paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0d60bd80-95d1-4d88-84ce-c93572a85644 | deepdeform-learning-non-rigid-rgb-d-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Bozic_DeepDeform_Learning_Non-Rigid_RGB-D_Reconstruction_With_Semi-Supervised_Data_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Bozic_DeepDeform_Learning_Non-Rigid_RGB-D_Reconstruction_With_Semi-Supervised_Data_CVPR_2020_paper.pdf | DeepDeform: Learning Non-Rigid RGB-D Reconstruction With Semi-Supervised Data | Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a n... | [' Matthias Niessner', ' Christian Theobalt', ' Michael Zollhofer', 'Aljaz Bozic'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 2.93491453e-01 7.95990750e-02 4.18124013e-02 -6.15716219e-01
-1.22850025e+00 -5.61610579e-01 5.86820543e-01 -3.21664572e-01
-3.39649141e-01 4.45493281e-01 5.91507435e-01 1.92682639e-01
1.36440881e-02 -5.19216299e-01 -1.01208079e+00 -4.81096894e-01
3.38655710e-01 6.55574739e-01 5.32846868e-01 -1.99947417... | [8.282771110534668, -2.495356321334839] |
d25b5b1b-e1f5-4588-bb9c-dc596ec56819 | bimodal-segnet-instance-segmentation-fusing | 2303.11228 | null | https://arxiv.org/abs/2303.11228v1 | https://arxiv.org/pdf/2303.11228v1.pdf | Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping | Object segmentation for robotic grasping under dynamic conditions often faces challenges such as occlusion, low light conditions, motion blur and object size variance. To address these challenges, we propose a Deep Learning network that fuses two types of visual signals, event-based data and RGB frame data. The propose... | ['Yahya Zweiri', 'Dimitrios Makris', 'Rajkumar Muthusamy', 'Fariborz Baghaei Naeini', 'Xiaoqian Huang', 'Sanket Kachole'] | 2023-03-20 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 4.14771438e-01 -1.74313530e-01 2.28379190e-01 -5.07187366e-01
-8.17377746e-01 -4.92199779e-01 1.28025621e-01 -1.54619798e-01
-5.02911270e-01 5.34775376e-01 -2.12008566e-01 1.92550123e-01
1.30218416e-01 -4.71413106e-01 -1.23211312e+00 -9.86404896e-01
1.58662703e-02 -6.54662997e-02 5.92190206e-01 2.54214078... | [9.230332374572754, -0.396270751953125] |
1bcb6c36-5a1c-4016-93d3-8e09a6c8c523 | cardiac-segmentation-with-strong-anatomical | 2006.08825 | null | https://arxiv.org/abs/2006.08825v1 | https://arxiv.org/pdf/2006.08825v1.pdf | Cardiac Segmentation with Strong Anatomical Guarantees | Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even ... | ['Pierre-Marc Jodoin', 'Youssef Skandarani', 'Thierry Judge', 'Olivier Bernard', 'Nathan Painchaud', 'Alain Lalande'] | 2020-06-15 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.11181360e-01 5.87045550e-01 2.73262322e-01 -5.01254916e-01
-7.30202734e-01 -9.61561799e-01 3.12583059e-01 1.24249673e-02
-3.84247720e-01 4.96194720e-01 1.00621395e-01 -4.93436068e-01
-1.89080834e-01 -6.68277979e-01 -6.36979401e-01 -7.01698661e-01
3.27036832e-03 8.57154846e-01 5.08108065e-02 1.42171040... | [14.167277336120605, -2.3564460277557373] |
58402ad2-f019-4ed1-95cb-8049843ebdd9 | impact-of-action-unit-occurrence-patterns-on | 2010.07982 | null | https://arxiv.org/abs/2010.07982v1 | https://arxiv.org/pdf/2010.07982v1.pdf | Impact of Action Unit Occurrence Patterns on Detection | Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. In this paper we investigate the impact of action unit occurrence patterns on detection of action units. To facilita... | ['Saandeep Aathreya', 'Shaun Canavan', 'Saurabh Hinduja'] | 2020-10-15 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 3.87591958e-01 -3.96385491e-02 -3.82387191e-01 -5.36110163e-01
-1.62916169e-01 -3.75354588e-01 5.47823668e-01 -5.60429990e-01
-1.76687837e-01 4.72187668e-01 1.15563229e-01 2.20197558e-01
2.53862828e-01 -5.97384393e-01 -3.03276211e-01 -7.31385112e-01
-2.06638247e-01 -3.98436278e-01 -3.91580731e-01 -3.13449502... | [13.57273006439209, 1.7866452932357788] |
a5601d62-762e-4c2c-be67-3fd2b19184c9 | utilizing-graph-measure-to-deduce-omitted | null | null | https://aclanthology.org/C18-2011 | https://aclanthology.org/C18-2011.pdf | Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs | This demo deals with the problem of capturing omitted arguments in relation extraction given a proper knowledge base for entities of interest. This paper introduces the concept of a salient entity and use this information to deduce omitted entities in the paragraph which allows improving the relation extraction quality... | ['Key-Sun Choi', 'Jiho Kim', 'Eun-Kyung Kim', 'Kijong Han'] | 2018-08-01 | utilizing-graph-measure-to-deduce-omitted-1 | https://aclanthology.org/C18-2011 | https://aclanthology.org/C18-2011.pdf | coling-2018-8 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 2.80241102e-01 1.27806306e+00 -4.39478874e-01 -1.54317126e-01
-3.47554624e-01 -6.12635732e-01 5.17005980e-01 1.06664908e+00
-4.44180906e-01 9.10786211e-01 6.18829012e-01 -3.77333581e-01
-6.32948339e-01 -1.35686231e+00 -4.83627290e-01 -1.41362026e-01
-3.43014628e-01 6.61725879e-01 6.96347594e-01 -4.52723265... | [9.275533676147461, 8.634690284729004] |
31bf46cc-bd50-4fa1-9d1f-6b07d9fa5a74 | depth-not-needed-an-evaluation-of-rgb-d | 1801.01235 | null | http://arxiv.org/abs/1801.01235v1 | http://arxiv.org/pdf/1801.01235v1.pdf | Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network | Scene understanding for autonomous vehicles is a challenging computer vision
task, with recent advances in convolutional neural networks (CNNs) achieving
results that notably surpass prior traditional feature driven approaches.
However, limited work investigates the application of such methods either
within the highly ... | ['Xiong Wei', 'Toby P. Breckon', 'Christopher J. Holder'] | 2018-01-04 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 7.52024889e-01 2.21439078e-02 2.67728776e-01 -1.01050341e+00
-3.54739815e-01 -4.77104455e-01 7.13053346e-01 -3.87242138e-01
-6.49678230e-01 4.93559361e-01 -1.37330785e-01 -8.81868839e-01
7.59688467e-02 -1.13986695e+00 -7.20648408e-01 -5.43887734e-01
2.93739408e-01 5.72454512e-01 5.09081125e-01 -5.17145574... | [8.468727111816406, -2.2385003566741943] |
2b9616c2-fed8-4b80-95ed-6df109b937a4 | point-gcc-universal-self-supervised-3d-scene | 2305.19623 | null | https://arxiv.org/abs/2305.19623v2 | https://arxiv.org/pdf/2305.19623v2.pdf | Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast | Geometry and color information provided by the point clouds are both crucial for 3D scene understanding. Two pieces of information characterize the different aspects of point clouds, but existing methods lack an elaborate design for the discrimination and relevance. Hence we explore a 3D self-supervised paradigm that c... | ['Kaisheng Ma', 'Wenkai Shi', 'Zekun Qi', 'Guofan Fan'] | 2023-05-31 | null | null | null | null | ['unsupervised-3d-semantic-segmentation', '3d-instance-segmentation-1', '3d-semantic-segmentation', 'scene-understanding', 'deep-clustering', 'deep-clustering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous', 'natural-language-processing'] | [-2.50665635e-01 -4.15201277e-01 -1.00089870e-01 -5.00928998e-01
-7.90619135e-01 -8.19904149e-01 7.29075253e-01 6.06141277e-02
-1.39605477e-01 -4.85973433e-02 -3.22499216e-01 -2.97071129e-01
-1.40081719e-01 -7.48712897e-01 -9.05674279e-01 -6.11749053e-01
-8.62970278e-02 6.28007829e-01 4.45427805e-01 -2.60061711... | [7.985340118408203, -3.1419389247894287] |
085e29ad-31f8-4760-a8e9-b707dca35d4c | detecting-adversarial-attacks-on-audio-visual | 1912.08639 | null | https://arxiv.org/abs/1912.08639v2 | https://arxiv.org/pdf/1912.08639v2.pdf | Detecting Adversarial Attacks On Audiovisual Speech Recognition | Adversarial attacks pose a threat to deep learning models. However, research on adversarial detection methods, especially in the multi-modal domain, is very limited. In this work, we propose an efficient and straightforward detection method based on the temporal correlation between audio and video streams. The main ide... | ['Stavros Petridis', 'Pingchuan Ma', 'Maja Pantic'] | 2019-12-18 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 1.69630185e-01 -1.24304451e-01 2.90933937e-01 1.19781315e-01
-1.13490045e+00 -9.86859381e-01 7.85606563e-01 1.32057471e-02
-2.68895715e-01 4.75311011e-01 1.83200184e-02 -1.80169106e-01
1.48845464e-01 -6.01712525e-01 -9.10269916e-01 -7.73787856e-01
-4.39915061e-01 -3.47208194e-02 5.37235081e-01 -2.04642773... | [14.001389503479004, 5.829612731933594] |
fc5f91c3-20ca-403f-a35a-dc600cb1bc1c | learning-representation-for-anomaly-detection | 2303.05000 | null | https://arxiv.org/abs/2303.05000v1 | https://arxiv.org/pdf/2303.05000v1.pdf | Learning Representation for Anomaly Detection of Vehicle Trajectories | Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are introduced to those history trajectories, the resulting anomalous (or adversarial) trajectories can significantly mislead the future traj... | ['Qi Zhu', 'Qi Alfred Chen', 'Xiaowei Yuan', 'Takami Sato', 'Xiangguo Liu', 'Juyang Bai', 'Ruochen Jiao'] | 2023-03-09 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 7.01798424e-02 4.61628735e-02 -2.25616291e-01 -3.80858123e-01
-5.27120948e-01 -5.68943799e-01 7.78918207e-01 3.36613238e-01
-3.53775173e-02 4.38523620e-01 2.69784778e-01 -5.86736083e-01
-3.33182141e-02 -8.09913337e-01 -9.52268422e-01 -7.90646970e-01
-3.03195000e-01 8.48598257e-02 6.16067767e-01 -3.03608268... | [7.388101100921631, 2.195969820022583] |
2862df61-8933-4c52-96b3-c28582d851e7 | deepchess-end-to-end-deep-neural-network-for | 1711.09667 | null | http://arxiv.org/abs/1711.09667v1 | http://arxiv.org/pdf/1711.09667v1.pdf | DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess | We present an end-to-end learning method for chess, relying on deep neural
networks. Without any a priori knowledge, in particular without any knowledge
regarding the rules of chess, a deep neural network is trained using a
combination of unsupervised pretraining and supervised training. The
unsupervised training extra... | ['Nathan S. Netanyahu', 'Lior Wolf', 'Eli David'] | 2017-11-27 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-1.54844150e-01 -1.83451232e-02 3.50896530e-02 -4.99888450e-01
-5.58204532e-01 -6.39120758e-01 3.94791275e-01 1.40472189e-01
-9.06541467e-01 6.12680197e-01 -1.61165416e-01 -5.08272231e-01
-1.77600175e-01 -1.16681290e+00 -7.82864690e-01 -4.18849468e-01
4.08247411e-02 7.92930126e-01 6.68640673e-01 -1.00200617... | [3.4441146850585938, 1.430984377861023] |
60c16aff-190f-40c8-ab59-211e0c6ae7b3 | analysis-and-adaptation-of-yolov4-for-object | 2203.10194 | null | https://arxiv.org/abs/2203.10194v1 | https://arxiv.org/pdf/2203.10194v1.pdf | Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images | The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for various computer vision tasks has paved the path for numerous opportunities to make them more effective and valuable. Object detection in aerial images is challenging due to variations in appearance, pose, and scale. Autonomous aerial flight ... | ['Satish Shenoy B', 'Karunakar A K', 'Soham Hans', 'Akshatha K R', 'Aryaman Singh Samyal'] | 2022-03-18 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.22630091e-02 -6.44610703e-01 3.43365848e-01 4.01556678e-02
5.38235046e-02 -8.96485746e-01 3.53368014e-01 5.63193671e-02
-5.60358107e-01 4.02557909e-01 -1.00630903e+00 -2.35140651e-01
-3.27574350e-02 -7.88184106e-01 -4.30126637e-01 -5.81041574e-01
-4.83790070e-01 2.77851164e-01 8.58148098e-01 -1.63088739... | [8.433639526367188, -1.0596481561660767] |
5d4e5d91-de1a-4560-bae4-877befe2c962 | small-object-detection-via-pixel-level | null | null | https://www.frontiersin.org/articles/10.3389/fphys.2022.911297/full#h1 | https://www.frontiersin.org/articles/10.3389/fphys.2022.911297/pdf | Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection | Object detection technology has been widely used in medical field, such as detecting the images of blood cell to count the changes and distribution for assisting the diagnosis of diseases. However, detecting small objects is one of the most challenging and important problems especially in medical scenarios. Most of the... | ['Qingjie Kong', 'Minglei Tong', 'Pengzhi Chu', 'Yang Liu', 'Bin Hu'] | 2022-06-17 | null | null | null | frontiers-in-physiology-2022-6 | ['cell-detection', 'small-object-detection', 'blood-cell-detection'] | ['computer-vision', 'computer-vision', 'medical'] | [-1.80060845e-02 -4.07198966e-01 -1.71145350e-02 -1.42445847e-01
