paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2b3e21b2-0eaa-436f-a875-807c7ff7137c | video-chatcaptioner-towards-the-enriched | 2304.04227 | null | https://arxiv.org/abs/2304.04227v3 | https://arxiv.org/pdf/2304.04227v3.pdf | Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions | Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment. Although there have been recent advances, generating detailed and enriched video descriptions continues to be a substantial challenge. In this work, we ... | ['Mohamed Elhoseiny', 'Xiang Li', 'Kilichbek Haydarov', 'Deyao Zhu', 'Jun Chen'] | 2023-04-09 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 2.09110573e-01 3.68396305e-02 -1.24736652e-01 -2.82884389e-01
-1.15858567e+00 -6.84101760e-01 5.66940010e-01 -2.45172366e-01
3.12045664e-02 7.71778941e-01 9.55438793e-01 1.32065594e-01
5.09773731e-01 -2.17615873e-01 -7.42456436e-01 -4.07990128e-01
2.28130911e-02 8.69581942e-03 3.28315496e-01 -2.02117145... | [10.537740707397461, 0.8803253173828125] |
41542ba7-80f5-4634-8c62-38c3edab361b | lmu-munichas-neural-machine-translation | null | null | https://aclanthology.org/W18-6446 | https://aclanthology.org/W18-6446.pdf | LMU Munich's Neural Machine Translation Systems at WMT 2018 | We present the LMU Munich machine translation systems for the English{--}German language pair. We have built neural machine translation systems for both translation directions (English→German and German→English) and for two different domains (the biomedical domain and the news domain). The systems were used for our par... | ['er', 'Viktor Hangya', 'Alex Fraser', 'Matthias Huck', 'Dario Stojanovski'] | 2018-10-01 | null | null | null | ws-2018-10 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.02652085e-01 3.95562619e-01 -4.44846123e-01 -5.08051157e-01
-1.35334492e+00 -5.18659770e-01 8.22574437e-01 3.77935544e-02
-7.55907714e-01 1.39803207e+00 5.68814635e-01 -1.00066090e+00
3.26352745e-01 -3.95974219e-01 -5.79310000e-01 -2.40914240e-01
4.53737438e-01 9.77614820e-01 -3.41656864e-01 -5.29894590... | [11.547478675842285, 10.41109848022461] |
324d6fcb-8349-4e38-b98e-cff3d840acb9 | dsvae-interpretable-disentangled | 2304.03323 | null | https://arxiv.org/abs/2304.03323v1 | https://arxiv.org/pdf/2304.03323v1.pdf | DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection | Tools to generate high quality synthetic speech signal that is perceptually indistinguishable from speech recorded from human speakers are easily available. Several approaches have been proposed for detecting synthetic speech. Many of these approaches use deep learning methods as a black box without providing reasoning... | ['Edward J. Delp', 'Stefano Tubaro', 'Paolo Bestagini', 'Ziyue Xiang', 'Kratika Bhagtani', 'Amit Kumar Singh Yadav'] | 2023-04-06 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 2.11273685e-01 6.20698750e-01 4.34593707e-01 -1.97437197e-01
-1.09112060e+00 -9.69471455e-01 7.58305490e-01 -4.30011272e-01
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2.10401952e-01 4.93290454e-01 -6.33533020e-03 -1.49894983... | [15.040329933166504, 6.481487274169922] |
88ea59cc-daad-4367-9fd2-79e941ca015d | robust-classification-of-high-dimensional-1 | 2306.13985 | null | https://arxiv.org/abs/2306.13985v1 | https://arxiv.org/pdf/2306.13985v1.pdf | Robust Classification of High-Dimensional Data using Data-Adaptive Energy Distance | Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These clas... | ['Subhajit Dutta', 'Sarbojit Roy', 'Aytijhya Saha', 'Jyotishka Ray Choudhury'] | 2023-06-24 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 3.59549999e-01 -2.51862139e-01 -6.80515110e-01 -4.92023051e-01
-4.96963084e-01 -1.76680431e-01 5.87435603e-01 3.94855261e-01
-2.73514062e-01 9.60471392e-01 -3.84913892e-01 -3.02152187e-01
-4.90704089e-01 -4.51138794e-01 -2.53949463e-01 -1.26979184e+00
-2.38014266e-01 3.36474121e-01 -1.09670922e-01 -5.62509149... | [8.057577133178711, 4.238565444946289] |
23731197-1bd5-47a6-8df3-31038ef347e6 | dialoguebert-a-self-supervised-learning-based | 2109.10480 | null | https://arxiv.org/abs/2109.10480v1 | https://arxiv.org/pdf/2109.10480v1.pdf | DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder | With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service timely. To satisfy the user, the conversational bots need to understand the user's intention, detect the user's emotion, and extract the key ... | ['Meng Chen', 'Tao Guo', 'Zhenyu Zhang'] | 2021-09-22 | null | null | null | null | ['role-embedding', 'intent-recognition', 'dialogue-understanding'] | ['graphs', 'natural-language-processing', 'natural-language-processing'] | [-2.44993091e-01 3.33610058e-01 -2.30924189e-02 -5.57816625e-01
-2.18881354e-01 -6.80175245e-01 7.41434693e-01 -1.14153624e-01
-5.63757837e-01 5.97579658e-01 6.29661500e-01 -2.07276717e-01
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8.53065923e-02 5.87086022e-01 -3.12561989e-02 -7.58042932... | [12.7887544631958, 7.7094831466674805] |
15d3a513-bbf7-4d9c-9ebe-1cc556737faf | from-big-to-small-adaptive-learning-to | 2203.07375 | null | https://arxiv.org/abs/2203.07375v1 | https://arxiv.org/pdf/2203.07375v1.pdf | From Big to Small: Adaptive Learning to Partial-Set Domains | Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show th... | ['Mingsheng Long', 'Jianmin Wang', 'Ziyang Zhang', 'Kaichao You', 'Zhangjie Cao'] | 2022-03-14 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.28236985e-01 1.91775441e-01 -4.55000699e-01 -4.86734152e-01
-7.40987539e-01 -1.07402360e+00 5.48914135e-01 -1.29477680e-01
-4.10319209e-01 1.31378245e+00 -1.22724980e-01 -1.74663857e-01
-1.11786850e-01 -1.01953411e+00 -1.13773692e+00 -7.14253545e-01
1.58676594e-01 8.36998820e-01 4.91079628e-01 -2.07907602... | [10.36670970916748, 3.1109607219696045] |
0c22cb69-253e-4ddf-981f-88ec1a9ad750 | avist-a-benchmark-for-visual-object-tracking | 2208.06888 | null | https://arxiv.org/abs/2208.06888v1 | https://arxiv.org/pdf/2208.06888v1.pdf | AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility | One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of ... | ['Fahad Shahbaz Khan', 'Luc van Gool', 'Salman Khan', 'Hisham Cholakkal', 'Martin Danelljan', 'Akshay Dudhane', 'Christoph Mayer', 'Daniya Najiha', 'Wafa Al Ghallabi', 'Mubashir Noman'] | 2022-08-14 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.18113130e-01 -7.67431557e-01 9.44911465e-02 2.54727975e-02
-1.90784752e-01 -1.20967913e+00 6.23262525e-01 -2.72710472e-01
-3.03916872e-01 7.93074131e-01 2.77340472e-01 -6.34849668e-02
4.70533073e-02 -2.15286955e-01 -7.51772523e-01 -7.39638805e-01
-5.08522451e-01 3.18613410e-01 9.22092021e-01 -3.28117311... | [6.338367938995361, -2.0704896450042725] |
6ee419b6-d68d-428f-9e4e-9513926f6702 | large-batch-optimization-for-dense-visual | 2210.11078 | null | https://arxiv.org/abs/2210.11078v1 | https://arxiv.org/pdf/2210.11078v1.pdf | Large-batch Optimization for Dense Visual Predictions | Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tackle this challenge. However, although the current advanced algorithms such as LARS and LAMB succeed in classification models, the complicate... | ['Ping Luo', 'Yu Liu', 'Liang Chen', 'Zhuofan Zong', 'Guanglu Song', 'Jianming Liang', 'Zeyue Xue'] | 2022-10-20 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [-1.56700656e-01 -2.68507320e-02 5.71765043e-02 -2.75900126e-01
-6.70360863e-01 -4.69438881e-01 3.98734957e-01 -4.85583305e-01
-4.51626033e-01 2.20306382e-01 -3.42557997e-01 -4.33984995e-01
3.17569405e-01 -5.82691669e-01 -1.08950663e+00 -9.14334595e-01
2.14699760e-01 4.33902651e-01 3.81608367e-01 1.10155838... | [9.425628662109375, 0.07300645858049393] |
d76a42db-3749-42d5-9f47-e0caffe2718d | novel-object-viewpoint-estimation-through-1 | 2006.03586 | null | https://arxiv.org/abs/2006.03586v1 | https://arxiv.org/pdf/2006.03586v1.pdf | Novel Object Viewpoint Estimation through Reconstruction Alignment | The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learnin... | ['Mohamed El Banani', 'Jason J. Corso', 'David F. Fouhey'] | 2020-06-05 | novel-object-viewpoint-estimation-through | http://openaccess.thecvf.com/content_CVPR_2020/html/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Banani_Novel_Object_Viewpoint_Estimation_Through_Reconstruction_Alignment_CVPR_2020_paper.pdf | cvpr-2020-6 | ['viewpoint-estimation'] | ['computer-vision'] | [ 3.28381747e-01 2.12348744e-01 -1.67335048e-01 -4.99175966e-01
-8.42138886e-01 -8.09766173e-01 7.72254944e-01 -4.02726382e-02
-2.89818525e-01 7.65648782e-02 1.07500270e-01 6.84044361e-02
9.89659950e-02 -4.23602968e-01 -1.09350407e+00 -5.55034697e-01
3.31912428e-01 9.09080744e-01 2.25776762e-01 2.06395052... | [8.364216804504395, -2.6861379146575928] |
0a2c63c2-7e1a-404b-866f-636de3228784 | utm-a-unified-multiple-object-tracking-model | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/You_UTM_A_Unified_Multiple_Object_Tracking_Model_With_Identity-Aware_Feature_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/You_UTM_A_Unified_Multiple_Object_Tracking_Model_With_Identity-Aware_Feature_CVPR_2023_paper.pdf | UTM: A Unified Multiple Object Tracking Model With Identity-Aware Feature Enhancement | Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association. Existing methods apply the three-step or two-step paradigm to generate robust trajectories, where identity association is independent of other components. However, the inde... | ['Changsheng Xu', 'Bing-Kun Bao', 'Hantao Yao', 'Sisi You'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['multiple-object-tracking'] | ['computer-vision'] | [-1.18360117e-01 -2.61301994e-01 -2.16293707e-01 -1.42044127e-01
-5.45051098e-01 -4.47059721e-01 7.50005066e-01 -1.24240488e-01
-3.37309331e-01 4.60580200e-01 4.50688511e-01 1.47855952e-01
-2.08660886e-01 -6.56140983e-01 -8.58098149e-01 -8.35580289e-01
2.01251522e-01 -1.83382019e-01 5.48508048e-01 -1.47978261... | [6.31104040145874, -2.1352765560150146] |
d736397b-0cbc-4b5f-83f9-48411da6fe62 | carigans-unpaired-photo-to-caricature | 1811.00222 | null | http://arxiv.org/abs/1811.00222v2 | http://arxiv.org/pdf/1811.00222v2.pdf | CariGANs: Unpaired Photo-to-Caricature Translation | Facial caricature is an art form of drawing faces in an exaggerated way to
convey humor or sarcasm. In this paper, we propose the first Generative
Adversarial Network (GAN) for unpaired photo-to-caricature translation, which
we call "CariGANs". It explicitly models geometric exaggeration and appearance
stylization usin... | ['Kaidi Cao', 'Jing Liao', 'Lu Yuan'] | 2018-11-01 | null | null | null | null | ['photo-to-caricature-translation', 'caricature'] | ['computer-vision', 'computer-vision'] | [ 1.17827766e-01 4.79766697e-01 4.61278588e-01 -4.03297544e-01
-2.78756857e-01 -8.20736110e-01 6.28799319e-01 -8.28503609e-01
1.81060195e-01 7.10203707e-01 -1.04346098e-02 6.82720989e-02
4.48974848e-01 -8.77472222e-01 -9.72393572e-01 -6.22017682e-01
7.10960627e-01 3.83681059e-01 -4.00077373e-01 -4.98986870... | [12.146077156066895, -0.37351420521736145] |
78bb47e2-9bcc-4dfc-b6ff-940b303256ce | what-s-behind-the-couch-directed-ray-distance | 2112.04481 | null | https://arxiv.org/abs/2112.04481v2 | https://arxiv.org/pdf/2112.04481v2.pdf | What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction | We present an approach for full 3D scene reconstruction from a single unseen image. We train on dataset of realistic non-watertight scans of scenes. Our approach predicts a distance function, since these have shown promise in handling complex topologies and large spaces. We identify and analyze two key challenges for p... | ['David F. Fouhey', 'Justin Johnson', 'Nilesh Kulkarni'] | 2021-12-08 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.61749887e-01 3.78912538e-01 4.44942087e-01 -8.83142471e-01
-5.89478731e-01 -5.14215112e-01 8.74927819e-01 -3.49080116e-01
-3.08513135e-01 2.82196313e-01 3.05709571e-01 -5.78086853e-01
-3.39020640e-01 -9.81020868e-01 -1.25772035e+00 -3.52547377e-01
-2.04149023e-01 1.02985251e+00 3.89477819e-01 -8.01845267... | [8.586544036865234, -3.4606258869171143] |
