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b84d3ca2-23e1-4547-a5ff-bed54621aef9 | keyword-spotting-system-and-evaluation-of | 2208.02765 | null | https://arxiv.org/abs/2208.02765v1 | https://arxiv.org/pdf/2208.02765v1.pdf | Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers | Keyword spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. The edge devices are usually always-on, so edge computing brings bandwidth savings and privacy protection. The devices typically have limited memory spaces, computational performances, power and costs, for example... | ['Shengchen Li', 'Jingyi Wang'] | 2022-08-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-7.45569393e-02 1.10165209e-01 -3.96399081e-01 -3.89290363e-01
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-5.11587597e-02 -4.12233949e-01 3.43997896e-01 6.79170340... | [8.41967487335205, 2.815565586090088] |
787bb7d6-50fe-4f16-a497-aa16f542af51 | modeling-motion-with-multi-modal-features-for | 2204.02547 | null | https://arxiv.org/abs/2204.02547v1 | https://arxiv.org/pdf/2204.02547v1.pdf | Modeling Motion with Multi-Modal Features for Text-Based Video Segmentation | Text-based video segmentation aims to segment the target object in a video based on a describing sentence. Incorporating motion information from optical flow maps with appearance and linguistic modalities is crucial yet has been largely ignored by previous work. In this paper, we design a method to fuse and align appea... | ['Yang You', 'Xinchao Wang', 'Fuzhao Xue', 'Xiangxiang Chu', 'Kai Wang', 'Wangbo Zhao'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Modeling_Motion_With_Multi-Modal_Features_for_Text-Based_Video_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['referring-expression-segmentation'] | ['computer-vision'] | [-6.01384789e-02 -4.34386522e-01 -2.23116934e-01 -5.90464532e-01
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21ea277d-44d5-4798-8192-f75ff7256e63 | hive-harnessing-human-feedback-for | 2303.09618 | null | https://arxiv.org/abs/2303.09618v1 | https://arxiv.org/pdf/2303.09618v1.pdf | HIVE: Harnessing Human Feedback for Instructional Visual Editing | Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human fee... | ['ran Xu', 'Caiming Xiong', 'Stefano Ermon', 'Silvio Savarese', 'Huan Wang', 'Zeyuan Chen', 'Ning Yu', 'Chia-Chih Chen', 'Can Qin', 'Yihao Feng', 'Xinyi Yang', 'Shu Zhang'] | 2023-03-16 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 3.17972422e-01 1.28622681e-01 -3.67159963e-01 -5.78750491e-01
-6.66948557e-01 -6.78144753e-01 7.03687131e-01 1.45077452e-01
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3.95640761e-01 1.12460785e-01 1.58859804e-01 -2.77992755... | [11.24560260772705, -0.07648859918117523] |
3f15a5b2-52b7-40ce-bbca-5d90a4573813 | learning-single-view-3d-reconstruction-with | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Guandao_Yang_A_Unified_Framework_ECCV_2018_paper.pdf | Learning Single-View 3D Reconstruction with Limited Pose Supervision | It is expensive to label images with 3D structure or precise camera pose. Yet, this is precisely the kind of annotation required to train single-view 3D reconstruction models. In contrast, unlabeled images or images with just category labels are easy to acquire, but few current models can use this weak supervision. We ... | ['Bharath Hariharan', 'Guandao Yang', 'Serge Belongie', 'Yin Cui'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 2.17437580e-01 2.09809259e-01 -1.49969041e-01 -4.79117155e-01
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5.47196031e-01 6.13614798e-01 3.09535444e-01 -9.59692225... | [8.443636894226074, -2.8792946338653564] |
5bdc1b7c-2608-4ad9-b1f5-88d416d0a74b | diagnosis-and-prognosis-of-head-and-neck | 2306.00034 | null | https://arxiv.org/abs/2306.00034v1 | https://arxiv.org/pdf/2306.00034v1.pdf | Diagnosis and Prognosis of Head and Neck Cancer Patients using Artificial Intelligence | Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they... | ['Ikboljon Sobirov'] | 2023-05-31 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-6.20462447e-02 3.44261587e-01 -6.98902488e-01 -3.07242155e-01
-1.20481551e+00 -2.62773894e-02 4.34030294e-01 3.72119337e-01
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8.46089497e-02 1.23518789e+00 6.67272089e-03 8.57042670... | [15.117195129394531, -2.637650489807129] |
ecabaa1b-e746-45a5-823d-800f04a17244 | mixtape-breaking-the-softmax-bottleneck | null | null | http://papers.nips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently | http://papers.nips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf | Mixtape: Breaking the Softmax Bottleneck Efficiently | The softmax bottleneck has been shown to limit the expressiveness of neural language models. Mixture of Softmaxes (MoS) is an effective approach to address such a theoretical limitation, but are expensive compared to softmax in terms of both memory and time. We propose Mixtape, an output layer that breaks the softmax b... | ['Russ R. Salakhutdinov', 'Quoc V. Le', 'Thang Luong', 'Zhilin Yang'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['tree-decomposition'] | ['graphs'] | [ 6.10901862e-02 1.14654541e-01 -7.30675697e-01 -4.06701028e-01
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-9.65219021e-01 9.00962114e-01 2.47363031e-01 -1.05175281e+00
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8.71935785e-02 5.62525809e-01 -1.50387257e-01 -2.62972385... | [10.892677307128906, 7.384946346282959] |
a71626a6-91de-4ec9-bee9-3b0335ffbf1e | counterfactual-prediction-under-outcome | 2302.11121 | null | https://arxiv.org/abs/2302.11121v2 | https://arxiv.org/pdf/2302.11121v2.pdf | Counterfactual Prediction Under Outcome Measurement Error | Across domains such as medicine, employment, and criminal justice, predictive models often target labels that imperfectly reflect the outcomes of interest to experts and policymakers. For example, clinical risk assessments deployed to inform physician decision-making often predict measures of healthcare utilization (e.... | ['Zhiwei Steven Wu', 'Kenneth Holstein', 'Amanda Coston', 'Luke Guerdan'] | 2023-02-22 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 6.28390312e-01 2.03042939e-01 -1.10363364e+00 -4.64686185e-01
-9.39206839e-01 -5.21473587e-01 3.93784583e-01 6.05358243e-01
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1.88486949e-01 6.54022336e-01 -7.22068727e-01 5.64234316... | [8.053650856018066, 5.429545879364014] |
73b0da22-830a-41a9-afb8-71c45c2f237a | faceformer-scale-aware-blind-face-restoration | 2207.0979 | null | https://arxiv.org/abs/2207.09790v1 | https://arxiv.org/pdf/2207.09790v1.pdf | FaceFormer: Scale-aware Blind Face Restoration with Transformers | Blind face restoration usually encounters with diverse scale face inputs, especially in the real world. However, most of the current works support specific scale faces, which limits its application ability in real-world scenarios. In this work, we propose a novel scale-aware blind face restoration framework, named Face... | ['Xintao Wang', 'Lei Sun', 'Gen Li', 'Aijin Li'] | 2022-07-20 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.20526813e-01 -2.81655341e-01 9.32801142e-02 -4.48718876e-01
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3.71079355e-01 -3.28538567e-01 -2.52813071e-01 -2.93935835... | [12.828965187072754, -0.03235628828406334] |
71bb1312-e084-47c1-bd49-f560d3b0f0d7 | large-scale-lexical-analysis | null | null | https://aclanthology.org/L12-1268 | https://aclanthology.org/L12-1268.pdf | Large Scale Lexical Analysis | The following paper presents a lexical analysis component as implemented in the PANACEA project. The goal is to automatically extract lexicon entries from crawled corpora, in an attempt to use corpus-based methods for high-quality linguistic text processing, and to focus on the quality of data without neglecting quanti... | ["Vera Aleksi{\\'c}", 'Christoph Schwarz', 'Gregor Thurmair'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['lexical-analysis'] | ['natural-language-processing'] | [-4.81907576e-02 3.13657075e-01 2.82364674e-02 -4.14441019e-01
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3.81190926e-01 1.05045867e+00 5.07424593e-01 -4.65313643... | [10.243531227111816, 10.029499053955078] |
e047729a-a44c-4e68-aacd-610d6a9ed436 | attention-based-transformers-for-instance | 2011.09763 | null | https://arxiv.org/abs/2011.09763v2 | https://arxiv.org/pdf/2011.09763v2.pdf | Attention-Based Transformers for Instance Segmentation of Cells in Microstructures | Detecting and segmenting object instances is a common task in biomedical applications. Examples range from detecting lesions on functional magnetic resonance images, to the detection of tumours in histopathological images and extracting quantitative single-cell information from microscopy imagery, where cell segmentati... | ['Heinz Koeppl', 'Christoph Reich', 'Tim Prangemeier'] | 2020-11-19 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 5.39805055e-01 3.22136283e-01 1.11266887e-02 -9.91652086e-02
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4.74495031e-02 1.18523538e+00 4.98677164e-01 6.43155277... | [14.544615745544434, -3.08376145362854] |
db5eadf8-e354-4a54-99e7-a72aade8540c | green-steganalyzer-a-green-learning-approach | 2306.04008 | null | https://arxiv.org/abs/2306.04008v1 | https://arxiv.org/pdf/2306.04008v1.pdf | Green Steganalyzer: A Green Learning Approach to Image Steganalysis | A novel learning solution to image steganalysis based on the green learning paradigm, called Green Steganalyzer (GS), is proposed in this work. GS consists of three modules: 1) pixel-based anomaly prediction, 2) embedding location detection, and 3) decision fusion for image-level detection. In the first module, GS deco... | ['C. -C. Jay Kuo', 'Ronald Salloum', 'Hong-Shuo Chen', 'Xinyu Wang', 'Yao Zhu'] | 2023-06-06 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 6.85133398e-01 -4.97404533e-03 -2.26056516e-01 2.78787225e-01
-5.23454726e-01 2.73856819e-01 1.66339904e-01 3.99298891e-02
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-2.73344547e-01 -2.81801254e-01 5.48287511e-01 -2.65401095... | [4.289546966552734, 8.060402870178223] |
704b7f41-cdb3-4e6f-b8b9-238f01796883 | self-supervised-video-representation-learning-8 | 2108.08426 | null | https://arxiv.org/abs/2108.08426v2 | https://arxiv.org/pdf/2108.08426v2.pdf | Self-Supervised Video Representation Learning with Meta-Contrastive Network | Self-supervised learning has been successfully applied to pre-train video representations, which aims at efficient adaptation from pre-training domain to downstream tasks. Existing approaches merely leverage contrastive loss to learn instance-level discrimination. However, lack of category information will lead to hard... | ['Yan Lu', 'Xun Guo', 'Yuanze Lin'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Lin_Self-Supervised_Video_Representation_Learning_With_Meta-Contrastive_Network_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Lin_Self-Supervised_Video_Representation_Learning_With_Meta-Contrastive_Network_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.58945721e-01 -3.89669925e-01 -7.23575890e-01 -4.04041260e-01
-9.49859321e-01 -2.29309738e-01 6.49285614e-01 -2.63244390e-01
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6.85477182e-02 9.29563344e-02 3.11522722e-01 -1.33382857... | [8.750097274780273, 0.8738946318626404] |
9fc1a4ca-bd86-4d21-aefd-967e88bdf9c6 | inferring-class-label-distribution-of | 2211.04157 | null | https://arxiv.org/abs/2211.04157v1 | https://arxiv.org/pdf/2211.04157v1.pdf | Inferring Class Label Distribution of Training Data from Classifiers: An Accuracy-Augmented Meta-Classifier Attack | Property inference attacks against machine learning (ML) models aim to infer properties of the training data that are unrelated to the primary task of the model, and have so far been formulated as binary decision problems, i.e., whether or not the training data have a certain property. However, in industrial and health... | ['György Dán', 'Raksha Ramakrishna'] | 2022-11-08 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.08369839e-01 5.68449736e-01 -4.32051122e-01 -3.88916224e-01
