 'Yi Li'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_VALHALLA_Visual_Hallucination_for_Machine_Translation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_VALHALLA_Visual_Hallucination_for_Machine_Translation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.68616283e-01 1.41136616e-01 -2.64325857e-01 -3.47175837e-01
-1.07297242e+00 -3.84235620e-01 9.93811190e-01 -4.32535827e-01
-9.03225467e-02 7.33027160e-01 2.62909591e-01 -4.41102594e-01
7.70525455e-01 -4.96575892e-01 -1.03548717e+00 -5.67404926e-01
6.64487362e-01 5.92115700e-01 -3.34956408e-01 -9.93198380... | [11.43542194366455, 1.4557660818099976] |
1ba5d297-e4ea-4dda-bdfb-2a23dcde91e0 | improving-robustness-of-jet-tagging | 2203.13890 | null | https://arxiv.org/abs/2203.13890v2 | https://arxiv.org/pdf/2203.13890v2.pdf | Improving Robustness of Jet Tagging Algorithms with Adversarial Training | Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are r... | ['Alexander Schmidt', 'Andrzej Novak', 'Spandan Mondal', 'Xavier Coubez', 'Annika Stein'] | 2022-03-25 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-5.42712733e-02 -3.59695405e-01 -7.52529502e-02 -4.40099478e-01
-8.28612328e-01 -1.10559726e+00 7.79384434e-01 4.54796076e-01
-3.52691978e-01 6.19627774e-01 -2.11307049e-01 -5.84936798e-01
-3.44342403e-02 -9.24417734e-01 -9.49804485e-01 -8.71225595e-01
1.61192231e-02 5.32206953e-01 3.74975294e-01 -2.53880441... | [15.684991836547852, 2.925086498260498] |
d1795b0d-70b8-4417-9c97-56cf68eea750 | soccer-line-mark-segmentation-with-stochastic | 2108.06432 | null | https://arxiv.org/abs/2108.06432v2 | https://arxiv.org/pdf/2108.06432v2.pdf | Soccer line mark segmentation and classification with stochastic watershed transform | Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detecti... | ['Narciso García', 'Carlos Cuevas', 'Daniel Berjón'] | 2021-08-14 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.55571178e-01 -1.72514662e-01 2.11760670e-01 4.30284888e-02
-3.92305940e-01 -7.23577261e-01 5.85507691e-01 4.90483314e-01
-5.60882568e-01 5.62902153e-01 -2.83663094e-01 -9.48933661e-02
2.37955227e-02 -8.16259563e-01 -7.25535691e-01 -2.74401098e-01
-6.95132688e-02 6.33029699e-01 9.28944767e-01 -5.62927783... | [8.14599895477295, -1.5667246580123901] |
5f3f6ebf-1df1-4335-b1a8-4efad34319bf | subtask-dominated-transfer-learning-for-long | 2112.00527 | null | https://arxiv.org/abs/2112.00527v1 | https://arxiv.org/pdf/2112.00527v1.pdf | Subtask-dominated Transfer Learning for Long-tail Person Search | Person search unifies person detection and person re-identification (Re-ID) to locate query persons from the panoramic gallery images. One major challenge comes from the imbalanced long-tail person identity distributions, which prevents the one-step person search model from learning discriminative person features for t... | ['Shibao Zheng', 'Qin Zhou', 'Hua Yang', 'Chuang Liu'] | 2021-12-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.20011546e-01 -5.23390889e-01 -5.36131151e-02 -3.77399027e-01
-9.74061131e-01 -3.39825720e-01 5.51258266e-01 -4.09705400e-01
-9.11168337e-01 5.20021319e-01 2.04046398e-01 2.16230527e-01
-1.54712006e-01 -5.83397985e-01 -4.70781267e-01 -6.28302336e-01
5.85972428e-01 7.37018943e-01 1.19770728e-01 8.80861878... | [14.833560943603516, 0.7984473705291748] |
b3c9735f-89b7-43da-9e30-17fcc077e255 | model-aware-gesture-to-gesture-translation | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hu_Model-Aware_Gesture-to-Gesture_Translation_CVPR_2021_paper.pdf | Model-Aware Gesture-to-Gesture Translation | Hand gesture-to-gesture translation is a significant and interesting problem, which serves as a key role in many applications, such as sign language production. This task involves fine-grained structure understanding of the mapping between the source and target gestures. Current works follow a data-driven paradigm ... | ['Houqiang Li', 'Weichao Zhao', 'Wengang Zhou', 'Weilun Wang', 'Hezhen Hu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['gesture-to-gesture-translation', 'sign-language-production'] | ['computer-vision', 'natural-language-processing'] | [ 3.94777685e-01 -3.28179747e-01 -1.31590515e-01 -3.31242740e-01
-4.46836054e-01 -3.77755105e-01 7.10769415e-01 -6.89236760e-01
-7.43457302e-02 3.54981095e-01 6.22713447e-01 1.90490812e-01
-2.85767228e-03 -7.46669412e-01 -6.57717645e-01 -8.21409166e-01
4.95176703e-01 5.66680312e-01 2.18053684e-01 -1.43809512... | [11.234539031982422, -0.897448718547821] |
7835c19f-c869-49cc-b83f-3db63a343f3a | shallow-attention-network-for-polyp | 2108.00882 | null | https://arxiv.org/abs/2108.00882v1 | https://arxiv.org/pdf/2108.00882v1.pdf | Shallow Attention Network for Polyp Segmentation | Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development of polyp segmentation. (i) Samples collected under different conditions show inconsistent colors, causing the feature... | ['Shuguang Cui', 'S. Kevin Zhou', 'Zhen Li', 'Ruimao Zhang', 'Yiwen Hu', 'Jun Wei'] | 2021-08-02 | null | null | null | null | ['video-polyp-segmentation'] | ['computer-vision'] | [ 2.02357382e-01 -2.10383870e-02 -1.66526318e-01 9.78119522e-02