812fa37f-e618-4677-af21-b9b2d45ae0c7 | one-ring-to-rule-them-all-a-simple-solution | 2104.05014 | null | https://arxiv.org/abs/2104.05014v1 | https://arxiv.org/pdf/2104.05014v1.pdf | One Ring to Rule Them All: a simple solution to multi-view 3D-Reconstruction of shapes with unknown BRDF via a small Recurrent ResNet | This paper proposes a simple method which solves an open problem of multi-view 3D-Reconstruction for objects with unknown and generic surface materials, imaged by a freely moving camera and a freely moving point light source. The object can have arbitrary (e.g. non-Lambertian), spatially-varying (or everywhere differen... | ['Imari Sato', 'Yinqiang Zheng', 'Richard Hartley', 'Hongdong Li', 'Ziang Cheng'] | 2021-04-11 | null | null | null | null | ['svbrdf-estimation'] | ['computer-vision'] | [ 2.28713840e-01 1.75074726e-01 2.93730706e-01 -1.08798780e-01
-3.87633145e-01 -5.50169706e-01 2.26614237e-01 -6.22180164e-01
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-1.03505338e-02 -7.55802453e-01 -1.01349676e+00 -9.42788720e-01
3.23443413e-01 2.98508108e-01 7.57729635e-02 -2.35742480... | [9.25064754486084, -3.1404192447662354] |
81fb8616-26e7-41ce-89ef-83c7be38234c | adaptive-online-value-function-approximation | 2204.11842 | null | https://arxiv.org/abs/2204.11842v1 | https://arxiv.org/pdf/2204.11842v1.pdf | Adaptive Online Value Function Approximation with Wavelets | Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear function approximation has desirable theoretical guarantees and often requires less compute and samples than neural networks, but most approaches suffer from an exponential growth in the numb... | ['George Konidaris', 'Steven James', 'Dean Wookey', 'Michael Mitchley', 'Michael Beukman'] | 2022-04-22 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-9.44711640e-02 -1.87728584e-01 -1.36822999e-01 -6.77156299e-02
-4.49723095e-01 -5.94686985e-01 4.97880161e-01 1.40734334e-02
-4.66121078e-01 1.07275522e+00 -2.76237667e-01 -3.59689713e-01
-2.39722803e-01 -8.73205543e-01 -6.13604605e-01 -8.23718131e-01
-3.89013946e-01 4.24489945e-01 3.92422408e-01 -5.63991487... | [4.343340873718262, 2.4022083282470703] |
255c1fe6-d1ea-4d08-8a16-6ed8aec11757 | are-we-really-making-much-progress-in | 2210.12941 | null | https://arxiv.org/abs/2210.12941v3 | https://arxiv.org/pdf/2210.12941v3.pdf | Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method | A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area. UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the context... | ['Xuemin Lin', 'Fan Zhang', 'Liping Wang', 'Yihong Huang'] | 2022-10-24 | null | null | null | null | ['graph-outlier-detection'] | ['graphs'] | [-2.78251082e-01 -2.83447742e-01 -1.08656496e-01 9.03536379e-02
-2.53907531e-01 -1.81470126e-01 4.26343739e-01 6.37072384e-01
-5.98522723e-02 3.40684533e-01 8.23924020e-02 -2.20449910e-01
-2.14563429e-01 -1.02530932e+00 -4.54523355e-01 -5.48110723e-01
-2.51549900e-01 1.05835572e-01 6.55095696e-01 -8.54105875... | [6.682646751403809, 5.792638778686523] |
64484b42-46e1-46a1-819b-36fc66cf26bb | funnel-activation-for-visual-recognition | 2007.11824 | null | https://arxiv.org/abs/2007.11824v2 | https://arxiv.org/pdf/2007.11824v2.pdf | Funnel Activation for Visual Recognition | We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition. The forms of ReLU and PReLU are y = max(x, 0) and y = max(x, px), respectively, while FReLU... | ['Xiangyu Zhang', 'Jian Sun', 'Ningning Ma'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1342_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560341.pdf | eccv-2020-8 | ['scene-generation'] | ['computer-vision'] | [-8.56577829e-02 7.32330158e-02 -1.46277785e-01 -5.02000451e-01
-1.47707537e-01 -7.13002980e-01 5.19430876e-01 -5.53506799e-02
-4.85348850e-01 1.99733019e-01 -8.99945199e-02 -7.26139843e-01
1.23663165e-01 -7.30809331e-01 -1.02012992e+00 -3.86079937e-01
-2.12739393e-01 -1.75669730e-01 4.22833532e-01 1.24688752... | [9.616273880004883, 0.6383764147758484] |
ab014ce3-8da3-4b1d-8d68-26c69a6daaff | multi-modal-pre-training-for-medical-vision | 2306.06494 | null | https://arxiv.org/abs/2306.06494v1 | https://arxiv.org/pdf/2306.06494v1.pdf | Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark | With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area of research and proven to be effective for various VL tasks such as visual-question answering. However, studies on VLP in the medical domain... | ['Xiao-Ming Wu', 'Lu Fan', 'Ameer Hamza Khan', 'Bo Liu', 'Li Xu'] | 2023-06-10 | null | null | null | null | ['visual-question-answering-1', 'medical-report-generation'] | ['computer-vision', 'medical'] | [ 4.68597770e-01 1.02569133e-01 -4.78062361e-01 -4.06919539e-01
-1.56026626e+00 -2.51162231e-01 5.55791795e-01 3.08969617e-01
-5.25892437e-01 5.96377730e-01 5.71359634e-01 -6.55247211e-01
2.72981077e-01 -4.05247480e-01 -7.51836598e-01 -4.57722902e-01
3.25066358e-01 5.08755624e-01 2.06293315e-01 7.65984803... | [14.938253402709961, -1.6496202945709229] |
c86a11eb-c17f-4385-9879-75627ae16261 | weighted-programming | 2202.07577 | null | https://arxiv.org/abs/2202.07577v2 | https://arxiv.org/pdf/2202.07577v2.pdf | Weighted Programming | We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weights can be numbers but also other obje... | ['Tobias Winkler', 'Joost-Pieter Katoen', 'Benjamin Lucien Kaminski', 'Adrian Gallus', 'Kevin Batz'] | 2022-02-15 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 9.71672088e-02 2.98153311e-01 -3.19007009e-01 -3.84882569e-01
-9.14159238e-01 -8.49107623e-01 5.93653023e-01 2.95444280e-01
-5.96573889e-01 3.82487714e-01 -5.91078587e-02 -9.52313960e-01
-4.95417088e-01 -1.26052189e+00 -8.37897301e-01 -5.91241002e-01
-6.27050877e-01 9.79066730e-01 6.01364315e-01 -1.10353865... | [8.469956398010254, 6.597517967224121] |
c59c867d-f0d9-4b9d-9c2f-889573c59c26 | compguesswhat-a-multi-task-evaluation | 2006.02174 | null | https://arxiv.org/abs/2006.02174v1 | https://arxiv.org/pdf/2006.02174v1.pdf | CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning | Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden representations, such as their ability to predict salient attributes or to generalise to unseen situations. To remedy this, we present GROLLA, an ev... | ['Stella Frank', 'Alessandro Suglia', 'Ioannis Konstas', 'Emanuele Bastianelli', 'Desmond Elliott', 'Andrea Vanzo', 'Oliver Lemon'] | 2020-06-03 | compguesswhat-a-multi-task-evaluation-1 | https://aclanthology.org/2020.acl-main.682 | https://aclanthology.org/2020.acl-main.682.pdf | acl-2020-6 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.69457436e-01 6.14845693e-01 -1.11359507e-02 -6.88836992e-01
-8.75665545e-01 -4.21047449e-01 8.67461145e-01 6.37824535e-01
-3.27280790e-01 6.28700376e-01 2.66849637e-01 5.96850142e-02
-2.35720485e-01 -1.08559752e+00 -8.83262098e-01 -5.87439358e-01
-1.25271857e-01 7.93577552e-01 1.81240097e-01 -4.67384636... | [10.360855102539062, 2.2295026779174805] |
906901da-2322-4e02-8007-5c9f86d373f5 | isointense-infant-brain-segmentation-with-a | 1710.05956 | null | http://arxiv.org/abs/1710.05956v4 | http://arxiv.org/pdf/1710.05956v4.pdf | Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network | Neonatal brain segmentation in magnetic resonance (MR) is a challenging
problem due to poor image quality and low contrast between white and gray
matter regions. Most existing approaches for this problem are based on
multi-atlas label fusion strategies, which are time-consuming and sensitive to
registration errors. As ... | ['Christian Desrosiers', 'Jing Yuan', 'Jose Dolz', 'Ismail Ben Ayed'] | 2017-10-16 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [ 1.37233198e-01 2.83214778e-01 1.00963645e-01 -6.15296960e-01
-6.55063450e-01 -3.22922051e-01 1.92241475e-01 3.04810643e-01
-7.83850431e-01 4.88116324e-01 1.96672887e-01 -1.87247247e-01
-6.93828240e-02 -4.58678991e-01 -7.93766916e-01 -5.05792260e-01
-3.03572059e-01 7.80293882e-01 6.29673421e-01 -7.88324401... | [14.230156898498535, -2.3860318660736084] |
03b3d525-25cb-4940-af0d-7a2dfb0f9475 | st-fl-style-transfer-preprocessing-in | 2203.13680 | null | https://arxiv.org/abs/2203.13680v1 | https://arxiv.org/pdf/2203.13680v1.pdf | ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation | Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. Most hospitals or countries can train their own models using in-house data, however empirical evidence... | ['Rob Otter', 'Sean Moran', 'Fran Silavong', 'Varun Babbar', 'Antonios Georgiadis'] | 2022-03-25 | null | null | null | null | ['covid-19-image-segmentation', 'covid-19-detection'] | ['computer-vision', 'medical'] | [-2.66436078e-02 3.51489484e-01 -7.53936842e-02 -3.94875139e-01
-1.23860729e+00 -6.58787072e-01 1.91816211e-01 -2.98502684e-01
-4.12604988e-01 6.56996012e-01 2.94499636e-01 -4.47924793e-01
-2.25655690e-01 -7.82276690e-01 -5.32637656e-01 -9.20418561e-01
1.58598963e-02 8.56572092e-01 -2.27339402e-01 3.51889208... | [6.126415252685547, 6.523619174957275] |
11d10114-74d9-4976-8c36-c0aef6d308ea | proceedings-10th-international-workshop-on | 2202.02144 | null | https://arxiv.org/abs/2202.02144v1 | https://arxiv.org/pdf/2202.02144v1.pdf | Proceedings 10th International Workshop on Theorem Proving Components for Educational Software | This EPTCS volume contains the proceedings of the ThEdu'21 workshop, promoted on 11 July 2021, as a satellite event of CADE-28. Due to the COVID-19 pandemic, CADE-28 and all its co-located events happened as virtual events. ThEdu'21 was a vibrant workshop, with an invited talk by Gilles Dowek (ENS Paris-Saclay), eleven... | ['Pedro Quaresma', 'Walther Neuper', 'João Marcos'] | 2022-02-02 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [-2.57140517e-01 5.96158385e-01 3.02260578e-01 -7.35247135e-02
-4.42905724e-01 -7.67554581e-01 5.79268336e-01 5.05542815e-01
-1.88316867e-01 9.01781499e-01 -1.46760151e-01 -8.78096044e-01
-3.08833301e-01 -8.75497401e-01 -1.03187466e+00 -8.22637901e-02
-3.79750371e-01 2.56033003e-01 3.87628347e-01 -3.81883860... | [8.88636589050293, 6.86614990234375] |
00ef12c2-1262-4b42-a243-ee6cc6cdd835 | co-attention-propagation-network-for-zero | 2304.03910 | null | https://arxiv.org/abs/2304.03910v1 | https://arxiv.org/pdf/2304.03910v1.pdf | Co-attention Propagation Network for Zero-Shot Video Object Segmentation | Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of i... | ['Heng-Tao Shen', 'Xingguo Huang', 'Dan Huang', 'Fumin Shen', 'Yazhou Yao', 'Gensheng Pei'] | 2023-04-08 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.71599972e-01 -1.72647163e-01 -1.85996398e-01 7.85173941e-03
-4.20812100e-01 -3.40078145e-01 4.29484636e-01 -2.51217633e-01
-2.69803196e-01 4.03681517e-01 2.45643303e-01 -8.34472626e-02
3.42712134e-01 -5.57279646e-01 -8.82740796e-01 -6.95172846e-01
-1.01849616e-01 1.75459255e-02 7.95366526e-01 2.48069763... | [9.197833061218262, -0.1326318234205246] |
a49dfac7-7f44-4a83-8222-ab2b58fa2af3 | towards-accurate-facial-landmark-detection-1 | 2208.10808 | null | https://arxiv.org/abs/2208.10808v1 | https://arxiv.org/pdf/2208.10808v1.pdf | Towards Accurate Facial Landmark Detection via Cascaded Transformers | Accurate facial landmarks are essential prerequisites for many tasks related to human faces. In this paper, an accurate facial landmark detector is proposed based on cascaded transformers. We formulate facial landmark detection as a coordinate regression task such that the model can be trained end-to-end. With self-att... | ['Jae-Joon Han', 'Seungju Han', 'Seon-Min Rhee', 'Zidong Guo', 'Hui Li'] | 2022-08-23 | towards-accurate-facial-landmark-detection | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Towards_Accurate_Facial_Landmark_Detection_via_Cascaded_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Towards_Accurate_Facial_Landmark_Detection_via_Cascaded_Transformers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-alignment', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision'] | [-1.34830967e-01 1.91843718e-01 -2.95723110e-01 -7.82592773e-01
-9.61697996e-01 -1.17526151e-01 3.89289081e-01 -1.07679449e-01
-3.38072985e-01 -2.24836683e-03 2.57001668e-01 3.04455400e-01
2.12937906e-01 -5.31280935e-01 -7.05006301e-01 -5.32791853e-01
6.06251433e-02 4.35301483e-01 1.07182100e-01 -1.73457205... | [13.49160385131836, 0.41838449239730835] |