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2.13537723e-01 3.91954958e-01 4.75182235e-02 3.14036459... | [5.898190021514893, 7.337635040283203] |
875dc21f-839e-464e-b08f-a3b3850a245a | weakly-supervised-scientific-document | 2306.07193 | null | https://arxiv.org/abs/2306.07193v1 | https://arxiv.org/pdf/2306.07193v1.pdf | Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training | Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To address this challenge, we propose a weakly-supervised approach for scientific document classification using label names only. In scientific doma... | ['Carl Yang', 'Joyce C. Ho', 'Yue Yu', 'ran Xu'] | 2023-06-12 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-6.25330349e-03 -3.17911923e-01 -4.76478577e-01 -6.29366457e-01
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4.14782166e-01 5.41870356e-01 -3.89300622e-02 3.15614015... | [9.835416793823242, 8.202319145202637] |
0ca3c6dd-c622-4fd4-96f1-fa485a86b29b | multilingual-named-entity-recognition-and | null | null | https://aclanthology.org/2021.bsnlp-1.9 | https://aclanthology.org/2021.bsnlp-1.9.pdf | Multilingual Named Entity Recognition and Matching Using BERT and Dedupe for Slavic Languages | This paper describes the University of Ljubljana (UL FRI) Group’s submissions to the shared task at the Balto-Slavic Natural Language Processing (BSNLP) 2021 Workshop. We experiment with multiple BERT-based models, pre-trained in multi-lingual, Croatian-Slovene-English and Slovene-only data. We perform training iterati... | ['Slavko Zitnik', 'Marko Prelevikj'] | null | null | null | null | eacl-bsnlp-2021-4 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-5.22152483e-01 4.06093188e-02 -4.92079966e-02 -3.77059489e-01
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4.59428042e-01 6.22581124e-01 -3.75124365e-02 -2.61227280... | [9.899370193481445, 9.761275291442871] |
c0bc88e5-a366-4202-a491-b70cd47e6f7c | pneumonia-detection-in-chest-x-rays-using | 2204.03618 | null | https://arxiv.org/abs/2204.03618v1 | https://arxiv.org/pdf/2204.03618v1.pdf | Pneumonia Detection in Chest X-Rays using Neural Networks | With the advancement in AI, deep learning techniques are widely used to design robust classification models in several areas such as medical diagnosis tasks in which it achieves good performance. In this paper, we have proposed the CNN model (Convolutional Neural Network) for the classification of Chest X-ray images fo... | ['Anwesh Reddy Paduri', 'Vivek Kumar', 'Ketul Kumar', 'Devendra Trivedi', 'Dany Bright', 'Ashish Ranjan', 'Narayana Darapaneni'] | 2022-04-07 | null | null | null | null | ['image-augmentation', 'pneumonia-detection'] | ['computer-vision', 'medical'] | [-7.22930161e-03 -7.98680335e-02 -1.35218233e-01 -1.18180424e-01
-3.36793065e-01 1.25076041e-01 3.76314551e-01 -9.36522335e-02
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-8.97343084e-02 4.71706182e-01 6.23460591e-01 -2.07016632... | [15.063911437988281, -2.4778056144714355] |
3cb57c75-54c9-4de8-b2dd-e1fd88b0f245 | what-can-we-learn-by-predicting-accuracy | 2208.01358 | null | https://arxiv.org/abs/2208.01358v2 | https://arxiv.org/pdf/2208.01358v2.pdf | What can we Learn by Predicting Accuracy? | This paper seeks to answer the following question: \textit{"What can we learn by predicting accuracy?"}. Indeed, classification is one of the most popular tasks in machine learning, and many loss functions have been developed to maximize this non-differentiable objective function. Unlike past work on loss function desi... | ['Olivier Risser-Maroix', 'Benjamin Chamand'] | 2022-08-02 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.92146523e-02 3.21749121e-01 -3.93097728e-01 -6.74647033e-01
-6.71853125e-01 -3.84622127e-01 4.16700006e-01 6.22723639e-01
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-4.81396288e-01 -8.19614291e-01 -7.24213898e-01 -6.60982549e-01
-1.80568025e-01 3.03168714e-01 1.00970685e-01 -3.06258827... | [8.069732666015625, 4.465197563171387] |
df16e5ff-d7fd-49b4-bff4-993d813f92c3 | memsum-extractive-summarization-of-long | 2107.08929 | null | https://arxiv.org/abs/2107.08929v2 | https://arxiv.org/pdf/2107.08929v2.pdf | MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes | We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects sentences into the summary, it considers a broad information set that wo... | ['Richard H. R. Hahnloser', 'Elliott Ash', 'Nianlong Gu'] | 2021-07-19 | null | https://aclanthology.org/2022.acl-long.450 | https://aclanthology.org/2022.acl-long.450.pdf | acl-2022-5 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 1.40868947e-01 3.22214812e-01 -4.79461014e-01 -3.29970047e-02
-1.26013219e+00 -6.18260026e-01 6.69478059e-01 1.10637879e+00
-4.82082099e-01 1.19144130e+00 1.09741879e+00 2.90324036e-02
-1.13216363e-01 -5.89140296e-01 -5.75682878e-01 -2.98405915e-01
-1.91847712e-01 5.93950868e-01 2.12560922e-01 -1.77956939... | [12.504096031188965, 9.502656936645508] |
d5760d0e-9b37-4131-8276-7213bddcecc4 | v2meow-meowing-to-the-visual-beat-via-music | 2305.06594 | null | https://arxiv.org/abs/2305.06594v1 | https://arxiv.org/pdf/2305.06594v1.pdf | V2Meow: Meowing to the Visual Beat via Music Generation | Generating high quality music that complements the visual content of a video is a challenging task. Most existing visual conditioned music generation systems generate symbolic music data, such as MIDI files, instead of raw audio waveform. Given the limited availability of symbolic music data, such methods can only gene... | ['Timo I. Denk', 'Mauro Verzetti', 'Yu Wang', 'Aren Jansen', 'Fei Sha', 'Chris Donahue', 'Joonseok Lee', 'Dima Kuzmin', 'Qingqing Huang', 'Judith Yue Li', 'Kun Su'] | 2023-05-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.93889666e-01 -3.88422579e-01 -3.55254300e-02 1.10248245e-01
-1.04919636e+00 -8.90895069e-01 5.46609640e-01 -1.31714776e-01
5.45154177e-02 3.86477441e-01 2.58522063e-01 2.08087206e-01
-1.96975008e-01 -5.76350570e-01 -7.71685958e-01 -4.16318774e-01
3.90475951e-02 2.05676958e-01 7.10729733e-02 -1.34255588... | [15.59449291229248, 5.402563095092773] |
3c9916bc-2c68-4a37-b767-2b8e709654ff | self-dictionary-sparse-regression-for | 1409.432 | null | http://arxiv.org/abs/1409.4320v2 | http://arxiv.org/pdf/1409.4320v2.pdf | Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related | This paper considers a recently emerged hyperspectral unmixing formulation
based on sparse regression of a self-dictionary multiple measurement vector
(SD-MMV) model, wherein the measured hyperspectral pixels are used as the
dictionary. Operating under the pure pixel assumption, this SD-MMV formalism is
special in that... | ['José M. Bioucas-Dias', 'Tsung-Han Chan', 'Wing-Kin Ma', 'Xiao Fu'] | 2014-09-15 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.77461052e-01 -4.44694668e-01 -1.02645040e-01 1.43867865e-01
-7.22182333e-01 -4.98072177e-01 4.77131426e-01 -1.94488287e-01
-7.29142055e-02 6.29864991e-01 -2.12208629e-02 -2.84444898e-01
-5.28158188e-01 -6.34292960e-01 -5.51871717e-01 -1.46744120e+00
1.94422454e-01 2.12106600e-01 -5.71004987e-01 -1.18692294... | [10.096426963806152, -2.0242714881896973] |
6cc241ea-1b7a-4a40-b4e0-09c3bd735b71 | tackling-face-verification-edge-cases-in | 2304.08134 | null | https://arxiv.org/abs/2304.08134v2 | https://arxiv.org/pdf/2304.08134v2.pdf | Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach | Nowadays, face recognition systems surpass human performance on several datasets. However, there are still edge cases that the machine can't correctly classify. This paper investigates the effect of a combination of machine and human operators in the face verification task. First, we look closer at the edge cases for s... | ['Gerhard Rigoll', 'Martin Knoche'] | 2023-04-17 | null | null | null | null | ['face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision'] | [-9.77939293e-02 -2.31998146e-01 -2.62879461e-01 -8.52100611e-01
-5.90083122e-01 -3.92470270e-01 6.17772102e-01 -3.96370769e-01
-2.14899689e-01 4.15321469e-01 -3.44573051e-01 -3.93244475e-01
1.18056133e-01 -1.38512000e-01 -3.98280382e-01 -5.74383736e-01
-1.04441710e-01 3.34698498e-01 -1.51071459e-01 -4.18732762... | [13.199227333068848, 0.8964056372642517] |
7a0c2f2b-76fc-4e2f-bbca-4a3a0c39a9e8 | votehmr-occlusion-aware-voting-network-for | 2110.08729 | null | https://arxiv.org/abs/2110.08729v1 | https://arxiv.org/pdf/2110.08729v1.pdf | VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds | 3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding. Previous works in this field either require high-quality 3D human scans or sequential point clouds, which cannot be easily applied to low-quality 3D scans captured by consumer-level depth sensors. ... | ['Lu Sheng', 'Yu Rong', 'Guanze Liu'] | 2021-10-17 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-1.33665189e-01 1.39777690e-01 -1.68445393e-01 -4.61083114e-01
-9.55153823e-01 1.04782842e-01 2.35006571e-01 -1.77530751e-01
-1.11628205e-01 5.55009484e-01 2.15924997e-02 2.43233591e-01
-2.12506533e-01 -9.22091067e-01 -8.17504764e-01 -4.89034534e-01
3.38721275e-02 1.12317157e+00 1.86789930e-01 -3.54523659... | [7.079288005828857, -1.1951502561569214] |
70a11e00-12e4-4f7f-8b77-ca494b64b1d2 | a-novel-privacy-preserving-deep-learning | 1908.07701 | null | https://arxiv.org/abs/1908.07701v2 | https://arxiv.org/pdf/1908.07701v2.pdf | A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component | Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To accomplish such a task usually needs one of the entities (the customer or the service provider) to provi... | ['Allen C. -H. Wu', 'Chin-Yu Sun', 'TingTing Hwang'] | 2019-08-21 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.47054568e-01 -1.41479731e-01 8.82165283e-02 -5.72632611e-01
6.53393343e-02 -7.81503081e-01 4.03754056e-01 -2.45431706e-01
-7.16417551e-01 7.09227741e-01 -3.48014981e-01 -6.40371382e-01
9.93510559e-02 -1.18166935e+00 -7.35941768e-01 -1.13780653e+00
-2.45692171e-02 -2.19755527e-03 9.36608613e-02 -2.02828452... | [5.878170490264893, 6.922840118408203] |
bea675dc-398e-4324-9989-c66b9c09362e | implicit-temporal-modeling-with-learnable | 2304.10465 | null | https://arxiv.org/abs/2304.10465v1 | https://arxiv.org/pdf/2304.10465v1.pdf | Implicit Temporal Modeling with Learnable Alignment for Video Recognition | Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While mod... | ['Yu-Gang Jiang', 'Han Hu', 'Zhi-Qi Cheng', 'Zuxuan Wu', 'Qi Dai', 'Shuyuan Tu'] | 2023-04-20 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [-8.14791992e-02 -2.41135269e-01 -4.23036188e-01 -4.24282789e-01
-7.10935473e-01 -2.16230601e-01 4.93752658e-01 -2.49467582e-01
-4.25634086e-01 4.36595321e-01 3.97222102e-01 -2.13911906e-01
-3.55704618e-03 -4.00898099e-01 -9.61489975e-01 -8.24972451e-01
-7.12260008e-02 -1.04105003e-01 2.85461247e-01 1.13002704... | [9.305630683898926, 0.19172224402427673] |
239b91dc-e8c8-4fe3-962b-ac3b023d793b | robust-online-multi-target-visual-tracking | 1908.03945 | null | https://arxiv.org/abs/1908.03945v6 | https://arxiv.org/pdf/1908.03945v6.pdf | Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning | We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while m... | ['Nathanael L. Baisa'] | 2019-08-11 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-1.47521511e-01 -6.02144420e-01 7.26977140e-02 -1.51806951e-01
-4.85336661e-01 -5.41363955e-01 7.23096371e-01 3.55733652e-03
-7.78468072e-01 8.79409611e-01 -1.83128312e-01 4.53633398e-01
-9.82141420e-02 -4.20310676e-01 -9.54662442e-01 -7.67532349e-01
-3.99743825e-01 7.37738431e-01 4.13276583e-01 2.80958086... | [6.398937702178955, -1.9489973783493042] |
ef2343bd-ec70-40bd-b4fb-255e25d55f87 | image-classification-of-stroke-blood-clot | 2305.16492 | null | https://arxiv.org/abs/2305.16492v1 | https://arxiv.org/pdf/2305.16492v1.pdf | Image Classification of Stroke Blood Clot Origin using Deep Convolutional Neural Networks and Visual Transformers | Stroke is one of two main causes of death worldwide. Many individuals suffer from ischemic stroke every year. Only in US more over 700,000 individuals meet ischemic stroke due to blood clot blocking an artery to the brain every year. The paper describes particular approach how to apply Artificial Intelligence for purpo... | ['David Azatyan'] | 2023-05-25 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.75471231e-01 4.94769476e-02 -2.39446133e-01 -2.71087259e-01