-4.05509442e-01 -2.25107953e-01 -1.57109529e-01 2.30488807e-01
-3.08774322e-01 4.21102643e-01 -4.34817597e-02 -3.12561214e-01
2.25844920e-01 -8.25544536e-01 -5.82910061e-01 -1.07124329e+00
3.98368955e-01 -1.55951634e-01 6.54910684e-01 1.01049036... | [14.640573501586914, -2.74558687210083] |
9ce41e91-b21c-4b51-82f7-c35460281857 | tensorizing-flows-a-tool-for-variational | 2305.02460 | null | https://arxiv.org/abs/2305.02460v1 | https://arxiv.org/pdf/2305.02460v1.pdf | Tensorizing flows: a tool for variational inference | Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training samples. Normalizing flows have also been applied successfully to variational inference, wherein one ... | ['Hongli Zhao', 'Michael Lindsey', 'Yuehaw Khoo'] | 2023-05-03 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 9.90449339e-02 1.88938141e-01 -7.79008865e-02 -3.33469272e-01
-5.51403821e-01 -7.71922171e-01 1.08864164e+00 -3.53563040e-01
-2.25701377e-01 9.70923781e-01 4.01837319e-01 -3.57468575e-01
-4.06837277e-02 -9.86268878e-01 -7.51777589e-01 -9.51616585e-01
1.84825450e-01 8.80152166e-01 -2.24423677e-01 4.69296686... | [6.966035842895508, 3.9199397563934326] |
2d508261-8c1b-4970-af66-281b1f46d960 | stable-learning-via-sparse-variable | 2212.00992 | null | https://arxiv.org/abs/2212.00992v1 | https://arxiv.org/pdf/2212.00992v1.pdf | Stable Learning via Sparse Variable Independence | The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the covariates when there is no explicit domain information about training data. However, with finite samples, it is difficult to achieve the des... | ['Xingxuan Zhang', 'Renzhe Xu', 'Yong Lin', 'Zheyan Shen', 'Yue He', 'Peng Cui', 'Han Yu'] | 2022-12-02 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.64451611e-01 -2.01121822e-01 -4.39171970e-01 -6.83001339e-01
-3.17881912e-01 -2.13830575e-01 7.16686323e-02 -4.48656641e-02
-4.06969994e-01 1.06733191e+00 2.24570587e-01 -2.75234073e-01
-3.53415549e-01 -6.80530369e-01 -5.47029495e-01 -9.87484336e-01
4.19359803e-02 5.57156950e-02 -1.81911826e-01 1.16302386... | [10.299111366271973, 3.274691581726074] |
283c421f-3425-424f-8bfd-cb6dc7f4e3b7 | coarsenconf-equivariant-coarsening-with | 2306.14852 | null | https://arxiv.org/abs/2306.14852v1 | https://arxiv.org/pdf/2306.14852v1.pdf | CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation | Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated screenings and enhanced structural exploration. Several generative models have been dev... | ['Aditi S. Krishnapriyan', 'Danny Reidenbach'] | 2023-06-26 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.00529216e-01 -6.73438609e-02 1.72654375e-01 -3.13267499e-01
-1.05694008e+00 -7.65340447e-01 6.91484451e-01 4.24017668e-01
-3.67858559e-02 1.39055848e+00 4.43645597e-01 -4.29802716e-01
1.01511247e-01 -1.22563517e+00 -1.11534834e+00 -9.74385619e-01
6.87450496e-03 7.05005884e-01 -3.03040594e-01 -3.96336764... | [4.968823432922363, 5.687071800231934] |
f9af235d-4eea-4616-8a2f-d6f45b4cb2a5 | using-interventions-to-improve-out-of | 2210.10636 | null | https://arxiv.org/abs/2210.10636v2 | https://arxiv.org/pdf/2210.10636v2.pdf | Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems | Given a user's input text, text-matching recommender systems output relevant items by comparing the input text to available items' description, such as product-to-product recommendation on e-commerce platforms. As users' interests and item inventory are expected to change, it is important for a text-matching system to ... | ['Amit Sharma', 'Emre Kiciman', 'Yashoteja Prabhu', 'Parikshit Bansal'] | 2022-10-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.09030384e-01 -2.70619220e-03 -4.44447875e-01 -5.72212994e-01
-3.67268920e-01 -6.90007091e-01 4.99054611e-01 2.92674452e-01
-2.96893507e-01 4.53113735e-01 4.13464785e-01 -4.22436386e-01
-5.26287377e-01 -9.17986572e-01 -9.52798605e-01 -3.50339025e-01
8.65904614e-02 4.03634667e-01 1.90855965e-01 -2.67925888... | [9.856765747070312, 5.588678359985352] |
25751f0f-1869-4390-a709-840b7b146bbc | convolutional-neural-networks-for-sentence | 1408.5882 | null | http://arxiv.org/abs/1408.5882v2 | http://arxiv.org/pdf/1408.5882v2.pdf | Convolutional Neural Networks for Sentence Classification | We report on a series of experiments with convolutional neural networks (CNN)
trained on top of pre-trained word vectors for sentence-level classification
tasks. We show that a simple CNN with little hyperparameter tuning and static