0eb4724f-4d84-43b7-af81-2e375d9767bb | pixel-level-face-image-quality-assessment-for | 2110.11001 | null | https://arxiv.org/abs/2110.11001v3 | https://arxiv.org/pdf/2110.11001v3.pdf | Pixel-Level Face Image Quality Assessment for Explainable Face Recognition | An essential factor to achieve high performance in face recognition systems is the quality of its samples. Since these systems are involved in daily life there is a strong need of making face recognition processes understandable for humans. In this work, we introduce the concept of pixel-level face image quality that d... | ['Arjan Kuijper', 'Kiran Raja', 'Florian Kirchbuchner', 'Naser Damer', 'Marco Huber', 'Philipp Terhörst'] | 2021-10-21 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 4.36348349e-01 -6.32519424e-02 1.16659537e-01 -8.10295582e-01
-1.63162515e-01 -4.76636291e-02 3.50475729e-01 -3.68007302e-01
-1.26543999e-01 6.70460999e-01 -8.36543068e-02 -5.64820617e-02
-3.88583839e-01 -8.03988338e-01 -6.15932286e-01 -7.32047915e-01
1.10594463e-02 -2.09700331e-01 -4.43591446e-01 -5.99790551... | [13.073864936828613, 0.7844659090042114] |
acca680b-db1b-481e-9bfa-34345587a27d | no-attention-is-needed-grouped-spatial | 2206.10810 | null | https://arxiv.org/abs/2206.10810v2 | https://arxiv.org/pdf/2206.10810v2.pdf | A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift | Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame information. However, existing deep learning methods often rely on complicated network architectures, such as optical flow estimation, deformable... | ['Xiaogang Wang', 'Simon See', 'Ka Chun Cheung', 'Hongsheng Li', 'Hongwei Qin', 'Yi Zhang', 'Xiaoyu Shi', 'Dasong Li'] | 2022-06-22 | a-simple-baseline-for-video-restoration-with | http://openaccess.thecvf.com//content/CVPR2023/html/Li_A_Simple_Baseline_for_Video_Restoration_With_Grouped_Spatial-Temporal_Shift_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_A_Simple_Baseline_for_Video_Restoration_With_Grouped_Spatial-Temporal_Shift_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 4.11420502e-02 -5.37921906e-01 -7.61081427e-02 -1.95316344e-01
-7.07394719e-01 -3.21388304e-01 3.83728862e-01 -4.02222782e-01
-3.06354225e-01 7.49407291e-01 5.05963027e-01 -2.29727641e-01
5.73002920e-02 -5.53381145e-01 -8.64417255e-01 -7.06242263e-01
7.75692686e-02 -5.53729773e-01 1.91272721e-01 -1.73103526... | [11.137659072875977, -1.9736356735229492] |
74ed9c8c-f630-4fd9-b87c-7d3c0daa22d4 | english-to-hindi-multi-modal-neural-machine | null | null | https://aclanthology.org/D19-5205 | https://aclanthology.org/D19-5205.pdf | English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning | With the widespread use of Machine Trans-lation (MT) techniques, attempt to minimizecommunication gap among people from di-verse linguistic backgrounds. We have par-ticipated in Workshop on Asian Transla-tion 2019 (WAT2019) multi-modal translationtask. There are three types of submissiontrack namely, multi-modal transl... | ['Sivaji yopadhyay', 'B', 'Rohit Pratap Singh', 'Sahinur Rahman Laskar', 'Partha Pakray'] | 2019-11-01 | null | null | null | ws-2019-11 | ['hindi-image-captioning'] | ['computer-vision'] | [-1.94359809e-01 -7.47337043e-02 -2.30163485e-01 -2.04158977e-01
-1.45953596e+00 -7.86329985e-01 9.67122436e-01 -4.06322658e-01
-5.86079121e-01 1.11936712e+00 5.29941022e-01 -8.60030353e-01
5.51518619e-01 -5.99776745e-01 -9.57534850e-01 -3.52009982e-01
6.67054117e-01 8.90883029e-01 -4.82381582e-01 -5.83433032... | [11.4982271194458, 1.5505329370498657] |
612d6254-a3d1-49b0-a7b1-e6db9066389e | viewset-diffusion-0-image-conditioned-3d | 2306.07881 | null | https://arxiv.org/abs/2306.07881v1 | https://arxiv.org/pdf/2306.07881v1.pdf | Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data | We present Viewset Diffusion: a framework for training image-conditioned 3D generative models from 2D data. Image-conditioned 3D generative models allow us to address the inherent ambiguity in single-view 3D reconstruction. Given one image of an object, there is often more than one possible 3D volume that matches the i... | ['Andrea Vedaldi', 'Christian Rupprecht', 'Stanislaw Szymanowicz'] | 2023-06-13 | null | null | null | null | ['single-view-3d-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.61173189e-01 4.04325664e-01 2.10218892e-01 -3.17049831e-01
-8.84242594e-01 -8.02532911e-01 7.62733161e-01 -6.85189605e-01
4.70436215e-02 3.22363764e-01 1.85416371e-01 -1.92482859e-01
1.97455108e-01 -9.36128020e-01 -9.64835405e-01 -6.26961768e-01
3.64082307e-01 1.11382568e+00 9.70951319e-02 1.23630494... | [9.072075843811035, -3.141407012939453] |
4fb79f97-bfd3-4864-af4f-b51e96f6d120 | seggpt-segmenting-everything-in-context | 2304.03284 | null | https://arxiv.org/abs/2304.03284v1 | https://arxiv.org/pdf/2304.03284v1.pdf | SegGPT: Segmenting Everything In Context | We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-contex... | ['Tiejun Huang', 'Chunhua Shen', 'Wen Wang', 'Yue Cao', 'Xiaosong Zhang', 'Xinlong Wang'] | 2023-04-06 | null | null | null | null | ['panoptic-segmentation', 'personalized-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 7.10462511e-01 9.47797392e-03 -4.27678764e-01 -6.01505578e-01
-7.88292944e-01 -9.00734603e-01 4.20849204e-01 -3.67864631e-02
-2.19974563e-01 3.57815981e-01 -1.91596538e-01 -3.50279272e-01
1.52185097e-01 -7.45196700e-01 -7.13805795e-01 -6.69270933e-01
3.06218237e-01 6.41956151e-01 4.98965025e-01 3.77612934... | [9.606740951538086, 0.5053882598876953] |
c22b4d2a-7016-4a79-b8f9-46801b1f77d9 | current-state-of-community-driven | 2212.14177 | null | https://arxiv.org/abs/2212.14177v2 | https://arxiv.org/pdf/2212.14177v2.pdf | Current State of Community-Driven Radiological AI Deployment in Medical Imaging | Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist read... | ['Rahul Choudhury', 'M. Jorge Cardoso', 'Hyeonhoon Lee', 'Haris Shuaib', 'Risto Haukioja', 'Matthew Lungren', 'David Bericat', 'Tessa Cook', 'Richard D. White', 'Eric Kerfoot', 'Sebastien Ourselin', 'Gigon Bae', 'Ming Melvin Qin', 'Andreas Michael Bucher', 'Tobias Penzkofer', 'Khaled Younis', 'Rickmer Braren', 'Felix N... | 2022-12-29 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 2.24188864e-01 5.18797398e-01 -6.56460300e-02 -3.40701967e-01
-1.03250504e+00 -6.54708862e-01 -1.14214756e-01 3.68308336e-01
-3.53077739e-01 2.83641398e-01 4.51687992e-01 -8.74694705e-01
-3.13043654e-01 -3.72554392e-01 -5.11647284e-01 -3.72856349e-01
4.84879948e-02 9.71997976e-01 -1.10627443e-01 2.88525522... | [14.849830627441406, -2.3291335105895996] |
d9de030b-f161-4ebb-8fd2-ed4ea8e6b3f0 | context-aware-visual-tracking-with-joint-meta | 2204.01513 | null | https://arxiv.org/abs/2204.01513v1 | https://arxiv.org/pdf/2204.01513v1.pdf | Context-aware Visual Tracking with Joint Meta-updating | Visual object tracking acts as a pivotal component in various emerging video applications. Despite the numerous developments in visual tracking, existing deep trackers are still likely to fail when tracking against objects with dramatic variation. These deep trackers usually do not perform online update or update singl... | ['Yongsheng Liang', 'Fanyang Meng', 'Xin Li', 'Qiuhong Shen'] | 2022-04-04 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-3.75265002e-01 -5.91727614e-01 -1.57691628e-01 -1.58650223e-02
-3.49905461e-01 -5.73645115e-01 4.13996011e-01 4.35416996e-02
-5.30851007e-01 5.65236032e-01 -1.15399376e-01 7.90559202e-02
3.99467461e-02 -3.46530110e-01 -7.54808962e-01 -8.81726682e-01
1.20269237e-02 1.00011081e-01 8.38238835e-01 1.36254663... | [6.340561389923096, -2.126520872116089] |
60c563f6-7e43-4e00-8711-79bd67c12c03 | the-ab-use-of-open-source-code-to-train-large | 2302.13681 | null | https://arxiv.org/abs/2302.13681v2 | https://arxiv.org/pdf/2302.13681v2.pdf | The (ab)use of Open Source Code to Train Large Language Models | In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields, such as Software Engineering. LLMs for Code are commonly trained on large unsanitized corpora of source code scraped from the Internet. Th... | ['Maliheh Izadi', 'Ali Al-Kaswan'] | 2023-02-27 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.02421492e-01 7.26961792e-01 -3.14706624e-01 -3.24401826e-01
-7.08093286e-01 -6.84554935e-01 7.39811838e-01 3.59193861e-01
-3.24045718e-01 5.70542395e-01 2.88762361e-01 -8.28082919e-01
3.75661343e-01 -4.92517412e-01 -9.29825366e-01 1.56508118e-01
2.11085156e-01 -3.92258674e-01 -1.12974346e-01 -1.24956958... | [7.900961875915527, 7.7956061363220215] |
027e0c33-7b84-4b73-aef6-dc163d4052dd | clinical-outcome-prediction-from-admission | 2102.04110 | null | https://arxiv.org/abs/2102.04110v1 | https://arxiv.org/pdf/2102.04110v1.pdf | Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration | Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decision support can be especially valuable, and contribute a novel admission to discharge task with four common outcome prediction targets: Diagn... | ['Alexander Löser', 'Felix A. Gers', 'Klemens Budde', 'Manuel Mayrdorfer', 'Jens-Michalis Papaioannou', 'Betty van Aken'] | 2021-02-08 | null | https://aclanthology.org/2021.eacl-main.75 | https://aclanthology.org/2021.eacl-main.75.pdf | eacl-2021-2 | ['medical-procedure', 'length-of-stay-prediction'] | ['medical', 'medical'] | [-1.43993691e-01 3.51248890e-01 -5.47242284e-01 -5.70247054e-01
-1.25287533e+00 -4.17992532e-01 1.79070830e-01 1.29178047e+00
-3.86170536e-01 9.12835240e-01 1.23504853e+00 -7.84963131e-01
-6.01178944e-01 -7.89435267e-01 -6.67160749e-02 -3.25297892e-01
-4.38105136e-01 1.07056832e+00 -3.47282529e-01 6.91255108... | [7.994041919708252, 6.492499351501465] |
63c46364-34f3-4d2b-a290-e8b7bfcd4d27 | whitenet-phishing-website-detection-by-visual | 1909.00300 | null | https://arxiv.org/abs/1909.00300v4 | https://arxiv.org/pdf/1909.00300v4.pdf | VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity | Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites - in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishin... | ['Sahar Abdelnabi', 'Katharina Krombholz', 'Mario Fritz'] | 2019-09-01 | null | null | null | null | ['phishing-website-detection'] | ['adversarial'] | [ 7.92184025e-02 -2.35192925e-01 3.29225231e-03 -8.84625763e-02
-3.01551640e-01 -1.36372447e+00 9.59044576e-01 1.47529751e-01
-7.06296787e-02 1.27548158e-01 -1.54534414e-01 -8.04701447e-01
1.89061239e-01 -7.33638585e-01 -6.13103211e-01 -3.13749641e-01
-5.84245138e-02 1.18493997e-01 4.41300273e-01 -3.53531480... | [7.793848037719727, 9.969390869140625] |
92b8c927-1d07-4875-8028-d9ea4fab3535 | complex-words-identification-using-word-level | null | null | https://aclanthology.org/2021.semeval-1.11 | https://aclanthology.org/2021.semeval-1.11.pdf | Complex words identification using word-level features for SemEval-2020 Task 1 | This article describes a system to predict the complexity of words for the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1) with a new annotated English dataset with a Likert scale. Located in the Lexical Semantics track, the task consisted of predicting the complexity value of the words ... | ["Arturo Montejo-R{\\'a}ez", 'Jenny A. Ortiz-Zambrano'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-3.10846329e-01 1.46784335e-01 -1.75753430e-01 -3.59203190e-01
-4.44477499e-01 -5.33994853e-01 7.32494116e-01 6.99210584e-01
-8.50777388e-01 6.74966276e-01 3.30094904e-01 -1.88077554e-01
-8.19493905e-02 -7.40797698e-01 -4.18721169e-01 -2.15090230e-01
-1.53090030e-01 2.54143000e-01 1.92082211e-01 -1.98675290... | [10.636628150939941, 10.427436828613281] |
a077b6db-5d04-416e-aeb2-05172eaa8e46 | clustop-an-unsupervised-and-integrated-text | 2301.00818 | null | https://arxiv.org/abs/2301.00818v1 | https://arxiv.org/pdf/2301.00818v1.pdf | ClusTop: An unsupervised and integrated text clustering and topic extraction framework | Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clusteri... | ['Yatong Zhou', 'Jingfei He', 'Siwei Duo', 'Chenghu Mi', 'Zhongtao Chen'] | 2023-01-03 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-2.04007745e-01 7.49551505e-02 -2.56516099e-01 -4.04634416e-01
-8.04146230e-01 -2.13685766e-01 7.08853245e-01 2.77279913e-01
-3.48230809e-01 7.58109763e-02 3.81997049e-01 -1.61376402e-01
-2.03217015e-01 -9.72571075e-01 -1.23196051e-01 -9.97676015e-01
2.12538391e-02 5.85538149e-01 1.73644662e-01 2.33301163... | [10.37619686126709, 6.829369068145752] |