-3.33493263e-01 -5.04864872e-01 6.80279255e-01 -6.09357581e-02
-7.46653974e-01 1.29984522e+00 7.37343490e-01 -8.59336436e-01
-1.71437442e-01 -9.40183103e-01 3.45544145e-02 -3.91220391e-01
-5.28370962e-03 8.35648000e-01 1.20340675e-01 3.17900889... | [14.19847297668457, -1.947218418121338] |
05d46fe1-5372-405f-8450-945f64544c30 | style-based-variational-autoencoder-for-real | 1912.10227 | null | https://arxiv.org/abs/1912.10227v2 | https://arxiv.org/pdf/1912.10227v2.pdf | Exploiting Style and Attention in Real-World Super-Resolution | Real-world image super-resolution (SR) is a challenging image translation problem. Low-resolution (LR) images are often generated by various unknown transformations rather than by applying simple bilinear down-sampling on high-resolution (HR) images. To address this issue, this paper proposes a novel pipeline which exp... | ['Yi Li', 'Mandi Luo', 'Huaibo Huang', 'Xin Ma', 'Ran He'] | 2019-12-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.67961675e-01 1.19554978e-02 2.88745016e-01 -4.38618362e-01
-9.44988251e-01 -1.91617429e-01 5.57957411e-01 -9.18897331e-01
-8.86029974e-02 7.23468721e-01 3.19603473e-01 1.75864384e-01
1.75703526e-01 -8.83720815e-01 -9.02946293e-01 -8.08704615e-01
2.72055089e-01 8.20046887e-02 2.99341649e-01 -4.75380003... | [11.036943435668945, -2.0703165531158447] |
dc5d372c-39db-41a9-8a61-a356878b7969 | ditch-the-gold-standard-re-evaluating | null | null | https://openreview.net/forum?id=L-j8a3P22cy | https://openreview.net/pdf?id=L-j8a3P22cy | Ditch the Gold Standard: Re-evaluating Conversational Question Answering | Conversational question answering (CQA) systems aim to provide natural-language answers to users in information-seeking conversations. Existing benchmarks compare CQA models on pre-collected human-human conversations, with ground-truth answers provided in conversational history. It remains unclear whether we can rely o... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['question-rewriting'] | ['natural-language-processing'] | [-1.25182435e-01 4.68524307e-01 2.00380668e-01 -5.47990918e-01
-1.07056916e+00 -8.31995130e-01 8.70694399e-01 2.84530014e-01
-3.72743547e-01 6.19737923e-01 6.97919011e-01 -7.41599083e-01
1.53777242e-01 -6.73882544e-01 6.65878120e-04 6.96500437e-03
2.10042074e-01 9.34901655e-01 5.04186571e-01 -9.12216604... | [12.082146644592285, 7.974800109863281] |
8bd329a8-0772-4581-93e3-d3519dd19f1f | low-resource-accent-classification-in | 2206.12759 | null | https://arxiv.org/abs/2206.12759v2 | https://arxiv.org/pdf/2206.12759v2.pdf | Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective | Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based pretrained models have achieved superb performances in both areas. However, most accent classification tasks focused on classifying differe... | ['Jie Yang', 'Peilin Zhou', 'Dading Chong', 'Qingcheng Zeng'] | 2022-06-26 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [-7.31210634e-02 -5.36489598e-02 5.52637428e-02 -5.75947165e-01
-1.06527066e+00 -4.25244242e-01 4.78902102e-01 5.55629805e-02
-6.38765335e-01 6.58814967e-01 5.82011104e-01 -4.70471084e-01
-1.53929800e-01 -4.69543993e-01 -1.01629518e-01 -8.87717068e-01
1.04635566e-01 4.23542649e-01 -2.88791150e-01 -3.49434435... | [14.313823699951172, 6.692679405212402] |
24620454-3cbd-485f-a48b-6967bdf34e2c | annotating-character-relationships-in | 1512.00728 | null | http://arxiv.org/abs/1512.00728v1 | http://arxiv.org/pdf/1512.00728v1.pdf | Annotating Character Relationships in Literary Texts | We present a dataset of manually annotated relationships between characters
in literary texts, in order to support the training and evaluation of automatic
methods for relation type prediction in this domain (Makazhanov et al., 2014;
Kokkinakis, 2013) and the broader computational analysis of literary character
(Elson ... | ['Philip Massey', 'Noah A. Smith', 'David Bamman', 'Patrick Xia'] | 2015-12-02 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-2.83666968e-01 3.21251214e-01 -2.60524005e-01 -4.51227218e-01
-1.63250610e-01 -7.92409599e-01 1.14905691e+00 7.11343467e-01
-4.95275974e-01 8.47469389e-01 5.89191377e-01 -2.08004102e-01
-3.34154278e-01 -5.99122524e-01 -1.71995118e-01 -2.21239135e-01
2.91374117e-01 7.82081544e-01 1.24581672e-01 -3.96802843... | [9.418426513671875, 10.152517318725586] |
b5c6be84-32e2-48b8-9d29-66bdd15ec6a9 | online-arbitrary-shaped-clustering-through | 2302.06335 | null | https://arxiv.org/abs/2302.06335v1 | https://arxiv.org/pdf/2302.06335v1.pdf | Online Arbitrary Shaped Clustering through Correlated Gaussian Functions | There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner, and requires no prior knowledge o... | ['Ole Christian Eidheim'] | 2023-02-13 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 2.22290173e-01 7.18200281e-02 -1.65445194e-01 -4.07389611e-01
-9.16861147e-02 -5.04148543e-01 7.71934628e-01 2.29454190e-01
-5.65941453e-01 6.06595993e-01 -1.41952038e-01 -1.16003178e-01
-5.50647795e-01 -4.20311034e-01 -8.49730551e-01 -9.34845686e-01
-3.83628279e-01 6.69584394e-01 1.06002845e-01 2.48238176... | [8.405793190002441, 3.2500386238098145] |
5783a566-f085-4bd8-b5a8-700615085d49 | approximate-sampling-and-estimation-of | 2209.10423 | null | https://arxiv.org/abs/2209.10423v1 | https://arxiv.org/pdf/2209.10423v1.pdf | Approximate sampling and estimation of partition functions using neural networks | We consider the closely related problems of sampling from a distribution known up to a normalizing constant, and estimating said normalizing constant. We show how variational autoencoders (VAEs) can be applied to this task. In their standard applications, VAEs are trained to fit data drawn from an intractable distribut... | ['George T. Cantwell'] | 2022-09-21 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.28054425e-01 1.57825217e-01 -2.24845618e-01 -3.66202146e-01
-8.01523864e-01 -5.10914683e-01 9.16671634e-01 -1.85000569e-01
-3.91014040e-01 9.24320817e-01 2.59911828e-03 -3.47025961e-01
-1.82239950e-01 -1.08514035e+00 -7.99518406e-01 -8.13604057e-01
1.24762937e-01 1.37369823e+00 -9.09307525e-02 -7.32441396... | [6.8247599601745605, 4.072566032409668] |
7cea2c0d-9f7c-4df1-a211-5718578b8730 | turku-semantic-dependency-parsing-as-a | null | null | https://aclanthology.org/S15-2161 | https://aclanthology.org/S15-2161.pdf | Turku: Semantic Dependency Parsing as a Sequence Classification | null | ['Juhani Luotolahti', 'Filip Ginter', 'Jenna Kanerva'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.390924453735352, 3.6337554454803467] |
57ea427d-2eb3-4856-a8d0-93c0669e44dc | applications-and-challenges-of-sentiment | 2301.09912 | null | https://arxiv.org/abs/2301.09912v1 | https://arxiv.org/pdf/2301.09912v1.pdf | Applications and Challenges of Sentiment Analysis in Real-life Scenarios | Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a driving force for research in SA. This chapter describes some of these applications and related challenges in real-life scenarios. In this chapter... | ['Aditya Joshi', 'Diptesh Kanojia'] | 2023-01-24 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.72841296e-01 -1.04867863e-02 -3.57726425e-01 -5.71191430e-01
-4.44403827e-01 -8.43600750e-01 4.06059086e-01 5.99825382e-01
-4.80012387e-01 6.76326811e-01 4.03552562e-01 -4.66315866e-01
1.38543118e-02 -8.41655493e-01 -2.96851933e-01 -4.46368873e-01
1.43082693e-01 2.00996444e-01 -3.98398966e-01 -6.55425012... | [11.082048416137695, 6.8795061111450195] |
fc0a9fb1-3c8b-4ec0-8088-7fa1fec1ed1b | evaluating-large-language-models-with | 2306.12567 | null | https://arxiv.org/abs/2306.12567v1 | https://arxiv.org/pdf/2306.12567v1.pdf | Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases | This paper investigates whether current large language models exhibit biases in logical reasoning, similar to humans. Specifically, we focus on syllogistic reasoning, a well-studied form of inference in the cognitive science of human deduction. To facilitate our analysis, we introduce a dataset called NeuBAROCO, origin... | ['Mitsuhiro Okada', 'Koji Mineshima', 'Hirohiko Abe', 'Takanobu Morishita', 'Risako Ando'] | 2023-06-21 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-4.52098757e-01 4.56219971e-01 -1.36361405e-01 -3.57744902e-01
1.40836880e-01 -5.53983986e-01 6.46305859e-01 3.63413662e-01
-4.85498816e-01 7.82639682e-01 2.47281864e-01 -1.12049246e+00
-3.08727622e-01 -9.74068344e-01 -5.54906785e-01 -4.53988789e-03
2.31304765e-01 7.41119087e-01 4.04961556e-02 -5.30514956... | [9.574106216430664, 7.323684215545654] |
0e3b0ccb-2fdd-46bf-ae50-0f6aa7481d7e | open-the-box-of-digital-neuromorphic | 2303.15224 | null | https://arxiv.org/abs/2303.15224v1 | https://arxiv.org/pdf/2303.15224v1.pdf | Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design | Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmark and optimize their computational cost without hardware in the loop. Although digital neuromorphic ... | ['Amirreza Yousefzadeh', 'Gert-Jan van Schaik', 'Manolis Sifalakis', 'Mario Konijnenburg', 'Stefano Traferro', 'Paul Detterer', 'Kevin Shidqi', 'Ali Safa', 'Guangzhi Tang'] | 2023-03-27 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.01633084e-01 -4.38206017e-01 1.82651579e-01 -3.88373621e-02
8.02507922e-02 -4.62940425e-01 1.30398363e-01 -7.31151998e-02
-7.38486707e-01 4.45915103e-01 -3.57498288e-01 -2.93370754e-01
-1.71329498e-01 -8.52651179e-01 -9.79294896e-01 -7.54873753e-01
-1.35212734e-01 2.77626067e-01 4.19855326e-01 -1.51638001... | [8.289698600769043, 2.556624412536621] |
e0afbe4d-787e-4d4d-9a04-b96b410b2fd1 | putting-figures-on-influences-on-moroccan | null | null | https://aclanthology.org/2018.gwc-1.46 | https://aclanthology.org/2018.gwc-1.46.pdf | Putting Figures on Influences on Moroccan Darija from Arabic, French and Spanish using the WordNet | Moroccan Darija is a variant of Arabic with many influences. Using the Open Multilingual WordNet (OMW), we compare the lemmas in the Moroccan Darija Wordnet (MDW) with the standard Arabic, French and Spanish ones. We then compared the lemmas in each synset with their translation equivalents. Transliteration is used to ... | ['Francis Bond', 'Khalil Mrini'] | null | null | null | null | gwc-2018-1 | ['transliteration'] | ['natural-language-processing'] | [-5.64796805e-01 1.13465935e-01 5.32879643e-02 2.33056217e-01
-3.58883917e-01 -1.13812077e+00 9.71667349e-01 4.39790010e-01
-5.61989486e-01 1.48734987e+00 4.63303119e-01 -6.54188991e-01
-1.11173429e-01 -8.47419441e-01 -3.14216465e-01 -3.03134263e-01
5.93256876e-02 6.57042384e-01 1.80454850e-01 -1.25248551... | [10.372654914855957, 10.40301513671875] |
dd18c5c1-875a-4e9a-bb97-bc5ce497e5b0 | reconstruction-of-perceived-images-from-fmri | 2202.12692 | null | https://arxiv.org/abs/2202.12692v1 | https://arxiv.org/pdf/2202.12692v1.pdf | Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs | Reconstructing perceived natural images from fMRI signals is one of the most engaging topics of neural decoding research. Prior studies had success in reconstructing either the low-level image features or the semantic/high-level aspects, but rarely both. In this study, we utilized an Instance-Conditioned GAN (IC-GAN) m... | ['Rufin VanRullen', 'Leila Reddy', 'Milad Mozafari', 'Bhavin Choksi', 'Furkan Ozcelik'] | 2022-02-25 | null | null | null | null | ['2048'] | ['playing-games'] | [ 7.50656188e-01 1.57289758e-01 2.00947657e-01 -6.09312177e-01
-8.05256248e-01 -2.20683530e-01 7.20044076e-01 -5.71591020e-01
-1.23016037e-01 8.12701702e-01 3.90319437e-01 3.12961012e-01
1.68635756e-01 -8.85752439e-01 -1.11523092e+00 -8.54389787e-01
2.18102634e-01 3.35766107e-01 -3.61265481e-01 1.10773839... | [10.690975189208984, 2.469329357147217] |
a067ad19-e479-4e11-8969-0864acb003a6 | an-investigation-of-preprocessing-filters-and | null | null | https://ieeexplore.ieee.org/abstract/document/9940921 | https://ieeexplore.ieee.org/iel7/6287639/6514899/09940921.pdf | An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data | The illegal exploitation of protected marine environments has consistently threatened the