vectors achieves excellent results on multiple benchmarks. Learning
task-specific vecto... | ['Yoon Kim'] | 2014-08-25 | convolutional-neural-networks-for-sentence-1 | https://aclanthology.org/D14-1181 | https://aclanthology.org/D14-1181.pdf | emnlp-2014-10 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.30197704e-01 -1.49746865e-01 -1.45875692e-01 -8.22498024e-01
-7.47857451e-01 -5.87298751e-01 4.91260082e-01 2.13176280e-01
-9.77877319e-01 4.48169321e-01 4.52962458e-01 -7.27082849e-01
1.64576516e-01 -5.20012438e-01 -5.62038124e-01 -3.17244828e-01
6.75963089e-02 6.35192692e-02 2.60087878e-01 -8.40519786... | [10.787358283996582, 7.766941070556641] |
120856c2-ef44-4a7f-9b7b-6924a56dc9d8 | semi-supervised-federated-learning-for-1 | 2305.05110 | null | https://arxiv.org/abs/2305.05110v1 | https://arxiv.org/pdf/2305.05110v1.pdf | Semi-Supervised Federated Learning for Keyword Spotting | Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly expanded the ability to train machine learning models by utilizing the computational and private data resources of numerous distributed devi... | ['Tao Zhang', 'Jie Ding', 'Eric W. Tramel', 'Enmao Diao'] | 2023-05-09 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.21768452e-01 -2.16059387e-01 -6.30991459e-01 -3.34671855e-01
-1.36949360e+00 -6.10714912e-01 1.80903584e-01 -1.03462920e-01
-1.24810874e-01 1.00790715e+00 1.67374220e-02 -5.66415370e-01
-2.32930183e-01 -4.62872058e-01 -7.33516574e-01 -4.88205194e-01
-1.15478113e-01 3.25981945e-01 5.72496690e-02 1.77445874... | [5.89973783493042, 6.243646144866943] |
361fab87-c854-43fa-ab83-4d7e2648deab | turku-neural-parser-pipeline-an-end-to-end | null | null | https://aclanthology.org/K18-2013 | https://aclanthology.org/K18-2013.pdf | Turku Neural Parser Pipeline: An End-to-End System for the CoNLL 2018 Shared Task | In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Compared to the last year, this year the shared task includes two new main metrics to measure the morphological tagging and lemmatization accuracies in addition to syntactic trees.... | ['Niko Miekka', 'Tapio Salakoski', 'Akseli Leino', 'Filip Ginter', 'Jenna Kanerva'] | 2018-10-01 | null | null | null | conll-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [-4.05187547e-01 3.54207307e-01 -1.04077473e-01 -5.58946848e-01
-1.55937839e+00 -1.11548483e+00 3.38941514e-01 4.10050273e-01
-8.12198639e-01 8.27138603e-01 5.46656191e-01 -4.90831435e-01
2.34714672e-01 -3.06903809e-01 -6.93807423e-01 -2.15501204e-01
4.10002656e-02 6.45653307e-01 1.10113300e-01 -7.90986642... | [10.443948745727539, 9.98353099822998] |
5020b002-e6f3-4571-a31c-bedaf1124316 | hybrid-coarse-fine-classification-for-head | 1901.06778 | null | https://arxiv.org/abs/1901.06778v2 | https://arxiv.org/pdf/1901.06778v2.pdf | Hybrid coarse-fine classification for head pose estimation | Head pose estimation, which computes the intrinsic Euler angles (yaw, pitch, roll) from the human, is crucial for gaze estimation, face alignment, and 3D reconstruction. Traditional approaches heavily relies on the accuracy of facial landmarks. It limits their performances, especially when the visibility of the face is... | ['Zhenghua Chen', 'Haofan Wang', 'Yi Zhou'] | 2019-01-21 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.99631023e-01 -3.37495878e-02 -2.30863944e-01 -6.04334295e-01
-5.48423290e-01 -1.72671169e-01 4.82297629e-01 -2.08853871e-01
-4.05962586e-01 5.75457931e-01 1.81314811e-01 -3.42558958e-02
2.89784849e-01 -1.67751774e-01 -5.65124571e-01 -7.89759576e-01
1.78086191e-01 3.32680047e-01 -6.28141239e-02 -1.55115321... | [13.647063255310059, 0.28115278482437134] |
fa1b0cfd-dea2-4ff7-bc3a-d3baa9eb5d87 | flsea-underwater-visual-inertial-and-stereo | 2302.12772 | null | https://arxiv.org/abs/2302.12772v1 | https://arxiv.org/pdf/2302.12772v1.pdf | FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets | Visibility underwater is challenging, and degrades as the distance between the subject and camera increases, making vision tasks in the forward-looking direction more difficult. We have collected underwater forward-looking stereo-vision and visual-inertial image sets in the Mediterranean and Red Sea. To our knowledge t... | ['Tali treibitz', 'Yelena Randall'] | 2023-02-24 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.16427436e-01 -2.29455590e-01 9.37568367e-01 -4.20408845e-01
-3.43971759e-01 -1.01929832e+00 2.28774175e-01 2.69710887e-02
-1.28224099e+00 6.21006012e-01 2.15182960e-01 1.09456889e-01
-1.18258387e-01 -8.55565429e-01 -8.77363026e-01 -7.31660783e-01
-3.84767592e-01 5.88147700e-01 4.11261708e-01 -4.37190533... | [7.518667221069336, -1.8004757165908813] |