fa1e2d0d-34ab-42ff-a53b-2dedfac2794f | towards-open-intent-detection | 2203.05823 | null | https://arxiv.org/abs/2203.05823v3 | https://arxiv.org/pdf/2203.05823v3.pdf | Learning Discriminative Representations and Decision Boundaries for Open Intent Detection | Open intent detection is a significant problem in natural language understanding, which aims to identify the unseen open intent while ensuring known intent identification performance. However, current methods face two major challenges. Firstly, they struggle to learn friendly representations to detect the open intent w... | ['Qianrui Zhou', 'Shaojie Zhao', 'Hua Xu', 'Hanlei Zhang'] | 2022-03-11 | null | null | null | null | ['open-intent-detection'] | ['natural-language-processing'] | [ 1.67459846e-01 -4.51260246e-02 -4.95144635e-01 -4.16408777e-01
-1.04673076e+00 -7.12556839e-01 4.03334409e-01 1.06977031e-01
-2.90622920e-01 5.41492105e-01 3.84301156e-01 -3.52568269e-01
-2.10703179e-01 -6.34123564e-01 -2.34267533e-01 -3.57396692e-01
-6.25701621e-04 3.61716449e-01 -2.90294103e-02 5.99612929... | [12.347663879394531, 7.398104190826416] |
7fa23c56-3c48-4ca8-8b47-ae0cc60411d8 | efficient-multi-stage-inference-on-tabular | 2303.11580 | null | https://arxiv.org/abs/2303.11580v1 | https://arxiv.org/pdf/2303.11580v1.pdf | Efficient Multi-stage Inference on Tabular Data | Many ML applications and products train on medium amounts of input data but get bottlenecked in real-time inference. When implementing ML systems, conventional wisdom favors segregating ML code into services queried by product code via Remote Procedure Call (RPC) APIs. This approach clarifies the overall software archi... | ['Igor L Markov', 'Daniel S Johnson'] | 2023-03-21 | null | null | null | null | ['automl'] | ['methodology'] | [-2.15737134e-01 3.29981774e-01 -6.35861158e-01 -9.65464413e-01
-8.93240035e-01 -1.05410361e+00 2.08975092e-01 -4.55484241e-02
-1.83107585e-01 5.52531660e-01 -2.85536408e-01 -1.06356966e+00
1.04738608e-01 -8.56496811e-01 -8.90611410e-01 -1.79036885e-01
1.01469129e-01 6.28115773e-01 2.65487600e-02 3.79669964... | [8.74677848815918, 3.8009073734283447] |
645f933a-6b88-4059-ac03-58c20d872751 | rise-of-the-machines-intraday-high-frequency | 2009.04200 | null | https://arxiv.org/abs/2009.04200v1 | https://arxiv.org/pdf/2009.04200v1.pdf | Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies | This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of r... | ['Wolfgang Karl Härdle', 'Raphael C. G. Reule', 'Alla A. Petukhina'] | 2020-09-09 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.83245051e-01 -1.77084133e-01 7.65888542e-02 -5.15093915e-02
-5.12747467e-01 -1.15604746e+00 1.25461233e+00 2.74100184e-01
-3.75673681e-01 6.68763578e-01 2.35795960e-01 -7.57655740e-01
-3.81298184e-01 -1.07941091e+00 -1.65725231e-01 -5.55892944e-01
-8.61560941e-01 5.56504786e-01 3.01288664e-01 -6.37134433... | [4.666437149047852, 4.12335729598999] |
178ee774-825b-4596-b0fb-eb8413e16a67 | the-nexus-between-job-burnout-and-emotional | 2208.04843 | null | https://arxiv.org/abs/2208.04843v1 | https://arxiv.org/pdf/2208.04843v1.pdf | The Nexus between Job Burnout and Emotional Intelligence on Turnover Intention in Oil and Gas Companies in the UAE | Currently, job satisfaction and turnover intentions are the significant issues for oil and gas companies in the United Arab Emirates (UAE). These issues need to be addressed soon for the performance of the oil and gas companies. Thus, the aim related to the current study is to examine the impact of job burnout, emotion... | ['Norziani Dahalan', 'Mohd Faiz Hilmi', 'Anas Abudaqa'] | 2022-08-06 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-8.01758945e-01 2.80610293e-01 -5.31564713e-01 -9.65959504e-02
2.81272769e-01 2.08108455e-01 -4.82207537e-02 -1.38120264e-01
-4.38184530e-01 3.05011898e-01 3.00628901e-01 -3.57053638e-01
-4.60101217e-01 -7.51876891e-01 2.03179076e-01 -7.50011265e-01
4.42799896e-01 1.99036598e-01 -4.74131823e-01 -5.80065429... | [9.012404441833496, 6.142904281616211] |
5a0bd13c-b909-41c4-9699-3cf8dcfdfaf3 | towards-end-to-end-open-conversational | 2210.07113 | null | https://arxiv.org/abs/2210.07113v1 | https://arxiv.org/pdf/2210.07113v1.pdf | Towards End-to-End Open Conversational Machine Reading | In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem's two successive sub-tasks: first with a hard-label decision ma... | ['Hai Zhao', 'Zhuosheng Zhang', 'Siru Ouyang', 'Sizhe Zhou'] | 2022-10-13 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 3.90506089e-01 8.67438495e-01 1.94016285e-02 -7.92414606e-01
-1.51237833e+00 -6.84164762e-01 8.98652613e-01 1.44661620e-01
-4.29759324e-01 6.66593194e-01 4.72616941e-01 -7.70183802e-01
1.44546792e-01 -4.76334423e-01 -5.69738030e-01 -1.15870431e-01
3.70066166e-01 8.35041583e-01 2.55907118e-01 -6.40947640... | [11.88630199432373, 8.054312705993652] |
2a26a34d-87c3-4036-8b8f-f9990082730a | monash-summ-longsumm-20-scisummpip-an | null | null | https://aclanthology.org/2020.sdp-1.37 | https://aclanthology.org/2020.sdp-1.37.pdf | Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline | The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task. It contains three shared tasks and we participate in the LongSumm shared task. In this paper, we describe our text summarization system, SciSummPip, inspired by SummPip (Zhao et al., 2020)... | ['Shirui Pan', 'Longxiang Gao', 'Ming Liu', 'Jiaxin Ju'] | null | null | null | null | emnlp-sdp-2020-11 | ['graph-clustering'] | ['graphs'] | [ 2.23217502e-01 3.80715847e-01 -1.81964207e-02 -1.76780134e-01
-9.70377505e-01 -7.63037920e-01 9.22342062e-01 6.36683464e-01
-8.28692988e-02 1.06536114e+00 1.08135760e+00 -1.73239201e-01
-4.08464670e-01 -6.92454398e-01 -5.06358922e-01 -1.26633346e-01
-1.18887685e-01 4.65923101e-01 3.34256262e-01 -3.36022347... | [12.470355987548828, 9.5418701171875] |
a38d80cf-8a24-417e-829f-11eedcf5c496 | scai-qrecc-shared-task-on-conversational | 2201.11094 | null | https://arxiv.org/abs/2201.11094v1 | https://arxiv.org/pdf/2201.11094v1.pdf | SCAI-QReCC Shared Task on Conversational Question Answering | Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI'21 was organised as an independent on-line event and featured a shared task on conversational question answering. Since all of the participant teams ex... | ['Maik Fröbe', 'Johannes Kiesel', 'Svitlana Vakulenko'] | 2022-01-26 | null | https://aclanthology.org/2022.lrec-1.525 | https://aclanthology.org/2022.lrec-1.525.pdf | lrec-2022-6 | ['conversational-search'] | ['natural-language-processing'] | [ 2.68220067e-01 7.68144667e-01 2.79149741e-01 -4.76833642e-01
-1.26452947e+00 -8.61800015e-01 1.25450444e+00 3.58372927e-01
-4.44265157e-01 9.94140506e-01 8.99075806e-01 -4.89508033e-01
-5.70160039e-02 -3.51977527e-01 -1.43374175e-01 1.17468335e-01
3.21756512e-01 1.23147225e+00 5.48665822e-01 -7.29732394... | [12.089581489562988, 7.973697662353516] |
a93e4dbc-79ea-446d-abe4-5dba0474bceb | f2-nerf-fast-neural-radiance-field-training | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_F2-NeRF_Fast_Neural_Radiance_Field_Training_With_Free_Camera_Trajectories_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_F2-NeRF_Fast_Neural_Radiance_Field_Training_With_Free_Camera_Trajectories_CVPR_2023_paper.pdf | F2-NeRF: Fast Neural Radiance Field Training With Free Camera Trajectories | This paper presents a novel grid-based NeRF called F^2-NeRF (Fast-Free-NeRF) for novel view synthesis, which enables arbitrary input camera trajectories and only costs a few minutes for training. Existing fast grid-based NeRF training frameworks, like Instant-NGP, Plenoxels, DVGO, or TensoRF, are mainly designed fo... | ['Wenping Wang', 'Christian Theobalt', 'Taku Komura', 'Ziwei Liu', 'Lingjie Liu', 'Zhaoxi Chen', 'YuAn Liu', 'Peng Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['novel-view-synthesis'] | ['computer-vision'] | [-1.78578883e-01 -4.62755919e-01 9.24172178e-02 -2.65132897e-02
-5.14171124e-01 -1.01560867e+00 8.03494692e-01 -6.09739542e-01
-2.30050012e-01 5.52787662e-01 2.36296967e-01 -5.83688796e-01
-1.58861250e-01 -7.35164404e-01 -7.33516216e-01 -6.00799859e-01
-1.97740062e-03 1.38099223e-01 4.09810901e-01 -4.20600504... | [10.131444931030273, -1.833882451057434] |
38f1d8d3-c0e8-4889-a00d-b2be7fd92ab8 | post-hoc-concept-bottleneck-models | 2205.15480 | null | https://arxiv.org/abs/2205.15480v2 | https://arxiv.org/pdf/2205.15480v2.pdf | Post-hoc Concept Bottleneck Models | Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees" in an input and which of these concepts are deemed ... | ['James Zou', 'Maggie Wang', 'Mert Yuksekgonul'] | 2022-05-31 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 3.20111930e-01 4.96778965e-01 -1.51079178e-01 -4.52752203e-01
-2.94029355e-01 -6.51709378e-01 2.82040566e-01 5.52864909e-01
-5.62562108e-01 5.50938070e-01 1.39384583e-01 -5.10445058e-01
-1.20893165e-01 -5.36905885e-01 -9.81386602e-01 -1.03776976e-01
-7.71329701e-02 7.66809404e-01 1.01453230e-01 -3.45642149... | [9.635565757751465, 7.123847007751465] |
caa958d0-0698-4d3c-8f9f-6a3d28c146f9 | towards-efficient-discriminative-pattern | 1908.06801 | null | https://arxiv.org/abs/1908.06801v1 | https://arxiv.org/pdf/1908.06801v1.pdf | Towards Efficient Discriminative Pattern Mining in Hybrid Domains | Discriminative pattern mining is a data mining task in which we find patterns that distinguish transactions in the class of interest from those in other classes, and is also called emerging pattern mining or subgroup discovery. One practical problem in discriminative pattern mining is how to handle numeric values in th... | ['Yoshitaka Kameya'] | 2019-08-15 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 4.92471844e-01 -3.27977598e-01 -6.27125263e-01 -5.80456674e-01
9.21340510e-02 -2.40412071e-01 2.59785116e-01 4.38554674e-01
-1.32372722e-01 7.65763938e-01 -1.99534521e-02 -3.90706182e-01
-5.15805721e-01 -1.12171483e+00 -1.87999502e-01 -7.64938951e-01
-3.57771188e-01 9.76916790e-01 5.20531595e-01 -9.54044610... | [8.302258491516113, 6.295173645019531] |
cb6130dc-a5f7-4b97-8dde-413f0a80a905 | early-and-in-season-crop-type-mapping-without | 2110.10275 | null | https://arxiv.org/abs/2110.10275v1 | https://arxiv.org/pdf/2110.10275v1.pdf | Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach | Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to ma... | ['Zhenong Jin', 'David B. Lobell', 'Jinwei Dong', 'Xiao-Peng Song', 'Liheng Zhong', 'Chenxi Lin'] | 2021-10-19 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 4.16881263e-01 -1.52959883e-01 -4.62157696e-01 -2.92519093e-01
-3.61891419e-01 -1.11771882e+00 3.39269310e-01 5.79510450e-01
-1.29627377e-01 1.01848888e+00 -3.14765126e-01 -9.71106648e-01
-2.37570301e-01 -1.54067206e+00 -7.62581408e-01 -7.59931624e-01
-3.49412709e-01 -2.10568868e-02 2.02264339e-02 -5.09972692... | [9.404327392578125, -1.5779999494552612] |
1df5f036-b7e7-45eb-bc1a-80561d0a19a2 | real-time-dense-stereo-embedded-in-a-uav-for | 1904.06017 | null | http://arxiv.org/abs/1904.06017v1 | http://arxiv.org/pdf/1904.06017v1.pdf | Real-Time Dense Stereo Embedded in A UAV for Road Inspection | The condition assessment of road surfaces is essential to ensure their
serviceability while still providing maximum road traffic safety. This paper
presents a robust stereo vision system embedded in an unmanned aerial vehicle
(UAV). The perspective view of the target image is first transformed into the
reference view, ... | ['Huaiyang Huang', 'Jie Pan', 'Jianhao Jiao', 'Rui Fan', 'Shaojie Shen', 'Ming Liu'] | 2019-04-12 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 7.79355764e-01 -1.28037006e-01 3.67911547e-01 -5.02169132e-02
1.37339756e-01 -3.90923291e-01 6.14313364e-01 -4.68208134e-01
-5.25392413e-01 7.01037586e-01 -5.99670231e-01 -3.97922367e-01
1.07594896e-02 -1.15030730e+00 -3.88904929e-01 -7.83308148e-01
4.00017589e-01 1.34288192e-01 4.65767145e-01 -2.62735263... | [8.908501625061035, -2.4322566986083984] |
8690cdf9-efd3-45e0-85c1-58d8d49092de | unsupervised-learning-of-stereo-matching | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Zhou_Unsupervised_Learning_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Unsupervised_Learning_of_ICCV_2017_paper.pdf | Unsupervised Learning of Stereo Matching | In recent years, convolutional neural networks have shown its strong power for stereo matching cost learning. Current approaches learn the parameters of their models from public datasets with ground truth disparity. However, due to the limitations of these datasets and the difficulty of collecting new stereo data, curr... | ['Chao Zhou', 'Xiaoyong Shen', 'Jiaya Jia', 'Hong Zhang'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['stereo-matching'] | ['computer-vision'] | [ 7.26088434e-02 -6.28285259e-02 -5.16587853e-01 -6.32813931e-01