biodiversity and economic development of coastal regions. Extensive monitoring in these – often remote
– areas is challenging. Machine learning methods are useful in object detection and classification tasks and
have the potent... | ['Karl Sammut', 'Russell S. A. Brinkworth', 'Phillip S. M. Skelton', 'Paulo E. Santos', 'Lucas Cesar Ferreira Domingos'] | 2022-11-07 | null | null | null | ieee-access-2022-11 | ['underwater-acoustic-classification'] | ['audio'] | [ 2.58907825e-01 -9.82364118e-02 9.52129006e-01 -3.48008364e-01
-5.27511597e-01 -6.40926182e-01 4.22676712e-01 5.12785673e-01
-1.31027544e+00 6.61537707e-01 -6.25421405e-02 -2.95843422e-01
-3.13257784e-01 -1.09171581e+00 -4.65257734e-01 -9.02813375e-01
-5.79797268e-01 -3.17350812e-02 3.94483387e-01 -5.16765118... | [8.526493072509766, -1.2099195718765259] |
973bb8ca-94e1-4b93-9cfb-89fa1471f5c4 | developing-asr-for-indonesian-english | null | null | https://aclanthology.org/2021.calcs-1.17 | https://aclanthology.org/2021.calcs-1.17.pdf | Developing ASR for Indonesian-English Bilingual Language Teaching | Usage-based analyses of teacher corpora and code-switching (Boztepe, 2003) are an important next stage in understanding language acquisition. Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. Using quantita... | ['Ben Foley', 'Zara Maxwelll-Smith'] | null | null | null | null | naacl-calcs-2021-6 | ['language-acquisition'] | ['natural-language-processing'] | [ 3.45050581e-02 -1.19464882e-01 -4.40131962e-01 -4.47980613e-01
-9.64302838e-01 -9.27311063e-01 3.53560150e-01 7.65222549e-01
-5.29457271e-01 3.87345254e-01 6.11364663e-01 -1.16318846e+00
-1.37453973e-01 -1.61229208e-01 -6.82636023e-01 -1.16343321e-02
4.42190617e-01 1.40863165e-01 1.78609639e-01 -4.35561180... | [10.790648460388184, 10.053860664367676] |
07d233e3-ff13-4a81-861e-a8dbf221fcd2 | nadi-2022-the-third-nuanced-arabic-dialect | 2210.09582 | null | https://arxiv.org/abs/2210.09582v2 | https://arxiv.org/pdf/2210.09582v2.pdf | NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task | We describe findings of the third Nuanced Arabic Dialect Identification Shared Task (NADI 2022). NADI aims at advancing state of the art Arabic NLP, including on Arabic dialects. It does so by affording diverse datasets and modeling opportunities in a standardized context where meaningful comparisons between models and... | ['Nizar Habash', 'Houda Bouamor', 'AbdelRahim Elmadany', 'Chiyu Zhang', 'Muhammad Abdul-Mageed'] | 2022-10-18 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-2.44930521e-01 -1.23196974e-01 -9.15348902e-02 -7.00168312e-01
-1.14469182e+00 -1.25153756e+00 9.08789814e-01 2.84965396e-01
-6.11634851e-01 6.57115459e-01 3.51257622e-01 -2.09121898e-01
-4.18059342e-03 -5.46535194e-01 -2.72994399e-01 -4.42079395e-01
-5.27639836e-02 9.46251690e-01 -4.82982337e-01 -1.09193385... | [10.182329177856445, 10.767207145690918] |
588835c5-e631-441f-8d27-9d5cec23819a | generalizing-graph-ode-for-learning-complex | 2307.04287 | null | https://arxiv.org/abs/2307.04287v1 | https://arxiv.org/pdf/2307.04287v1.pdf | Generalizing Graph ODE for Learning Complex System Dynamics across Environments | Learning multi-agent system dynamics has been extensively studied for various real-world applications, such as molecular dynamics in biology. Most of the existing models are built to learn single system dynamics from observed historical data and predict the future trajectory. In practice, however, we might observe mult... | ['Wei Wang', 'Yizhou Sun', 'Zijie Huang'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'physical-simulations'] | ['computer-vision', 'methodology', 'miscellaneous'] | [-8.82914364e-02 -2.67386049e-01 8.04290622e-02 2.02360496e-01
6.11353293e-03 -6.37323081e-01 7.04611480e-01 1.07467532e-01
-1.04142083e-02 8.80698085e-01 -3.37565728e-02 -1.37212321e-01
-5.07952809e-01 -6.82491481e-01 -8.58413458e-01 -1.21260655e+00
-5.41686893e-01 4.88839835e-01 1.13728508e-01 -3.07207674... | [6.529656887054443, 4.048830986022949] |
552727c8-9ded-49c5-9468-141e9f008662 | deep-monocular-visual-odometry-for-ground | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9201526 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9201526 | Deep Monocular Visual Odometry for Ground Vehicle | Monocular visual odometry, with the ability to help robots to locate themselves in unexplored
environments, has been a crucial research problem in robotics. Though the existed learning-based endto-end methods can reduce engineering efforts such as accurate camera calibration and tedious case-by-case
parameter tuning,... | ['HUI ZHANG', 'Xiangwei Wang'] | 2020-09-21 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.43287039e-01 8.25354010e-02 -1.24537848e-01 -1.73740044e-01
1.35301119e-02 -4.06632990e-01 3.46033126e-01 -5.88312268e-01
-7.78101742e-01 4.66719061e-01 -3.49020541e-01 -3.99442792e-01
-8.87602866e-02 -3.99554700e-01 -8.84287715e-01 -7.25915611e-01
3.36027034e-02 5.21216929e-01 4.29383218e-01 -2.87920207... | [7.989333629608154, -2.1770999431610107] |
934af0b1-2573-4b08-9350-0e8b3597148a | kam-a-kernel-attention-module-for-emotion | 2208.08161 | null | https://arxiv.org/abs/2208.08161v2 | https://arxiv.org/pdf/2208.08161v2.pdf | KAM -- a Kernel Attention Module for Emotion Classification with EEG Data | In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks. The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The desig... | ['Craig Michoski', 'Dongyang Kuang'] | 2022-08-17 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 1.23240814e-01 3.98563623e-01 2.05851555e-01 -5.87225199e-01
-3.88056427e-01 1.53853949e-02 9.80810598e-02 2.77503997e-01
-5.96149147e-01 7.32767999e-01 -3.28792930e-02 -1.43214121e-01
-2.68596470e-01 -1.08998001e-01 -5.82969189e-01 -6.44774139e-01
-4.75853801e-01 -3.28363515e-02 -1.85321897e-01 -1.40287027... | [13.152473449707031, 3.452599048614502] |
1ea29977-8447-47be-822c-7014c1195e4c | geometric-anchor-correspondence-mining-with | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Geometric_Anchor_Correspondence_Mining_With_Uncertainty_Modeling_for_Universal_Domain_CVPR_2022_paper.pdf | Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation | Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" ... | ['Minghua Deng', 'Tao Bai', 'Jianzhong He', 'Yihang Lou', 'Liang Chen'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 1.90311953e-01 1.39354318e-01 -4.43192631e-01 -4.09103096e-01
-1.29794037e+00 -7.35824287e-01 4.99825388e-01 8.53768829e-03
1.16807267e-01 8.93291712e-01 2.55849748e-03 -4.19770405e-02
1.78855047e-01 -8.74211609e-01 -9.37910378e-01 -9.94612157e-01
2.11733043e-01 7.45292902e-01 2.42657110e-01 1.38505414... | [10.371963500976562, 3.0738039016723633] |
eb29a7d2-48c8-4422-9e61-ea4ee8916145 | integration-of-regularized-l1-tracking-and | 1912.12883 | null | https://arxiv.org/abs/1912.12883v1 | https://arxiv.org/pdf/1912.12883v1.pdf | Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking | We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that e... | ['Bilge Gunsel', 'Filiz Gurkan'] | 2019-12-30 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-4.66946214e-01 -3.03828925e-01 -2.21233554e-02 2.95223087e-01
-4.19901341e-01 -5.95551193e-01 7.85441399e-01 -4.09571268e-02
-5.87224603e-01 5.22868156e-01 -7.79839233e-02 3.92798275e-01
2.82365739e-01 -8.27434734e-02 -7.76928961e-01 -8.21453512e-01
-1.23816885e-01 4.30820197e-01 8.43858361e-01 1.69968113... | [6.436605453491211, -2.072625160217285] |
9741a251-b169-4c34-9a4f-0e8f5dacd756 | self-supervised-video-representation-learning-3 | 2008.02531 | null | https://arxiv.org/abs/2008.02531v2 | https://arxiv.org/pdf/2008.02531v2.pdf | Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework | We propose a self-supervised method to learn feature representations from videos. A standard approach in traditional self-supervised methods uses positive-negative data pairs to train with contrastive learning strategy. In such a case, different modalities of the same video are treated as positives and video clips from... | ['Toshihiko Yamasaki', 'Xueting Wang', 'Li Tao'] | 2020-08-06 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 9.99490395e-02 -5.58070421e-01 -6.96438670e-01 -5.30823529e-01
-8.84881020e-01 -6.22715116e-01 7.06935704e-01 -1.76269904e-01
-5.75934529e-01 5.22137880e-01 1.75486371e-01 6.64661899e-02
1.67949334e-01 -4.60767210e-01 -1.03197575e+00 -7.94830799e-01
-4.32473600e-01 -8.42722282e-02 3.26019585e-01 -3.94596532... | [8.757645606994629, 0.8115352392196655] |
8e9f9781-a41b-4a3b-967a-e2bce634f7ba | a-two-steps-approach-to-improve-the | 2205.08265 | null | https://arxiv.org/abs/2205.08265v1 | https://arxiv.org/pdf/2205.08265v1.pdf | A two-steps approach to improve the performance of Android malware detectors | The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In this paper, we propose GUIDED RETRAINING, a supervised representation learning-based method that boosts... | ['Jacques Klein', 'Tegawendé F. Bissyandé', 'Kevin Allix', 'Nadia Daoudi'] | 2022-05-17 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.90934497e-01 2.14752909e-02 -6.73617303e-01 -7.37301707e-02
-6.59775972e-01 -6.48868203e-01 6.06658876e-01 -9.03925598e-02
-1.58892155e-01 4.31367993e-01 -3.18266690e-01 -8.44587028e-01
3.18267077e-01 -6.43484175e-01 -8.40179622e-01 -4.44302827e-01
-3.65944147e-01 2.28790045e-01 3.13629299e-01 -1.57576308... | [14.418176651000977, 9.676521301269531] |
3c056325-9a25-4fb7-99be-2cb7cdb0b518 | towards-making-the-most-of-dialogue | 2109.00668 | null | https://arxiv.org/abs/2109.00668v1 | https://arxiv.org/pdf/2109.00668v1.pdf | Towards Making the Most of Dialogue Characteristics for Neural Chat Translation | Neural Chat Translation (NCT) aims to translate conversational text between speakers of different languages. Despite the promising performance of sentence-level and context-aware neural machine translation models, there still remain limitations in current NCT models because the inherent dialogue characteristics of chat... | ['Jie zhou', 'Jinsong Su', 'Yufeng Chen', 'Jinan Xu', 'Fandong Meng', 'Chulun Zhou', 'Yunlong Liang'] | 2021-09-02 | null | https://aclanthology.org/2021.emnlp-main.6 | https://aclanthology.org/2021.emnlp-main.6.pdf | emnlp-2021-11 | ['speaker-identification'] | ['speech'] | [ 1.85581282e-01 -1.56745762e-02 -1.99139297e-01 -5.41038871e-01
-8.61306310e-01 -3.39043230e-01 8.51018190e-01 -4.66216654e-01
-2.19233289e-01 9.51269031e-01 6.09316230e-01 -3.89263600e-01
3.82096380e-01 -3.79313678e-01 -1.24260567e-01 -4.50267941e-01
4.25122619e-01 5.58364868e-01 -2.80493736e-01 -5.47257960... | [12.654387474060059, 8.2705078125] |
3064f027-d1b8-4dcd-9c6d-e19f7ab70daa | mask-focal-loss-for-dense-crowd-counting-with | 2212.11542 | null | https://arxiv.org/abs/2212.11542v2 | https://arxiv.org/pdf/2212.11542v2.pdf | Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks | As a fundamental computer vision task, crowd counting predicts the number of pedestrians in a scene, which plays an important role in risk perception and early warning, traffic control and scene statistical analysis. Currently, deep learning based head detection is a promising method for crowd counting. However, the hi... | ['Zongze Wu', 'Weixiang Liu', 'Yuanlong Deng', 'Guankun Wang', 'Xiaopin Zhong'] | 2022-12-22 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-2.67293125e-01 -3.23636979e-01 5.74812554e-02 -3.21991563e-01
-3.46941233e-01 -2.91375141e-03 5.77761889e-01 2.74710834e-01
-1.01101351e+00 9.08707738e-01 1.10217810e-01 2.40479968e-02
2.20774427e-01 -1.02636850e+00 -5.35036743e-01 -9.16879833e-01
-3.90816666e-02 5.65055132e-01 8.94222200e-01 9.68844723... | [8.274250984191895, -0.3998216688632965] |
1a2b6512-0cba-4625-97ac-51cd198593ff | towards-plug-n-play-task-level-autonomy-for | 2207.09713 | null | https://arxiv.org/abs/2207.09713v1 | https://arxiv.org/pdf/2207.09713v1.pdf | Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models | To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical ro... | ['Ronen I. Brafman', 'Dan R. Suissa', 'Or Wertheim'] | 2022-07-20 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.56752282e-01 5.16755283e-01 -1.43628970e-01 -6.10136464e-02