1.17584698e-01 1.86954334e-01 -6.73905015e-02 5.14596939e-01
-5.53253949e-01 3.27542424e-01 -7.82035515e-02 1.83490425e-01
1.67642394e-03 -1.03007388e+00 -3.12613785e-01 -1.15950704e+00
1.73306361e-01 2.11248487e-01 8.53465259e-01 1.08403154... | [14.780082702636719, -3.052536964416504] |
9942c9a4-4648-401b-9d77-8687602c4292 | characterizing-the-value-of-information-in | 2010.03574 | null | https://arxiv.org/abs/2010.03574v2 | https://arxiv.org/pdf/2010.03574v2.pdf | Characterizing the Value of Information in Medical Notes | Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize ... | ['Chenhao Tan', 'Ziad Obermeyer', 'Sendhil Mullainathan', 'Shantanu Karnwal', 'Chao-Chun Hsu'] | 2020-10-07 | null | https://aclanthology.org/2020.findings-emnlp.187 | https://aclanthology.org/2020.findings-emnlp.187.pdf | findings-of-the-association-for-computational | ['readmission-prediction'] | ['medical'] | [ 7.95482397e-02 5.47475815e-01 -6.98175848e-01 -4.40174997e-01
-1.32788229e+00 -4.61513162e-01 2.54397858e-02 9.20109928e-01
-4.09250319e-01 8.30500066e-01 9.18536961e-01 -5.35014749e-01
-4.80064750e-01 -7.93477952e-01 -3.75813514e-01 -4.90222186e-01
-1.93764679e-02 8.48361135e-01 -2.29909346e-01 2.21507311... | [8.025598526000977, 6.58673095703125] |
15f156a7-3888-443a-b0b0-ab55bef7e9dd | joint-spectrum-and-power-allocation-for-v2x | 2302.14704 | null | https://arxiv.org/abs/2302.14704v1 | https://arxiv.org/pdf/2302.14704v1.pdf | Joint Spectrum and Power Allocation for V2X Communications with Imperfect CSI | In Vehicle-to-Everything (V2X) communication, the high mobility of vehicles generates the Doppler shift which leads to channel uncertainties. Moreover, the reasons for channel uncertainties also include the finite channel feedback, channels state information (CSI) loss and latency. With this concern, we formulate a joi... | ['Li Feng', 'Guanhua Chai', 'Jiayi Liu', 'Weihua Wu', 'Peng Wang'] | 2023-02-21 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-3.08474619e-03 3.85795951e-01 -4.48392123e-01 2.53016442e-01
-8.23736608e-01 -3.02572191e-01 3.54511701e-02 -3.82488370e-01
-1.76357150e-01 1.16732252e+00 -1.98244140e-01 -6.99715793e-01
-6.16168141e-01 -6.42390966e-01 -4.60641086e-01 -1.19203830e+00
-1.91628754e-01 -2.53507495e-01 1.33775130e-01 -2.80221866... | [6.111091613769531, 1.4518405199050903] |
60281f9e-94d2-41d9-9fa7-54a177b31790 | deeptagger-knowledge-enhanced-named-entity | 2306.17413 | null | https://arxiv.org/abs/2306.17413v1 | https://arxiv.org/pdf/2306.17413v1.pdf | DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries | Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major challenges remain unresolved: web queries differ from natural language, on which pre-trained models are trained; web queries are short and lack c... | ['Denis Charles', 'Jian Jiao', 'Qiang Lou', 'Xinyu Hu', 'Pengfei Tang', 'Simiao Zuo'] | 2023-06-30 | null | null | null | null | ['named-entity-recognition-ner', 'cg'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.17171133e-02 2.12603793e-01 -3.71769458e-01 -7.06862152e-01
-1.29602766e+00 -8.76558304e-01 5.91878653e-01 -1.65525585e-01
-7.05903113e-01 6.09838247e-01 3.16363662e-01 -2.75906235e-01
2.42265612e-01 -8.86561930e-01 -7.15922058e-01 -3.77731174e-02
2.29935661e-01 5.36468267e-01 3.19286615e-01 -5.52528739... | [9.898639678955078, 9.589803695678711] |
12017bc3-4b01-436f-ad94-0fa8775e7f4b | hiface-high-fidelity-3d-face-reconstruction | 2303.11225 | null | https://arxiv.org/abs/2303.11225v1 | https://arxiv.org/pdf/2303.11225v1.pdf | HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details | 3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image. The facial surface is influenced by the coarse shape, as well as the static detail (e,g., person-specific appearance) and dynamic detail (e.g., expression-driven wrinkles). Previous... | ['Jiang Bian', 'Chun Yuan', 'Sheng Zhao', 'Runnan Li', 'HsiangTao Wu', 'Tadas Baltrusaitis', 'Xu Tan', 'Tianyu He', 'Tianke Zhang', 'Zenghao Chai'] | 2023-03-20 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.86419263e-02 1.92904800e-01 1.45995291e-02 -5.11871576e-01
-7.92700231e-01 -3.66305113e-01 5.71044326e-01 -4.92856085e-01
2.76315808e-01 5.05495191e-01 2.19986811e-01 3.72218341e-01
3.57717514e-01 -8.20812523e-01 -8.26710820e-01 -7.61690974e-01
1.14888139e-01 5.50148547e-01 -8.18138421e-02 -2.76943773... | [12.779732704162598, -0.3126075267791748] |
b6aa761d-b079-475a-92ba-914cfaf0510b | automatic-speech-summarisation-a-scoping | 2008.11897 | null | https://arxiv.org/abs/2008.11897v1 | https://arxiv.org/pdf/2008.11897v1.pdf | Automatic Speech Summarisation: A Scoping Review | Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps th... | ['Ying Wang', 'A. Baki Kocaballi', 'Liliana Laranjo', 'Juan C. Quiroz', 'Shlomo Berkovsky', 'Enrico Coiera', 'Dana Rezazadegan'] | 2020-08-27 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 8.92804146e-01 6.29558682e-01 -6.68733954e-01 -2.10519791e-01
-1.18443775e+00 -3.44742209e-01 6.19649351e-01 8.62706363e-01
-6.00481510e-01 8.19011390e-01 1.14776576e+00 -6.01830125e-01
-2.15916187e-01 -3.44612867e-01 -2.00889364e-01 -3.82514834e-01
1.50832996e-01 3.69727790e-01 2.86170058e-02 7.12535158... | [12.356876373291016, 9.560173034667969] |
e6c017a7-94b5-4bf4-bad5-756264eb01c6 | how-to-reduce-change-detection-to-semantic | 2206.07557 | null | https://arxiv.org/abs/2206.07557v2 | https://arxiv.org/pdf/2206.07557v2.pdf | How to Reduce Change Detection to Semantic Segmentation | Change detection (CD) aims to identify changes that occur in an image pair taken different times. Prior methods devise specific networks from scratch to predict change masks in pixel-level, and struggle with general segmentation problems. In this paper, we propose a new paradigm that reduces CD to semantic segmentation... | ['Chengjie Wang', 'Bin-Bin Gao', 'Guo-Hua Wang'] | 2022-06-15 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 4.70378548e-01 -1.70518190e-01 -2.36540228e-01 -2.85600096e-01
-5.01908422e-01 -5.65486610e-01 4.76109535e-01 -8.52064788e-02
-3.52374434e-01 2.45681241e-01 -2.65380919e-01 -1.83440223e-01
7.38708004e-02 -7.73599088e-01 -4.68006581e-01 -8.10551763e-01
4.80119102e-02 7.07111433e-02 8.95793200e-01 -3.85036230... | [9.624187469482422, -0.6590315699577332] |
7ec5607c-8755-4b91-aec2-a3e82e345faa | sensing-of-side-lobes-interference-for | 2306.17650 | null | https://arxiv.org/abs/2306.17650v1 | https://arxiv.org/pdf/2306.17650v1.pdf | Sensing of Side Lobes Interference for Blockage Prediction in Dense mmWave Networks | The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networks. This paper focuses on millimeter-wave communications, which are subject to severe attenuation due to blockages, ultimately detrimental t... | ['Benoit Denis', 'Hiba Dakdouk', 'Mohamed Sana'] | 2023-06-30 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 4.59424525e-01 3.12999159e-01 -3.03460956e-01 2.13803947e-01
-3.17948371e-01 -7.41215646e-01 1.79898307e-01 8.52131918e-02
-2.59987980e-01 1.09626245e+00 -1.22491628e-01 -8.12394142e-01
-6.36512101e-01 -7.82792449e-01 -6.83039278e-02 -1.18314397e+00
-5.27224958e-01 -9.17346478e-02 -1.68226734e-02 1.35234103... | [6.234353542327881, 1.2528659105300903] |
be50c43e-4ba8-40a0-af77-dbcb1ff48e85 | stock-price-prediction-using-bert-and-gan | 2107.09055 | null | https://arxiv.org/abs/2107.09055v1 | https://arxiv.org/pdf/2107.09055v1.pdf | Stock price prediction using BERT and GAN | The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time a... | ['Anukriti Bansal', 'Vikas Bajpai', 'Priyank Sonkiya'] | 2021-07-18 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-8.22848797e-01 -2.63778001e-01 -1.11284375e-01 -2.41526559e-01
-3.37721705e-01 -7.22964227e-01 6.73197448e-01 -2.26069614e-01
-3.27809572e-01 8.98249030e-01 5.06554842e-01 -6.67618752e-01
4.38207716e-01 -1.25839972e+00 -3.78293484e-01 -5.97459674e-01
-9.47476327e-02 2.26969257e-01 -2.57017344e-01 -7.97617972... | [4.433347702026367, 4.255926609039307] |
8ab3fbb0-d420-4301-882c-d082d2e91f4c | streaming-robust-submodular-maximization-a | 1711.02598 | null | http://arxiv.org/abs/1711.02598v1 | http://arxiv.org/pdf/1711.02598v1.pdf | Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach | We study the classical problem of maximizing a monotone submodular function
subject to a cardinality constraint k, with two additional twists: (i) elements
arrive in a streaming fashion, and (ii) m items from the algorithm's memory are
removed after the stream is finished. We develop a robust submodular algorithm
STAR-... | ['Ashkan Norouzi-Fard', 'Slobodan Mitrović', 'Volkan Cevher', 'Jakub Tarnawski', 'Ilija Bogunovic'] | 2017-11-07 | streaming-robust-submodular-maximization-a-1 | http://papers.nips.cc/paper/7042-streaming-robust-submodular-maximization-a-partitioned-thresholding-approach | http://papers.nips.cc/paper/7042-streaming-robust-submodular-maximization-a-partitioned-thresholding-approach.pdf | neurips-2017-12 | ['data-summarization'] | ['miscellaneous'] | [ 5.28346360e-01 3.48039597e-01 -6.82571352e-01 -2.05946863e-01
-9.09628630e-01 -9.79804397e-01 -1.26119377e-02 7.82942951e-01
-3.66255194e-01 7.69201636e-01 3.71914774e-01 8.11492279e-02
-5.09892941e-01 -7.10135043e-01 -9.71623957e-01 -7.46481180e-01
-4.58877832e-01 9.53439116e-01 2.99105823e-01 1.59820188... | [6.535688400268555, 4.949305057525635] |
57929ee6-bbbc-4cf7-bdcb-51117c31e2fe | gan-based-joint-activity-detection-and | 2204.01731 | null | https://arxiv.org/abs/2204.01731v1 | https://arxiv.org/pdf/2204.01731v1.pdf | Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access | Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only achieve either activity detection or channel estimation, but not both. In this pa... | ['Yong Zhou', 'Yinan Zou', 'Shuang Liang'] | 2022-04-04 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.56730813e-01 1.89139515e-01 -1.77286580e-01 7.66208693e-02
-7.34465003e-01 -2.12221041e-01 3.23429972e-01 -5.79065561e-01
-1.52519047e-01 9.52897370e-01 3.09464544e-01 -5.04165590e-01
8.21197778e-02 -8.58863413e-01 -6.99158251e-01 -1.17365980e+00
1.71806179e-02 -2.83801466e-01 -2.02053800e-01 1.20929137... | [6.3975067138671875, 1.5150320529937744] |
aa333897-3f1a-4ec3-ae7a-fc35f97d5fd7 | coordinate-based-texture-inpainting-for-pose | 1811.11459 | null | https://arxiv.org/abs/1811.11459v2 | https://arxiv.org/pdf/1811.11459v2.pdf | Coordinate-based Texture Inpainting for Pose-Guided Image Generation | We present a new deep learning approach to pose-guided resynthesis of human photographs. At the heart of the new approach is the estimation of the complete body surface texture based on a single photograph. Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that co... | ['Victor Lempitsky', 'Alexander Vakhitov', 'Artur Grigorev', 'Artem Sevastopolsky'] | 2018-11-28 | null | null | null | null | ['pose-guided-image-generation'] | ['computer-vision'] | [ 6.39591455e-01 5.44061065e-01 1.69461608e-01 -3.94547313e-01
-6.38838887e-01 -3.11855406e-01 2.48170257e-01 -6.09327495e-01
-9.40704793e-02 5.69852769e-01 1.12583555e-01 5.39921641e-01
4.99224156e-01 -9.24188673e-01 -1.26898074e+00 -5.40263116e-01
4.93657827e-01 6.26673222e-01 2.95520313e-02 -2.52602041... | [11.753835678100586, -0.8038618564605713] |
796b9df1-ab9c-452d-9ee0-a35f68ca2503 | clam-selective-clarification-for-ambiguous | 2212.07769 | null | https://arxiv.org/abs/2212.07769v2 | https://arxiv.org/pdf/2212.07769v2.pdf | CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models | Users often ask dialogue systems ambiguous questions that require clarification. We show that current language models rarely ask users to clarify ambiguous questions and instead provide incorrect answers. To address this, we introduce CLAM: a framework for getting language models to selectively ask for clarification ab... | ['Sebastian Farquhar', 'Yarin Gal', 'Lorenz Kuhn'] | 2022-12-15 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [ 3.67109388e-01 7.26885259e-01 2.43502948e-02 -7.30243862e-01