-6.07617199e-01 -3.38080466e-01 4.28394794e-01 -2.79407613e-02
-9.65825915e-01 8.80156994e-01 2.92705476e-01 2.92529371e-02
1.78784356e-01 -9.54015195e-01 -9.08871174e-01 -3.75843287e-01
1.79557860e-01 5.99235892e-01 3.62465024e-01 -1.13236956... | [8.755315780639648, -2.2914960384368896] |
c688061e-ffc1-46e8-b9bd-def096f29030 | embeddia-project-cross-lingual-embeddings-for | null | null | https://aclanthology.org/2022.eamt-1.36 | https://aclanthology.org/2022.eamt-1.36.pdf | EMBEDDIA project: Cross-Lingual Embeddings for Less- Represented Languages in European News Media | EMBEDDIA project developed a range of resources and methods for less-resourced EU languages, focusing on applications for media industry, including keyword extraction, comment moderation and article generation. | ['Andraž Pelicon', 'Senja Pollak'] | null | null | null | null | eamt-2022-6 | ['keyword-extraction'] | ['natural-language-processing'] | [-1.83230400e-01 7.58196592e-01 -9.39732611e-01 5.58592498e-01
-8.64805937e-01 -9.54680800e-01 1.17131507e+00 5.87674975e-01
-5.59724569e-01 1.18087947e+00 1.22462726e+00 -7.02180266e-01
5.46722040e-02 -5.65139353e-01 -1.97322704e-02 8.16653967e-02
-8.61050040e-02 2.15921357e-01 -1.91017136e-01 -2.92741686... | [12.035922050476074, 9.493075370788574] |
1077f29b-d368-42f9-8c5f-62a4f78f9fd0 | efficient-search-of-comprehensively-robust | 2305.07308 | null | https://arxiv.org/abs/2305.07308v1 | https://arxiv.org/pdf/2305.07308v1.pdf | Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation | Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NAS only consider $l_{\infty}$ norm-based adversarial noises. In order to improve the robustness of DNN models against multiple types of noise... | ['Xiaoqian Chen', 'Tingsong Jiang', 'Wen Yao', 'Jialiang Sun'] | 2023-05-12 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.39863724e-01 -6.52608871e-01 3.63876313e-01 -4.60565597e-01
-9.09636259e-01 -6.21179700e-01 5.04230969e-02 -3.60766262e-01
-6.70388758e-01 5.43935478e-01 8.03576317e-03 -2.90927202e-01
-4.91502881e-01 -6.68077290e-01 -6.78702593e-01 -8.75540435e-01
2.99487174e-01 -1.78716853e-01 2.29350224e-01 -3.08777690... | [5.547145366668701, 7.9163594245910645] |
a68b8cf5-259c-44a3-b94d-9acdb55d8b88 | robust-regression-via-model-based-methods | 2106.10759 | null | https://arxiv.org/abs/2106.10759v4 | https://arxiv.org/pdf/2106.10759v4.pdf | Robust Regression via Model Based Methods | The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, it is not robust to outliers. In contrast, l_p norms are known to be robust, but cannot be optimized ... | ['Edmund Yeh', 'Stratis Ioannidis', 'Khashayar Kamran', 'Armin Moharrer'] | 2021-06-20 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 2.82308497e-02 -1.37071595e-01 1.22132279e-01 -3.82837087e-01
-1.05136597e+00 -2.84682900e-01 2.13758603e-01 1.96059495e-01
-6.13554120e-01 8.32327902e-01 -3.43801305e-02 -1.55320168e-01
-3.90786767e-01 -3.83307785e-01 -1.09204650e+00 -7.24056005e-01
-4.32622619e-02 3.64974529e-01 -2.05358908e-01 -1.88788146... | [7.163823127746582, 4.414854526519775] |
d7afff57-5bfe-47d9-9b1c-c8b6d04af8b7 | language-model-analysis-for-ontology | 2302.06761 | null | https://arxiv.org/abs/2302.06761v3 | https://arxiv.org/pdf/2302.06761v3.pdf | Language Model Analysis for Ontology Subsumption Inference | Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently. However, existing works focus on simple, triple-based, relational KBs, but omit more sophisticated, logic-based, conceptualised KBs such as OWL ontologies. To investigate an LM's kn... | ['Ian Horrocks', 'Hang Dong', 'Ernesto Jiménez-Ruiz', 'Jiaoyan Chen', 'Yuan He'] | 2023-02-14 | null | null | null | null | ['ontology-subsumption-inferece'] | ['knowledge-base'] | [-2.03958854e-01 6.85792446e-01 -6.44002378e-01 -6.27317369e-01
-2.75044411e-01 -5.19048452e-01 5.47269106e-01 2.01906756e-01
-1.12375073e-01 1.22553873e+00 1.87147602e-01 -6.64086223e-01
-5.00689089e-01 -1.48921132e+00 -1.04940736e+00 2.53070265e-01
-4.09325123e-01 9.06768084e-01 7.68603742e-01 -4.02195245... | [9.974486351013184, 7.868868827819824] |
35f5607b-e3ed-4cd5-8421-4f4b5e6e4ac3 | a-latent-diffusion-model-for-protein | 2305.04120 | null | https://arxiv.org/abs/2305.04120v1 | https://arxiv.org/pdf/2305.04120v1.pdf | A Latent Diffusion Model for Protein Structure Generation | Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synthetic biology applications, including drug discovery. However, it remains a challenging computational task due to the large modeling space o... | ['Shuiwang Ji', 'Xiaoning Qian', 'Kanji Uchino', 'Koji Maruhashi', 'Tao Komikado', 'Michael McThrow', 'Wing Yee Au', 'Limei Wang', 'Keqiang Yan', 'Cong Fu'] | 2023-05-06 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.76941320e-01 2.18526088e-02 -5.83408661e-02 -2.59329349e-01
-2.73215979e-01 -6.29658997e-01 4.22938496e-01 -7.53432373e-03
-1.06447197e-01 8.88935626e-01 4.92219359e-01 -3.50026876e-01
9.00436193e-02 -9.38193679e-01 -9.61797655e-01 -1.24869013e+00
2.22347975e-01 5.04985154e-01 2.01339424e-02 -1.92428529... | [4.6735734939575195, 5.695454120635986] |
9aa14daf-3747-40cb-bb34-f6b2b96c4204 | optimization-of-iot-enabled-physical-location | 2204.04664 | null | https://arxiv.org/abs/2204.04664v1 | https://arxiv.org/pdf/2204.04664v1.pdf | Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR | This study shows an enhancement of IoT that gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDad... | ['Manoj Himmatrao Devare', 'Ajitkumar Sureshrao Shitole'] | 2022-04-10 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.48675805e-02 -3.99691463e-01 8.81315768e-03 -5.83058596e-01
2.90953308e-01 -2.13389561e-01 7.68761337e-02 -3.87744568e-02
-5.59934042e-02 7.55339622e-01 -1.96567163e-01 -3.08880270e-01
-3.54601026e-01 -1.17676687e+00 5.54874539e-02 -7.85832286e-01
3.40820625e-02 1.08896323e-01 -6.09267913e-02 1.72644779... | [13.346487045288086, 1.4653000831604004] |
c88ed29f-5f2b-437c-a575-76c7d62aa46d | 190600544 | 1906.00544 | null | https://arxiv.org/abs/1906.00544v1 | https://arxiv.org/pdf/1906.00544v1.pdf | 3D Magic Mirror: Automatic Video to 3D Caricature Translation | Caricature is an abstraction of a real person which distorts or exaggerates certain features, but still retains a likeness. While most existing works focus on 3D caricature reconstruction from 2D caricatures or translating 2D photos to 2D caricatures, this paper presents a real-time and automatic algorithm for creating... | ['Juyong Zhang', 'Yudong Guo', 'Luo Jiang', 'Lin Cai'] | 2019-06-03 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.56311172e-01 3.15378010e-01 1.82112023e-01 -4.16522115e-01
-4.54209089e-01 -9.06320274e-01 7.43795633e-01 -1.21720350e+00
4.33343589e-01 5.10030210e-01 4.23767060e-01 2.26767287e-01
5.35658896e-01 -5.89339375e-01 -7.76046813e-01 -5.46240330e-01
5.35505950e-01 4.71571982e-01 -5.67560077e-01 -4.06156778... | [12.689620018005371, -0.2617032527923584] |
8b0abb56-c0c0-40ca-a1d9-57df6a6d8a48 | towards-label-free-scene-understanding-by | 2306.03899 | null | https://arxiv.org/abs/2306.03899v1 | https://arxiv.org/pdf/2306.03899v1.pdf | Towards Label-free Scene Understanding by Vision Foundation Models | Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classification and segmentation tasks. However, the incorporation of CLIP and SAM for label-free scene understanding has yet to be explored. In this pap... | ['Wenping Wang', 'Tongliang Liu', 'Yuexin Ma', 'Xinge Zhu', 'Nenglun Chen', 'Lingdong Kong', 'Youquan Liu', 'Runnan Chen'] | 2023-06-06 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 3.94021988e-01 2.69117713e-01 -1.44392177e-01 -6.94026411e-01
-6.65302157e-01 -5.71013749e-01 6.16246343e-01 -3.38403970e-01
-4.66412842e-01 3.28253448e-01 -9.18061808e-02 -3.58170152e-01
2.11677983e-01 -5.44997692e-01 -8.90533447e-01 -4.05230880e-01
2.21461102e-01 4.20485079e-01 2.73801029e-01 4.65563275... | [9.643895149230957, 0.705441415309906] |
e284fd59-6e36-4f94-8a6c-e7221c8be763 | defect-transformer-an-efficient-hybrid | 2207.08319 | null | https://arxiv.org/abs/2207.08319v1 | https://arxiv.org/pdf/2207.08319v1.pdf | Defect Transformer: An Efficient Hybrid Transformer Architecture for Surface Defect Detection | Surface defect detection is an extremely crucial step to ensure the quality of industrial products. Nowadays, convolutional neural networks (CNNs) based on encoder-decoder architecture have achieved tremendous success in various defect detection tasks. However, due to the intrinsic locality of convolution, they commonl... | ['Zhengsheng Wang', 'Jinjin Wang', 'Fuju Yan', 'Guili Xu', 'Junpu Wang'] | 2022-07-17 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.32736930e-01 -2.59334117e-01 2.35825807e-01 -2.45973751e-01
-5.89241624e-01 1.97713211e-01 1.65245086e-01 1.30925581e-01
3.09968852e-02 2.16017216e-01 4.88677435e-02 -2.28645802e-02
-1.49770640e-02 -1.16984153e+00 -8.18039715e-01 -9.06950057e-01
1.72527313e-01 4.91140075e-02 7.57439852e-01 -2.23385647... | [7.541024684906006, 1.524172067642212] |
65d6b9b1-cad9-4396-867d-cb16c4f307d8 | adaptive-intrusion-detection-in-the | null | null | https://ieeexplore.ieee.org/document/9296578 | https://ieeexplore.ieee.org/document/9296578 | Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning | Predominant network intrusion detection systems (NIDS) aim to identify malicious traffic patterns based on a handcrafted dataset of rules. Recently, the application of machine learning in NIDS helps alleviate the enormous effort of human observation. Federated learning (FL) is a collaborative learning scheme concerning... | ['Hideya Ochiai.', 'Hiroshi Esaki', 'Yuwei Sun'] | 2020-12-16 | null | null | null | ieee-open-journal-of-the-communications | ['network-intrusion-detection'] | ['miscellaneous'] | [-1.36720061e-01 -4.02410448e-01 -2.74975151e-01 -3.60121310e-01
-8.50671902e-02 -5.68390846e-01 6.55693114e-01 1.35531977e-01
-3.26198846e-01 7.92023242e-01 -5.49147427e-01 -5.43057740e-01
-6.52291238e-01 -8.45492661e-01 -1.81193516e-01 -4.45605814e-01
-3.30516040e-01 6.39078975e-01 7.13123024e-01 -1.61901154... | [5.288809776306152, 7.187138080596924] |
dc6579a6-c36d-4a83-9743-400eab36dea6 | contrimix-unsupervised-disentanglement-of | 2306.04527 | null | https://arxiv.org/abs/2306.04527v2 | https://arxiv.org/pdf/2306.04527v2.pdf | ContriMix: Unsupervised disentanglement of content and attribute for domain generalization in microscopy image analysis | Domain generalization is critical for real-world applications of machine learning models to microscopy images, including histopathology and fluorescence imaging. Artifacts in histopathology arise through a complex combination of factors relating to tissue collection and laboratory processing, as well as factors intrins... | ['Amaro Taylor-Weiner', 'Justin Lee', 'John Abel', 'Anand Sampat', 'Michael Griffin', 'Sai Chowdary Gullapally', 'Chintan Shah', 'Shima Nofallah', 'Aaditya Prakash', 'Jin Li', 'Dinkar Juyal', 'Tan H. Nguyen'] | 2023-06-07 | null | null | null | null | ['domain-generalization', 'disentanglement'] | ['methodology', 'methodology'] | [ 7.99748003e-01 -2.35270262e-01 3.46632265e-02 -6.37283385e-01
-7.93557346e-01 -8.58066261e-01 6.95384920e-01 2.93656915e-01
-7.51392007e-01 1.10939145e+00 -1.55803099e-01 -2.90245593e-01
-1.31536245e-01 -4.26186740e-01 -8.49245071e-01 -1.02009141e+00
8.23729858e-02 7.03253865e-01 -1.22138672e-01 1.48841143... | [15.039206504821777, -2.9207327365875244] |
bd1b83c5-feda-49c7-bee1-e55a5d0950fa | blind-image-deblurring-using-class-adapted | 1709.01710 | null | http://arxiv.org/abs/1709.01710v1 | http://arxiv.org/pdf/1709.01710v1.pdf | Blind image deblurring using class-adapted image priors | Blind image deblurring (BID) is an ill-posed inverse problem, usually
addressed by imposing prior knowledge on the (unknown) image and on the
blurring filter. Most of the work on BID has focused on natural images, using
image priors based on statistical properties of generic natural images.