-7.64239907e-01 -6.60012424e-01 4.29828823e-01 -5.81199378e-02
-1.96527854e-01 7.70833492e-01 3.22414027e-03 -3.69175494e-01
-3.82799953e-01 -7.78953910e-01 -6.81974411e-01 -4.65327114e-01
3.16507593e-02 7.53638446e-01 5.15254855e-01 -3.15572947... | [4.505429744720459, 1.1556674242019653] |
c1ecf257-0a81-4da2-9fbd-0e9690c0d5db | grounding-plural-phrases-countering | null | null | https://aclanthology.org/2021.alvr-1.4 | https://aclanthology.org/2021.alvr-1.4.pdf | Grounding Plural Phrases: Countering Evaluation Biases by Individuation | Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects ... | ['Anette Frank', 'Letitia Parcalabescu', 'Julia Suter'] | null | null | null | null | naacl-alvr-2021-6 | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.62776935e-01 3.63354594e-01 -1.33756578e-01 -2.32695073e-01
-1.11063230e+00 -9.13158655e-01 7.24673986e-01 7.09846854e-01
-6.50039017e-01 6.96870446e-01 5.37425756e-01 -1.41616300e-01
2.03033134e-01 -7.84868836e-01 -8.42642069e-01 -5.96920907e-01
3.34383026e-02 4.65259194e-01 4.29779798e-01 -5.62105417... | [10.67442512512207, 1.4921914339065552] |
031d99b3-9061-47b5-83f7-be8f16b29ca0 | snowformer-scale-aware-transformer-via | 2208.09703 | null | https://arxiv.org/abs/2208.09703v3 | https://arxiv.org/pdf/2208.09703v3.pdf | SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing | Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task. As prior arts can not handle it ideally, we propose a novel transformer, SnowFormer, which explores efficient cross-attentions to build local-global context interaction across patches and surpasses existing... | ['ErKang Chen', 'Yun Liu', 'Tian Ye', 'Sixiang Chen'] | 2022-08-20 | null | null | null | null | ['single-image-desnowing'] | ['computer-vision'] | [ 1.35519952e-01 -3.74239981e-01 -2.85041593e-02 -2.23997608e-01
-1.14788067e+00 -2.21496880e-01 5.44923484e-01 -1.02969907e-01
-2.72698671e-01 5.95373511e-01 7.48706341e-01 -1.91136464e-01
-4.09365743e-02 -7.41789043e-01 -9.26355302e-01 -1.04476202e+00
7.25716352e-02 -1.12357557e-01 3.18101272e-02 -4.78300899... | [11.179513931274414, -2.695082426071167] |
dc086e58-79ab-4a45-91f9-55463cd44de5 | hierarchical-matching-and-reasoning-for-multi | 2306.1446 | null | https://arxiv.org/abs/2306.14460v1 | https://arxiv.org/pdf/2306.14460v1.pdf | Hierarchical Matching and Reasoning for Multi-Query Image Retrieval | As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglects the hierarchical guidance of multi-level similaritie... | ['Xuelong Li', 'Yanwei Pang', 'Haoran Wang', 'Yan Zhang', 'Zhihao LI', 'Zhong Ji'] | 2023-06-26 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 0.16811563 -0.36565572 -0.43042713 -0.2590881 -1.1950161 -0.40017614
0.5810723 0.32425722 -0.19476227 0.02973298 0.5309805 -0.04924569
-0.4692287 -0.7582248 -0.27113715 -0.5754841 0.35863993 0.18104436
0.78252846 -0.45607406 0.43098426 0.3636227 -1.6613067 0.50724316
0.8182324 1.159301 0.... | [10.6735258102417, 1.2245707511901855] |
f7c7efe8-75e0-47c4-8724-eb3267c824af | taca-upgrading-your-visual-foundation-model | 2306.12642 | null | https://arxiv.org/abs/2306.12642v1 | https://arxiv.org/pdf/2306.12642v1.pdf | TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter | Visual foundation models like CLIP excel in learning feature representations from extensive datasets through self-supervised methods, demonstrating remarkable transfer learning and generalization capabilities. A growing number of applications based on visual foundation models are emerging, including innovative solution... | ['Mike Zheng Shou', 'Ying Shan', 'Xuyuan Xu', 'Yixiao Ge', 'Binjie Zhang'] | 2023-06-22 | null | null | null | null | ['video-recognition', 'visual-question-answering-1', 'video-text-retrieval', 'retrieval', 'transfer-learning', 'question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'natural-language-processing'] | [-1.62592143e-01 -4.93095636e-01 -2.19707131e-01 -3.99421334e-01
-8.44967961e-01 -7.34255970e-01 4.09294754e-01 -1.23615712e-01
-2.37262473e-01 2.36335427e-01 4.24553305e-02 -5.50744176e-01
-1.18982151e-01 -3.71449590e-01 -7.29576349e-01 -1.78122833e-01
-4.58568931e-02 1.38947755e-01 2.74456531e-01 -2.13555485... | [10.20382308959961, 1.9163975715637207] |
d8c3cc29-45d6-41fd-88a4-f06ba5f08bed | deep-learning-models-delineates-multiple | 1802.04427 | null | http://arxiv.org/abs/1802.04427v2 | http://arxiv.org/pdf/1802.04427v2.pdf | Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections | Nuclear segmentation is an important step for profiling aberrant regions of
histology sections. However, segmentation is a complex problem as a result of
variations in nuclear geometry (e.g., size, shape), nuclear type (e.g.,
epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy).
The problem is ... | ['Bahram Parvin', 'Mina Khoshdeli'] | 2018-02-13 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 2.64900178e-01 1.33223431e-02 2.72747539e-02 -1.57475412e-01
-7.42811263e-01 -1.00285864e+00 3.13648224e-01 6.22052550e-01
-4.37779397e-01 8.41330409e-01 1.25128791e-01 -2.36555681e-01
-1.67222485e-01 -6.14719510e-01 -6.05467796e-01 -9.66523588e-01
1.05665766e-01 6.90600216e-01 2.23361462e-01 6.68692589... | [14.939221382141113, -3.2189347743988037] |
982c02bd-b757-4f2d-8ee9-30f08c3caf3f | autonomous-manipulation-learning-for-similar | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ren_Autonomous_Manipulation_Learning_for_Similar_Deformable_Objects_via_Only_One_CVPR_2023_paper.pdf | Autonomous Manipulation Learning for Similar Deformable Objects via Only One Demonstration | In comparison with most methods focusing on 3D rigid object recognition and manipulation, deformable objects are more common in our real life but attract less attention. Generally, most existing methods for deformable object manipulation suffer two issues, 1) Massive demonstration: repeating thousands of robot-obje... | ['Yang Cong', 'Ronghan Chen', 'Yu Ren'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-recognition', 'deformable-object-manipulation'] | ['computer-vision', 'robots'] | [ 5.50713129e-02 -7.41852373e-02 2.12299991e-02 -3.45866531e-01
-1.15503706e-01 -7.33390391e-01 3.89232993e-01 -2.49355689e-01
-1.99594155e-01 6.07869804e-01 -3.00988913e-01 1.19611472e-01
-3.90929401e-01 -7.30114818e-01 -1.20166969e+00 -7.83324242e-01
-2.01704040e-01 9.06253636e-01 6.50072396e-01 -3.01625878... | [4.97944974899292, 0.24209393560886383] |
322166d3-713b-4d7d-a0b2-368e1dd61c52 | single-stage-visual-relationship-learning | 2306.05689 | null | https://arxiv.org/abs/2306.05689v1 | https://arxiv.org/pdf/2306.05689v1.pdf | Single-Stage Visual Relationship Learning using Conditional Queries | Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationships. While showing promising results, the pipeline structure induces large parameter and computation overhead, and typically hinders end-to-en... | ['Nuno Vasconcelos', 'Subarna Tripathi', 'Tz-Ying Wu', 'Alakh Desai'] | 2023-06-09 | null | null | null | null | ['scene-graph-generation', 'multi-task-learning'] | ['computer-vision', 'methodology'] | [ 4.90142494e-01 2.33556792e-01 -2.71863133e-01 -3.14363599e-01
-1.06691694e+00 -4.43941027e-01 5.16606688e-01 3.18727314e-01
-3.07835132e-01 6.84476674e-01 3.00820433e-02 -3.01617920e-01
1.85903132e-01 -1.00185108e+00 -9.28982615e-01 -3.49717170e-01
2.01761365e-01 8.67057025e-01 5.20705104e-01 1.65551737... | [10.286669731140137, 1.6825331449508667] |
34fa740d-b26a-49e7-8a9e-4d51e3944bfd | tallformer-temporal-action-localization-with | 2204.0168 | null | https://arxiv.org/abs/2204.01680v2 | https://arxiv.org/pdf/2204.01680v2.pdf | TALLFormer: Temporal Action Localization with a Long-memory Transformer | Most modern approaches in temporal action localization divide this problem into two parts: (i) short-term feature extraction and (ii) long-range temporal boundary localization. Due to the high GPU memory cost caused by processing long untrimmed videos, many methods sacrifice the representational power of the short-term... | ['Gedas Bertasius', 'Feng Cheng'] | 2022-04-04 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.25599736e-01 -4.98217702e-01 -4.08810109e-01 -3.50820236e-02
-9.49092388e-01 -6.29662991e-01 3.06400478e-01 -2.03147978e-01
-6.99118316e-01 4.54673916e-01 2.32969269e-01 -1.06175646e-01
1.12078115e-01 -5.04742622e-01 -7.11176455e-01 -6.81216657e-01
-1.62829041e-01 -4.66439761e-02 6.82474732e-01 2.51609534... | [8.683733940124512, 0.4298623502254486] |
738b77e4-a4e0-480a-9db1-e1d83d6231be | sequential-clique-optimization-for-video | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Yeong_Jun_Koh_Sequential_Clique_Optimization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yeong_Jun_Koh_Sequential_Clique_Optimization_ECCV_2018_paper.pdf | Sequential Clique Optimization for Video Object Segmentation | A novel algorithm to segment out objects in a video sequence is proposed in this work. First, we extract object instances in each frame. Then, we select a visually important object instance in each frame to construct the salient object track through the sequence. This can be formulated as finding the maximal weight cl... | ['Chang-Su Kim', 'Young-Yoon Lee', 'Yeong Jun Koh'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.28948581e-01 1.41685888e-01 -5.23060024e-01 -1.91859156e-02
-7.84118772e-01 -5.84810317e-01 -2.27717385e-01 2.43687838e-01
-2.47449234e-01 6.34101093e-01 -2.09649190e-01 2.37697959e-01
-2.13432208e-01 -5.50825298e-01 -1.02471745e+00 -7.32287765e-01
-2.91962266e-01 5.29274046e-01 1.12841058e+00 3.28329176... | [9.166877746582031, -0.22267374396324158] |
b75806db-39e2-402d-95e3-47c3f09c1cfa | understanding-the-quality-of-container | 2101.03844 | null | https://arxiv.org/abs/2101.03844v1 | https://arxiv.org/pdf/2101.03844v1.pdf | Understanding the Quality of Container Security Vulnerability Detection Tools | Virtualization enables information and communications technology industry to better manage computing resources. In this regard, improvements in virtualization approaches together with the need for consistent runtime environment, lower overhead and smaller package size has led to the growing adoption of containers. This... | ['Salman Toor', 'Omar Javed'] | 2021-01-11 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-6.29853785e-01 -6.16848648e-01 1.03770174e-01 1.27507403e-01
-4.08326119e-01 -1.12320375e+00 1.91858679e-01 3.43459696e-01
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1.54000325e-02 -3.31833929e-01 3.29090744e-01 -2.66647428... | [14.37150764465332, 9.65654182434082] |
7fae61c5-d5d4-4585-a5f0-482ca0e2f5ae | optimization-and-interpretability-of-graph | 2305.16196 | null | https://arxiv.org/abs/2305.16196v1 | https://arxiv.org/pdf/2305.16196v1.pdf | Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications | For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parame... | ['Wolfgang Utschick', 'Michael Botsch', 'Sebastian Dorn', 'Andreas Tollkühn', 'Marion Neumeier'] | 2023-05-25 | null | null | null | null | ['graph-attention'] | ['graphs'] | [ 4.95785624e-02 5.60766041e-01 -4.90220875e-01 -1.98576376e-01
-1.53834522e-01 8.06611404e-03 5.14342725e-01 3.91274124e-01
-7.92567432e-02 6.67777121e-01 1.08711831e-01 -6.94525838e-01
-4.40488219e-01 -8.52355361e-01 -6.03861511e-01 -4.76368964e-01
-1.49910524e-01 4.06644523e-01 2.22013474e-01 -3.73160630... | [7.219631195068359, 6.260894775390625] |
3355fbaa-2df8-4c45-b4c2-44aec7113c65 | diffute-universal-text-editing-diffusion | 2305.10825 | null | https://arxiv.org/abs/2305.10825v2 | https://arxiv.org/pdf/2305.10825v2.pdf | DiffUTE: Universal Text Editing Diffusion Model | Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which... | ['Weiqiang Wang', 'Huijia Zhu', 'Changhua Meng', 'Yaohui Li', 'Xing Zheng', 'Jun Lan', 'Zhangxuan Gu', 'Zhuoer Xu', 'Haoxing Chen'] | 2023-05-18 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 2.91892290e-01 1.01900727e-01 -2.67723221e-02 -2.29375631e-01
-3.36725622e-01 -5.65658152e-01 8.46221447e-01 -1.60675615e-01
-1.28070191e-01 4.99541759e-01 2.96662390e-01 -2.51261979e-01
2.43300661e-01 -8.62771690e-01 -7.23455012e-01 -2.71000266e-01
4.90821719e-01 2.43981540e-01 9.45999697e-02 -3.06270808... | [11.443243980407715, -0.35327932238578796] |