c2c776f0-039c-4985-8cfa-6a48bcf022ce | one-shot-and-partially-supervised-cell-image | 2304.07991 | null | https://arxiv.org/abs/2304.07991v1 | https://arxiv.org/pdf/2304.07991v1.pdf | One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt | Semantic segmentation of microscopic cell images using deep learning is an important technique, however, it requires a large number of images and ground truth labels for training. To address the above problem, we consider an efficient learning framework with as little data as possible, and we propose two types of learn... | ['Kazuhiro Hotta', 'Sota Kato'] | 2023-04-17 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 3.79853338e-01 1.14642151e-01 -8.03424791e-02 -4.66416806e-01
-7.35929966e-01 -3.49109471e-01 1.03699155e-01 4.10634607e-01
-9.90225196e-01 9.69180703e-01 -5.22441030e-01 6.36715963e-02
1.13750279e-01 -6.95199609e-01 -7.25576580e-01 -1.04069316e+00
3.79240423e-01 5.25247097e-01 7.45828807e-01 4.32813466... | [14.651575088500977, -2.9231162071228027] |
a985f5ae-8863-4e0a-aaef-fe509440198d | automatic-relation-aware-graph-network | 2205.15678 | null | https://arxiv.org/abs/2205.15678v1 | https://arxiv.org/pdf/2205.15678v1.pdf | Automatic Relation-aware Graph Network Proliferation | Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information. Moreover, due to di... | ['Qingming Huang', 'Zheng-Jun Zha', 'Jiebo Luo', 'Xinzhe Han', 'Liang Li', 'Shaofei Cai'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_Automatic_Relation-Aware_Graph_Network_Proliferation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_Automatic_Relation-Aware_Graph_Network_Proliferation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['graph-regression'] | ['graphs'] | [ 4.80639264e-02 2.54471600e-01 -5.08740067e-01 4.99965101e-02
-1.65846407e-01 -4.59777594e-01 6.77450418e-01 3.47623646e-01
-2.67794847e-01 5.98972201e-01 -2.32368290e-01 -7.08238006e-01
-5.89769721e-01 -1.40865803e+00 -5.69191933e-01 -6.69671535e-01
-2.31817067e-01 6.60771668e-01 4.01869655e-01 -2.69353598... | [7.014630317687988, 6.157727241516113] |
c8f425b5-1bb3-4120-bb0d-112af9d9cb07 | review-learning-alleviating-catastrophic | 2210.09394 | null | https://arxiv.org/abs/2210.09394v1 | https://arxiv.org/pdf/2210.09394v1.pdf | Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator | When a deep learning model is sequentially trained on different datasets, it forgets the knowledge acquired from previous data, a phenomenon known as catastrophic forgetting. It deteriorates performance of the deep learning model on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applicat... | ['Kwangsoo Kim', 'Hyeong-Jin Yoon', 'Rae Woong Park', 'Dae Jung Kim', 'Hyung Joon Joo', 'Yaeji Lim', 'Dongkyeong Lim', 'Jieun Choi', 'Suhyeon Kim', 'Ye Seul Yang', 'Sunghyuk Choi', 'Jaesung Yoo'] | 2022-10-17 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 3.26310508e-02 1.11736089e-01 -1.57072231e-01 -3.10140699e-01
-3.72280598e-01 -3.31894875e-01 1.81205183e-01 3.84691983e-01
-8.02188277e-01 1.55920982e+00 -7.26639852e-02 -1.14367507e-01
-7.66589120e-02 -8.45147669e-01 -1.03765643e+00 -8.58728528e-01
-9.08904672e-02 2.36178096e-02 2.27396488e-01 3.49942535... | [9.792078971862793, 3.4514923095703125] |
7d0eb565-9011-41f9-bbe4-a0168b87a8a2 | looking-into-your-speech-learning-cross-modal | 2104.02775 | null | https://arxiv.org/abs/2104.02775v1 | https://arxiv.org/pdf/2104.02775v1.pdf | Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation | In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing. Most conventional approaches utilize frame-wise matching criteria to extract shared information between co-occurring audio and video. Thus, their performance heavily depends on the accuracy of... | ['Kwanghoon Sohn', 'Hong-Goo Kang', 'Sunok Kim', 'Soo-Whan Chung', 'Jiyoung Lee'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_Looking_Into_Your_Speech_Learning_Cross-Modal_Affinity_for_Audio-Visual_Speech_CVPR_2021_paper.pdf | cvpr-2021-1 | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 1.61058828e-01 -5.44920266e-01 -1.81771368e-01 -3.79119486e-01
-8.93132567e-01 -5.39151192e-01 2.62182772e-01 3.03329360e-02
-3.67074579e-01 5.31660199e-01 8.14183801e-02 3.12078655e-01
-4.67066556e-01 -8.08000490e-02 -5.68217576e-01 -8.24214637e-01
-2.03859136e-01 -1.05533920e-01 4.29310381e-01 1.59820259... | [14.114029884338379, 4.839443683624268] |
134dbd24-3376-45a7-8dce-f0798031d41a | the-paradox-of-choice-using-attention-in | 2201.09653 | null | https://arxiv.org/abs/2201.09653v1 | https://arxiv.org/pdf/2201.09653v1.pdf | The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning | Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth... | ['Doina Precup', 'Khimya Khetarpal', 'Andrei Nica'] | 2022-01-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.45224208e-01 2.40220740e-01 -6.35794818e-01 -6.16054051e-02
-4.98043597e-01 -5.95857024e-01 5.81619322e-01 1.55559555e-01