However, in many application... | ['Mário A. T. Figueiredo', 'Marina Ljubenović'] | 2017-09-06 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 5.90347648e-01 -2.42749974e-01 1.78881213e-01 -2.44005816e-03
-4.53236163e-01 -4.23880786e-01 7.83529937e-01 -2.32356504e-01
-3.91974032e-01 7.76961923e-01 2.34541908e-01 1.77605689e-01
-2.68505484e-01 -4.84492093e-01 -7.12204456e-01 -9.85021114e-01
2.71681190e-01 2.66485006e-01 -1.95777752e-02 1.04858078... | [11.60057258605957, -2.563585042953491] |
15509130-bef8-4e3d-9ea2-692ccd20f828 | correcting-for-selection-bias-and-missing | 2303.16800 | null | https://arxiv.org/abs/2303.16800v2 | https://arxiv.org/pdf/2303.16800v2.pdf | Correcting for Selection Bias and Missing Response in Regression using Privileged Information | When estimating a regression model, we might have data where some labels are missing, or our data might be biased by a selection mechanism. When the response or selection mechanism is ignorable (i.e., independent of the response variable given the features) one can use off-the-shelf regression methods; in the nonignora... | ['Onno Zoeter', 'Joris M. Mooij', 'Mathijs de Jong', 'Noud de Kroon', 'Philip Boeken'] | 2023-03-29 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 5.90252697e-01 -3.01883910e-02 -5.48110306e-01 -7.54315376e-01
-8.90186250e-01 -3.40560079e-01 4.24989879e-01 -4.96192910e-02
-7.14434147e-01 1.50488961e+00 -1.26298845e-01 -4.87966359e-01
-2.88042814e-01 -9.98915493e-01 -1.04573619e+00 -9.01697755e-01
1.28732607e-01 4.85256314e-01 -3.03304851e-01 -5.30847274... | [8.744263648986816, 4.6465983390808105] |
4bad6632-cb69-4105-a362-a771f131ffb8 | long-scale-error-control-in-low-light-image | 2206.01334 | null | https://arxiv.org/abs/2206.01334v1 | https://arxiv.org/pdf/2206.01334v1.pdf | Long Scale Error Control in Low Light Image and Video Enhancement Using Equivariance | Image frames obtained in darkness are special. Just multiplying by a constant doesn't restore the image. Shot noise, quantization effects and camera non-linearities mean that colors and relative light levels are estimated poorly. Current methods learn a mapping using real dark-bright image pairs. These are very hard to... | ['David Forsyth', 'Sara Aghajanzadeh'] | 2022-06-02 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.71124536e-01 -3.55891079e-01 3.29866827e-01 -3.16798598e-01
-7.37113535e-01 -5.40586114e-01 3.57300341e-01 -3.25785100e-01
-3.34015936e-01 9.82337296e-01 2.84222186e-01 -2.44010482e-02
4.09834951e-01 -6.04072213e-01 -8.72648239e-01 -8.41338336e-01
-1.12183601e-01 4.35619196e-03 3.62762690e-01 -3.79145682... | [10.834465026855469, -2.3189361095428467] |
2a699390-bcbb-4677-a19c-e3e1bc489c69 | image-segmentation-based-unsupervised | 2212.10124 | null | https://arxiv.org/abs/2212.10124v1 | https://arxiv.org/pdf/2212.10124v1.pdf | Image Segmentation-based Unsupervised Multiple Objects Discovery | Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery. The proposed approach is a two-stage framework. First... | ['Quoc-Cuong Pham', 'Florian Chabot', 'Hejer Ammar', 'Sandra Kara'] | 2022-12-20 | null | null | null | null | ['object-discovery', 'class-agnostic-object-detection'] | ['computer-vision', 'computer-vision'] | [ 4.83986467e-01 2.58818507e-01 -2.87499100e-01 -6.02554083e-01
-8.95393312e-01 -6.37400687e-01 4.52935427e-01 5.84455788e-01
-4.91052538e-01 2.54387081e-01 -2.89829284e-01 3.39274436e-01
-1.70671687e-01 -8.27005565e-01 -8.14576328e-01 -6.34895563e-01
1.54211059e-01 7.58229017e-01 8.15659523e-01 4.11703408... | [9.440848350524902, 0.6542792916297913] |
002ca917-378b-4729-9fcb-16dbd19c21af | triplet-loss-less-center-loss-sampling | 2302.04108 | null | https://arxiv.org/abs/2302.04108v1 | https://arxiv.org/pdf/2302.04108v1.pdf | Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios | Facial expressions convey massive information and play a crucial role in emotional expression. Deep neural network (DNN) accompanied by deep metric learning (DML) techniques boost the discriminative ability of the model in facial expression recognition (FER) applications. DNN, equipped with only classification loss fun... | ['Fatemeh Afghah', 'Adham Atyabi', 'Fatemeh Lotfi', 'Hossein Rajoli'] | 2023-02-08 | null | null | null | null | ['metric-learning', 'facial-expression-recognition', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.80647218e-01 -1.86993912e-01 -1.93480134e-01 -8.31333816e-01
-6.22221291e-01 2.42645033e-02 4.30925727e-01 -3.78686190e-01
-5.72001576e-01 7.32346356e-01 1.37316570e-01 2.94757038e-01
-3.77959548e-03 -6.30410433e-01 -4.24420029e-01 -9.13444340e-01
-4.23965417e-02 6.58939704e-02 -3.59736174e-01 -3.46148938... | [13.578397750854492, 1.6433298587799072] |
7b260f8c-2d07-4009-82b1-da78f9931db9 | robust-and-efficient-memory-network-for-video | 2304.11840 | null | https://arxiv.org/abs/2304.11840v1 | https://arxiv.org/pdf/2304.11840v1.pdf | Robust and Efficient Memory Network for Video Object Segmentation | This paper proposes a Robust and Efficient Memory Network, referred to as REMN, for studying semi-supervised video object segmentation (VOS). Memory-based methods have recently achieved outstanding VOS performance by performing non-local pixel-wise matching between the query and memory. However, these methods have two ... | ['Enhua Wu', 'Zhi-Xin Yang', 'Dingwei Zhang', 'Yadang Chen'] | 2023-04-24 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.61078060e-01 -2.96614528e-01 -2.06922442e-01 -2.59480953e-01
-5.83150923e-01 -7.32406080e-02 -5.15196808e-02 -1.29740775e-01
-7.77036190e-01 5.91520607e-01 -4.17684764e-01 -1.38696671e-01
2.47891366e-01 -7.09174931e-01 -9.86245573e-01 -6.82490408e-01
4.08944637e-02 2.82346457e-02 9.73740041e-01 3.27504218... | [9.19897747039795, -0.13850954174995422] |
af68eeb6-e404-4c56-b55d-d0400f14647a | unsupervised-learning-of-object-keypoints-for | 1906.11883 | null | https://arxiv.org/abs/1906.11883v2 | https://arxiv.org/pdf/1906.11883v2.pdf | Unsupervised Learning of Object Keypoints for Perception and Control | The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning ... | ['Volodymyr Mnih', 'Tejas Kulkarni', 'Sebastian Borgeaud', 'Catalin Ionescu', 'Andrew Zisserman', 'Malcolm Reynolds', 'Ankush Gupta'] | 2019-06-19 | unsupervised-learning-of-object-keypoints-for-1 | http://papers.nips.cc/paper/9256-unsupervised-learning-of-object-keypoints-for-perception-and-control | http://papers.nips.cc/paper/9256-unsupervised-learning-of-object-keypoints-for-perception-and-control.pdf | neurips-2019-12 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.66014016e-01 1.34552583e-01 -6.04429662e-01 -1.45859241e-01
-6.33371532e-01 -5.90612054e-01 7.59390295e-01 2.93246299e-01
-5.73234856e-01 6.50752008e-01 -8.31317753e-02 1.27803281e-01
-3.96954060e-01 -6.21043205e-01 -1.28053403e+00 -7.50514269e-01
-4.97005433e-01 2.78194547e-01 3.17701787e-01 7.32376501... | [4.698873043060303, 0.7201289534568787] |
a8799b5f-abb7-468d-8ae3-f43baab94b21 | zero-shot-slot-and-intent-detection-in-low | 2304.13292 | null | https://arxiv.org/abs/2304.13292v1 | https://arxiv.org/pdf/2304.13292v1.pdf | Zero-Shot Slot and Intent Detection in Low-Resource Languages | Intent detection and slot filling are critical tasks in spoken and natural language understanding for task-oriented dialog systems. In this work we describe our participation in the slot and intent detection for low-resource language varieties (SID4LR; Aepli et al. (2023)). We investigate the slot and intent detection ... | ['Muhammad Abdul-Mageed', 'Alcides Alcoba Inciarte', 'El Moatez Billah Nagoudi', 'Gagan Bhatia', 'Sang Yun Kwon'] | 2023-04-26 | null | null | null | null | ['intent-detection', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.88650885e-02 5.77579618e-01 -2.24269480e-01 -4.95565414e-01
-8.22900653e-01 -6.28130436e-01 1.01937664e+00 -2.00257868e-01
-7.39952028e-01 9.05161202e-01 7.62561083e-01 -5.77113390e-01
3.14334780e-01 3.28246877e-02 -1.11512914e-01 3.39816436e-02
-6.54001674e-03 1.00108659e+00 2.52329111e-01 -5.81883132... | [12.562447547912598, 7.889482021331787] |
a5987816-5f51-4073-b322-4b60244ba797 | learning-human-objectives-by-evaluating | 1912.05652 | null | https://arxiv.org/abs/1912.05652v2 | https://arxiv.org/pdf/1912.05652v2.pdf | Learning Human Objectives by Evaluating Hypothetical Behavior | We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and ... | ['Siddharth Reddy', 'Jan Leike', 'Shane Legg', 'Sergey Levine', 'Anca D. Dragan'] | 2019-12-05 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/664-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/664-Paper.pdf | icml-2020-1 | ['carracing-v0'] | ['playing-games'] | [-1.31496400e-01 3.76604050e-01 -3.19669515e-01 -2.79199362e-01
-8.37562621e-01 -7.66456783e-01 5.87857902e-01 -2.19995916e-01
-7.32363522e-01 8.20142329e-01 2.98171416e-02 -4.40248668e-01
1.45503223e-01 -7.06791759e-01 -8.60687256e-01 -5.15599370e-01
-4.01727289e-01 7.61684239e-01 3.42565030e-02 -4.79076087... | [4.1641411781311035, 1.6643182039260864] |
506b8e3c-27cb-4020-8101-51089d1af279 | adaptive-experimentation-at-scale-bayesian | 2303.11582 | null | https://arxiv.org/abs/2303.11582v3 | https://arxiv.org/pdf/2303.11582v3.pdf | Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches | Standard bandit algorithms that assume continual reallocation of measurement effort are challenging to implement due to delayed feedback and infrastructural/organizational difficulties. Motivated by practical instances involving a handful of reallocation epochs in which outcomes are measured in batches, we develop a co... | ['Hongseok Namkoong', 'Ethan Che'] | 2023-03-21 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.39747158e-01 9.52330455e-02 -6.23643756e-01 -3.06339592e-01
-1.07488656e+00 -6.63443506e-01 6.38453364e-01 2.40659099e-02
-6.36684120e-01 1.03978395e+00 2.37651974e-01 -8.36513340e-01
-7.02618718e-01 -6.71293736e-01 -8.04929376e-01 -5.39590359e-01
-8.96222070e-02 6.35873258e-01 -1.84140727e-01 1.34901792... | [4.5119805335998535, 3.2117879390716553] |
c5187280-f5e9-42d3-8d69-6c24906339ee | a-one-class-classifier-based-framework-using | 1612.01349 | null | http://arxiv.org/abs/1612.01349v1 | http://arxiv.org/pdf/1612.01349v1.pdf | A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset | Evaluation of hydrocarbon reservoir requires classification of petrophysical
properties from available dataset. However, characterization of reservoir
attributes is difficult due to the nonlinear and heterogeneous nature of the
subsurface physical properties. In this context, present study proposes a
generalized one cl... | ['Mamata Jenamani', 'Aurobinda Routray', 'Soumi Chaki', 'William K. Mohanty', 'Akhilesh Kumar Verma'] | 2016-12-02 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-3.53383794e-02 -3.04768890e-01 1.00887856e-02 -3.94956946e-01
-2.37899736e-01 -5.98791599e-01 9.02895808e-01 9.36295509e-01
-7.87583590e-02 1.04598868e+00 3.93484831e-01 -3.35637003e-01
-4.31346804e-01 -1.25692773e+00 -1.77252337e-01 -9.73159254e-01
-6.64549530e-01 7.16842175e-01 3.33525807e-01 -4.41159129... | [6.439683437347412, 3.04349946975708] |
fd56a9b9-7cbe-4a61-89e7-6d7e1022fa3a | data-free-point-cloud-network-for-3d-face | 1911.04731 | null | https://arxiv.org/abs/1911.04731v1 | https://arxiv.org/pdf/1911.04731v1.pdf | Data-Free Point Cloud Network for 3D Face Recognition | Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point clouds as input is still under study. Two main factors account for this: One is how to get discrimi... | ['Yu', 'Yi', 'Da', 'Ziyu', 'Feipeng', 'Zhang'] | 2019-11-12 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-1.78756922e-01 -2.33386531e-02 1.91232979e-01 -9.23350096e-01
-3.73476595e-01 -3.80630732e-01 4.62473303e-01 -6.54267907e-01
-5.60392775e-02 1.77855536e-01 -5.97934008e-01 -7.64993131e-02
7.41435140e-02 -9.85278606e-01 -9.30547237e-01 -4.90420461e-01
2.71504316e-02 8.38086188e-01 1.55744433e-01 -2.85312742... | [13.3261079788208, 0.20587414503097534] |
da92315a-da8d-4c5d-bb7b-5f3b41a74f0c | humaniflow-ancestor-conditioned-normalising | 2305.06968 | null | https://arxiv.org/abs/2305.06968v1 | https://arxiv.org/pdf/2305.06968v1.pdf | HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution Estimation | Monocular 3D human pose and shape estimation is an ill-posed problem since multiple 3D solutions can explain a 2D image of a subject. Recent approaches predict a probability distribution over plausible 3D pose and shape parameters conditioned on the image. We show that these approaches exhibit a trade-off between three... | ['Roberto Cipolla', 'Ignas Budvytis', 'Akash Sengupta'] | 2023-05-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_Flows_on_SO3_Manifolds_for_Human_Pose_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sengupta_HuManiFlow_Ancestor-Conditioned_Normalising_Flows_on_SO3_Manifolds_for_Human_Pose_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-and-shape-estimation', 'normalising-flows'] | ['computer-vision', 'methodology'] | [-7.16380775e-02 3.88633311e-01 -2.30193600e-01 -3.33646983e-01
-9.72956061e-01 -3.91013712e-01 5.20473123e-01 -5.54149747e-01
-1.96496770e-01 7.39359140e-01 4.15605426e-01 3.27844024e-01
-1.57702312e-01 -2.42661446e-01 -9.64468002e-01 -4.61700559e-01
-1.94472373e-01 1.00901461e+00 -2.05378118e-03 1.38317078... | [6.992441654205322, -1.0190507173538208] |
658d84cf-8271-425f-ba97-e43447c65ab9 | small-object-detection-for-near-real-time | 2106.06403 | null | https://arxiv.org/abs/2106.06403v1 | https://arxiv.org/pdf/2106.06403v1.pdf | Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario | Detecting small objects in video streams of head-worn augmented reality devices in near real-time is a huge challenge: training data is typically scarce, the input video stream can be of limited quality, and small objects are notoriously hard to detect. In industrial scenarios, however, it is often possible to leverage... | ['Martin Ruskowski', 'Christiane Plociennik', 'Parsha Pahlevannejad', 'Snehal Walunj', 'Hooman Tavakoli'] | 2021-06-11 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.98306459e-01 -5.09712212e-02 3.65780145e-01 -1.09295659e-01
-7.53851593e-01 -5.03274441e-01 1.71026096e-01 -1.80867091e-02
-2.28068680e-01 3.78627121e-01 -2.11448148e-01 -3.04039299e-01
3.61530989e-01 -4.81625229e-01 -9.25511777e-01 -2.31098354e-01