ca688a70-51af-41c9-8cfd-597988fbadc4 | cover-a-heuristic-greedy-adversarial-attack | 2306.05659 | null | https://arxiv.org/abs/2306.05659v2 | https://arxiv.org/pdf/2306.05659v2.pdf | COVER: A Heuristic Greedy Adversarial Attack on Prompt-based Learning in Language Models | Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead th... | ['Yongjian Huang', 'Wenbin Zhu', 'Qingliang Chen', 'Zihao Tan'] | 2023-06-09 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 6.05145060e-02 -8.25666711e-02 1.25787318e-01 2.30561029e-02
-8.05881977e-01 -8.09457242e-01 8.47153366e-01 -2.95300633e-02
-5.01726210e-01 5.49636066e-01 -1.35145009e-01 -5.87480903e-01
-8.49689543e-02 -8.67773473e-01 -5.74528754e-01 -4.53827530e-01
-2.21373111e-01 1.62313715e-01 7.70544052e-01 -5.75637519... | [5.8648247718811035, 7.945981502532959] |
76475404-7539-414d-96e2-c3aad3053dc6 | phrase-retrieval-for-open-domain | 2306.04293 | null | https://arxiv.org/abs/2306.04293v1 | https://arxiv.org/pdf/2306.04293v1.pdf | Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning | Open-Domain Conversational Question Answering (ODConvQA) aims at answering questions through a multi-turn conversation based on a retriever-reader pipeline, which retrieves passages and then predicts answers with them. However, such a pipeline approach not only makes the reader vulnerable to the errors propagated from ... | ['Jong C. Park', 'Sung Ju Hwang', 'Jinheon Baek', 'Soyeong Jeong'] | 2023-06-07 | null | null | null | null | ['conversational-question-answering'] | ['natural-language-processing'] | [-7.97748193e-03 1.20267354e-01 4.15563107e-01 -3.79031301e-01
-1.32854199e+00 -8.44201982e-01 5.89454949e-01 1.49234712e-01
-3.91787708e-01 5.97153604e-01 5.78160107e-01 -3.79594028e-01
-6.94894406e-05 -8.74078691e-01 -5.13150811e-01 -4.78642881e-01
4.94759709e-01 7.90569842e-01 5.37544191e-01 -6.74281299... | [11.68349838256836, 8.042598724365234] |
10019f74-e756-4b62-be99-198deed7b4d1 | knowledge-graph-question-answering-via-sparql | 2109.09475 | null | https://arxiv.org/abs/2109.09475v1 | https://arxiv.org/pdf/2109.09475v1.pdf | Knowledge Graph Question Answering via SPARQL Silhouette Generation | Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large-scale Knowledge Graphs (KGs). Recently Neural Machine Translation based approaches are gaining momentum that translates natural language queries to structured query languages thereby solvin... | ['G P Shrivatsa Bhargav', 'Dinesh Khandelwal', 'Dinesh Garg', 'Saswati Dana', 'Sukannya Purkayastha'] | 2021-09-06 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [ 1.19329587e-01 6.01297915e-01 -1.07136779e-02 -2.21382692e-01
-1.15892196e+00 -7.30494261e-01 2.46894404e-01 2.94413388e-01
-4.72470284e-01 7.39731252e-01 1.49618939e-01 -5.46923578e-01
1.92806982e-02 -1.51598895e+00 -1.14569497e+00 -1.73562154e-01
1.19786613e-01 1.06084490e+00 4.16396588e-01 -6.40488625... | [10.132826805114746, 7.857876777648926] |
54e3ba56-445a-48f2-8a65-2036cbde3b8b | cde-iiith-at-semeval-2016-task-12-extraction | null | null | https://aclanthology.org/S16-1192 | https://aclanthology.org/S16-1192.pdf | CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques | null | ['Veera Raghavendra Chikka'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.256299018859863, 3.879796028137207] |
fffa1325-7fd6-4c2e-8338-9d9f27faa451 | anomal-e-a-self-supervised-network-intrusion | 2207.06819 | null | https://arxiv.org/abs/2207.06819v5 | https://arxiv.org/pdf/2207.06819v5.pdf | Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks | This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning to generalise graph representations and output embeddings. As network flows are naturally g... | ['Marius Portmann', 'Siamak Layeghy', 'Wai Weng Lo', 'Evan Caville'] | 2022-07-14 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-7.75017515e-02 2.12146100e-02 1.47326402e-02 -2.83742309e-01
8.85894179e-01 -5.19561648e-01 8.45546901e-01 4.47143972e-01
-3.96885306e-01 2.54023582e-01 -2.51491427e-01 -1.14690804e+00
-4.60343182e-01 -1.27266383e+00 -1.42654717e-01 -7.00400174e-02
-8.02575409e-01 7.09627807e-01 6.17866278e-01 -7.38456905... | [5.492465496063232, 7.188541889190674] |
9be78d35-d482-4d00-aeed-158c82a7af27 | anomaly-classification-in-distribution | 1805.04979 | null | http://arxiv.org/abs/1805.04979v1 | http://arxiv.org/pdf/1805.04979v1.pdf | Anomaly Classification in Distribution Networks Using a Quotient Gradient System | The classification of anomalies or sudden changes in power networks versus
normal abrupt changes or switching actions is essential to take appropriate
maintenance actions that guarantee the quality of power delivery. This issue
has increased in importance and has become more complicated with the
proliferation of volati... | [] | 2018-05-14 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 1.00572318e-01 -5.61476588e-01 -1.41556626e-02 -2.33502835e-01
-4.13206846e-01 -4.40471768e-01 4.12108481e-01 4.52948600e-01
1.81051999e-01 1.05278325e+00 -2.48832449e-01 -4.38914001e-01
-6.82079017e-01 -7.04829991e-01 -1.76390000e-02 -1.04485345e+00
-4.86098856e-01 2.13461444e-01 -1.00063533e-01 -1.71348110... | [6.146108150482178, 2.5638577938079834] |
4355d890-6c26-4779-86e2-9483469dc277 | multi-domain-incremental-learning-for | 2110.12205 | null | https://arxiv.org/abs/2110.12205v1 | https://arxiv.org/pdf/2110.12205v1.pdf | Multi-Domain Incremental Learning for Semantic Segmentation | Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment performed sequentially on three popular road scene segmentation datasets demonstrates that existing segmentation frameworks fail at incrementally... | ['C. V. Jawahar', 'Anbumani Subramanian', 'Chetan Arora', 'Vineeth N Balasubramanian', 'Rohit Saluja', 'Prachi Garg'] | 2021-10-23 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.57545704e-01 4.87503149e-02 -3.39055985e-01 -4.42192286e-01
-9.14088190e-01 -6.82574928e-01 6.05515897e-01 1.50032982e-01
-5.64490497e-01 9.31648433e-01 -1.14545032e-01 -3.12380344e-01
-2.27907628e-01 -7.89180100e-01 -8.43374729e-01 -5.52143157e-01
-1.13963626e-01 7.77828395e-01 8.10613394e-01 -1.54437020... | [9.803983688354492, 1.5857502222061157] |
b928a92d-773d-4a6a-9792-90a43abf8356 | entity-cloze-by-date-understanding-what-lms | null | null | https://openreview.net/forum?id=InPbQdUhmUH | https://openreview.net/pdf?id=InPbQdUhmUH | Entity Cloze By Date: Understanding what LMs know about unseen entities | Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. Our world, however, is dynamic, and new entities constantly arise. We propose a framework to analyze what LMs can infer about new entities that did not exist when the LMs were pretrained. We derive a datas... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['date-understanding'] | ['reasoning'] | [-3.12235683e-01 5.08575797e-01 -3.92563045e-01 -3.40754360e-01
-6.92448020e-01 -1.13233960e+00 9.42719638e-01 6.85382903e-01
-9.34082031e-01 1.06293356e+00 3.95280212e-01 -3.20065469e-01
8.51961225e-02 -9.51017559e-01 -9.70902503e-01 1.42495215e-01
-3.28455657e-01 8.00275087e-01 6.48942888e-01 -2.15156227... | [9.457036972045898, 8.877546310424805] |
94860e21-6c18-43b1-8f23-b1180dd40a21 | single-image-intrinsic-decomposition-without | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Wei-Chiu_Single_Image_Intrinsic_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Wei-Chiu_Single_Image_Intrinsic_ECCV_2018_paper.pdf | Single Image Intrinsic Decomposition without a Single Intrinsic Image | Intrinsic image decomposition---decomposing a natural image into a set of images corresponding to different physical causes---is one of the key and fundamental problems of computer vision. Previous intrinsic decomposition approaches either address the problem in a fully supervised manner, or require multiple images of ... | ['Wei-Chiu Ma', 'Raquel Urtasun', 'Bolei Zhou', 'Antonio Torralba', 'Hang Chu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.09882832e-01 1.25906855e-01 -2.77027972e-02 -8.36142898e-02
-6.84769392e-01 -3.58209550e-01 4.56084311e-01 -2.97730923e-01
-2.41971046e-01 4.78152633e-01 -7.80866817e-02 -2.70046517e-02
-1.20778702e-01 -7.35438824e-01 -8.60834062e-01 -8.58638763e-01
4.05439496e-01 2.11017475e-01 1.62445411e-01 -1.11985341... | [9.618846893310547, -2.63651442527771] |
0c251e7b-092d-483b-8442-dafc68e2fef7 | single-image-deraining-via-rain-steaks-aware | 2209.07808 | null | https://arxiv.org/abs/2209.07808v2 | https://arxiv.org/pdf/2209.07808v2.pdf | Single Image Deraining via Rain-Steaks Aware Deep Convolutional Neural Network | It is challenging to remove rain-steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. This problem is studied in this paper by combining conventional image processing techniques and deep learning based techniques. An improved weighted guided image filter (iWGIF) is proposed... | ['Shiqian Wu', 'Yuwen Li', 'Chaobing Zheng'] | 2022-09-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.90477911e-02 -5.21547735e-01 4.16021466e-01 -6.01175368e-01
-2.29043260e-01 -7.72253349e-02 -3.01245712e-02 -5.31319499e-01
-3.36604655e-01 1.01909971e+00 -6.23621866e-02 -1.29554778e-01
-5.45679927e-02 -1.11744225e+00 -6.07574880e-01 -1.24968815e+00
-3.74775618e-01 5.95424436e-02 1.38437673e-01 -6.14468634... | [10.925448417663574, -3.2691171169281006] |
b0b2c8be-3972-4eb4-9a19-1eefcc0b5048 | machine-learning-computer-vision-applications | 2303.0756 | null | https://arxiv.org/abs/2303.07560v1 | https://arxiv.org/pdf/2303.07560v1.pdf | Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California | We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-fie... | ['Kostas Alexandridis'] | 2023-03-14 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 4.12065893e-01 -4.42097872e-01 3.91165167e-01 -5.54989994e-01
-3.49018037e-01 -9.27151620e-01 5.03188968e-01 -2.59452730e-01
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-5.05970120e-01 4.95122910e-01 1.34964213e-01 -4.62892175... | [9.42424488067627, -1.4027458429336548] |
5c3419d8-ee39-4d78-bd3c-18b1b63e602c | deletion-and-insertion-tests-in-regression | 2205.12423 | null | https://arxiv.org/abs/2205.12423v2 | https://arxiv.org/pdf/2205.12423v2.pdf | Deletion and Insertion Tests in Regression Models | A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function $f$. The insertion and deletion tests of Petsiuk et al. (2018) are used to judge the quality of algorithms that rank pixels from most to least important for a classification. Motivated by reg... | ['Art B. Owen', 'Masayoshi Mase', 'Naofumi Hama'] | 2022-05-25 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.53814250e-01 1.33400112e-01 -2.40577564e-01 -5.65828741e-01
-7.10085213e-01 -7.85455525e-01 3.18175167e-01 2.65792340e-01
-5.04642606e-01 8.87095749e-01 -1.91284701e-01 -7.53152132e-01
-5.77566445e-01 -8.38180125e-01 -1.07837737e+00 -7.06462681e-01
-3.03312361e-01 3.34706604e-01 -3.81063595e-02 -1.72667921... | [8.206751823425293, 4.954085826873779] |
14776783-02d2-4126-bf3a-29c19ba7f07c | boosting-the-convergence-of-reinforcement | 2107.08815 | null | https://arxiv.org/abs/2107.08815v1 | https://arxiv.org/pdf/2107.08815v1.pdf | Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data | Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for applications in power-constrained scenarios such as embedded systems. Reinforcement learning (RL)-based auto-pruning has been further proposed to... | ['Wei zhang', 'Wei Lin', 'Jun Yang', 'Feiwen Zhu', 'Mengdi Wang', 'Jiandong Mu'] | 2021-07-16 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 9.10794288e-02 -1.36316657e-01 -2.10600942e-01 -3.39045733e-01
-3.75786200e-02 1.13321841e-01 1.25324532e-01 -9.93842185e-02
-1.00045907e+00 8.50050509e-01 -5.64716816e-01 -6.80042624e-01
-2.92129312e-02 -1.20059669e+00 -7.53271639e-01 -6.18352056e-01
1.40282154e-01 2.49695107e-01 4.14261818e-01 -1.13914400... | [8.510058403015137, 3.0427474975585938] |