-8.68094206e-01 1.07535124e+00 3.68585289e-01 -5.01812577e-01
-5.15161216e-01 -7.65648067e-01 -4.31039929e-01 -6.21320367e-01
-4.45799202e-01 6.27668619e-01 2.64290094e-01 -4.39108521... | [4.10783052444458, 1.5081250667572021] |
f07622ac-498a-48eb-b692-114ff30bd3f2 | single-independent-component-recovery-and | 2110.05887 | null | https://arxiv.org/abs/2110.05887v3 | https://arxiv.org/pdf/2110.05887v3.pdf | Discovery of Single Independent Latent Variable | Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture of two statistically independent components and assume that one of the components is observed while the other is hidden. Our goal is to rec... | ['Ronen Talmon', 'Ori Katz', 'Jonathan Svirsky', 'Uri Shaham'] | 2021-10-12 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 6.00022733e-01 3.25089425e-01 -1.44842520e-01 -2.59196348e-02
-5.33628166e-01 -5.27843237e-01 3.96614850e-01 -7.68066570e-02
-1.88164666e-01 8.35113943e-01 9.68058854e-02 -1.39169693e-01
-1.41091526e-01 -4.19193268e-01 -7.24551439e-01 -1.32384527e+00
-7.52566978e-02 3.95710856e-01 -5.59390545e-01 3.32051039... | [15.235723495483398, 5.711827754974365] |
4da2cb4e-7163-4c14-8207-f95cdf8b2acb | material-classification-with-thermal-imagery | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Saponaro_Material_Classification_With_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Saponaro_Material_Classification_With_2015_CVPR_paper.pdf | Material Classification With Thermal Imagery | Material classification is an important area of research in computer vision. Typical algorithms use color and texture information for classification, but there are problems due to varying lighting conditions and diversity of colors in a single material class. In this work we study the use of long wave infrared (i.e. th... | ['Scott Sorensen', 'Philip Saponaro', 'Chandra Kambhamettu', 'Abhishek Kolagunda'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['material-classification'] | ['computer-vision'] | [ 3.59784752e-01 -1.20514119e+00 -2.46073008e-01 -2.99965322e-01
-2.94053346e-01 -6.92265332e-01 6.91244125e-01 -1.02873631e-01
-1.44339621e-01 6.66512549e-01 -2.71219909e-01 -9.85291693e-03
-2.18464836e-01 -9.97108340e-01 -1.21712409e-01 -1.25632298e+00
3.19758914e-02 1.49262741e-01 3.60218167e-01 -1.33284569... | [10.304221153259277, -2.5051040649414062] |
3512dc73-bc03-4220-863a-c418a2e6e061 | outlining-and-filling-hierarchical-query | 2111.00732 | null | https://arxiv.org/abs/2111.00732v2 | https://arxiv.org/pdf/2111.00732v2.pdf | Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs | Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL sy... | ['Tenggou Wang', 'Tianxing Wu', 'Guilin Qi', 'Huiying Li', 'Yongrui Chen'] | 2021-11-01 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [-6.88821450e-02 2.96006918e-01 -2.34082699e-01 -6.01892054e-01
-1.19836080e+00 -4.71117824e-01 4.90656234e-02 2.77312934e-01
-2.13280186e-01 3.46685350e-01 3.23187053e-01 -4.22373921e-01
-2.30260044e-01 -1.33394217e+00 -9.59167957e-01 -1.94586605e-01
8.43247399e-02 9.48046625e-01 5.38278699e-01 -3.95511389... | [10.037015914916992, 7.82706356048584] |
afcca557-e1d0-48ad-9612-08ee9435a213 | seeking-salient-facial-regions-for-cross | 2111.15361 | null | https://arxiv.org/abs/2111.15361v3 | https://arxiv.org/pdf/2111.15361v3.pdf | Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition | Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is face... | ['Mengting Wei', 'Jiateng Liu', 'Wenming Zheng', 'Yuan Zong', 'Xingxun Jiang'] | 2021-11-30 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 5.98500036e-02 -5.55875540e-01 -1.21457450e-01 -3.66522372e-01
-7.62004793e-01 -4.93820533e-02 8.68394300e-02 -6.67870224e-01
-2.40859706e-02 4.15978640e-01 3.09664935e-01 6.61315262e-01
-3.56424376e-02 -3.63986254e-01 -2.59076923e-01 -1.16981304e+00
2.08449915e-01 -9.20631811e-02 -2.02689573e-01 -2.84115106... | [13.617894172668457, 1.6194441318511963] |
91648906-a228-44b7-b0f0-05367e28783b | automatic-chord-recognition-with-higher-order | 1808.05341 | null | http://arxiv.org/abs/1808.05341v1 | http://arxiv.org/pdf/1808.05341v1.pdf | Automatic Chord Recognition with Higher-Order Harmonic Language Modelling | Common temporal models for automatic chord recognition model chord changes on
a frame-wise basis. Due to this fact, they are unable to capture musical
knowledge about chord progressions. In this paper, we propose a temporal model
that enables explicit modelling of chord changes and durations. We then apply
N-gram model... | ['Filip Korzeniowski', 'Gerhard Widmer'] | 2018-08-16 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 1.04685634e-01 -1.97992951e-01 -1.09105691e-01 -1.32851020e-01
-7.36933887e-01 -7.84489036e-01 7.35240877e-01 8.98361206e-02
-6.67253792e-01 2.42841288e-01 5.57517886e-01 -3.00595611e-01