6.71229586e-02 5.82642138e-01 6.84694469e-01 -1.84746668... | [7.228487491607666, -2.1761057376861572] |
f836023e-f1ab-4416-bd14-a17effb72137 | a-probit-tensor-factorization-model-for | 2111.03943 | null | https://arxiv.org/abs/2111.03943v2 | https://arxiv.org/pdf/2111.03943v2.pdf | A Probit Tensor Factorization Model For Relational Learning | With the proliferation of knowledge graphs, modeling data with complex multirelational structure has gained increasing attention in the area of statistical relational learning. One of the most important goals of statistical relational learning is link prediction, i.e., predicting whether certain relations exist in the ... | ['Yanghua Xiao', 'Wenbin Lu', 'Rui Song', 'Ye Liu'] | 2021-11-06 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-2.94820487e-01 2.66714618e-02 -5.00826776e-01 -2.51888484e-01
1.73789784e-01 -3.38021100e-01 4.86161351e-01 6.35979950e-01
9.50270966e-02 7.08912551e-01 6.19424954e-02 -4.30389553e-01
-8.38528693e-01 -1.11344826e+00 -3.17565411e-01 -4.41394687e-01
-2.63120860e-01 6.47966802e-01 3.49387437e-01 -2.83885539... | [8.640690803527832, 7.7816481590271] |
8ebf78fb-ce40-41a2-86de-52d47b348283 | newsnet-a-novel-dataset-for-hierarchical | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_NewsNet_A_Novel_Dataset_for_Hierarchical_Temporal_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_NewsNet_A_Novel_Dataset_for_Hierarchical_Temporal_Segmentation_CVPR_2023_paper.pdf | NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation | Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks. Recent works have studied several levels of granularity to segment a video, such as shot, event, and scene. Those segmentations can help compare... | ['Bernard Ghanem', 'Chia-Wen Lin', 'Raghavendra Ramachandra', 'Wentian Zhang', 'Mengmeng Xu', 'Bo Ren', 'Liangsheng Xu', 'Bei Gan', 'Xiujun Shu', 'Ruizhi Qiao', 'Bing Li', 'Mingchen Zhuge', 'Haozhe Liu', 'Keyu Chen', 'Haoqian Wu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-semantic-segmentation'] | ['computer-vision'] | [ 1.35611400e-01 -2.36530617e-01 -7.41569340e-01 -3.70843679e-01
-5.55201709e-01 -5.85892320e-01 2.50721008e-01 3.11314672e-01
-2.25232840e-01 5.10363102e-01 6.72603846e-01 -1.49000049e-01
-2.25574709e-02 -5.13307333e-01 -8.48941624e-01 -4.83384281e-01
-1.90225661e-01 -5.17809093e-02 9.26199853e-01 -1.74399152... | [9.341115951538086, 0.5558005571365356] |
570af657-8b27-4dec-ad67-a800ac590b4d | modeling-context-in-referring-expressions | 1608.00272 | null | http://arxiv.org/abs/1608.00272v3 | http://arxiv.org/pdf/1608.00272v3.pdf | Modeling Context in Referring Expressions | Humans refer to objects in their environments all the time, especially in
dialogue with other people. We explore generating and comprehending natural
language referring expressions for objects in images. In particular, we focus
on incorporating better measures of visual context into referring expression
models and find... | ['Alexander C. Berg', 'Tamara L. Berg', 'Licheng Yu', 'Shan Yang', 'Patrick Poirson'] | 2016-07-31 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 1.37669966e-01 2.70166636e-01 1.57253563e-01 -5.71178675e-01
-4.77393150e-01 -7.00618923e-01 9.29245651e-01 2.77306795e-01
-2.87378550e-01 6.48784995e-01 8.57664227e-01 -5.87661155e-02
3.37584257e-01 -5.70529044e-01 -4.86025691e-01 -1.63642555e-01
3.37286294e-01 4.56505477e-01 3.85467559e-02 -3.87877107... | [10.611825942993164, 1.5999025106430054] |
95314cb6-fb7f-4f7a-ada2-e4f2eb76d033 | adaattn-revisit-attention-mechanism-in | 2108.03647 | null | https://arxiv.org/abs/2108.03647v2 | https://arxiv.org/pdf/2108.03647v2.pdf | AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer | Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse deep style feature into deep content feature without considering feature distributions, or adaptively ... | ['Errui Ding', 'Qian Li', 'Zhengxing Sun', 'Xin Li', 'Meiling Wang', 'Fu Li', 'Dongliang He', 'Tianwei Lin', 'Songhua Liu'] | 2021-08-08 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_AdaAttN_Revisit_Attention_Mechanism_in_Arbitrary_Neural_Style_Transfer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_AdaAttN_Revisit_Attention_Mechanism_in_Arbitrary_Neural_Style_Transfer_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-style-transfer'] | ['computer-vision'] | [ 3.06859344e-01 -5.64600050e-01 -6.73565492e-02 -5.30396760e-01
-6.06101811e-01 -5.36143959e-01 4.17706966e-01 -5.42203523e-02
-3.81072372e-01 6.40544236e-01 3.17413837e-01 2.98514038e-01
-1.27831459e-01 -9.58555639e-01 -6.51993692e-01 -8.82148564e-01
4.53175336e-01 4.18342475e-04 1.49751067e-01 -2.98891693... | [11.525264739990234, -0.7820078730583191] |
28d1a0be-351d-4813-bef3-eedc90cd8c92 | source-free-domain-adaptation-via-avatar | 2106.15326 | null | https://arxiv.org/abs/2106.15326v1 | https://arxiv.org/pdf/2106.15326v1.pdf | Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation | We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challeng... | ['Mingkui Tan', 'Qing Du', 'Yanxia Liu', 'Shuaicheng Niu', 'Hongbin Lin', 'Yifan Zhang', 'Zhen Qiu'] | 2021-06-18 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.39184213e-01 6.02402166e-02 -3.71974081e-01 -6.60405993e-01
-7.18774915e-01 -6.36161566e-01 6.84143245e-01 1.08059250e-01
-3.19327742e-01 7.64167845e-01 -4.50350121e-02 3.28270383e-02
1.56312287e-01 -6.59710884e-01 -6.25571787e-01 -7.32110441e-01
4.66845781e-01 7.46987343e-01 -9.54606663e-03 -4.12614532... | [10.38912296295166, 3.0901811122894287] |
3f9202f6-6a2b-4300-a52a-405802e963a9 | not-all-tokens-are-equal-human-centric-visual | 2204.08680 | null | https://arxiv.org/abs/2204.08680v3 | https://arxiv.org/pdf/2204.08680v3.pdf | Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer | Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine repr... | ['Wanli Ouyang', 'Xiaogang Wang', 'Ping Luo', 'Chen Qian', 'Wentao Liu', 'Sheng Jin', 'Wang Zeng'] | 2022-04-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.pdf | cvpr-2022-1 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-2.57320613e-01 9.13291499e-02 -1.24269687e-01 -1.67281032e-01
-3.72809231e-01 -2.28659764e-01 3.97115767e-01 -5.66276396e-03
-3.22462738e-01 3.75291288e-01 2.78917462e-01 3.79382819e-01
2.56145507e-01 -6.57073975e-01 -6.52779639e-01 -8.34762335e-01
5.26533246e-01 8.67057502e-01 7.14362562e-01 -9.97776687... | [7.9678473472595215, -0.47895488142967224] |
d0103744-bb7a-4b88-9cf4-101b287e0eb4 | a-fixed-point-model-for-pancreas-segmentation | 1612.08230 | null | http://arxiv.org/abs/1612.08230v4 | http://arxiv.org/pdf/1612.08230v4.pdf | A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans | Deep neural networks have been widely adopted for automatic organ
segmentation from abdominal CT scans. However, the segmentation accuracy of
some small organs (e.g., the pancreas) is sometimes below satisfaction,
arguably because deep networks are easily disrupted by the complex and variable
background regions which o... | ['Yuyin Zhou', 'Elliot K. Fishman', 'Wei Shen', 'Lingxi Xie', 'Alan L. Yuille', 'Yan Wang'] | 2016-12-25 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 1.10559694e-01 3.65870923e-01 -7.41576180e-02 -3.18043888e-01
-4.22870815e-01 -6.02639019e-01 4.24415208e-02 4.04570460e-01
-6.89720094e-01 7.58701205e-01 -2.00921968e-01 -2.35048130e-01
1.03313498e-01 -6.16243362e-01 -5.93749940e-01 -8.94861042e-01
-9.28551778e-02 6.01763248e-01 3.73354316e-01 2.08475024... | [14.520011901855469, -2.6318166255950928] |
2ddb0534-5647-42aa-a9fc-f715a835d820 | processing-long-legal-documents-with-pre | 2211.00974 | null | https://arxiv.org/abs/2211.00974v2 | https://arxiv.org/pdf/2211.00974v2.pdf | Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer | Pre-trained Transformers currently dominate most NLP tasks. They impose, however, limits on the maximum input length (512 sub-words in BERT), which are too restrictive in the legal domain. Even sparse-attention models, such as Longformer and BigBird, which increase the maximum input length to 4,096 sub-words, severely ... | ['Ilias Chalkidis', 'Ion Androutsopoulos', 'Petros Tsotsi', 'Dimitris Mamakas'] | 2022-11-02 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-2.40410015e-01 -1.23215988e-02 -2.61212260e-01 -1.25109598e-01
-1.00965834e+00 -9.41141367e-01 8.26726675e-01 2.46763110e-01
-6.84902906e-01 7.85040855e-01 4.95171279e-01 -9.15866971e-01
-4.05657530e-01 -9.45640028e-01 -6.95440531e-01 -5.88794172e-01
-9.13741440e-02 8.89415085e-01 2.25858897e-01 -4.46227789... | [10.56991195678711, 8.921576499938965] |
649ecc5d-1640-4afc-bb8e-738dcf3479a9 | application-of-symmetric-uncertainty-and | 1211.0613 | null | https://arxiv.org/abs/1211.0613v2 | https://arxiv.org/pdf/1211.0613v2.pdf | Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images | Remote sensing is a technology to acquire data for disatant substances, necessary to construct a model knowledge for applications as classification. Recently Hyperspectral Images (HSI) becomes a high technical tool that the main goal is to classify the point of a region. The HIS is more than a hundred bidirectional mea... | ['Driss Aboutajdine', 'Ahmed Hammouch', 'Elkebir Sarhrouni'] | 2012-11-03 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 5.28454423e-01 -3.85424882e-01 4.12674667e-03 -4.20708388e-01
-3.99113357e-01 -3.85883152e-01 3.89782101e-01 6.37514815e-02
-2.81180233e-01 9.74991202e-01 1.52411625e-01 8.84289294e-02
-8.88972878e-01 -1.19961452e+00 -1.31397069e-01 -9.44603264e-01
-2.12760061e-01 3.33055556e-01 2.19061032e-01 -3.97174925... | [9.725162506103516, -1.8372302055358887] |
23d072e2-f5af-4579-8842-d81e291e4809 | 190600424 | 1906.00424 | null | https://arxiv.org/abs/1906.00424v1 | https://arxiv.org/pdf/1906.00424v1.pdf | Plain English Summarization of Contracts | Unilateral contracts, such as terms of service, play a substantial role in modern digital life. However, few users read these documents before accepting the terms within, as they are too long and the language too complicated. We propose the task of summarizing such legal documents in plain English, which would enable u... | ['Laura Manor', 'Junyi Jessy Li'] | 2019-06-02 | plain-english-summarization-of-contracts | https://aclanthology.org/W19-2201 | https://aclanthology.org/W19-2201.pdf | ws-2019-6 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 3.60039115e-01 4.20092225e-01 -4.60099697e-01 -5.57981491e-01
-1.31442583e+00 -9.49502170e-01 6.92544639e-01 3.57488602e-01
-2.20643476e-01 8.03531110e-01 9.33706820e-01 -6.66275561e-01
1.92813153e-04 -4.02152836e-01 -5.19909143e-01 1.24982305e-01
3.54556441e-01 5.65501511e-01 -4.31346707e-02 -5.09239614... | [12.287991523742676, 9.539031982421875] |
ccd218d1-8e62-4ed9-8c3e-82b57c6288ee | multistage-stochastic-optimization-via | 2303.06515 | null | https://arxiv.org/abs/2303.06515v1 | https://arxiv.org/pdf/2303.06515v1.pdf | Multistage Stochastic Optimization via Kernels | We develop a non-parametric, data-driven, tractable approach for solving multistage stochastic optimization problems in which decisions do not affect the uncertainty. The proposed framework represents the decision variables as elements of a reproducing kernel Hilbert space and performs functional stochastic gradient de... | ['Kimberly Villalobos Carballo', 'Dimitris Bertsimas'] | 2023-03-11 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.35025659e-01 -3.45061980e-02 -3.56820852e-01 -2.92926848e-01
-9.95878518e-01 -6.07693255e-01 6.49897903e-02 2.58022118e-02
-4.37491864e-01 9.96419132e-01 -7.71564096e-02 -3.66868794e-01
-4.67514634e-01 -5.03382266e-01 -8.44631493e-01 -8.64759743e-01
-2.00102523e-01 6.64765596e-01 -2.29001239e-01 7.51187280... | [5.471462249755859, 3.728276252746582] |
cf48f5f1-d4b5-4988-aae4-bf5aa6778853 | black-lscdiscovery-shared-task-glossreader-at | null | null | https://aclanthology.org/2022.lchange-1.22 | https://aclanthology.org/2022.lchange-1.22.pdf | black[LSCDiscovery shared task] GlossReader at LSCDiscovery: Train to Select a Proper Gloss in English – Discover Lexical Semantic Change in Spanish | The contextualized embeddings obtained from neural networks pre-trained as Language Models (LM) or Masked Language Models (MLM) are not well suitable for solving the Lexical Semantic Change Detection (LSCD) task because they are more sensitive to changes in word forms rather than word meaning, a property previously kno... | ['Nikolay Arefyev', 'Maxim Rachinskiy'] | null | null | null | null | lchange-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [ 2.31241807e-01 -7.89006576e-02 2.48658238e-03 -4.03353274e-01
-5.84292173e-01 -6.88837409e-01 5.85796595e-01 6.11043990e-01
-1.07873273e+00 5.81381321e-01 3.53991538e-01 -3.74868393e-01
1.82288930e-01 -8.11361551e-01 -5.45570076e-01 -5.26101410e-01
3.70912135e-01 3.73650432e-01 4.84720975e-01 -6.03999853... | [10.478604316711426, 9.15447998046875] |
d9939993-32a6-45be-b888-cd1d832e8346 | locking-and-quacking-stacking-bayesian-model | 2305.07334 | null | https://arxiv.org/abs/2305.07334v1 | https://arxiv.org/pdf/2305.07334v1.pdf | Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition | Combining predictions from different models is a central problem in Bayesian inference and machine learning more broadly. Currently, these predictive distributions are almost exclusively combined using linear mixtures such as Bayesian model averaging, Bayesian stacking, and mixture of experts. Such linear mixtures impo... | ['Yann McLatchie', 'Diego Mesquita', 'Luiz Max Carvalho', 'Yuling Yao'] | 2023-05-12 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 4.87796128e-01 1.15157031e-01 2.03050360e-01 -4.68204528e-01
-1.08744633e+00 -6.03587151e-01 1.04225767e+00 -2.23558426e-01
-3.63107324e-01 9.52531815e-01 1.84793264e-01 -4.29873466e-01
-6.15962267e-01 -5.62466979e-01 -5.15765786e-01 -1.20591450e+00
2.74051160e-01 8.46308351e-01 3.06866050e-01 3.09371471... | [6.777148723602295, 3.9094014167785645] |
e94d5d7d-f666-4422-aeb1-0918bda8bf2a | deep-emotion-facial-expression-recognition | 1902.01019 | null | http://arxiv.org/abs/1902.01019v1 | http://arxiv.org/pdf/1902.01019v1.pdf | Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network | Facial expression recognition has been an active research area over the past
few decades, and it is still challenging due to the high intra-class variation.