-1.13333094e+00 -1.25530183e+00 6.62764549e-01 2.08251402e-01
-3.20768505e-01 1.09355354e+00 6.20415270e-01 -1.16517377e+00
-4.32082117e-02 -4.11356419e-01 -1.77606419e-02 3.14775527e-01
7.63098240e-01 7.50295997e-01 2.62019247e-01 -6.87544882... | [12.058337211608887, 7.971707820892334] |
7bf96b58-3942-42f8-808f-f44a4d24177e | three-stream-joint-network-for-zero-shot | 2204.05666 | null | https://arxiv.org/abs/2204.05666v1 | https://arxiv.org/pdf/2204.05666v1.pdf | Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval | The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency between seen and unseen categories. Previous literature bridges seen and unseen categories by semantic embedding, which requires prior know... | ['Xin-Shun Xu', 'Zhen-Duo Chen', 'Yongxin Wang', 'Xin Luo', 'Yu-Wei Zhan'] | 2022-04-12 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.29297012e-01 -3.07327151e-01 -2.83310890e-01 -3.94909441e-01
-4.45591271e-01 -5.92090011e-01 8.37366164e-01 -2.74835557e-01
-2.43653178e-01 4.92709339e-01 1.53584689e-01 2.63899833e-01
-8.63291100e-02 -9.57859695e-01 -6.26663446e-01 -4.45587516e-01
5.24137437e-01 2.22549915e-01 5.47005057e-01 -2.73170680... | [11.613899230957031, 0.6836234331130981] |
bf502ed2-944b-48e4-844d-7af60b370ac8 | efficient-video-instance-segmentation-via | 2203.01853 | null | https://arxiv.org/abs/2203.01853v1 | https://arxiv.org/pdf/2203.01853v1.pdf | Efficient Video Instance Segmentation via Tracklet Query and Proposal | Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is util... | ['Gerard Medioni', 'Jayan Eledath', 'Junsong Yuan', 'Tian Lan', 'Hui Liang', 'Sudhir Yarram', 'Jialian Wu'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Efficient_Video_Instance_Segmentation_via_Tracklet_Query_and_Proposal_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Efficient_Video_Instance_Segmentation_via_Tracklet_Query_and_Proposal_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-instance-segmentation'] | ['computer-vision'] | [-7.00748935e-02 -1.71084091e-01 -4.69225645e-01 -3.01744521e-01
-1.36086607e+00 -6.30070925e-01 3.16630572e-01 1.63303148e-02
-4.86407131e-01 4.04266268e-01 -1.50004759e-01 7.67285675e-02
-2.87470296e-02 -4.58587438e-01 -1.30475521e+00 -2.33041242e-01
-1.47179067e-01 4.40662950e-01 7.88451195e-01 2.23068669... | [9.132604598999023, -0.02941025421023369] |
b534b910-0dad-4f5e-9ca9-b8c84b28cafc | contrastive-language-action-pre-training-for | 2204.12293 | null | https://arxiv.org/abs/2204.12293v1 | https://arxiv.org/pdf/2204.12293v1.pdf | Contrastive Language-Action Pre-training for Temporal Localization | Long-form video understanding requires designing approaches that are able to temporally localize activities or language. End-to-end training for such tasks is limited by the compute device memory constraints and lack of temporal annotations at large-scale. These limitations can be addressed by pre-training on large dat... | ['Loris Bazzani', 'Michael Donoser', 'Bernard Ghanem', 'Maksim Lapin', 'Erhan Gundogdu', 'Mengmeng Xu'] | 2022-04-26 | null | null | null | null | ['few-shot-temporal-action-localization', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.61100912e-01 -1.17176391e-01 -5.68789482e-01 -3.00415993e-01
-7.80858457e-01 -6.12454176e-01 5.37597299e-01 -1.13497026e-01
-4.66691792e-01 5.79765975e-01 3.54483426e-01 -4.69160601e-02
2.08206818e-01 -2.57643998e-01 -1.09372687e+00 -3.55461866e-01
-1.71196401e-01 -8.41981769e-02 4.89403695e-01 2.86828160... | [8.566803932189941, 0.6729558706283569] |
ba758621-171f-4185-b597-222b32ca343a | enrich-multi-purpose-dataset-for-benchmarking | null | null | https://www.sciencedirect.com/science/article/pii/S0924271623000539 | https://doi.org/10.1016/j.isprsjprs.2023.03.002 | ENRICH: Multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry | The availability of high-resolution data and accurate ground truth is essential to evaluate and compare methods and algorithms properly. Moreover, it is often difficult to acquire real data for a given application domain that is sufficiently representative and heterogeneous in terms of scene representation, acquisition... | ['Fabio Remondino', 'Gianluigi Ciocca', 'Simone Bianco', 'Elisa Mariarosaria Farella', 'Luca Morelli', 'Davide Marelli'] | 2023-04-01 | null | null | null | isprs-journal-of-photogrammetry-and-remote-10 | ['3d-scene-reconstruction', '3d-reconstruction', 'monocular-depth-estimation', 'key-point-matching'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.75669134e-01 -5.21607757e-01 1.94757834e-01 -4.25609291e-01
-6.99022055e-01 -6.31155014e-01 7.35054910e-01 1.83262572e-01
-3.21369171e-01 6.05836451e-01 -1.69053778e-01 -4.85606119e-02
-3.51291001e-01 -1.04830539e+00 -5.30962408e-01 -5.83398879e-01
1.01866171e-01 5.99161744e-01 1.28254652e-01 -3.20947051... | [8.550796508789062, -2.4482765197753906] |
3e060078-1140-4957-8618-a0db909ebe02 | data-driven-predictive-latency-for-5g-a | 2307.02329 | null | https://arxiv.org/abs/2307.02329v2 | https://arxiv.org/pdf/2307.02329v2.pdf | Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements | The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within... | ['Roberto Verdone', 'Simone Bizzarri', 'Giorgio Ghinamo', 'Davide Micheli', 'Andrea Orsi', 'Nicol Sarcone Grande', 'Francesca Conserva', 'Marco Skocaj'] | 2023-07-05 | null | null | null | null | ['anomaly-detection', 'management', 'decision-making'] | ['methodology', 'miscellaneous', 'reasoning'] | [ 1.89632382e-02 2.46719569e-01 -3.52095723e-01 -4.64230895e-01
-5.57053864e-01 -3.64203244e-01 3.77748758e-01 1.33869663e-01
1.34649530e-01 1.01024926e+00 -2.67154455e-01 -1.19341838e+00
-7.80795515e-01 -7.01320171e-01 -4.12302971e-01 -4.76221442e-01
-1.10796118e+00 9.02267277e-01 3.90686005e-01 6.08352311... | [6.092363357543945, 1.638914704322815] |
7e98ba38-03c7-4bb3-b564-32c2dbd54ccf | toward-more-accurate-and-generalizable | 2306.03984 | null | https://arxiv.org/abs/2306.03984v2 | https://arxiv.org/pdf/2306.03984v2.pdf | Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs | Measurement of interaction quality is a critical task for the improvement of spoken dialog systems. Existing approaches to dialog quality estimation either focus on evaluating the quality of individual turns, or collect dialog-level quality measurements from end users immediately following an interaction. In contrast t... | ['Anuj Goyal', 'Timothy Leffel', 'Aram Galstyan', 'Spyros Matsoukas', 'Angeliki Metallinou', 'Nagesh Panyam Chandrasekarasastry', 'Abishek Komma'] | 2023-06-06 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [-3.90205115e-01 4.04419988e-01 -8.62584263e-02 -1.02003741e+00
-9.80431497e-01 -9.18546617e-01 7.05141425e-01 4.15774792e-01
-2.85507083e-01 7.87615895e-01 7.98794448e-01 -1.10671453e-01
-5.75935505e-02 -5.41663587e-01 1.16753317e-01 -3.66469137e-02
3.85119498e-01 8.88926744e-01 1.28419593e-01 -6.76921606... | [12.870697021484375, 8.016271591186523] |
6c73eb1d-7d2a-4d3d-8cae-94d3c4482a17 | nmtpy-a-flexible-toolkit-for-advanced-neural | 1706.00457 | null | http://arxiv.org/abs/1706.00457v1 | http://arxiv.org/pdf/1706.00457v1.pdf | NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems | In this paper, we present nmtpy, a flexible Python toolkit based on Theano
for training Neural Machine Translation and other neural sequence-to-sequence
architectures. nmtpy decouples the specification of a network from the training
and inference utilities to simplify the addition of a new architecture and
reduce the a... | ['Loïc Barrault', 'Mercedes García-Martínez', 'Walid Aransa', 'Ozan Caglayan', 'Adrien Bardet', 'Fethi Bougares'] | 2017-06-01 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.62313864e-01 2.84715593e-01 -3.24778587e-01 -6.31147981e-01
-9.90441322e-01 -5.71374178e-01 6.86662257e-01 -3.38423103e-01
-4.17374760e-01 9.11672473e-01 2.66582936e-01 -1.07211995e+00
5.69189787e-01 -3.60561252e-01 -1.11070657e+00 -1.95348382e-01
4.78086382e-01 9.03564751e-01 -3.62822741e-01 -3.05209011... | [11.605818748474121, 10.3318510055542] |
c7889377-51ff-40c3-a0d6-e4d4f8a79db3 | average-outward-flux-skeletons-for | 2111.13826 | null | https://arxiv.org/abs/2111.13826v1 | https://arxiv.org/pdf/2111.13826v1.pdf | Average Outward Flux Skeletons for Environment Mapping and Topology Matching | We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an online procedure that robustly computes a retraction of its boundaries. In this article, we first present the online construction of a topological map and the implementati... | ['Kaleem Siddiqi', 'Gregory Dudek', 'Ioannis Rekleitis', 'Elham Karimi', 'Babak Samari', 'Morteza Rezanejad'] | 2021-11-27 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.57709730e-01 5.66456318e-01 3.70027184e-01 -2.60516733e-01
-2.40502372e-01 -6.52898252e-01 5.89637756e-01 3.97069991e-01
-5.00645578e-01 5.50706565e-01 -3.67231816e-01 -3.53390038e-01
-7.43043959e-01 -1.15913391e+00 -8.85628819e-01 -3.43879968e-01
-3.80506337e-01 1.07967639e+00 5.57689011e-01 -5.18229187... | [7.245418548583984, -2.0703253746032715] |
0b4607ff-44c0-4c8b-9fb6-49ee2d38d035 | summarize-before-aggregate-a-global-to-local | null | null | https://aclanthology.org/2020.coling-main.367 | https://aclanthology.org/2020.coling-main.367.pdf | Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition | Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications. Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances. Phrases carry... | ['Haozhuang Liu', 'Haitao Zheng', 'Ying Shen', 'Dong Wang', 'Dongming Sheng'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.41895090e-03 2.38067999e-01 -1.29344672e-01 -8.22478831e-01
-6.49627030e-01 -3.44774723e-01 4.28433776e-01 5.48795938e-01
1.74354225e-01 4.97111768e-01 8.40654671e-01 3.88152599e-01
1.50924981e-01 -5.15563428e-01 -4.41389322e-01 -7.59593785e-01
-1.54582158e-01 6.68133097e-03 -2.05505162e-01 -4.92245942... | [12.925463676452637, 6.22299861907959] |
8cdf42d4-11db-44cb-bd09-805024fd8ac3 | variational-bayesian-sequence-to-sequence | 2102.06143 | null | https://arxiv.org/abs/2102.06143v1 | https://arxiv.org/pdf/2102.06143v1.pdf | Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation | Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate argume... | ['Dimitris N. Metaxas', 'Sotirios Chatzis', 'Dimitrios Kosmopoulos', 'Andreas Voskou', 'Harris Partaourides'] | 2021-02-11 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 7.17807293e-01 4.08800066e-01 -9.90730599e-02 -3.43845874e-01
-9.04718041e-01 1.50279507e-01 4.51705426e-01 -5.75000107e-01
-6.64671481e-01 7.27121115e-01 6.89611554e-01 -4.33134943e-01
-7.70699531e-02 -4.16149229e-01 -7.55048394e-01 -1.01858950e+00
2.56839246e-01 6.63845420e-01 1.97795406e-01 6.70405179... | [7.072738170623779, 3.8194572925567627] |
44e3abbc-deed-4db7-9c92-12417e4d0241 | backdoor-attacks-against-transfer-learning | 2001.03274 | null | https://arxiv.org/abs/2001.03274v2 | https://arxiv.org/pdf/2001.03274v2.pdf | Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models | Transfer learning provides an effective solution for feasibly and fast customize accurate \textit{Student} models, by transferring the learned knowledge of pre-trained \textit{Teacher} models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and mainta... | ['Tianle Chen', 'Shangyu Chen', 'Carsten Rudolph', 'Surya Nepal', 'Marthie Grobler', 'Shuo Wang'] | 2020-01-10 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 5.13177395e-01 -1.40658572e-01 -4.81793657e-02 -2.53313273e-01
-8.61687601e-01 -1.06742072e+00 1.94747746e-01 -5.26414812e-02
-6.27668619e-01 8.73761594e-01 -7.73675382e-01 -7.41218030e-01
-4.26159114e-01 -8.50945950e-01 -1.14626682e+00 -9.29745734e-01
-3.93476158e-01 3.80813587e-03 1.99804649e-01 -2.51702994... | [5.5751953125, 7.8257670402526855] |