-1.66871503e-01 -5.44995368e-01 -3.15092534e-01 -1.88290194e-01
-2.43500099e-01 2.14338854e-01 5.43380320e-01 -3.73066664... | [15.91412353515625, 5.364935874938965] |
1e59e422-1e98-4626-830a-2df8816472d6 | structure-aware-robustness-certificates-for | 2306.11915 | null | https://arxiv.org/abs/2306.11915v2 | https://arxiv.org/pdf/2306.11915v2.pdf | Structure-Aware Robustness Certificates for Graph Classification | Certifying the robustness of a graph-based machine learning model poses a critical challenge for safety. Current robustness certificates for graph classifiers guarantee output invariance with respect to the total number of node pair flips (edge addition or edge deletion), which amounts to an $l_{0}$ ball centred on the... | ['Xiaowen Dong', 'Henry Kenlay', 'Pierre Osselin'] | 2023-06-20 | null | null | null | null | ['graph-classification', 'classification-1'] | ['graphs', 'methodology'] | [ 4.63507444e-01 3.88299286e-01 -5.98845771e-04 -1.72055364e-02
-6.28921747e-01 -1.23009169e+00 5.51877499e-01 5.34195244e-01
-3.05383861e-01 4.53938663e-01 -1.55561432e-01 -8.84336531e-01
-7.35118315e-02 -9.46870267e-01 -9.99642015e-01 -9.10744131e-01
-5.64411402e-01 5.57675920e-02 4.81297374e-01 -6.70614839... | [5.94571590423584, 7.406943321228027] |
3cb3c406-08a3-434f-af70-e81ea31f9f60 | weisfeiler-and-leman-go-relational | 2211.17113 | null | https://arxiv.org/abs/2211.17113v1 | https://arxiv.org/pdf/2211.17113v1.pdf | Weisfeiler and Leman Go Relational | Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emerged recently, often outperforming shallow architectures. However, the design of such multi-relational graph neural networks is ad-hoc, drive... | ['Miguel Romero Orth', 'Christopher Morris', 'Mikhail Galkin', 'Pablo Barcelo'] | 2022-11-30 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.04106873e-01 5.37429810e-01 -4.80959862e-01 -2.91782737e-01
-2.31481701e-01 -7.73773074e-01 4.05571282e-01 2.98876345e-01
-4.54728641e-02 5.77541411e-01 7.59782940e-02 -7.74187148e-01
-6.78818345e-01 -1.37744415e+00 -9.38756645e-01 -4.34799850e-01
-4.97341007e-01 6.81836009e-01 2.35086486e-01 -3.34957778... | [7.02721643447876, 6.332330703735352] |
584a7cc9-ea1b-48fa-a595-d22dcfc8c003 | zoomnas-searching-for-whole-body-human-pose | 2208.11547 | null | https://arxiv.org/abs/2208.11547v1 | https://arxiv.org/pdf/2208.11547v1.pdf | ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild | This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale... | ['Xiaogang Wang', 'Ping Luo', 'Wanli Ouyang', 'Chen Qian', 'Wentao Liu', 'Sheng Jin', 'Lumin Xu'] | 2022-08-23 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-5.80032408e-01 2.66059786e-01 -1.23812808e-02 -2.50185013e-01
-3.94105643e-01 1.04348827e-02 8.78069270e-03 -4.00379092e-01
-3.94056708e-01 2.53855824e-01 3.16395402e-01 7.55012035e-01
3.20513062e-02 -3.96163821e-01 -6.44104004e-01 -1.48468599e-01
-1.56163275e-01 9.27103281e-01 2.61764973e-01 -2.66149580... | [7.028871536254883, -0.8862003087997437] |
5d6e42d2-d73b-4253-9162-da52950da4ba | learning-to-exploit-temporal-structure-for | 2301.04558 | null | https://arxiv.org/abs/2301.04558v2 | https://arxiv.org/pdf/2301.04558v2.pdf | Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing | Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment ... | ['Fernando Pérez-García', 'Ozan Oktay', 'Javier Alvarez-Valle', 'Aditya Nori', 'Matthew P. Lungren', 'Maria Wetscherek', 'Anton Schwaighofer', 'Anja Thieme', 'Kenza Bouzid', 'Harshita Sharma', 'Benedikt Boecking', 'Daniel C. Castro', 'Maximilian Ilse', 'Qianchu Liu', 'Stephanie Hyland', 'Shruthi Bannur'] | 2023-01-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bannur_Learning_To_Exploit_Temporal_Structure_for_Biomedical_Vision-Language_Processing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bannur_Learning_To_Exploit_Temporal_Structure_for_Biomedical_Vision-Language_Processing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 5.89233875e-01 9.59054567e-03 -3.26356798e-01 -5.80821395e-01
-1.63635242e+00 -6.04379892e-01 9.51291740e-01 3.71756375e-01
-4.76744205e-01 6.12772584e-01 5.86160123e-01 -1.91836685e-01
-1.14841163e-01 -2.94086456e-01 -5.88834405e-01 -4.57295179e-01
4.73576263e-02 5.09431362e-01 1.16977192e-01 6.93249255... | [14.932223320007324, -1.820898413658142] |
0fa6b82a-2997-4bea-bb05-fa030fe9ebc1 | local-gaussian-process-extrapolation-for-bart | 2204.10963 | null | https://arxiv.org/abs/2204.10963v2 | https://arxiv.org/pdf/2204.10963v2.pdf | Local Gaussian process extrapolation for BART models with applications to causal inference | Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically provide inaccurate prediction and overly narrow prediction intervals at points outside the range of the trai... | ['P. Richard Hahn', 'Jingyu He', 'Meijiang Wang'] | 2022-04-23 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 3.82339120e-01 2.29518577e-01 -4.23762858e-01 -5.32664716e-01