Traditional approaches for this problem rely on hand-crafted features such as
SIFT, HOG and LBP, followed by a classifier trained on a database of images or
vide... | ['Amirali Abdolrashidi', 'Shervin Minaee'] | 2019-02-04 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [-2.75279135e-01 -3.99298161e-01 -8.26903805e-02 -7.04489708e-01
-7.35564977e-02 -4.59505841e-02 5.80023468e-01 -3.27735394e-01
-4.00014609e-01 3.43589008e-01 5.12410738e-02 3.53287935e-01
1.26022607e-01 -5.92551053e-01 -3.93286645e-01 -6.68523192e-01
-2.70128638e-01 -2.37663537e-02 6.32829145e-02 -5.28519511... | [13.563329696655273, 1.8116379976272583] |
f765a076-119c-43ed-aad9-b2a6941e2c2d | geometry-based-adaptive-symbolic | 1403.0820 | null | http://arxiv.org/abs/1403.0820v2 | http://arxiv.org/pdf/1403.0820v2.pdf | Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds | In this paper, we consider the problem of fast and efficient indexing
techniques for sequences evolving in non-Euclidean spaces. This problem has
several applications in the areas of human activity analysis, where there is a
need to perform fast search, and recognition in very high dimensional spaces.
The problem is ma... | ['Pavan Turaga', 'Rushil Anirudh'] | 2014-03-04 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 4.41721678e-01 -3.97193670e-01 9.41478088e-02 4.35845740e-02
-3.57499659e-01 -6.26590908e-01 5.92767835e-01 4.18407500e-01
-5.43843985e-01 6.86164081e-01 -9.46716145e-02 -2.62550831e-01
-7.43615746e-01 -8.28553140e-01 -3.24631602e-01 -8.14165056e-01
-5.10278940e-01 5.79426050e-01 4.85715508e-01 -2.08391249... | [7.357458114624023, 3.56378173828125] |
ab7fd16e-033c-4d27-a951-856fda69c543 | gog-relation-aware-graph-over-graph-network | 2109.08475 | null | https://arxiv.org/abs/2109.08475v3 | https://arxiv.org/pdf/2109.08475v3.pdf | GoG: Relation-aware Graph-over-Graph Network for Visual Dialog | Visual dialog, which aims to hold a meaningful conversation with humans about a given image, is a challenging task that requires models to reason the complex dependencies among visual content, dialog history, and current questions. Graph neural networks are recently applied to model the implicit relations between objec... | ['Jie zhou', 'Peng Li', 'Fandong Meng', 'Xiuyi Chen', 'Feilong Chen'] | 2021-09-17 | null | https://aclanthology.org/2021.findings-acl.20 | https://aclanthology.org/2021.findings-acl.20.pdf | findings-acl-2021-8 | ['implicit-relations'] | ['natural-language-processing'] | [-8.33639503e-02 4.20103878e-01 -6.10632487e-02 -5.55500567e-01
-2.94717133e-01 -5.00710309e-01 8.85474384e-01 1.49864525e-01
-1.20707102e-01 2.78790146e-01 8.13388824e-01 -2.00133294e-01
2.08209634e-01 -6.14379585e-01 -5.50020218e-01 -3.54251415e-01
3.13081175e-01 7.10250497e-01 5.19078374e-01 -5.64298153... | [10.80474853515625, 1.6134154796600342] |
02e18e13-f90d-4abe-9a5d-8441d633d65f | segment-based-fusion-of-multi-sensor-multi | 2211.15938 | null | https://arxiv.org/abs/2211.15938v1 | https://arxiv.org/pdf/2211.15938v1.pdf | Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals | Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typicall... | ['Davood Akbarid', 'Saeid Niazmardi', 'Iman Khosravi', 'Hossein Bagheri', 'Reza Attarzadeh'] | 2022-11-29 | null | null | null | null | ['soil-moisture-estimation', 'data-integration'] | ['computer-vision', 'knowledge-base'] | [ 4.66917306e-01 -3.19378197e-01 -1.24808429e-02 -3.24605674e-01
-7.74281502e-01 -4.17838961e-01 6.60449982e-01 9.06355619e-01
-4.76510435e-01 9.55855668e-01 -2.07778722e-01 -4.47597295e-01
-4.62965995e-01 -1.50089657e+00 -2.76121199e-01 -1.11486828e+00
9.10737291e-02 1.32158035e-02 4.27807748e-01 -6.36336446... | [9.529847145080566, -1.6628894805908203] |
86e7120b-a5af-4a53-9751-a23aa69c671e | cross-view-hierarchy-network-for-stereo-image | 2304.06236 | null | https://arxiv.org/abs/2304.06236v1 | https://arxiv.org/pdf/2304.06236v1.pdf | Cross-View Hierarchy Network for Stereo Image Super-Resolution | Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. To attain superior performance, many methods have prioritized designing complex modules to fuse similar information across views, yet overlooking the importance of intra-... | ['Ming Tan', 'Zhongxin Yu', 'Mingchao Jiang', 'Yunchen Zhang', 'Liang Chen', 'Hongxia Gao', 'Wenbin Zou'] | 2023-04-13 | null | null | null | null | ['image-super-resolution', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 1.82860404e-01 -1.60483703e-01 3.15004140e-02 -3.83892149e-01
-1.00515854e+00 -9.71698463e-02 2.38243759e-01 -4.28371787e-01
1.12446561e-01 6.13555193e-01 6.81326926e-01 1.47165194e-01
-1.58909902e-01 -8.09687912e-01 -7.12031424e-01 -7.11030304e-01
4.18505043e-01 -2.57138312e-01 2.94832587e-01 -3.54894698... | [10.726139068603516, -2.184730052947998] |
c05ad90c-d90d-46d9-83c6-2a9860eb0aa1 | sliding-line-point-regression-for-shape | 1801.09969 | null | http://arxiv.org/abs/1801.09969v1 | http://arxiv.org/pdf/1801.09969v1.pdf | Sliding Line Point Regression for Shape Robust Scene Text Detection | Traditional text detection methods mostly focus on quadrangle text. In this
study we propose a novel method named sliding line point regression (SLPR) in
order to detect arbitrary-shape text in natural scene. SLPR regresses multiple
points on the edge of text line and then utilizes these points to sketch the
outlines o... | ['Yixing Zhu', 'Jun Du'] | 2018-01-30 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 2.47479547e-02 -1.49152771e-01 1.43941313e-01 1.77762471e-02
-4.82650846e-01 -4.36772585e-01 4.38473374e-01 7.16596022e-02
-4.97245044e-01 1.33757755e-01 5.30677401e-02 -1.72360092e-01
3.42140228e-01 -8.11609626e-01 -6.53099120e-01 -5.21872282e-01
5.97854197e-01 4.96691287e-01 7.68017054e-01 -2.27233514... | [12.125288009643555, 2.2398176193237305] |
3e9a9946-2498-4fa0-a7a9-775c971e8aff | scene-graphs-a-survey-of-generations-and | 2104.01111 | null | https://arxiv.org/abs/2104.01111v5 | https://arxiv.org/pdf/2104.01111v5.pdf | A Comprehensive Survey of Scene Graphs: Generation and Application | Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images; instead, people look forward... | ['Alex Hauptmann', 'Xiaojiang Chen', 'Zhihui Li', 'Pengfei Xu', 'Pengzhen Ren', 'Xiaojun Chang'] | 2021-03-17 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.13086855e-01 6.22526146e-02 2.95789186e-02 -6.28814101e-01
-1.32215425e-01 -6.28742576e-01 4.93890405e-01 3.37747008e-01
6.77837953e-02 1.57528073e-01 5.04967347e-02 -2.79239655e-01
-2.52470002e-02 -9.74717438e-01 -5.69775820e-01 -5.55170357e-01
2.79363751e-01 1.78055450e-01 3.46833408e-01 -4.18449827... | [10.36331844329834, 1.5292459726333618] |
6933b067-1e5f-4a51-8bb0-aa1d5f0ab2ee | distributional-reinforcement-learning-with-2 | 2003.10903 | null | https://arxiv.org/abs/2003.10903v2 | https://arxiv.org/pdf/2003.10903v2.pdf | Distributional Reinforcement Learning with Ensembles | It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we propose an extension to categorical reinforcement learning, where ... | ['Karl-Olof Lindahl', 'Björn Lindenberg', 'Jonas Nordqvist'] | 2020-03-24 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 1.38421506e-01 7.56280422e-02 -2.58955508e-01 -5.14694035e-01
-9.45020139e-01 -6.81326687e-01 7.46057987e-01 2.91329265e-01
-7.56697655e-01 1.26679027e+00 3.33164722e-01 -2.94420600e-01
-3.82415742e-01 -9.56435025e-01 -5.43981493e-01 -8.41940105e-01
-2.64041573e-01 4.21528071e-01 -1.46254674e-01 -4.06116635... | [4.0936360359191895, 2.455345392227173] |
63583b2a-3b75-4856-88ea-55902d3a1260 | super-pixel-cloud-detection-using | 1810.08352 | null | http://arxiv.org/abs/1810.08352v1 | http://arxiv.org/pdf/1810.08352v1.pdf | Super-pixel cloud detection using Hierarchical Fusion CNN | Cloud detection plays a very important role in the process of remote sensing
images. This paper designs a super-pixel level cloud detection method based on
convolutional neural network (CNN) and deep forest. Firstly, remote sensing
images are segmented into super-pixels through the combination of SLIC and
SEEDS. Struct... | ['Qi Tian', 'Han Liu', 'Dan Zeng'] | 2018-10-19 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 5.08382201e-01 -7.26356626e-01 5.13337031e-02 -1.56706214e-01
-2.75132388e-01 -5.23712814e-01 3.40056807e-01 -6.35030270e-02
-4.18657690e-01 5.89104474e-01 -3.88358235e-01 -4.59511250e-01
8.27093050e-02 -1.65065253e+00 -2.97521502e-01 -8.40947986e-01
1.03026638e-02 1.20549470e-01 4.20127809e-01 4.08160426... | [9.801094055175781, -1.7136873006820679] |
8871086e-6265-4afd-b0ef-c90ef5768e4f | improved-visual-relocalization-by-discovering | 1811.04370 | null | http://arxiv.org/abs/1811.04370v1 | http://arxiv.org/pdf/1811.04370v1.pdf | Improved Visual Relocalization by Discovering Anchor Points | We address the visual relocalization problem of predicting the location and
camera orientation or pose (6DOF) of the given input scene. We propose a method
based on how humans determine their location using the visible landmarks. We
define anchor points uniformly across the route map and propose a deep learning
archite... | ['C. V. Jawahar', 'Girish Varma', 'Soham Saha'] | 2018-11-11 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [-1.49457633e-01 -3.44032273e-02 7.88658932e-02 -5.09936392e-01
-7.76735723e-01 -9.11047995e-01 7.18467295e-01 4.09889668e-01
-1.00174844e+00 6.39954507e-01 2.06008270e-01 2.91686952e-02
-2.57193834e-01 -6.50206625e-01 -1.30207241e+00 -2.64254272e-01
-1.29346862e-01 5.68137705e-01 3.01366776e-01 -9.74649116... | [7.661238670349121, -2.1701743602752686] |
99c718f4-d624-4eae-b0f9-0af5f138c6b4 | mme-crs-multi-metric-evaluation-based-on | 2206.09403 | null | https://arxiv.org/abs/2206.09403v1 | https://arxiv.org/pdf/2206.09403v1.pdf | MME-CRS: Multi-Metric Evaluation Based on Correlation Re-Scaling for Evaluating Open-Domain Dialogue | Automatic open-domain dialogue evaluation is a crucial component of dialogue systems. Recently, learning-based evaluation metrics have achieved state-of-the-art performance in open-domain dialogue evaluation. However, these metrics, which only focus on a few qualities, are hard to evaluate dialogue comprehensively. Fur... | ['Chunyang Yuan', 'Cao Liu', 'Song Han', 'Jian Wang', 'Kaidong Yu', 'Xiaohui Hu', 'Pengfei Zhang'] | 2022-06-19 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.84983450e-01 8.92704055e-02 1.01058908e-01 -5.41666389e-01
-1.07731485e+00 -6.65793002e-01 9.35003400e-01 2.38583073e-01
-4.46537495e-01 9.68228221e-01 6.98382139e-01 -8.59266892e-03
-3.50412697e-01 -5.44075429e-01 1.27342388e-01 -2.51117766e-01
1.26170143e-01 6.11501396e-01 5.75769305e-01 -1.08987033... | [12.738694190979004, 8.168157577514648] |
97c573b8-a47c-466b-96f2-8cf8f077d909 | passive-motion-detection-via-mmwave | 2203.14588 | null | https://arxiv.org/abs/2203.14588v1 | https://arxiv.org/pdf/2203.14588v1.pdf | Passive Motion Detection via mmWave Communication System | In this paper, an integrated passive sensing and communication system working in 60 GHz band is elaborated, and the sensing performance is investigated in an application of hand gesture recognition. Specifically, in this integrated system, there are two radio frequency (RF) chains at the receiver and one at the transmi... | ['Rui Wang', 'Yifei Sun', 'Yan Luo', 'Chao Yu', 'Jie Li'] | 2022-03-28 | null | null | null | null | ['hand-gesture-recognition', 'motion-detection', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.06870496e-01 -1.82679649e-02 -1.36882022e-01 -6.59388527e-02
-3.05439740e-01 -5.52012384e-01 2.14562565e-01 -5.53242803e-01
-5.26521206e-01 3.95852894e-01 5.88249117e-02 -1.13913506e-01
-4.70002264e-01 -8.75334501e-01 5.58573194e-02 -1.32464027e+00
-1.73980728e-01 4.13367115e-02 -1.80523291e-01 2.37134084... | [6.548395156860352, 0.8196328282356262] |
b0254bf1-a646-48df-b9b8-f90a39b4c34e | 190503042 | 1905.03042 | null | http://arxiv.org/abs/1905.03042v1 | http://arxiv.org/pdf/1905.03042v1.pdf | Rumour Detection via News Propagation Dynamics and User Representation Learning | Rumours have existed for a long time and have been known for serious
consequences. The rapid growth of social media platforms has multiplied the
negative impact of rumours; it thus becomes important to early detect them.
Many methods have been introduced to detect rumours using the content or the
social context of news... | ['Xiao Luo', 'Tien Huu Do', 'Nikos Deligiannis', 'Duc Minh Nguyen'] | 2019-04-18 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.75275934e-01 -4.21510190e-01 -3.49853665e-01 -3.85353893e-01
9.90797728e-02 -1.86813548e-01 1.09226704e+00 5.66009641e-01
-2.52289742e-01 5.71931124e-01 6.56095147e-01 -1.53909668e-01
1.68533608e-01 -9.80546713e-01 -3.49406600e-01 -2.57293433e-01
-4.40308541e-01 2.35954285e-01 5.58285296e-01 -6.15928352... | [8.166282653808594, 10.145696640014648] |
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