8099dc34-dc9e-4d80-9c8b-a47f054eed97 | lessons-from-computational-modelling-of | 2011.07398 | null | https://arxiv.org/abs/2011.07398v2 | https://arxiv.org/pdf/2011.07398v2.pdf | Lessons from Computational Modelling of Reference Production in Mandarin and English | Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in Mandarin was introduced. In the present paper, we annotate this corpus, evaluate classic REG algorithms on it, and compare the results with ... | ['Kees Van Deemter', 'Guanyi Chen'] | 2020-11-14 | null | https://aclanthology.org/2020.inlg-1.33 | https://aclanthology.org/2020.inlg-1.33.pdf | inlg-acl-2020-12 | ['referring-expression-generation'] | ['computer-vision'] | [ 1.30899519e-01 5.87499201e-01 -5.00896014e-02 -5.70205986e-01
-1.06204808e+00 -8.81390274e-01 6.73327506e-01 -7.19029009e-02
-2.54435390e-01 1.08617210e+00 8.26865613e-01 -5.90285361e-01
-9.51702744e-02 -4.42736119e-01 -3.45633000e-01 -2.53696978e-01
1.79706618e-01 2.71864712e-01 -6.87223673e-02 -7.19310701... | [10.413121223449707, 9.192953109741211] |
88f29f25-0a09-45b3-b77f-4ecf113b418f | uniflg-unified-facial-landmark-generator-from | 2302.14337 | null | https://arxiv.org/abs/2302.14337v2 | https://arxiv.org/pdf/2302.14337v2.pdf | UniFLG: Unified Facial Landmark Generator from Text or Speech | Talking face generation has been extensively investigated owing to its wide applicability. The two primary frameworks used for talking face generation comprise a text-driven framework, which generates synchronized speech and talking faces from text, and a speech-driven framework, which generates talking faces from spee... | ['Kei Sawada', 'Yukiya Hono', 'Kentaro Mitsui'] | 2023-02-28 | null | null | null | null | ['talking-face-generation', 'face-generation', 'speech-synthesis'] | ['computer-vision', 'computer-vision', 'speech'] | [ 4.34297800e-01 6.37721479e-01 2.41914347e-01 -6.29129052e-01
-1.06651533e+00 -2.27927864e-01 9.61195171e-01 -8.74510169e-01
3.56013447e-01 3.49732816e-01 6.02524042e-01 1.82563350e-01
6.07110977e-01 -6.65325999e-01 -5.00660956e-01 -6.32034421e-01
2.57268190e-01 3.13487351e-01 -7.97111094e-02 -9.56970304... | [13.235435485839844, -0.40774106979370117] |
67fda977-f4d1-4cd6-8fb7-50836f6dba60 | xbd-a-dataset-for-assessing-building-damage | 1911.09296 | null | https://arxiv.org/abs/1911.09296v1 | https://arxiv.org/pdf/1911.09296v1.pdf | xBD: A Dataset for Assessing Building Damage from Satellite Imagery | We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-pe... | ['Howie Choset', 'Eric Heim', 'Nirav Patel', 'Ritwik Gupta', 'Matthew Gaston', 'Jigar Doshi', 'Sandra Sajeev', 'Richard Hosfelt', 'Bryce Goodman'] | 2019-11-21 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 3.13930959e-01 -2.07220271e-01 -4.64605540e-02 -5.07197976e-02
-7.72626400e-01 -5.54623485e-01 3.62936348e-01 9.78345990e-01
-6.28081858e-01 5.26142538e-01 1.13615906e+00 -3.90587658e-01
-3.09621811e-01 -1.60480845e+00 -1.97805807e-01 -4.97385651e-01
-3.83549124e-01 1.94577381e-01 -4.62571159e-02 -4.65622038... | [9.52580451965332, -1.2854713201522827] |
495cacc0-9004-4905-b95b-29e99de5d427 | maskreid-a-mask-based-deep-ranking-neural | 1804.03864 | null | http://arxiv.org/abs/1804.03864v2 | http://arxiv.org/pdf/1804.03864v2.pdf | MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification | Person retrieval faces many challenges including cluttered background,
appearance variations (e.g., illumination, pose, occlusion) among different
camera views and the similarity among different person's images. To address
these issues, we put forward a novel mask based deep ranking neural network
with a skipped fusing... | ['Yang Gao', 'Lei Qi', 'Jing Huo', 'Yinghuan Shi', 'Lei Wang'] | 2018-04-11 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [ 1.24278881e-01 -8.86232316e-01 3.77286196e-01 -4.87748563e-01
-4.07442689e-01 -2.20988303e-01 4.95360047e-01 -9.78747904e-02
-5.99078834e-01 6.19390368e-01 1.58752158e-01 4.42912459e-01
-2.09305793e-01 -6.11294150e-01 -4.77343112e-01 -9.16705549e-01
3.50821435e-01 -2.79881675e-02 3.02447319e-01 2.81439647... | [14.7142915725708, 0.8891558647155762] |
74af0af5-41ba-4352-ab0b-38afbdaa7531 | metal-artifact-correction-in-cone-beam | 2208.08288 | null | https://arxiv.org/abs/2208.08288v1 | https://arxiv.org/pdf/2208.08288v1.pdf | Metal artifact correction in cone beam computed tomography using synthetic X-ray data | Metal artifact correction is a challenging problem in cone beam computed tomography (CBCT) scanning. Metal implants inserted into the anatomy cause severe artifacts in reconstructed images. Widely used inpainting-based metal artifact reduction (MAR) methods require segmentation of metal traces in the projections as a f... | ['Simo Särkkä', 'Ari Hietanen', 'Harshit Agrawal'] | 2022-08-17 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.74344236e-01 1.35298893e-01 5.52542269e-01 -2.28580683e-01
-1.13513255e+00 -7.64394179e-02 1.53678000e-01 4.77733202e-02
-3.90113354e-01 5.44532835e-01 3.83209512e-02 -2.11331427e-01
1.04027679e-02 -7.07308412e-01 -8.94843340e-01 -5.40354013e-01
1.48692176e-01 9.75785255e-01 7.89288759e-01 -6.29817788... | [13.508267402648926, -2.605604410171509] |
8d7065c2-808b-4314-a8c5-83c1df188034 | question-aware-memory-network-for-multi-hop | 2104.13173 | null | https://arxiv.org/abs/2104.13173v1 | https://arxiv.org/pdf/2104.13173v1.pdf | Question-Aware Memory Network for Multi-hop Question Answering in Human-Robot Interaction | Knowledge graph question answering is an important technology in intelligent human-robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different... | ['Quanjun Yin', 'Keping Yu', 'Qian Li', 'Mamoun Alazab', 'Xinmeng Li'] | 2021-04-27 | null | null | null | null | ['graph-question-answering', 'multi-hop-question-answering'] | ['graphs', 'knowledge-base'] | [-1.49970412e-01 5.80096006e-01 -2.28420556e-01 -2.86962092e-01
-4.29866940e-01 -4.24205661e-01 3.33974093e-01 4.18278456e-01
-5.33863485e-01 5.99081397e-01 5.11953235e-01 -5.61141670e-01
-3.37096781e-01 -1.16128159e+00 -7.85236239e-01 4.62984741e-02
3.35141689e-01 9.56045210e-01 1.08138371e+00 -6.01044595... | [10.52880859375, 7.9095892906188965] |
fdc28e7a-f7a0-459f-abde-4ce61f1e1f61 | speech-to-sql-towards-speech-driven-sql-query | 2201.01209 | null | https://arxiv.org/abs/2201.01209v1 | https://arxiv.org/pdf/2201.01209v1.pdf | Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question | Speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets in our daily lives, since voice is the most easiest and efficient way for human-computer interaction. This paper works towards designing more effective speech-based interfaces to query the structured data in relati... | ['Di Jiang', 'Xuefang Zhao', 'Raymond Chi-Wing Wong', 'Yuanfeng Song'] | 2022-01-04 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 6.86348900e-02 1.61949903e-01 6.54606149e-02 -6.88506424e-01
-1.08659506e+00 -4.45200264e-01 5.24303019e-01 1.38520226e-01
-4.13711399e-01 2.25843996e-01 2.16177702e-01 -7.42451072e-01
3.29214424e-01 -9.07472968e-01 -6.75030351e-01 -9.10424367e-02
5.03185689e-01 6.59989715e-01 3.85139674e-01 -6.04455829... | [14.218802452087402, 6.968374729156494] |
94d7e4e4-24af-4c51-825b-7d21fb02afcb | integrating-heterogeneous-domain-information | 2212.10714 | null | https://arxiv.org/abs/2212.10714v1 | https://arxiv.org/pdf/2212.10714v1.pdf | Integrating Heterogeneous Domain Information into Relation Extraction: A Case Study on Drug-Drug Interaction Extraction | The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. However, research on the effe... | ['Masaki Asada'] | 2022-12-21 | null | null | null | null | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 2.91009128e-01 2.95254439e-01 -8.06515098e-01 -5.15140444e-02
-5.87251246e-01 -2.05730811e-01 2.72376716e-01 7.85533249e-01
-2.17335150e-01 1.24910820e+00 3.93065006e-01 -4.02241290e-01
-5.25147021e-01 -1.15424585e+00 -5.79682767e-01 -6.67003214e-01
-2.90258437e-01 4.64155704e-01 -2.42397159e-01 -2.09480703... | [8.462570190429688, 8.652029037475586] |
c83e0854-6b59-406d-ba4d-d71e2fbebaff | unsupervised-domain-expansion-from-multiple | 2005.12544 | null | https://arxiv.org/abs/2005.12544v1 | https://arxiv.org/pdf/2005.12544v1.pdf | Unsupervised Domain Expansion from Multiple Sources | Given an existing system learned from previous source domains, it is desirable to adapt the system to new domains without accessing and forgetting all the previous domains in some applications. This problem is known as domain expansion. Unlike traditional domain adaptation in which the target domain is the domain defin... | ['Lu Sheng', 'Jing Zhang', 'Philip Ogunbona', 'Chang Tang', 'Wanqing Li'] | 2020-05-26 | null | null | null | null | ['unsupervised-domain-expansion'] | ['methodology'] | [ 4.03636426e-01 2.50694543e-01 -3.74659717e-01 -5.98705530e-01
-4.17796820e-01 -7.49935508e-01 3.73216122e-01 -7.58392811e-02
-4.33715612e-01 1.40940881e+00 7.00692460e-02 3.13312978e-01
-3.46977413e-02 -6.80777133e-01 -5.09962618e-01 -7.62804925e-01
3.78957242e-01 1.04619098e+00 5.88053286e-01 -1.39486104... | [10.364853858947754, 3.095060348510742] |
7dd4b2af-321b-424f-98d8-57c83a100674 | disentangled-representations-for-domain | 2008.11514 | null | https://arxiv.org/abs/2008.11514v1 | https://arxiv.org/pdf/2008.11514v1.pdf | Disentangled Representations for Domain-generalized Cardiac Segmentation | Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. Since the acquisition and annotation of medical data are costly and time-consuming, recent work focuses on domain adaptation and generalizati... | ["Alison O'Neil", 'Spyridon Thermos', 'Xiao Liu', 'Agisilaos Chartsias', 'Sotirios A. Tsaftaris'] | 2020-08-26 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 5.04537940e-01 2.92250644e-02 -8.42569694e-02 -5.90297401e-01
-9.83018219e-01 -6.23096764e-01 2.99700171e-01 -2.62991227e-02
-3.83851945e-01 7.05213964e-01 3.20340425e-01 -1.06451930e-02
-9.98463109e-03 -3.82298261e-01 -3.89872849e-01 -7.82203436e-01
1.62807286e-01 7.45818019e-01 1.88951969e-01 1.43056557... | [14.606988906860352, -2.0287351608276367] |
f3c0e481-3393-45c4-a1c4-168b3da6089a | nearest-neighbor-based-out-of-distribution | 2303.16616 | null | https://arxiv.org/abs/2303.16616v1 | https://arxiv.org/pdf/2303.16616v1.pdf | Nearest Neighbor Based Out-of-Distribution Detection in Remote Sensing Scene Classification | Deep learning models for image classification are typically trained under the "closed-world" assumption with a predefined set of image classes. However, when the models are deployed they may be faced with input images not belonging to the classes encountered during training. This type of scenario is common in remote se... | ['Vladimir Risojević', 'Mitar Simić', 'Dajana Dimitrić'] | 2023-03-29 | null | null | null | null | ['scene-classification', 'remote-sensing-image-classification'] | ['computer-vision', 'miscellaneous'] | [ 6.11131430e-01 -2.87189484e-01 -4.78426218e-02 -7.10941315e-01
-4.62366790e-01 -5.12962401e-01 6.58987343e-01 2.66006887e-01
-7.59293139e-01 5.08529902e-01 -4.09667641e-01 -3.92535388e-01
-4.68748629e-01 -1.26861048e+00 -7.18812704e-01 -8.92059624e-01
-1.67087093e-01 5.87742746e-01 -1.57791659e-01 1.09255306... | [9.643939018249512, -1.3386949300765991] |
c545e26f-b233-4ba1-baee-507d74eb0441 | discovering-topics-with-neural-topic-models | null | null | https://openreview.net/forum?id=Skx24yHFDr | https://openreview.net/pdf?id=Skx24yHFDr | Discovering Topics With Neural Topic Models Built From PLSA Loss | In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to re... | ['Sileye Ba'] | 2019-09-25 | null | null | null | null | ['document-embedding', 'topic-models'] | ['methodology', 'natural-language-processing'] | [-1.49874881e-01 5.40895343e-01 -4.94569957e-01 -5.45913696e-01
-8.71790707e-01 -1.64048046e-01 1.18267000e+00 3.26158643e-01
-2.86031634e-01 6.40270829e-01 7.63738573e-01 -1.38971388e-01
-1.18697388e-03 -1.18465054e+00 -6.95328951e-01 -6.30708516e-01
-2.22398683e-01 1.07192600e+00 5.61977662e-02 8.08779970... | [10.413408279418945, 6.954501628875732] |
3239eba6-6c62-4e1b-92a3-1a76b036ccc9 | shape-preserving-half-projective-warps-for | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Chang_Shape-Preserving_Half-Projective_Warps_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Chang_Shape-Preserving_Half-Projective_Warps_2014_CVPR_paper.pdf | Shape-Preserving Half-Projective Warps for Image Stitching | This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation ... | ['Yung-Yu Chuang', 'Yoichi Sato', 'Che-Han Chang'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['image-stitching'] | ['computer-vision'] | [ 3.26820910e-01 -1.76817939e-01 7.66129941e-02 -1.63921997e-01