-8.77586186e-01 3.08155045e-02 7.51357436e-01 2.57275134e-01
-2.02464178e-01 1.48444784e+00 1.20019995e-01 -7.20949829e-01
-4.96863276e-01 -9.30756330e-01 -7.42323816e-01 -7.72578001e-01
-1.43156216e-01 8.76230538e-01 2.69807369e-01 2.45112643... | [7.415706157684326, 4.348930835723877] |
9a5c032e-bd9f-4076-afe3-80fd13190a61 | how-useful-are-educational-questions | 2304.06638 | null | https://arxiv.org/abs/2304.06638v1 | https://arxiv.org/pdf/2304.06638v1.pdf | How Useful are Educational Questions Generated by Large Language Models? | Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse question generation can dramatically reduce the load on teachers and improve the quality of their educational content. Recent work in this domai... | ['Iulian Serban', 'Jackie C. K. Cheung', 'Ekaterina Kochmar', 'Sabina Elkins'] | 2023-04-13 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-1.73728690e-02 5.13548851e-01 3.00751757e-02 -6.62195534e-02
-1.02376926e+00 -8.73639345e-01 5.10330081e-01 4.69541699e-01
-1.13604695e-01 6.95324838e-01 5.86499572e-01 -8.41648579e-01
-2.78943390e-01 -9.01893377e-01 -4.75569308e-01 -2.74544895e-01
6.49350524e-01 4.38079387e-01 4.40560609e-01 -4.55371499... | [10.87680435180664, 7.707876682281494] |
71da6519-d9e6-4c7b-a13c-a7af24257117 | interpretable-3d-human-action-analysis-with | 1704.04516 | null | http://arxiv.org/abs/1704.04516v1 | http://arxiv.org/pdf/1704.04516v1.pdf | Interpretable 3D Human Action Analysis with Temporal Convolutional Networks | The discriminative power of modern deep learning models for 3D human action
recognition is growing ever so potent. In conjunction with the recent
resurgence of 3D human action representation with 3D skeletons, the quality and
the pace of recent progress have been significant. However, the inner workings
of state-of-the... | ['Austin Reiter', 'Tae Soo Kim'] | 2017-04-14 | null | null | null | null | ['action-analysis', 'multimodal-activity-recognition', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.10611993e-01 1.79454803e-01 -3.56338710e-01 -3.15643966e-01
-2.18487233e-01 -1.60264209e-01 8.33296657e-01 -3.95041108e-01
-1.81360662e-01 2.53570020e-01 7.23217309e-01 -4.35345262e-01
-1.24952674e-01 -3.91771793e-01 -5.92362165e-01 -3.90191317e-01
-1.55511707e-01 3.51928174e-01 1.06623005e-02 -8.07951167... | [7.954879283905029, 0.4834100306034088] |
c65dffdc-e85d-4cf5-bfc7-4d274f3cf076 | methodological-aspects-of-developing-and | null | null | https://aclanthology.org/2020.lrec-1.392 | https://aclanthology.org/2020.lrec-1.392.pdf | Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB-2.0 | Diachronic lexical information is not only important in the field of historical linguistics, but is also increasingly used in NLP, most recently for machine translation of low resource languages. Therefore, there is a need for fine-grained, large-coverage and accurate etymological lexical resources. In this paper, we p... | ['Beno{\\^\\i}t Sagot', "Cl{\\'e}mentine Fourrier"] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cognate-prediction'] | ['natural-language-processing'] | [ 1.71146184e-01 1.11419909e-01 -5.52113891e-01 -2.41523888e-02
-2.63206601e-01 -9.84471023e-01 7.66005039e-01 6.70709968e-01
-6.91910923e-01 1.42614436e+00 5.20559967e-01 -4.53735501e-01
-6.21612146e-02 -7.95648336e-01 -2.65563041e-01 -1.15708739e-01
3.78972381e-01 1.11605251e+00 -2.01934204e-02 -4.87119049... | [10.322925567626953, 10.120329856872559] |
6baefd82-2066-4ccb-9be8-772551dffbdb | quantifying-context-overlap-for-training-word | null | null | https://aclanthology.org/D18-1057 | https://aclanthology.org/D18-1057.pdf | Quantifying Context Overlap for Training Word Embeddings | Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data t... | ['Yinhe Zheng', 'Yimeng Zhuang', 'Xuan Zhu', 'Jinghui Xie'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['learning-word-embeddings'] | ['methodology'] | [-2.02729642e-01 6.54368028e-02 -5.77330351e-01 -4.77008909e-01
-1.89380348e-01 -1.79940924e-01 6.73664391e-01 6.78649843e-01
-1.01347077e+00 4.52453107e-01 8.80345583e-01 -3.40850741e-01
-1.91731751e-01 -7.78989553e-01 -1.87658670e-03 -4.85857010e-01
-2.64528453e-01 2.74939984e-01 1.06888317e-01 -4.22901005... | [10.39834213256836, 8.69887638092041] |