39e69067-ad9c-42b2-8b56-f1240c644fa0 | uncovering-hidden-challenges-in-query-based | 2009.00325 | null | https://arxiv.org/abs/2009.00325v2 | https://arxiv.org/pdf/2009.00325v2.pdf | Uncovering Hidden Challenges in Query-Based Video Moment Retrieval | The query-based moment retrieval is a problem of localising a specific clip from an untrimmed video according a query sentence. This is a challenging task that requires interpretation of both the natural language query and the video content. Like in many other areas in computer vision and machine learning, the progress... | ['Janne Heikkilä', 'Esa Rahtu', 'Yuta Nakashima', 'Mayu Otani'] | 2020-09-01 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.77990937e-01 -2.08601058e-01 -2.03901082e-01 -3.58488530e-01
-1.17420840e+00 -8.51871848e-01 9.16732430e-01 1.50991693e-01
-4.48164582e-01 3.00289512e-01 4.97815102e-01 -1.32994607e-01
-2.37618148e-01 -1.12558797e-01 -7.43396282e-01 -5.37710130e-01
-2.26484299e-01 3.21048856e-01 5.04542112e-01 -3.29759061... | [10.236137390136719, 0.7879320383071899] |
169a652a-106d-4513-82e9-4554c15656ed | diverse-demonstrations-improve-in-context | 2212.068 | null | https://arxiv.org/abs/2212.06800v3 | https://arxiv.org/pdf/2212.06800v3.pdf | Diverse Demonstrations Improve In-context Compositional Generalization | In-context learning has shown great success in i.i.d semantic parsing splits, where the training and test sets are drawn from the same distribution. In this setup, models are typically prompted with demonstrations that are similar to the input utterance. However, in the setup of compositional generalization, where mode... | ['Jonathan Berant', 'Ben Bogin', 'Itay Levy'] | 2022-12-13 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.03911221e-01 2.20920846e-01 -5.62809184e-02 -7.22041130e-01
-8.35678875e-01 -1.07939422e+00 4.42687094e-01 1.59868076e-01
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2.01726109e-01 7.29029477e-01 3.05690199e-01 -1.25339657... | [10.73022747039795, 8.868307113647461] |
15b60a9a-a3b3-4362-8a1d-a540ca108f27 | polygen-an-autoregressive-generative-model-of | 2002.1088 | null | https://arxiv.org/abs/2002.10880v1 | https://arxiv.org/pdf/2002.10880v1.pdf | PolyGen: An Autoregressive Generative Model of 3D Meshes | Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches have avoided the challenges of working with 3D meshes, instead using alternative object representations that are more compatible with neur... | ['Peter W. Battaglia', 'S. M. Ali Eslami', 'Charlie Nash', 'Yaroslav Ganin'] | 2020-02-23 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6917-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6917-Paper.pdf | icml-2020-1 | ['3d-shape-generation'] | ['computer-vision'] | [ 2.73395330e-01 5.29292643e-01 1.32548422e-01 -4.50036824e-01
-1.02534318e+00 -3.03464413e-01 6.97376609e-01 -9.26794261e-02
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-4.93873954e-02 -1.29741120e+00 -1.24842775e+00 -1.18108541e-01
-2.19890445e-01 1.28102505e+00 3.34150225e-01 1.38432249... | [8.670619010925293, -3.6567227840423584] |
2293501f-8f63-4a77-bc7b-9dc26cdec9a0 | wepamadm-outlier-detection-weighted-outlier | 2306.06139 | null | https://arxiv.org/abs/2306.06139v1 | https://arxiv.org/pdf/2306.06139v1.pdf | WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining | Weighted Outlier Detection is a method for identifying unusual or anomalous data points in a dataset, which can be caused by various factors like human error, fraud, or equipment malfunctions. Detecting outliers can reveal vital information about system faults, fraudulent activities, and patterns in the data, assisting... | ['Madhuri Bhavsar', 'Rachna Jain', 'Jai Prakash Verma', 'Ravindrakumar Purohit'] | 2023-06-09 | null | null | null | null | ['outlier-detection', 'fault-detection'] | ['methodology', 'miscellaneous'] | [ 3.50031853e-01 -8.38644952e-02 -1.61949694e-01 -3.59338343e-01
8.69342387e-02 -2.47231200e-01 1.71225235e-01 6.70419097e-01
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-5.45009136e-01 -4.33529854e-01 -6.87400162e-01 -4.72288251e-01
-4.97877389e-01 3.50403368e-01 7.07159117e-02 7.51670450... | [7.546064376831055, 2.5818352699279785] |
bb495b46-ba88-4971-b75a-ff0648678329 | diagnosis-of-covid-19-based-on-chest | 2212.13032 | null | https://arxiv.org/abs/2212.13032v1 | https://arxiv.org/pdf/2212.13032v1.pdf | Diagnosis of COVID-19 based on Chest Radiography | The Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019 and now becoming a pandemic. When COVID-19 patients undergo radiography examination, radiologists can observe the present of radiographic abnormalities from their chest X-ray (CXR) images. In this study, a deep convolut... | ['Hoi Leong Lee', 'Mei Gah Lim'] | 2022-12-26 | null | null | null | null | ['image-augmentation', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 1.22152783e-01 -2.79251367e-01 -5.08002788e-02 -1.05318971e-01
-2.84393519e-01 -3.12753290e-01 1.99688375e-01 1.20142542e-01
-5.97029328e-01 6.42593563e-01 7.21506476e-02 -7.21220315e-01
-4.48190831e-02 -7.71752656e-01 -5.59984922e-01 -6.02736890e-01
-5.82465380e-02 4.69199270e-01 -3.72603051e-02 2.02189848... | [15.52871036529541, -1.7487891912460327] |
121b8637-25f3-4f0c-b06c-5d03d09e4e3a | counterfactual-edits-for-generative | 2303.01555 | null | https://arxiv.org/abs/2303.01555v1 | https://arxiv.org/pdf/2303.01555v1.pdf | Counterfactual Edits for Generative Evaluation | Evaluation of generative models has been an underrepresented field despite the surge of generative architectures. Most recent models are evaluated upon rather obsolete metrics which suffer from robustness issues, while being unable to assess more aspects of visual quality, such as compositionality and logic of synthesi... | ['Giorgos Stamou', 'Konstantinos Thomas', 'Giorgos Filandrianos', 'Maria Lymperaiou'] | 2023-03-02 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 5.01199186e-01 5.23395598e-01 9.11884308e-02 -2.81976968e-01
-1.31604806e-01 -8.39213908e-01 1.26940167e+00 1.81827098e-01
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-3.29891175e-01 -9.74436164e-01 -7.59506643e-01 -4.73921239e-01
3.25799674e-01 5.26854634e-01 2.49999110e-02 -1.89340740... | [11.225885391235352, 0.49538999795913696] |
d5e4c16b-46c8-49da-9cba-070af4558a05 | 191209654 | 1912.09654 | null | https://arxiv.org/abs/1912.09654v1 | https://arxiv.org/pdf/1912.09654v1.pdf | JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds | In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. Firstly, we build an effective backbone network to extract robust features from the raw point clouds. Secondly, to obt... | ['Wenbing Tao', 'Lin Zhao'] | 2019-12-20 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-1.50472508e-04 5.23445383e-02 -2.36895587e-02 -5.64032972e-01
-6.30675852e-01 -3.21643263e-01 3.98734242e-01 3.62656303e-02
-6.35862947e-02 8.05348530e-02 -2.24953398e-01 -1.44611010e-02
-2.84291416e-01 -1.02990341e+00 -8.23615670e-01 -5.27598262e-01
1.59660608e-01 5.75200498e-01 5.46647608e-01 -8.18533301... | [7.96361780166626, -3.3369433879852295] |
aa68b9e3-f9fe-4eae-bcb7-070780ca5cfd | identifying-the-origin-of-finger-vein-samples | 2102.03992 | null | https://arxiv.org/abs/2102.03992v1 | https://arxiv.org/pdf/2102.03992v1.pdf | Identifying the Origin of Finger Vein Samples Using Texture Descriptors | Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments. Motivated by shortcomings of the photo response non-uniformity (PRNU) based method i... | ['Andreas Uhl', 'Babak Maser'] | 2021-02-08 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 8.41353357e-01 -3.30489993e-01 -2.92140186e-01 -2.49999180e-01
-5.77037811e-01 -9.43383992e-01 6.94315135e-01 4.68811810e-01
-2.86928952e-01 3.00934047e-01 -1.37637928e-01 -1.38200596e-01
-2.91987926e-01 -8.59382927e-01 -2.03798153e-03 -8.89300406e-01
1.32364541e-01 5.37514448e-01 1.98711187e-01 1.06218681... | [13.010711669921875, 1.0179308652877808] |
150ec280-420a-4cdd-a181-980f08fcf7e7 | a-hybrid-transmission-model-for-plasmodium | 2208.10403 | null | https://arxiv.org/abs/2208.10403v1 | https://arxiv.org/pdf/2208.10403v1.pdf | A hybrid transmission model for Plasmodium vivax accounting for superinfection, immunity and the hypnozoite reservoir | Malaria is a vector-borne disease that exacts a grave toll in the Global South. The epidemiology of Plasmodium vivax, the most geographically expansive agent of human malaria, is characterised by the accrual of a reservoir of dormant parasites known as hypnozoites. Relapses, arising from hypnozoite activation events, c... | ['Jennifer A. Flegg', 'James M. McCaw', 'Peter G. Taylor', 'Somya Mehra'] | 2022-08-22 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 9.67606977e-02 -3.14909399e-01 2.71480203e-01 1.61248803e-01
-1.75482705e-02 -5.69765449e-01 7.38783419e-01 3.33741933e-01
-5.46148479e-01 9.23418641e-01 -1.22572489e-01 -4.62131798e-01
-4.33777153e-01 -8.71599615e-01 -3.06812912e-01 -1.29578900e+00
-1.06408882e+00 7.66402423e-01 -4.62137274e-02 -2.75177807... | [5.9448442459106445, 4.408061504364014] |
78d968c0-a5cb-4931-946a-37e190c3b4a8 | hhh-an-online-medical-chatbot-system-based-on-1 | 2002.0314 | null | https://arxiv.org/abs/2002.03140v1 | https://arxiv.org/pdf/2002.03140v1.pdf | HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention | This paper proposes a chatbot framework that adopts a hybrid model which consists of a knowledge graph and a text similarity model. Based on this chatbot framework, we build HHH, an online question-and-answer (QA) Healthcare Helper system for answering complex medical questions. HHH maintains a knowledge graph construc... | ['Jiamou Liu', 'Lin Ni', 'Qiming Bao'] | 2020-02-08 | hhh-an-online-medical-chatbot-system-based-on | https://www.researchgate.net/publication/338926786_HHH_An_Online_Medical_Chatbot_System_based_on_Knowledge_Graph_and_Hierarchical_Bi-Directional_Attention | https://www.researchgate.net/publication/338926786_HHH_An_Online_Medical_Chatbot_System_based_on_Knowledge_Graph_and_Hierarchical_Bi-Directional_Attention | proceedings-of-the-australasian-computer | ['medical-question-pair-similarity-computation'] | ['natural-language-processing'] | [-1.27467394e-01 8.07575762e-01 2.23131493e-01 -3.90014648e-01
-1.18646872e+00 2.54910618e-01 2.27755383e-01 2.99446404e-01
-4.55149710e-01 5.98249137e-01 5.62277734e-01 -5.29609978e-01
-1.91823065e-01 -1.02597499e+00 -3.01003337e-01 -2.18596503e-01
1.08474493e-01 9.17000175e-01 4.93049920e-01 -6.66528404... | [8.849519729614258, 8.659587860107422] |
4dff9bdb-2e38-441e-ac39-c0a66f179bb5 | diagnosing-and-preventing-instabilities-in | 2010.05099 | null | https://arxiv.org/abs/2010.05099v3 | https://arxiv.org/pdf/2010.05099v3.pdf | Diagnosing and Preventing Instabilities in Recurrent Video Processing | Recurrent models are a popular choice for video enhancement tasks such as video denoising or super-resolution. In this work, we focus on their stability as dynamical systems and show that they tend to fail catastrophically at inference time on long video sequences. To address this issue, we (1) introduce a diagnostic t... | ['Gregory Slabaugh', 'Ales Leonardis', 'Philip Torr', 'Puneet K. Dokania', 'Matteo Maggioni', 'Aivar Sootla', 'Thomas Tanay'] | 2020-10-10 | null | null | null | null | ['video-denoising', 'video-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.78869265e-01 -1.14492431e-01 1.93923429e-01 8.19332376e-02
-1.12109080e-01 -4.26992863e-01 5.90327621e-01 -3.15570563e-01
-2.92767316e-01 4.06192541e-01 2.88054556e-01 -2.30531096e-01
-2.84798294e-02 -3.48101825e-01 -7.20134616e-01 -9.32458639e-01
-2.66417861e-01 -4.24649984e-01 6.56826913e-01 -4.08348352... | [10.861577987670898, -1.5203399658203125] |
15d80f7d-10b5-4db7-82c9-a988446d2062 | patch-based-discriminative-feature-learning | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Patch-Based_Discriminative_Feature_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Patch-Based_Discriminative_Feature_Learning_for_Unsupervised_Person_Re-Identification_CVPR_2019_paper.pdf | Patch-Based Discriminative Feature Learning for Unsupervised Person Re-Identification | While discriminative local features have been shown effective in solving the person re-identification problem, they are limited to be trained on fully pairwise labelled data which is expensive to obtain. In this work, we overcome this problem by proposing a patch-based unsupervised learning framework in order to learn ... | [' Wei-Shi Zheng', ' Ancong Wu', ' Hong-Xing Yu', 'Qize Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.65751104e-02 -1.68722406e-01 -4.06609088e-01 -7.75881886e-01