-4.67605323e-01 -8.56092632e-01 7.14421451e-01 -3.03300530e-01
-2.02840701e-01 5.01136184e-01 1.44905090e-01 1.09722555e-01
-2.14542568e-01 -7.88706183e-01 -5.18055081e-01 -8.51906955e-01
2.22460389e-01 3.87292355e-01 8.73606443e-01 -5.67509830... | [9.394255638122559, -2.3560335636138916] |
d377d624-5b93-4939-87f4-ffd0b0c4c867 | online-dictionary-learning-based-fault-and | 2108.10990 | null | https://arxiv.org/abs/2108.10990v1 | https://arxiv.org/pdf/2108.10990v1.pdf | Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems | The emerging wide area monitoring systems (WAMS) have brought significant improvements in electric grids' situational awareness. However, the newly introduced system can potentially increase the risk of cyber-attacks, which may be disguised as normal physical disturbances. This paper deals with the event and intrusion ... | ['Yu Zhang', 'Gabriel Intriago'] | 2021-08-24 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 2.71557361e-01 -3.62041503e-01 -1.30165204e-01 -1.69941559e-01
-4.50058877e-01 -4.63985801e-01 3.93388510e-01 7.75361657e-01
2.45760903e-01 7.97765493e-01 -2.80137837e-01 -4.27561402e-01
-3.36525679e-01 -8.24498951e-01 -4.00170356e-01 -9.03544664e-01
-8.12443793e-01 1.64513469e-01 1.36819050e-01 -2.58382738... | [6.189124584197998, 2.5547306537628174] |
1bbb47af-d740-4417-b598-12d9f33b276f | pk-icr-persona-knowledge-interactive-context | 2302.06674 | null | https://arxiv.org/abs/2302.06674v1 | https://arxiv.org/pdf/2302.06674v1.pdf | PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue | Identifying relevant Persona or Knowledge for conversational systems is a critical component of grounded dialogue response generation. However, each grounding has been studied in isolation with more practical multi-context tasks only recently introduced. We define Persona and Knowledge Dual Context Identification as th... | ['Guoyin Wang', 'Jiwei Li', 'Joosung Lee', 'Minsik Oh'] | 2023-02-13 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.21415931e-01 5.16586721e-01 6.10970110e-02 -2.13650122e-01
-1.24530566e+00 -7.02634811e-01 1.17286742e+00 1.18864991e-01
-4.53519583e-01 1.10060263e+00 6.57515407e-01 -4.83296067e-02
-4.43833917e-01 -6.22612715e-01 -1.35302976e-01 -4.48156059e-01
2.76129425e-01 7.56625175e-01 2.02305242e-01 -6.47579193... | [12.526223182678223, 8.10345458984375] |
f4cd8d1d-ff40-41ad-bc07-1fb0722eca9c | a-unified-model-for-arabizi-detection-and | null | null | https://aclanthology.org/2020.wanlp-1.15 | https://aclanthology.org/2020.wanlp-1.15.pdf | A Unified Model for Arabizi Detection and Transliteration using Sequence-to-Sequence Models | While online Arabic is primarily written using the Arabic script, a Roman-script variety called Arabizi is often seen on social media. Although this representation captures the phonology of the language, it is not a one-to-one mapping with the Arabic script version. This issue is exacerbated by the fact that Arabizi on... | ['Nizar Habash', 'Aiza Usman', 'Ali Shazal'] | null | null | null | null | coling-wanlp-2020-12 | ['transliteration'] | ['natural-language-processing'] | [ 2.35780533e-02 -4.18686420e-01 6.82041422e-02 -4.33827907e-01
-8.30553889e-01 -1.16258526e+00 4.26661938e-01 -2.10346162e-01
-1.74056649e-01 2.69360662e-01 3.23223323e-01 -3.50086451e-01
4.57767248e-01 -6.37394905e-01 -4.39700603e-01 -4.28795636e-01
5.04112124e-01 9.18430507e-01 -2.64440496e-02 -9.13185835... | [10.392294883728027, 10.562074661254883] |
65a33468-ce85-4937-921d-fa6ef77ae96c | entity-relation-extraction-as-dependency | 2110.09915 | null | https://arxiv.org/abs/2110.09915v1 | https://arxiv.org/pdf/2110.09915v1.pdf | Entity Relation Extraction as Dependency Parsing in Visually Rich Documents | Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored. In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity re... | ['Zuyi Bao', 'Chen Li', 'Junjie Cao', 'Rui Wang', 'Bo Zhang', 'Yue Zhang'] | 2021-10-19 | null | https://aclanthology.org/2021.emnlp-main.218 | https://aclanthology.org/2021.emnlp-main.218.pdf | emnlp-2021-11 | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.10941000e-01 4.80302334e-01 -1.20650068e-01 -2.92616487e-01
-5.73742032e-01 -8.72567892e-01 5.45147538e-01 4.40860122e-01
-3.77800643e-01 4.86824751e-01 6.20557010e-01 -6.94984496e-01
1.73210666e-01 -9.16585445e-01 -7.26619303e-01 -1.64440334e-01
-7.17010424e-02 3.48977834e-01 4.21809524e-01 -1.42804250... | [9.282936096191406, 8.128338813781738] |
ca4f658c-23ef-4429-92a9-90448cc7f452 | attention-w-net-improved-skip-connections-for | 2110.08811 | null | https://arxiv.org/abs/2110.08811v2 | https://arxiv.org/pdf/2110.08811v2.pdf | Attention W-Net: Improved Skip Connections for better Representations | Segmentation of macro and microvascular structures in fundoscopic retinal images plays a crucial role in the detection of multiple retinal and systemic diseases, yet it is a difficult problem to solve. Most neural network approaches face several issues such as lack of enough parameters, overfitting and/or incompatibili... | ['Sayantari Ghosh', 'Saumik Bhattacharya', 'Shikhar Mohan'] | 2021-10-17 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 1.44193754e-01 2.44478434e-01 -5.69529012e-02 -1.87379941e-01
-4.87220466e-01 -1.77699283e-01 2.86233902e-01 -6.64412156e-02
-5.25286198e-01 6.34515822e-01 3.08077365e-01 -5.41141033e-01
-1.60748512e-01 -5.01569271e-01 -5.00030041e-01 -3.30978751e-01
3.39435823e-02 -2.13452294e-01 4.85100240e-01 4.23242152... | [15.796382904052734, -3.968369245529175] |
87da1274-c428-44c6-ac40-61564f20c97e | progressive-transformers-for-end-to-end-sign | 2004.14874 | null | https://arxiv.org/abs/2004.14874v2 | https://arxiv.org/pdf/2004.14874v2.pdf | Progressive Transformers for End-to-End Sign Language Production | The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need ... | ['Richard Bowden', 'Necati Cihan Camgoz', 'Ben Saunders'] | 2020-04-30 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1430_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560664.pdf | eccv-2020-8 | ['sign-language-production'] | ['natural-language-processing'] | [ 7.60435224e-01 3.45161945e-01 1.41947985e-01 -5.73959231e-01
-1.00661421e+00 -5.71189225e-01 8.12066674e-01 -8.99780393e-01
-5.72642148e-01 6.40387297e-01 8.96000385e-01 -5.23986340e-01
2.61240095e-01 -3.84097099e-01 -8.67629588e-01 -3.74540567e-01
8.50204378e-02 7.01698124e-01 3.41125876e-01 -4.60573614... | [9.212510108947754, -6.5379228591918945] |
11df4460-9827-4601-abfe-f43fe1153af7 | rast-domain-robust-dialogue-rewriting-as | null | null | https://aclanthology.org/2021.emnlp-main.402 | https://aclanthology.org/2021.emnlp-main.402.pdf | RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging | The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context. Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset. We address this robustness i... | ['Dong Yu', 'Zhaopeng Tu', 'Kun Xu', 'LiWei Wang', 'Linfeng Song', 'Jie Hao'] | null | null | null | null | emnlp-2021-11 | ['dialogue-rewriting'] | ['natural-language-processing'] | [ 4.86524850e-01 7.04823554e-01 1.02671809e-01 -4.02179658e-01
-9.31517363e-01 -6.12092078e-01 8.43126833e-01 -2.35807329e-01
-1.96267828e-01 1.18025434e+00 7.65851617e-01 -2.35070884e-01
5.50099671e-01 -5.09199381e-01 -5.71464300e-01 -2.70017356e-01
5.02490819e-01 6.27825439e-01 2.11919755e-01 -7.59476781... | [12.487499237060547, 8.408202171325684] |
9a187593-f51c-4fc9-9535-6f1617436d79 | forecasting-with-deep-learning-s-p-500-index | 2103.14080 | null | https://arxiv.org/abs/2103.14080v1 | https://arxiv.org/pdf/2103.14080v1.pdf | Forecasting with Deep Learning: S&P 500 index | Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the... | ['Ikhlaas Gurrib', 'Linda Smail', 'Firuz Kamalov'] | 2021-03-21 | null | null | null | null | ['stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-6.49397671e-01 -4.52533513e-01 -1.90668255e-01 -5.59803545e-01
1.15991816e-01 -3.52773428e-01 5.24479568e-01 -1.47649318e-01
-4.93816167e-01 7.66778827e-01 1.66316912e-01 -5.01115978e-01
5.19800298e-02 -1.28401458e+00 -4.28410798e-01 -3.02234977e-01
-1.54475585e-01 1.22831874e-01 3.53988975e-01 -4.38976765... | [4.414137840270996, 4.213064670562744] |
7069ac2e-7e81-4577-80cc-800c62c5644c | two-stage-movie-script-summarization-an | null | null | https://aclanthology.org/2022.creativesumm-29.9 | https://aclanthology.org/2022.creativesumm-29.9.pdf | Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization | The Creative Summarization Shared Task at COLING 2022 aspires to generate summaries given long-form texts from creative writing. This paper presents the system architecture and the results of our participation in the Scriptbase track that focuses on generating movie plots given movie scripts. The core innovation in our... | ['Vera Demberg', 'Ernie Chang', 'Pin-Jie Lin', 'Xudong Hong', 'Dongqi Pu'] | null | null | https://aclanthology.org/2022.creativesumm-1.9 | https://aclanthology.org/2022.creativesumm-1.9.pdf | coling-creativesumm-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 7.11324811e-01 4.24449503e-01 9.80844647e-02 -3.86047453e-01
-1.39548564e+00 -8.26319039e-01 8.13929796e-01 -2.03047723e-01
-2.43901417e-01 7.70396769e-01 1.20775831e+00 1.36811033e-01
2.88295418e-01 -2.40565553e-01 -6.04414940e-01 -1.31076172e-01
3.67186487e-01 4.98519182e-01 1.10477665e-02 -2.51311153... | [12.393659591674805, 9.40395736694336] |
03ba238b-aa7c-4486-9041-94cd90d8d73e | explore-propose-and-assemble-an-interpretable | 1906.05210 | null | https://arxiv.org/abs/1906.05210v1 | https://arxiv.org/pdf/1906.05210v1.pdf | Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension | Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr). First, the Document Explorer ite... | ['Mohit Bansal', 'Yen-Chun Chen', 'Yichen Jiang', 'Nitish Joshi'] | 2019-06-12 | explore-propose-and-assemble-an-interpretable-1 | https://aclanthology.org/P19-1261 | https://aclanthology.org/P19-1261.pdf | acl-2019-7 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 5.77432394e-01 6.37759268e-01 2.66021565e-02 -6.36853695e-01
-1.34139669e+00 -7.37360597e-01 1.17576748e-01 8.59305978e-01
-4.51250046e-01 3.70962709e-01 7.72894681e-01 -7.87551403e-01
-3.34757566e-01 -6.60574198e-01 -8.80814493e-01 7.16388524e-02
2.97110498e-01 7.18553364e-01 3.29290956e-01 -2.96610296... | [11.118263244628906, 7.94274377822876] |
42a71d21-c325-43f2-8e40-11c7a8b39152 | learning-video-conditioned-policies-for | 2305.06289 | null | https://arxiv.org/abs/2305.06289v1 | https://arxiv.org/pdf/2305.06289v1.pdf | Learning Video-Conditioned Policies for Unseen Manipulation Tasks | The ability to specify robot commands by a non-expert user is critical for building generalist agents capable of solving a large variety of tasks. One convenient way to specify the intended robot goal is by a video of a person demonstrating the target task. While prior work typically aims to imitate human demonstration... | ['Ivan Laptev', 'Cordelia Schmid', 'Elliot Chane-Sane'] | 2023-05-10 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition', 'robot-manipulation'] | ['computer-vision', 'computer-vision', 'robots'] | [ 4.59918529e-01 1.23903016e-02 -4.24882360e-02 -1.75918430e-01
-5.60383499e-01 -7.06107199e-01 1.00623631e+00 -3.97448242e-01
-8.78333390e-01 7.31914878e-01 8.61386955e-02 -8.80016461e-02
1.48531273e-01 -1.60146803e-01 -1.17580175e+00 -6.17246270e-01
-1.30328804e-01 8.87292445e-01 7.27352686e-03 -2.16870219... | [4.552096366882324, 0.7894750237464905] |
937580b3-b935-42e2-83a9-b478e59063b9 | evaluation-of-induced-expert-knowledge-in | 2301.01817 | null | https://arxiv.org/abs/2301.01817v1 | https://arxiv.org/pdf/2301.01817v1.pdf | Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS | Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data d... | ['Gabriel Terejanu', 'Rezaur Rashid', 'Jawad Chowdhury'] | 2023-01-04 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.18083876e-01 5.57712555e-01 -7.73362458e-01 -1.87612414e-01