61a879ba-3e2d-4d20-8dcb-cc07e74a458a | flare7k-a-phenomenological-nighttime-flare | 2210.06570 | null | https://arxiv.org/abs/2210.06570v1 | https://arxiv.org/pdf/2210.06570v1.pdf | Flare7K: A Phenomenological Nighttime Flare Removal Dataset | Artificial lights commonly leave strong lens flare artifacts on images captured at night. Nighttime flare not only affects the visual quality but also degrades the performance of vision algorithms. Existing flare removal methods mainly focus on removing daytime flares and fail in nighttime. Nighttime flare removal is c... | ['Chen Change Loy', 'Ruicheng Feng', 'Shangchen Zhou', 'Chongyi Li', 'Yuekun Dai'] | 2022-10-12 | null | null | null | null | ['flare-removal'] | ['computer-vision'] | [ 4.74409729e-01 -1.16201341e+00 6.51964009e-01 -3.36876541e-01
-5.00078201e-01 -1.17608213e+00 5.27713954e-01 -6.31790459e-01
1.32853642e-01 1.01795769e+00 3.33742559e-01 8.50062668e-02
-2.61299312e-01 -6.32368982e-01 -6.88192248e-01 -1.08499050e+00
9.12287012e-02 -1.68384001e-01 1.64307266e-01 -6.51753962... | [10.751118659973145, -3.1057324409484863] |
c96bb7de-acd5-48f6-bc48-ee17de880015 | siatrans-siamese-transformer-network-for-rgb | 2207.04224 | null | https://arxiv.org/abs/2207.04224v1 | https://arxiv.org/pdf/2207.04224v1.pdf | SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification | RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth information. Although these methods improve the accuracy of saliency prediction through various cro... | ['Yanjun Peng', 'Dongye Changlei', 'Xingzhao Jia'] | 2022-07-09 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.12564158e-01 2.50468627e-02 -2.31220275e-01 -1.94006562e-01
-7.04894066e-01 -1.02587594e-02 1.33415639e-01 -1.30467594e-01
-2.95114249e-01 3.27941686e-01 1.95074737e-01 -7.02390680e-04
2.18924545e-02 -8.06314111e-01 -6.38502479e-01 -8.69855046e-01
4.06738013e-01 -2.23052263e-01 8.83427918e-01 -4.71729517... | [9.666224479675293, -0.8335204720497131] |
cffae4bf-9007-448b-a79e-5bc323c8f848 | classification-of-social-media-toxic-comments | 2304.06934 | null | https://arxiv.org/abs/2304.06934v1 | https://arxiv.org/pdf/2304.06934v1.pdf | Classification of social media Toxic comments using Machine learning models | The abstract outlines the problem of toxic comments on social media platforms, where individuals use disrespectful, abusive, and unreasonable language that can drive users away from discussions. This behavior is referred to as anti-social behavior, which occurs during online debates, comments, and fights. The comments ... | ['S. Ayyasamy', 'M. Arun Kuamr Reddy', 'A. Sai Charish', 'K. Poojitha'] | 2023-04-14 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-3.56406599e-01 2.39291847e-01 -9.19347107e-02 1.16533246e-02
1.04439147e-01 -9.58872616e-01 5.36179781e-01 5.67496836e-01
-2.83862203e-01 8.13167453e-01 5.43895304e-01 -5.08805633e-01
4.85783279e-01 -4.97513175e-01 1.44384906e-01 -4.62080181e-01
5.46679020e-01 -3.86931866e-01 -3.15590888e-01 -6.26428306... | [8.695298194885254, 10.57834243774414] |
ce356143-f2b1-43d2-9f91-afdd180b73fe | rapid-rl-a-reconfigurable-architecture-with | 2109.08231 | null | https://arxiv.org/abs/2109.08231v1 | https://arxiv.org/pdf/2109.08231v1.pdf | RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning | Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexity associated with the underlying deep neural networks (DNNs) leads to power-hungry implementations. This makes deep RL systems unsuitable fo... | ['Kaushik Roy', 'Arijit Raychowdhury', 'Priyadarshini Panda', 'Malik Aqeel Anwar', 'Adarsh Kumar Kosta'] | 2021-09-16 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [-1.55448750e-01 1.02163538e-01 -2.56139547e-01 -2.12126732e-01
-3.37693810e-01 -5.39202213e-01 4.57421869e-01 -3.32835734e-01
-9.09014344e-01 8.33444834e-01 -4.60837930e-01 -7.20824420e-01
-7.38873286e-03 -1.04213238e+00 -9.20539439e-01 -6.55070961e-01
-3.55573803e-01 5.20850003e-01 3.62621307e-01 -4.68067616... | [3.8459224700927734, 1.4765948057174683] |
6815cc5b-10e9-4f64-bd9e-88ca9ad7af8b | multimodal-engagement-analysis-from-facial | 2101.04215 | null | https://arxiv.org/abs/2101.04215v2 | https://arxiv.org/pdf/2101.04215v2.pdf | Multimodal Engagement Analysis from Facial Videos in the Classroom | Student engagement is a key construct for learning and teaching. While most of the literature explored the student engagement analysis on computer-based settings, this paper extends that focus to classroom instruction. To best examine student visual engagement in the classroom, we conducted a study utilizing the audiov... | ['Enkelejda Kasneci', 'Ulrich Trautwein', 'Peter Gerjets', "Sidney D'Mello", 'Patricia Goldberg', 'Ömer Sümer'] | 2021-01-11 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 3.91207188e-02 3.03820729e-01 -1.79453529e-02 -5.34523249e-01
-8.35576892e-01 -5.06619513e-01 1.06576972e-01 6.61715090e-01
-4.74342555e-01 1.71368316e-01 1.71322942e-01 -2.78232187e-01
-4.57557201e-01 -4.13717628e-01 -6.42927587e-01 -6.40856206e-01
9.99794975e-02 -1.41960412e-01 -1.33342773e-01 1.19837530... | [13.496199607849121, 2.4262328147888184] |
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