-8.91492307e-01 -4.39162493e-01 4.10666466e-01 3.10588423e-02
-3.36120009e-01 4.50084716e-01 3.98938924e-01 3.27675283e-01
-1.11226626e-01 -6.75292253e-01 -6.22665763e-01 -6.82404220e-01
3.29156190e-01 2.41144121e-01 -1.45372868e-01 2.35186651... | [14.751791954040527, 0.9868013262748718] |
5d44bfc9-dfc4-446a-b3a9-d9bdf73aab64 | good-view-hunting-learning-photo-composition | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wei_Good_View_Hunting_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Good_View_Hunting_CVPR_2018_paper.pdf | Good View Hunting: Learning Photo Composition From Dense View Pairs | Finding views with good photo composition is a challenging task for machine learning methods. A key difficulty is the lack of well annotated large scale datasets. Most existing datasets only provide a limited number of annotations for good views, while ignoring the comparative nature of view selection. In this work, w... | ['Dimitris Samaras', 'Minh Hoai', 'RadomÃ\xadr Mech', 'Xiaohui Shen', 'Zijun Wei', 'Zhe Lin', 'Jianming Zhang'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['image-cropping'] | ['computer-vision'] | [ 4.66148823e-01 -1.97273210e-01 -1.13730900e-01 -3.44355106e-01
-1.20429325e+00 -9.01885092e-01 5.80177248e-01 -3.89668822e-01
-1.61413446e-01 5.39526820e-01 4.50857490e-01 2.29017753e-02
4.51349020e-01 -3.98949265e-01 -1.14364076e+00 -3.29604119e-01
2.97691911e-01 4.61719424e-01 4.16928172e-01 -2.45656013... | [11.332436561584473, -0.3236837387084961] |
9a5b1d5c-6589-4c09-afad-32a3b4a63cba | backpropagation-clipping-for-deep-learning | 2202.05089 | null | https://arxiv.org/abs/2202.05089v2 | https://arxiv.org/pdf/2202.05089v2.pdf | Backpropagation Clipping for Deep Learning with Differential Privacy | We present backpropagation clipping, a novel variant of differentially private stochastic gradient descent (DP-SGD) for privacy-preserving deep learning. Our approach clips each trainable layer's inputs (during the forward pass) and its upstream gradients (during the backward pass) to ensure bounded global sensitivity ... | ['Joseph P. Near', 'David Slater', 'Calvin Hirsch', 'David Darais', 'Ivoline C. Ngong', 'Timothy Stevens'] | 2022-02-10 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.78205505e-01 1.18654341e-01 -3.86844389e-02 -6.70247078e-01
-8.97137642e-01 -7.44760573e-01 2.66558468e-01 -5.19245230e-02
-1.05149710e+00 1.06964540e+00 -1.64829746e-01 -8.39589119e-01
2.81785041e-01 -6.69446886e-01 -8.92416954e-01 -7.52992332e-01
-3.83263350e-01 -2.94578522e-01 6.57594278e-02 1.89850956... | [5.893701076507568, 6.882406234741211] |
f71d6d46-039e-4dc0-994f-12fc13064572 | domain-adaptive-self-supervised-pre-training | 2211.10641 | null | https://arxiv.org/abs/2211.10641v2 | https://arxiv.org/pdf/2211.10641v2.pdf | Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings | Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic... | ['Tevfik Metin Sezgin', 'Deniz Yuret', 'Barış Batuhan Topal'] | 2022-11-19 | null | null | null | null | ['body-detection', 'face-detection', 'weakly-supervised-object-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.40513673e-02 3.28090265e-02 -2.74191141e-01 -4.09297705e-01
-4.49202567e-01 -9.28380489e-01 6.96407437e-01 -1.31064877e-01
-1.02305062e-01 3.52650374e-01 -1.95886977e-02 -1.36279404e-01
3.99428010e-01 -7.44482279e-01 -7.45661318e-01 -1.71612069e-01
2.58784235e-01 6.46997213e-01 3.05044711e-01 -2.10441977... | [11.642226219177246, 0.21742670238018036] |
e1d5c1cd-39da-4d99-b3d8-1622c32c8829 | compact-global-descriptor-for-neural-networks | 1907.09665 | null | https://arxiv.org/abs/1907.09665v10 | https://arxiv.org/pdf/1907.09665v10.pdf | Compact Global Descriptor for Neural Networks | Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convolutional operations to enlarge receptive fields nor recent nonlocal modules is computationally efficient. In this paper, we present a generi... | ['Qinghao Hu', 'Qiang Chen', 'Peisong Wang', 'Jian Cheng', 'Xiangyu He', 'Ke Cheng'] | 2019-07-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-4.22784597e-01 -8.16836834e-01 -8.50050747e-02 -3.91946137e-01
-6.98900759e-01 -5.61093509e-01 7.99018681e-01 5.28216325e-02
-8.11503232e-01 7.10290492e-01 1.93287849e-01 6.14604950e-02
-2.35291898e-01 -6.96022809e-01 -7.51232088e-01 -9.47126150e-01
-4.18409526e-01 -3.29840720e-01 6.26206756e-01 5.89825585... | [9.057138442993164, 0.8334511518478394] |
918c7313-ee7a-47ea-82ce-c35949f54fc9 | limsi-cot-at-semeval-2017-task-12-neural | null | null | https://aclanthology.org/S17-2098 | https://aclanthology.org/S17-2098.pdf | LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives | In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks. | ["Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Olivier Ferret', 'Xavier Tannier', 'Julien Tourille'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-4.39916365e-02 6.33012354e-01 -2.44057730e-01 -6.39116585e-01
-5.53388298e-01 -4.29820776e-01 1.07527328e+00 2.39554718e-01
-1.16878450e+00 1.06284583e+00 2.39174172e-01 -4.61786419e-01
-5.91986887e-02 -5.72269619e-01 -4.83628750e-01 1.80674016e-01
-6.42676353e-01 9.58329976e-01 5.19828498e-01 -3.66068095... | [9.297877311706543, 9.061139106750488] |
7b641199-6616-48ac-94c2-2cf84230540f | streamlining-cross-document-coreference | 2009.11032 | null | https://arxiv.org/abs/2009.11032v3 | https://arxiv.org/pdf/2009.11032v3.pdf | Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling | Recent evaluation protocols for Cross-document (CD) coreference resolution have often been inconsistent or lenient, leading to incomparable results across works and overestimation of performance. To facilitate proper future research on this task, our primary contribution is proposing a pragmatic evaluation methodology ... | ['Gabriel Stanovsky', 'Ido Dagan', 'Mandar Joshi', 'Arie Cattan', 'Alon Eirew'] | 2020-09-23 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 4.36863959e-01 3.49566638e-01 -7.57498860e-01 -3.98741663e-01
-1.42864120e+00 -7.17635989e-01 1.00889266e+00 3.92642803e-02
-7.26853013e-01 8.98672163e-01 1.06587017e+00 -3.77964377e-01
-4.98012215e-01 -2.42182478e-01 -3.90256941e-01 -2.50435829e-01
2.47392029e-01 1.12966502e+00 1.27545431e-01 -3.10221136... | [9.30803394317627, 9.569612503051758] |
7f308bea-f8f6-450d-af9b-a789cb00ede7 | hatemonitors-language-agnostic-abuse | 1909.12642 | null | https://arxiv.org/abs/1909.12642v1 | https://arxiv.org/pdf/1909.12642v1.pdf | HateMonitors: Language Agnostic Abuse Detection in Social Media | Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful c... | ['Binny Mathew', 'Punyajoy Saha', 'Animesh Mukherjee', 'Pawan Goyal'] | 2019-09-27 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-3.44343901e-01 -4.37772945e-02 -2.54298449e-01 -4.27121408e-02
-5.38607776e-01 -9.59479272e-01 8.19013596e-01 1.36737868e-01
-5.16476452e-01 8.56900573e-01 4.69350994e-01 -4.82521296e-01
2.30917335e-01 -5.15570819e-01 -1.44233868e-01 -4.79465395e-01
1.10538580e-01 8.86253268e-02 -1.45308048e-01 -2.53835559... | [8.756953239440918, 10.588008880615234] |
6092704f-0695-4326-80fc-9dc5f1d1ce7f | single-perspective-warps-in-natural-image | 1802.04645 | null | http://arxiv.org/abs/1802.04645v2 | http://arxiv.org/pdf/1802.04645v2.pdf | Single-Perspective Warps in Natural Image Stitching | Results of image stitching can be perceptually divided into
single-perspective and multiple-perspective. Compared to the
multiple-perspective result, the single-perspective result excels in
perspective consistency but suffers from projective distortion. In this paper,
we propose two single-perspective warps for natural... | ['Tianli Liao', 'Nan Li'] | 2018-02-13 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 3.12794417e-01 -4.31148618e-01 8.34260583e-02 1.60971969e-01
-4.00732964e-01 -7.02383399e-01 7.35968292e-01 -3.73647183e-01
7.87361115e-02 3.78787339e-01 5.46311259e-01 7.26881623e-02
-4.11986336e-02 -4.98923928e-01 -5.95819771e-01 -9.89001095e-01
4.66787219e-01 1.72757342e-01 6.25064373e-01 -5.51177859... | [9.391593933105469, -2.3689541816711426] |
8f8d29d1-7ae4-4fb9-b590-99fb7b92d1cd | multi-reference-image-super-resolution-a | 2212.09988 | null | https://arxiv.org/abs/2212.09988v1 | https://arxiv.org/pdf/2212.09988v1.pdf | Multi-Reference Image Super-Resolution: A Posterior Fusion Approach | Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper... | ['Tsz Fung Yau', 'Haining Tan', 'Ke Zhao'] | 2022-12-20 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 4.57945645e-01 -2.75702298e-01 -5.60545586e-02 -5.38068235e-01
-1.62935007e+00 1.06979504e-01 5.54076433e-01 -3.72306675e-01
-2.21604422e-01 9.43733990e-01 4.73133028e-01 5.57740271e-01
-1.89122185e-01 -6.16683543e-01 -3.05543184e-01 -7.05865681e-01
2.56560594e-01 -1.28507778e-01 7.47491121e-01 -4.12985176... | [11.011517524719238, -2.1305227279663086] |
6fd4bfe7-1c86-42fd-b168-02550298d960 | neural-feature-extraction-for-contextual | null | null | https://aclanthology.org/R19-1091 | https://aclanthology.org/R19-1091.pdf | Neural Feature Extraction for Contextual Emotion Detection | This paper describes a new approach for the task of contextual emotion detection. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a classifier, that can be neural or SVM-based. We evaluated the model with the dataset of the task 3 of S... | ['Leila Kosseim', 'Elham Mohammadi', 'Hessam Amini'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.13333739e-01 3.11054081e-01 7.43620545e-02 -4.50443745e-01
-5.82081139e-01 -4.14198935e-01 4.13025141e-01 1.00735590e-01
-7.91701257e-01 6.90714240e-01 3.59898150e-01 -3.94621670e-01
5.26589632e-01 -4.81851697e-01 -4.25148010e-01 -7.21204221e-01
9.68408864e-03 3.02676946e-01 -2.47983970e-02 -3.42667550... | [13.117466926574707, 6.141024112701416] |
5c07dbec-d7f2-4119-a521-41475f46f413 | model-driven-ct-reconstruction-algorithm-for | 2305.08882 | null | https://arxiv.org/abs/2305.08882v1 | https://arxiv.org/pdf/2305.08882v1.pdf | Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging | The limited imaging performance of low-density objects in a zone plate based nano-resolution hard X-ray computed tomography (CT) system can be significantly improved by accessing the phase information. To do so, a grating-based Lau interferometer needs to be integrated. However, the nano-resolution phase contrast CT, d... | ['Yongshuai Ge', 'Peiping Zhu', 'Jinyou Xu', 'Hairong Zheng', 'Dong Liang', 'Ting Su', 'Yuhang Tan', 'Xuebao Cai'] | 2023-05-14 | null | null | null | null | ['image-reconstruction', 'computed-tomography-ct'] | ['computer-vision', 'methodology'] | [ 6.55100286e-01 -1.23688340e-01 5.25992334e-01 -2.08203271e-01
-9.26466286e-01 4.04385209e-01 3.94256920e-01 -5.85076988e-01
-6.02995932e-01 8.29862893e-01 -1.29756808e-01 1.82791278e-01
-5.19352496e-01 -8.94347191e-01 -4.36390877e-01 -1.31226945e+00
4.18919861e-01 9.77535129e-01 4.39749867e-01 1.21144608... | [12.838422775268555, -2.7492172718048096] |
c3292121-e775-4841-8407-35a7b38fd04f | ganomaly-semi-supervised-anomaly-detection | 1805.06725 | null | http://arxiv.org/abs/1805.06725v3 | http://arxiv.org/pdf/1805.06725v3.pdf | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | Anomaly detection is a classical problem in computer vision, namely the
determination of the normal from the abnormal when datasets are highly biased
towards one class (normal) due to the insufficient sample size of the other
class (abnormal). While this can be addressed as a supervised learning problem,
a significantl... | ['Amir Atapour-Abarghouei', 'Toby P. Breckon', 'Samet Akcay'] | 2018-05-17 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 0.6428143 0.3440319 0.15126453 -0.4588701 -0.67756927 -0.28850558
0.7144701 0.05918167 -0.10927697 0.41719112 -0.17406169 -0.23729904
0.06104729 -0.7128489 -0.8720928 -1.1627333 0.07032381 0.7316051
0.07215493 0.2615033 0.32444045 0.44745117 -1.6424408 0.286679
0.71092856 1.1552966 -0.249... | [7.633440971374512, 2.269700050354004] |
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