-3.31531912e-01 -5.41485548e-01 5.33280432e-01 3.02718639e-01
-2.08341494e-01 1.34675562e+00 6.10309243e-01 -7.95209169e-01
-7.66364872e-01 -1.00706458e+00 -1.23384416e+00 -3.57653081e-01
-3.10035706e-01 2.20991746e-01 -3.81811932e-02 2.67925054... | [8.170515060424805, 5.371395587921143] |
6c2afada-7cb1-495d-88ac-992dba6a8ce1 | incongruity-detection-between-bangla-news | 2211.07709 | null | https://arxiv.org/abs/2211.07709v1 | https://arxiv.org/pdf/2211.07709v1.pdf | Incongruity Detection between Bangla News Headline and Body Content through Graph Neural Network | Incongruity between news headlines and the body content is a common method of deception used to attract readers. Profitable headlines pique readers' interest and encourage them to visit a specific website. This is usually done by adding an element of dishonesty, using enticements that do not precisely reflect the conte... | ['Ryan Mohammad Bin Shahjahan', 'MD Abdullah Al Nasim', 'Kawsarul Islam', 'Akib Khan', 'Md Aminul Haque Palash'] | 2022-10-26 | null | null | null | null | ['incongruity-detection', 'stance-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-4.37779576e-01 3.20769623e-02 -4.44860995e-01 -2.86274731e-01
-4.78317499e-01 -4.39705223e-01 5.96927643e-01 6.48304045e-01
-4.69927758e-01 5.21962345e-01 8.22585285e-01 -3.45011562e-01
2.75707722e-01 -8.09019446e-01 -5.46581268e-01 -3.97936493e-01
4.27742213e-01 2.49887377e-01 1.93180770e-01 -9.56997633... | [8.754631042480469, 10.511886596679688] |
efb1749a-507a-4451-89c0-e7abd1117b66 | moocradar-a-fine-grained-and-multi-aspect | 2304.02205 | null | https://arxiv.org/abs/2304.02205v1 | https://arxiv.org/pdf/2304.02205v1.pdf | MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs | Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effec... | ['Jie Tang', 'Juanzi Li', 'Hai-Tao Zheng', 'Lei Hou', 'Manli Li', 'Xiaoya Li', 'Zhengshan Liao', 'Shangqing Tu', 'Zijun Yao', 'Qingyang Zhong', 'Mengying Lu', 'Jifan Yu'] | 2023-04-05 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-3.21153343e-01 -1.32200181e-01 -4.54915464e-01 -4.36651677e-01
-4.33206409e-01 -7.20314264e-01 2.20964804e-01 5.15912652e-01
-8.36060289e-03 7.75769353e-01 1.45578161e-01 -5.34335315e-01
-8.03897262e-01 -1.13231313e+00 -5.12773454e-01 -2.29229018e-01
5.54600477e-01 4.38413918e-01 5.15224338e-01 -2.30781078... | [10.118603706359863, 7.2196173667907715] |
e2bc3e1b-04d7-4a3f-a769-0aa95dbd1017 | non-invasive-blood-pressure-estimation-from | null | null | https://doi.org/10.3390/s18041160 | https://www.mdpi.com/280924 | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques | Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data ar... | ['Ana Madevska Bogdanova', 'Matjaž Gams', 'Martin Gjoreski', 'Monika Simjanoska'] | 2018-04-18 | null | null | null | sensors-2018-4 | ['blood-pressure-estimation'] | ['medical'] | [ 1.15721911e-01 -3.28007750e-02 9.61108580e-02 -6.90441668e-01
-4.27794516e-01 -1.59550563e-01 -2.43378773e-01 5.38709283e-01
-3.82915378e-01 1.04895592e+00 -2.21748158e-01 -5.21501899e-01
-8.44162628e-02 -9.24525857e-01 -2.04030499e-01 -5.75868905e-01
-3.37431610e-01 1.75896838e-01 1.70741722e-01 1.58096790... | [14.087732315063477, 2.9890785217285156] |
0dc0a347-7058-42e1-8e23-4a47130d3104 | master-thesis-neural-sign-language | 2011.09289 | null | https://arxiv.org/abs/2011.09289v1 | https://arxiv.org/pdf/2011.09289v1.pdf | Master Thesis: Neural Sign Language Translation by Learning Tokenization | In this thesis, we propose a multitask learning based method to improve Neural Sign Language Translation (NSLT) consisting of two parts, a tokenization layer and Neural Machine Translation (NMT). The tokenization part focuses on how Sign Language (SL) videos should be represented to be fed into the other part. It has n... | ['Alptekin Orbay'] | 2020-11-18 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 2.83262819e-01 -6.58015683e-02 -4.62415874e-01 -3.66664886e-01
-9.69700515e-01 -4.80770051e-01 6.16050601e-01 -4.02308792e-01
-6.82889044e-01 7.22100317e-01 4.52313662e-01 -2.51852334e-01
2.58779734e-01 -3.32488656e-01 -7.61675656e-01 -4.80865985e-01
3.60943437e-01 3.13154459e-01 2.62995869e-01 -2.34205961... | [9.182079315185547, -6.502620220184326] |
d5b89ab7-fd1d-4f2b-b90d-ec3b11c5dafe | cmta-covid-19-misinformation-multilingual | null | null | https://aclanthology.org/2021.acl-srw.28 | https://aclanthology.org/2021.acl-srw.28.pdf | CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter | The internet has actually come to be an essential resource of health knowledge for individuals around the world in the present situation of the coronavirus condition pandemic(COVID-19). During pandemic situations, myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapi... | ['Genoveva Vargas-Solar', 'Ambesh Shekhar', 'Mehrdad Farokhenajd', 'Raj Pranesh'] | 2021-08-01 | null | null | null | acl-2021-5 | ['rumour-detection'] | ['natural-language-processing'] | [-2.53329575e-01 1.26153678e-01 -1.68238744e-01 3.16458195e-02
-6.39841855e-01 -5.71879268e-01 1.10596585e+00 7.78923154e-01
-5.83376706e-01 9.23893094e-01 5.74061275e-01 -5.12926817e-01
3.63101780e-01 -9.45344627e-01 -6.64304733e-01 -4.75889266e-01
-1.01254627e-01 9.39105690e-01 -2.50942379e-01 -7.27381945... | [8.431573867797852, 9.856377601623535] |
2c48db47-cbbc-4ba9-8cd2-fcbf443915d3 | multi-microphone-complex-spectral-mapping-for | 2010.01703 | null | https://arxiv.org/abs/2010.01703v2 | https://arxiv.org/pdf/2010.01703v2.pdf | Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation | We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditions. We aim at both speaker separation and dereverberation. Our study first investigates offline utterance-wise speaker separation and then e... | ['DeLiang Wang', 'Peidong Wang', 'Zhong-Qiu Wang'] | 2020-10-04 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 2.40862086e-01 -5.32989800e-01 7.46646941e-01 -2.25697979e-01
-1.46913195e+00 -7.62203813e-01 2.09992364e-01 -3.33783895e-01
-2.89688706e-01 3.56387258e-01 4.80888098e-01 -6.09856009e-01
-2.18902841e-01 -3.12202740e-02 -6.87375247e-01 -1.07028103e+00
-3.23597789e-01 -6.44548014e-02 -2.34691307e-01 -2.01432124... | [15.01480484008789, 5.931805610656738] |
d834f819-cc29-462b-88c2-1d8afc73d3fa | low-cost-relevance-generation-and-evaluation | 2205.10298 | null | https://arxiv.org/abs/2205.10298v1 | https://arxiv.org/pdf/2205.10298v1.pdf | Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI | Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking. In this paper we propose a low cost relevance generation framework by generating features using cu... | ['Kurtis Voris', 'Haotian Jiang', 'Jitesh Mehta', 'Mina Ghashami', 'Venkat Varada'] | 2022-05-20 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-1.99728861e-01 5.02111793e-01 8.46958235e-02 -5.72233796e-01
-1.01061285e+00 -5.39386868e-01 5.01383901e-01 2.53774405e-01
-4.17149276e-01 1.13593066e+00 4.17766809e-01 -8.26491639e-02
-5.88816822e-01 -8.62488627e-01 -1.20679304e-01 1.11715861e-01
-6.85101897e-02 1.08910251e+00 2.91514546e-01 -7.88787127... | [9.478676795959473, 8.869369506835938] |
c09280ce-ae55-4c2d-ac88-472b09adc4c4 | spatio-temporal-crop-aggregation-for-video | 2211.17042 | null | https://arxiv.org/abs/2211.17042v2 | https://arxiv.org/pdf/2211.17042v2.pdf | Spatio-Temporal Crop Aggregation for Video Representation Learning | We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model builds long-range video features by learning from sets of video clip-level features extracted with a pre-trained backbone. To train the model,... | ['Paolo Favaro', 'Simon Jenni', 'Sepehr Sameni'] | 2022-11-30 | null | null | null | null | ['action-classification', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 5.12894511e-01 -5.66118099e-02 -4.44440931e-01 -3.39019984e-01
-1.09505785e+00 -4.48557615e-01 5.17585754e-01 -2.40270883e-01
-1.96530163e-01 4.48682278e-01 5.09909868e-01 2.50558615e-01
-4.73010167e-02 -6.04142308e-01 -1.34329557e+00 -6.23663306e-01
-4.93877918e-01 2.63497233e-01 5.16208448e-03 1.80394873... | [8.744112968444824, 0.7508679032325745] |
df4d0208-1387-4f39-995f-970dfa0a2206 | two-shot-video-object-segmentation | 2303.12078 | null | https://arxiv.org/abs/2303.12078v1 | https://arxiv.org/pdf/2303.12078v1.pdf | Two-shot Video Object Segmentation | Previous works on video object segmentation (VOS) are trained on densely annotated videos. Nevertheless, acquiring annotations in pixel level is expensive and time-consuming. In this work, we demonstrate the feasibility of training a satisfactory VOS model on sparsely annotated videos-we merely require two labeled fram... | ['Yan Lu', 'Ping Wang', 'Chenbin Zhang', 'Jinglu Wang', 'Fangyun Wei', 'Xiao Li', 'Kun Yan'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_Two-Shot_Video_Object_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Two-Shot_Video_Object_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-object-segmentation', 'video-semantic-segmentation', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.79776394e-01 6.88246489e-02 -5.14542699e-01 -5.22603393e-01
-8.98593664e-01 -5.65079927e-01 1.32977247e-01 -3.04531842e-01
-4.79476869e-01 5.95836639e-01 -1.47633761e-01 -1.11613683e-02
6.16188288e-01 -4.25332397e-01 -9.79932606e-01 -5.65737963e-01
3.58040422e-01 4.11920398e-01 6.65000558e-01 2.03053489... | [9.18018627166748, 0.06681721657514572] |
5f2c7d44-25ec-458e-aa0f-eff5f3a11e3c | knowledge-enhanced-attentive-learning-for | 1912.07915 | null | https://arxiv.org/abs/1912.07915v1 | https://arxiv.org/pdf/1912.07915v1.pdf | Knowledge-Enhanced Attentive Learning for Answer Selection in Community Question Answering Systems | In the community question answering (CQA) system, the answer selection task aims to identify the best answer for a specific question, and thus is playing a key role in enhancing the service quality through recommending appropriate answers for new questions. Recent advances in CQA answer selection focus on enhancing the... | ['Fengshi Jing', 'Qingpeng Zhang'] | 2019-12-17 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-3.68252367e-01 1.23750299e-01 2.62737311e-02 -1.43715203e-01
-6.01324141e-01 -5.18994868e-01 3.89435530e-01 5.12053072e-01
-3.80885988e-01 2.10807964e-01 7.40116239e-01 -2.33646885e-01
-5.56500137e-01 -9.06741202e-01 -1.39352724e-01 -6.57726943e-01
3.68561953e-01 5.80844879e-01 7.78974652e-01 -4.73843038... | [11.419778823852539, 7.976271152496338] |
f35fe6b2-a6a2-49dc-a8f7-5ae4307ce7c4 | novel-deep-learning-model-for-traffic-sign | 1805.04424 | null | http://arxiv.org/abs/1805.04424v1 | http://arxiv.org/pdf/1805.04424v1.pdf | Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks | Convolutional neural networks are the most widely used deep learning
algorithms for traffic signal classification till date but they fail to capture
pose, view, orientation of the images because of the intrinsic inability of max
pooling layer.This paper proposes a novel method for Traffic sign detection
using deep lear... | ['Amara Dinesh Kumar'] | 2018-05-11 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [-3.74642581e-01 -3.45624536e-01 4.98527549e-02 -4.47474986e-01
-2.68685043e-01 -8.27446759e-01 3.50907832e-01 -1.04501128e+00
-4.42088515e-01 2.77315438e-01 -3.51416916e-01 -3.62958133e-01
-7.16973469e-02 -5.98048151e-01 -8.06328356e-01 -9.96986210e-01
-3.30895066e-01 2.57630318e-01 8.06555867e-01 3.03339306... | [8.01069164276123, -0.8027552962303162] |
ebcc982b-1863-483d-ac97-a7e3c371f1cc | discomat-distantly-supervised-composition | 2207.01079 | null | https://arxiv.org/abs/2207.01079v3 | https://arxiv.org/pdf/2207.01079v3.pdf | DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles | A crucial component in the curation of KB for a scientific domain is information extraction from tables in the domain's published articles -- tables carry important information (often numeric), which must be adequately extracted for a comprehensive machine understanding of an article. Existing table extractors assume p... | ['Mausam', 'N. M. Anoop Krishnan', 'Mohd Zaki', 'Tanishq Gupta'] | 2022-07-03 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [ 2.35470966e-01 3.22436512e-01 -3.60793710e-01 -2.78832883e-01
-1.04728639e+00 -1.11026490e+00 3.89989257e-01 1.06059492e+00
-1.56844586e-01 9.22911763e-01 4.14232284e-01 -6.35585010e-01
1.16528600e-01 -9.28351104e-01 -1.22418523e+00 -1.24970347e-01
1.41123995e-01 9.24255073e-01 -8.18646327e-02 -2.57077247... | [9.608098030090332, 7.907163619995117] |
f859104f-c974-4aa0-9700-53eed032f935 | interpretable-simultaneous-localization-of | 2306.00473 | null | https://arxiv.org/abs/2306.00473v1 | https://arxiv.org/pdf/2306.00473v1.pdf | Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5 | Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kinds of inter-hemispheric communication. Thus, subtle changes in CC might be associated with various ne... | ['Sandhya M', 'Pramod Kumar Pal', 'Jitender Saini', 'Neelam Sinha', 'Debanjali Bhattacharya', 'Vamshi Krishna Kancharla'] | 2023-06-01 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-1.61205113e-01 2.37559676e-01 -9.66382548e-02 9.31675360e-03
-8.67695436e-02 -1.31069615e-01 6.09731257e-01 -1.95004195e-01
-2.58056760e-01 7.98158586e-01 4.69377041e-01 -6.20441139e-03
-2.32534319e-01 -4.30839479e-01 7.78451649e-05 -7.86898434e-01
-6.42272234e-01 5.27019083e-01 1.63627341e-01 1.25991497... | [14.100139617919922, -1.9926878213882446] |
b85d7caf-75f2-49d7-9a40-958e3f227452 | ladi-vton-latent-diffusion-textual-inversion | 2305.13501 | null | https://arxiv.org/abs/2305.13501v2 | https://arxiv.org/pdf/2305.13501v2.pdf | LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On | The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer experience. At the same time, recent advancements in the development of diffusion models have enabled generative networks to create remarkably realistic images. In this context, image-based virtual try... | ['Rita Cucchiara', 'Marco Bertini', 'Marcella Cornia', 'Giuseppe Cartella', 'Alberto Baldrati', 'Davide Morelli'] | 2023-05-22 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 2.14740455e-01 1.96917892e-01 -1.32372573e-01 -2.69242257e-01
-5.88055372e-01 -3.25978279e-01 8.71212602e-01 -4.14615512e-01
4.84371148e-02 2.94464141e-01 2.35466331e-01 7.85742104e-02
5.28786518e-02 -9.53203738e-01 -9.34365451e-01 -8.76234412e-01
1.58554778e-01 3.23267609e-01 -3.73450398e-01 -4.35234129... | [11.490551948547363, -0.5384437441825867] |
0f93e15f-d358-40ed-a612-8ede345e55ae | language-models-in-word-sense-disambiguation | 2111.13982 | null | https://arxiv.org/abs/2111.13982v1 | https://arxiv.org/pdf/2111.13982v1.pdf | Language models in word sense disambiguation for Polish | In the paper, we test two different approaches to the {unsupervised} word sense disambiguation task for Polish. In both methods, we use neural language models to predict words similar to those being disambiguated and, on the basis of these words, we predict the partition of word senses in different ways. In the first m... | ['Piotr Rychlik', 'Agnieszka A. Mykowiecka', 'Agnieszka Mykowiecka'] | 2021-11-27 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.96110755e-01 3.20577562e-01 -1.79983392e-01 -1.11106612e-01
-2.94977307e-01 -6.30681336e-01 6.21730983e-01 8.52166653e-01
-1.04743147e+00 9.73172605e-01 4.02116328e-01 -3.88928562e-01
-4.09902632e-01 -9.64103460e-01 7.64818192e-02 -7.25369811e-01
4.17737067e-01 7.90188491e-01 9.31978971e-02 -8.04542601... | [10.193241119384766, 9.201297760009766] |
f773823d-a146-4ea1-b6e7-1a9587c6997a | korc-knowledge-oriented-reading-comprehension | 2307.03115 | null | https://arxiv.org/abs/2307.03115v1 | https://arxiv.org/pdf/2307.03115v1.pdf | KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding | Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which lead... | ['Juanzi Li', 'Lei Hou', 'Jifan Yu', 'Shulin Cao', 'Xin Lv', 'Yantao Liu', 'Zijun Yao'] | 2023-07-06 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-2.56686926e-01 6.06457628e-02 -3.36828232e-01 -3.32717180e-01
-9.23840106e-01 -8.22523057e-01 3.25740516e-01 -9.88811031e-02
-4.83091921e-01 9.49291885e-01 3.03448558e-01 -2.89107561e-01
-2.41443634e-01 -7.83638239e-01 -6.05005324e-01 -3.23908091e-01
7.25220084e-01 7.42933810e-01 4.57259983e-01 -4.38158035... | [10.840435028076172, 8.153508186340332] |
3b4fb46a-70fc-4683-b713-d46195ce3fa7 | equitable-multi-task-learning | 2306.09373 | null | https://arxiv.org/abs/2306.09373v2 | https://arxiv.org/pdf/2306.09373v2.pdf | Equitable Multi-task Learning | Multi-task learning (MTL) has achieved great success in various research domains, such as CV, NLP and IR etc. Due to the complex and competing task correlation, naive training all tasks may lead to inequitable learning, i.e. some tasks are learned well while others are overlooked. Multi-task optimization (MTO) aims to ... | ['Rui Zhang', 'Jun Yuan'] | 2023-06-15 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 1.24053277e-01 -6.80200100e-01 -3.43867987e-01 -3.51003677e-01
-8.98763955e-01 -3.75231206e-01 9.76032242e-02 -2.10313872e-01
-4.10515577e-01 8.72513175e-01 1.16966985e-01 -1.07037142e-01
-6.68942034e-01 -1.58707276e-01 -5.99835992e-01 -8.13980639e-01
2.88850248e-01 2.49892846e-01 5.54683320e-02 -2.30268016... | [9.441699981689453, 4.093064785003662] |
28761f50-9fde-4b82-a7b7-cd9c2fd60195 | beyond-learned-metadata-based-raw-image | 2306.12058 | null | https://arxiv.org/abs/2306.12058v1 | https://arxiv.org/pdf/2306.12058v1.pdf | Beyond Learned Metadata-based Raw Image Reconstruction | While raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels, they are not widely adopted by general users due to their substantial storage requirements. Very recent studies propose to compress raw images by designing sampling masks within the pixel space of the raw i... | ['Bihan Wen', 'Alex C. Kot', 'Lap-Pui Chau', 'Lanqing Guo', 'Wenhan Yang', 'Yi Yu', 'YuFei Wang'] | 2023-06-21 | null | null | null | null | ['image-reconstruction', 'image-compression', 'quantization'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.59181988e-01 -3.71470302e-01 -4.38278139e-01 -3.11008066e-01
-4.32863355e-01 2.85665272e-03 2.32130423e-01 -3.30888145e-02
-2.43782356e-01 4.76276338e-01 3.39597315e-01 5.26974536e-02
-4.50109988e-01 -1.05713558e+00 -5.13864458e-01 -1.03330481e+00
1.89514130e-01 -2.57825702e-01 2.41646141e-01 -6.16257638... | [11.250225067138672, -1.6791279315948486] |
29c1b3a0-a18c-4b54-bd48-d692c7cf9db9 | concentrated-document-topic-model | 2102.04449 | null | https://arxiv.org/abs/2102.04449v1 | https://arxiv.org/pdf/2102.04449v1.pdf | Concentrated Document Topic Model | We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In particular, an exponential entropy penalty is imposed on the document topic distribution. Documents that have diverse topic distributions are pena... | ['Ying Chen', 'Hao Lei'] | 2021-02-06 | null | null | null | null | ['unsupervised-text-classification'] | ['natural-language-processing'] | [-1.03897631e-01 4.66445923e-01 -6.79389715e-01 -6.32397294e-01
-5.85699320e-01 -2.59844270e-02 8.65531564e-01 4.14912671e-01
-8.72403458e-02 7.01727867e-01 5.72319508e-01 1.06222175e-01
-3.65060978e-02 -8.61816108e-01 -3.61835212e-01 -1.05377793e+00
-1.61547828e-02 1.06939399e+00 -4.08524610e-02 4.33536649... | [10.392657279968262, 6.947778701782227] |
47a27570-4467-4690-8a3d-7daecfa55c22 | bert-lid-leveraging-bert-to-improve-spoken | 2203.00328 | null | https://arxiv.org/abs/2203.00328v3 | https://arxiv.org/pdf/2203.00328v3.pdf | BERT-LID: Leveraging BERT to Improve Spoken Language Identification | Language identification is the task of automatically determining the identity of a language conveyed by a spoken segment. It has a profound impact on the multilingual interoperability of an intelligent speech system. Despite language identification attaining high accuracy on medium or long utterances(>3s), the performa... | ['Jinfeng Bai', 'Wei-Qiang Zhang', 'Junhong Zhao', 'Yuting Nie'] | 2022-03-01 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-1.77562431e-01 -1.52068079e-01 -2.77959973e-01 -5.46864748e-01
-1.21738136e+00 -1.01075327e+00 5.67835927e-01 -2.10698709e-01
-6.96091175e-01 4.04311478e-01 1.28183410e-01 -7.96568990e-01
3.95210028e-01 -8.21748897e-02 -5.04363418e-01 -4.15509254e-01
1.63988158e-01 6.76207602e-01 3.47314551e-02 -1.66578859... | [14.180026054382324, 6.717308521270752] |
37c97c17-b0ed-439c-9c2f-4cc80e6be323 | end-to-end-clinical-event-extraction-from | 2208.09354 | null | https://arxiv.org/abs/2208.09354v1 | https://arxiv.org/pdf/2208.09354v1.pdf | End-to-end Clinical Event Extraction from Chinese Electronic Health Record | Event extraction is an important work of medical text processing. According to the complex characteristics of medical text annotation, we use the end-to-end event extraction model to enhance the output formatting information of events. Through pre training and fine-tuning, we can extract the attributes of the four dime... | ['Yun Liu', 'Huiting Sun', 'Yun Yu', 'Ruochen Huang', 'Wei Feng'] | 2022-08-19 | null | null | null | null | ['event-extraction', 'text-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.58877397e-01 2.38234118e-01 -3.05950373e-01 -3.22762400e-01
-7.42025018e-01 -2.06135556e-01 2.21877232e-01 6.99473977e-01
-8.30351532e-01 9.58366036e-01 5.33507526e-01 -4.09972072e-01
-9.58380699e-02 -8.48987162e-01 -1.35510638e-01 -4.17732537e-01
-2.36908108e-01 2.91943580e-01 2.19171032e-01 1.28237680... | [8.439409255981445, 8.738224983215332] |
fc97be20-31b5-463c-b7e1-54563276de43 | weak-shot-object-detection-through-mutual | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Du_Weak-Shot_Object_Detection_Through_Mutual_Knowledge_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Du_Weak-Shot_Object_Detection_Through_Mutual_Knowledge_Transfer_CVPR_2023_paper.pdf | Weak-Shot Object Detection Through Mutual Knowledge Transfer | Weak-shot Object Detection methods exploit a fully-annotated source dataset to facilitate the detection performance on the target dataset which only contains image-level labels for novel categories. To bridge the gap between these two datasets, we aim to transfer the object knowledge between the source (S) and targ... | ['Chen Li', 'Chong Sun', 'Weitao Wan', 'Xuanyi Du'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['multiple-instance-learning'] | ['methodology'] | [ 5.26507080e-01 3.84605438e-01 -2.30958834e-01 -3.67165089e-01
-1.07863188e+00 -4.10512447e-01 5.74785411e-01 1.34507805e-01
-4.60622400e-01 5.72000742e-01 -5.51373124e-01 2.69768357e-01
-7.20892623e-02 -8.22528839e-01 -9.13164258e-01 -9.56456184e-01
3.15424442e-01 2.77628511e-01 7.26047158e-01 2.23353729... | [9.354456901550293, 1.404369831085205] |
1b734fa6-bd43-4137-a979-85d54674c44c | rm-prt-realistic-robotic-manipulation | 2306.11335 | null | https://arxiv.org/abs/2306.11335v2 | https://arxiv.org/pdf/2306.11335v2.pdf | RM-PRT: Realistic Robotic Manipulation Simulator and Benchmark with Progressive Reasoning Tasks | Recently, the advent of pre-trained large-scale language models (LLMs) like ChatGPT and GPT-4 have significantly advanced the machine's natural language understanding capabilities. This breakthrough has allowed us to seamlessly integrate these open-source LLMs into a unified robot simulator environment to help robots a... | ['Xiaodan Liang', 'Mas Ma', 'Fengda Zhu', 'Yuhang Wen', 'Zixuan Li', 'Hetao Zheng', 'Kaidong Zhang', 'Pengzhen Ren'] | 2023-06-20 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-2.37600878e-01 2.16860086e-01 4.05169129e-02 -4.97151792e-01
-6.67154193e-01 -7.76524842e-01 5.11522174e-01 -1.79276407e-01
-8.07899088e-02 2.92887896e-01 2.06599265e-01 -4.96524841e-01
-2.08495557e-02 -8.00779700e-01 -1.15097880e+00 3.21431225e-03
-1.92123830e-01 9.14451003e-01 1.54869124e-01 -8.81870389... | [4.455013275146484, 0.7481858134269714] |
7d1a46e1-9155-4619-a4c8-f967b7a72304 | abusive-language-detection-with-graph | 1904.04073 | null | http://arxiv.org/abs/1904.04073v1 | http://arxiv.org/pdf/1904.04073v1.pdf | Abusive Language Detection with Graph Convolutional Networks | Abuse on the Internet represents a significant societal problem of our time.
Previous research on automated abusive language detection in Twitter has shown
that community-based profiling of users is a promising technique for this task.
However, existing approaches only capture shallow properties of online
communities b... | ['Ekaterina Shutova', 'Helen Yannakoudakis', 'Marco del Tredici', 'Pushkar Mishra'] | 2019-04-05 | abusive-language-detection-with-graph-1 | https://aclanthology.org/N19-1221 | https://aclanthology.org/N19-1221.pdf | naacl-2019-6 | ['abuse-detection'] | ['natural-language-processing'] | [-7.79590458e-02 7.21769556e-02 -6.05540276e-01 -1.18538633e-01
-4.67855856e-03 -8.77588868e-01 8.18723023e-01 9.00063097e-01
-2.36450374e-01 2.88029611e-01 3.78012508e-01 -4.85751331e-01
3.02916795e-01 -1.13773906e+00 -1.70897543e-01 -5.39891832e-02
-4.62722540e-01 5.43625295e-01 3.76020998e-01 -5.19854546... | [8.446728706359863, 10.339388847351074] |
c9b4050b-471b-490d-8e7a-975b7695cb2b | playing-fps-games-with-deep-reinforcement | 1609.05521 | null | http://arxiv.org/abs/1609.05521v2 | http://arxiv.org/pdf/1609.05521v2.pdf | Playing FPS Games with Deep Reinforcement Learning | Advances in deep reinforcement learning have allowed autonomous agents to
perform well on Atari games, often outperforming humans, using only raw pixels
to make their decisions. However, most of these games take place in 2D
environments that are fully observable to the agent. In this paper, we present
the first archite... | ['Devendra Singh Chaplot', 'Guillaume Lample'] | 2016-09-18 | null | null | null | null | ['game-of-doom', 'fps-games'] | ['playing-games', 'playing-games'] | [-7.59050250e-02 1.01403192e-01 3.00061852e-01 8.67447928e-02
-2.64402926e-01 -7.37565398e-01 6.19940758e-01 6.90350356e-03
-9.69269872e-01 7.09025264e-01 -4.23313886e-01 -1.30380571e-01
1.91997856e-01 -8.33963573e-01 -7.47692645e-01 -5.82846820e-01
-4.43466723e-01 7.97425807e-01 2.75345951e-01 -7.07767904... | [3.8398807048797607, 1.3932883739471436] |
651b322f-a917-46b8-afea-c1882c6f42c7 | closing-the-loop-fast-interactive-semi | null | null | https://aclanthology.org/D11-1136/ | https://aclanthology.org/D11-1136.pdf | Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances | This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e.g., words) and instances (e.g., documents). We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, a... | ['Burr Settles'] | 2011-07-01 | null | null | null | proceedings-of-the-2011-conference-on | ['information-extraction'] | ['natural-language-processing'] | [ 2.46623561e-01 5.70615232e-01 -6.40339136e-01 -6.56011462e-01
-1.32063043e+00 -1.01014841e+00 8.77936959e-01 6.76782072e-01
-5.83669543e-01 8.83472204e-01 -1.73489437e-01 -3.85227472e-01
1.35515286e-02 -3.58456552e-01 -3.76914740e-01 -4.38014597e-01
-3.07403803e-01 9.30411935e-01 2.01292634e-01 2.29685791... | [9.665583610534668, 4.468758583068848] |
a3d39a74-ba08-4897-88a1-b1ddef5b1004 | arb-sen-at-semeval-2018-task1-a-new-set-of | null | null | https://aclanthology.org/S18-1055 | https://aclanthology.org/S18-1055.pdf | ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets | This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a s... | ['El Moatez Billah Nagoudi'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-3.79553348e-01 1.15290940e-01 -1.63245544e-01 -9.25102711e-01
-6.41400099e-01 -6.68677509e-01 5.73750973e-01 6.20665252e-01
-7.61811554e-01 5.78761935e-01 5.67314208e-01 3.68533373e-01
4.07287419e-01 -6.48271203e-01 2.22276594e-03 -1.09726191e-01
8.76263976e-02 4.91458744e-01 -4.94089842e-01 -1.08412075... | [11.27458667755127, 6.881308555603027] |
0d547d52-13cd-4c84-9cdf-aced18ae27fe | grafenne-learning-on-graphs-with | 2306.03447 | null | https://arxiv.org/abs/2306.03447v1 | https://arxiv.org/pdf/2306.03447v1.pdf | GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets | Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice. Existing methods partly address this issue through feature imputation. However, these techniques (i) assume uniformity of feature set acro... | ['Srikanta Bedathur', 'Sayan Ranu', 'Sahil Manchanda', 'Shubham Gupta'] | 2023-06-06 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 3.60600829e-01 2.37479344e-01 -5.75059932e-03 -2.46048853e-01
-2.29870692e-01 -6.78255498e-01 6.22472405e-01 2.97309548e-01
-3.59815806e-01 8.46763194e-01 -2.75027126e-01 -3.43939841e-01
-5.54288566e-01 -1.31068063e+00 -1.01318824e+00 -6.94470406e-01
-3.93474251e-01 4.06857133e-01 5.95707782e-02 -3.45947146... | [6.9033331871032715, 6.032680511474609] |
6c8a0254-f1fc-48c6-83c2-1ef147d99d04 | cost-sensitive-bert-for-generalisable | null | null | https://aclanthology.org/D19-5018 | https://aclanthology.org/D19-5018.pdf | Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data | The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprisi... | ['Michael Castelle', 'Harish Tayyar Madabushi', 'Elena Kochkina'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 2.23506987e-01 -1.09217197e-01 -2.18801185e-01 -5.29217005e-01
-5.77184439e-01 -6.27090693e-01 1.17309070e+00 7.81000495e-01
-5.78446090e-01 6.99913323e-01 5.78099012e-01 -4.69650447e-01
-7.92667270e-02 -7.51954854e-01 -1.70138285e-01 -5.82104027e-01
-8.81263614e-02 5.83524227e-01 3.55877392e-02 -7.42988646... | [8.583970069885254, 10.378092765808105] |
9a4918b4-4523-4720-9372-3e9f8a7507af | d4ft-a-deep-learning-approach-to-kohn-sham | 2303.00399 | null | https://arxiv.org/abs/2303.00399v1 | https://arxiv.org/pdf/2303.00399v1.pdf | D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory | Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method. Behind the SCF loop is the physics intuition of solving a system of non-interactive single-electron wave functions under an effective potential. In this work, we propose a deep learning approach to KS-D... | ['Shuicheng Yan', 'Kostya S. Novoselov', 'A. H. Castro Neto', 'Kenji Kawaguchi', 'Giovanni Vignale', 'Kunhao Zheng', 'Zheyuan Hu', 'Min Lin', 'Tianbo Li'] | 2023-03-01 | null | null | null | null | ['numerical-integration', 'total-energy'] | ['miscellaneous', 'miscellaneous'] | [ 8.68143514e-02 -3.69265825e-01 1.59698695e-01 -3.42699468e-01
-9.76938128e-01 -1.72730893e-01 3.58277321e-01 1.00885583e-02
-7.31931150e-01 1.31500280e+00 -2.55205482e-01 -4.04792398e-01
-3.48358974e-02 -9.01984632e-01 -8.21788251e-01 -1.15800393e+00
-6.63544312e-02 3.28551352e-01 -6.87793344e-02 -1.69906721... | [5.419877052307129, 5.066863536834717] |
f4ca912b-ab9a-4f37-bddb-a65a8cf872ce | alignot-an-optimal-transport-based-algorithm | 2210.09361 | null | https://arxiv.org/abs/2210.09361v1 | https://arxiv.org/pdf/2210.09361v1.pdf | AlignOT: An optimal transport based algorithm for fast 3D alignment with applications to cryogenic electron microscopy density maps | Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which... | ['K. Dao Duc', 'A. Condon', 'F. Poitevin', 'G. Woollard', 'A. Tajmir Riahi'] | 2022-10-17 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 5.55555075e-02 -4.04540390e-01 9.24213827e-02 -3.63323510e-01
-8.26308489e-01 -7.13862836e-01 6.74419224e-01 2.78655171e-01
-6.57911062e-01 8.13578606e-01 -1.49481595e-01 -4.45280224e-01
-4.90037017e-02 -2.96912163e-01 -6.74465179e-01 -8.54775906e-01
-1.12121820e-01 9.13757026e-01 3.49809527e-01 9.88623966... | [13.300750732421875, -3.065548896789551] |
57e51737-2919-4ca2-bcdc-01f408c0e0dd | aiatrack-attention-in-attention-for | 2207.09603 | null | https://arxiv.org/abs/2207.09603v2 | https://arxiv.org/pdf/2207.09603v2.pdf | AiATrack: Attention in Attention for Transformer Visual Tracking | Transformer trackers have achieved impressive advancements recently, where the attention mechanism plays an important role. However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement. To address this i... | ['Junsong Yuan', 'Xinggang Wang', 'Chao Ma', 'Chunluan Zhou', 'Shenyuan Gao'] | 2022-07-20 | null | null | null | null | ['visual-tracking', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.38563359e-01 -5.78316450e-01 -2.37890244e-01 -1.51414782e-01
-5.77618241e-01 -2.58501172e-01 7.16503799e-01 -1.29804075e-01
-2.85333425e-01 2.32330456e-01 3.78609061e-01 -7.86759555e-02
7.50666335e-02 -5.51849961e-01 -5.29171169e-01 -5.95996797e-01
2.77518528e-03 8.54658112e-02 6.79366410e-01 -1.41345650... | [6.275995254516602, -2.128560781478882] |
49cabfc7-1150-422e-ad18-cb3ef4ef489d | towards-robust-text-prompted-semantic | 2304.14672 | null | https://arxiv.org/abs/2304.14672v1 | https://arxiv.org/pdf/2304.14672v1.pdf | Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment | The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies. Contemporary supervised opinion-driven VQA strategies predominantly hinge on training from expensive human annotations for quality scores, which limited the sca... | ['Weisi Lin', 'Qiong Yan', 'Wenxiu Sun', 'Jingwen Hou', 'Chaofeng Chen', 'Annan Wang', 'Liang Liao', 'HaoNing Wu'] | 2023-04-28 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 2.63885617e-01 -4.70140874e-01 7.35540986e-02 -4.16323513e-01
-1.11262536e+00 -7.01861441e-01 5.13472736e-01 1.02506116e-01
-4.23745304e-01 3.48884851e-01 5.16201913e-01 -1.09699339e-01
-4.32237685e-01 -4.62728590e-01 -4.69709963e-01 -5.72319508e-01
6.62726611e-02 -7.60260373e-02 1.85080752e-01 -3.83527607... | [11.815016746520996, -1.8229495286941528] |
17a5d936-5f8e-4fd5-a8d5-8f269ac728fc | fully-self-supervised-learning-for-semantic | 2202.11981 | null | https://arxiv.org/abs/2202.11981v1 | https://arxiv.org/pdf/2202.11981v1.pdf | Fully Self-Supervised Learning for Semantic Segmentation | In this work, we present a fully self-supervised framework for semantic segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation, which saves efforts for the huge amount of annotation, is crucial for building customized models from end-to-end for open-world domains. This application is eagerly needed... | ['Wenwu Zhu', 'Qi Ju', 'Zhi Wang', 'Yucong Li', 'Wei Zhuo', 'YuAn Wang'] | 2022-02-24 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 3.61191392e-01 4.45444942e-01 5.64470403e-02 -5.44950604e-01
-5.55523634e-01 -5.25886536e-01 3.28843832e-01 6.45133555e-02
-3.13299537e-01 4.48350817e-01 -1.06255271e-01 1.03293180e-01
1.26619980e-01 -9.35726762e-01 -7.74031520e-01 -7.01711595e-01
1.47097364e-01 6.22624874e-01 9.62157190e-01 -1.23610824... | [9.632708549499512, 0.5910286903381348] |
c8c9660a-4bae-42f4-af5e-570ed3b6ab64 | neural-monocular-3d-human-motion-capture-with | 2105.01057 | null | https://arxiv.org/abs/2105.01057v1 | https://arxiv.org/pdf/2105.01057v1.pdf | Neural Monocular 3D Human Motion Capture with Physical Awareness | We present a new trainable system for physically plausible markerless 3D human motion capture, which achieves state-of-the-art results in a broad range of challenging scenarios. Unlike most neural methods for human motion capture, our approach, which we dub physionical, is aware of physical and environmental constraint... | ['Christian Theobalt', 'Patrick Pérez', 'Weipeng Xu', 'Vladislav Golyanik', 'Soshi Shimada'] | 2021-05-03 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.04078546e-01 2.30953291e-01 -3.92084010e-02 1.53147861e-01
-6.92027211e-01 -5.83061695e-01 5.47467470e-01 -3.85048062e-01
-5.97803712e-01 6.83926702e-01 1.19953915e-01 -8.64241272e-02
-1.01411760e-01 -3.03547442e-01 -9.79341030e-01 -6.61964893e-01
-1.82642445e-01 6.79431558e-01 4.75460827e-01 -5.11806250... | [7.12580680847168, -0.7799766063690186] |
cef59fac-18aa-4efc-8c0e-a1bb0d145b7b | exploiting-hybrid-models-of-tensor-train | 2201.10609 | null | https://arxiv.org/abs/2201.10609v1 | https://arxiv.org/pdf/2201.10609v1.pdf | Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition | This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a deep hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC pipeline. Our comma... | ['Javier Tejedor', 'Jun Qi'] | 2022-01-11 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [-1.07968085e-01 -1.50053412e-01 -5.27498201e-02 -6.28802896e-01
-8.42869997e-01 -5.22948086e-01 2.85770327e-01 -6.41786516e-01
-6.10325158e-01 -7.68371820e-02 2.85503358e-01 -5.61081588e-01
6.01654291e-01 -3.02774400e-01 -6.22092009e-01 -6.21500552e-01
4.50980477e-02 1.33105993e-01 1.08506233e-01 -1.98754951... | [14.271072387695312, 6.313569068908691] |
cc7d47f9-72b8-4aed-832e-12d970a878c5 | deep-imitator-handwriting-calligraphy | null | null | https://www.sciencedirect.com/science/article/pii/S0031320319303814?via%3Dihub | https://www.sciencedirect.com/science/article/pii/S0031320319303814?via%3Dihub | Deep imitator: Handwriting calligraphy imitation via deep attention networks | Calligraphy imitation (CI) from a handful of target handwriting samples is such a challenging task that most of the existing writing style analysis or handwriting generation methods do not exhibit satisfactory performance. In this paper, we propose a novel multi-module framework to address the problem of CI. Firstly, w... | ['Ye Bai', 'Cunhang Fan', 'Zhengkun Tian', 'Minghao Yang', 'JianHua Tao', 'Bocheng Zhao'] | 2020-08-01 | null | null | null | pattern-recognition-2020-8 | ['deep-attention', 'handwriting-generation', 'deep-attention'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.37320676e-01 -3.43471378e-01 -2.62686431e-01 -2.34386846e-01
-5.79498827e-01 -7.18167245e-01 8.44444811e-01 -8.03869843e-01
7.34721264e-03 6.19453609e-01 3.85524929e-01 -3.17006022e-01
-7.90470243e-02 -6.57817960e-01 -7.23505199e-01 -7.52610147e-01
7.59092510e-01 4.97533917e-01 -2.14038074e-01 7.07281055... | [11.911980628967285, 2.252321720123291] |
9a73e483-8678-46c1-bbdf-8dd2ebd67e4e | danish-stance-classification-and-rumour | 1907.01304 | null | https://arxiv.org/abs/1907.01304v1 | https://arxiv.org/pdf/1907.01304v1.pdf | Danish Stance Classification and Rumour Resolution | The Internet is rife with flourishing rumours that spread through microblogs and social media. Recent work has shown that analysing the stance of the crowd towards a rumour is a good indicator for its veracity. One state-of-the-art system uses an LSTM neural network to automatically classify stance for posts on Twitter... | ['Anders Edelbo Lillie', 'Emil Refsgaard Middelboe'] | 2019-07-02 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.43091035e-01 3.81478459e-01 -3.61842722e-01 -1.68622226e-01
-2.41400018e-01 -2.62791425e-01 9.85668838e-01 5.09746850e-01
-2.32925236e-01 8.53188872e-01 2.08858147e-01 -3.96474212e-01
3.57258111e-01 -1.02967906e+00 -2.43967667e-01 -6.08915210e-01
1.07277632e-01 5.46491146e-01 3.80478978e-01 -6.18038177... | [8.24662971496582, 10.120420455932617] |
c57e27a5-78ad-43d7-9614-ff00cc308922 | leveraging-code-generation-to-improve-code | 2002.10198 | null | https://arxiv.org/abs/2002.10198v2 | https://arxiv.org/pdf/2002.10198v2.pdf | Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning | Code summarization generates brief natural language description given a source code snippet, while code retrieval fetches relevant source code given a natural language query. Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to ... | ['Xiaoyin Wang', 'Tianxiang Hu', 'Shikun Zhang', 'Wei Ye', 'Rui Xie', 'Jinglei Zhang'] | 2020-02-24 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 2.24975899e-01 -9.46651474e-02 -3.65313202e-01 -2.83555597e-01
-1.40988696e+00 -5.64445257e-01 5.20102143e-01 4.17991728e-01
-1.38152584e-01 9.07992572e-02 4.88013923e-01 -4.40768600e-01
4.03781652e-01 -4.77736503e-01 -6.91734433e-01 -7.86153376e-02
-7.86547177e-03 1.87345482e-02 2.52219409e-01 -1.99415293... | [7.626534938812256, 7.94273042678833] |
bebbf2d5-8e51-4910-80e9-c11cc45fea54 | bodiffusion-diffusing-sparse-observations-for | 2304.11118 | null | https://arxiv.org/abs/2304.11118v1 | https://arxiv.org/pdf/2304.11118v1.pdf | BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis | Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand movements, leading to a limited reconstruction of full-body motion due to variability in lower body configurations. We propose BoDiffusion -- a ge... | ['Artsiom Sanakoyeu', 'Ali Thabet', 'Pablo Arbeláez', 'Albert Pumarola', 'Guillaume Jeanneret', 'Maria Escobar', 'Angela Castillo'] | 2023-04-21 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-1.31367013e-01 -1.63758837e-03 -2.16590628e-01 -4.13596779e-02
-8.35304022e-01 -3.79679054e-01 6.14120364e-01 -8.86367321e-01
-1.29987046e-01 7.91489780e-01 5.75721502e-01 -5.70647009e-02
2.87389427e-01 -3.70375663e-01 -7.28570998e-01 -4.31987762e-01
-6.05451781e-03 3.69611233e-01 1.30922750e-01 -1.05539158... | [7.192646026611328, -0.5321860313415527] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.