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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7b2a74f2-070f-4f04-82dd-ac07dd4cd3c7 | handling-divergent-reference-texts-when | 1906.01081 | null | https://arxiv.org/abs/1906.01081v1 | https://arxiv.org/pdf/1906.01081v1.pdf | Handling Divergent Reference Texts when Evaluating Table-to-Text Generation | Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio, often contain reference texts that diverge from the information in the corresponding semi-structured data. We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlat... | ['Ming-Wei Chang', 'Manaal Faruqui', 'Dipanjan Das', 'Ankur Parikh', 'William W. Cohen', 'Bhuwan Dhingra'] | 2019-06-03 | handling-divergent-reference-texts-when-1 | https://aclanthology.org/P19-1483 | https://aclanthology.org/P19-1483.pdf | acl-2019-7 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.27445239e-01 8.37763250e-01 -1.13228681e-02 -2.97684848e-01
-1.26337767e+00 -9.12108779e-01 1.16385293e+00 6.27378047e-01
-5.11751592e-01 1.13914406e+00 8.62544298e-01 -5.08116521e-02
-8.07176977e-02 -8.74191582e-01 -6.53470397e-01 -5.54929003e-02
3.79259020e-01 1.07369161e+00 1.39697060e-01 -4.26707059... | [11.64137077331543, 8.996068000793457] |
109a6273-52b1-4aef-8a38-5a095c434a60 | anchor-changing-regularized-natural-policy | 2206.05357 | null | https://arxiv.org/abs/2206.05357v2 | https://arxiv.org/pdf/2206.05357v2.pdf | Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning | We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off. We propose an Anchor-changing Reg... | ['Chao Tian', 'P. R. Kumar', 'Dileep Kalathil', 'Tao Liu', 'Ruida Zhou'] | 2022-06-10 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-9.17489231e-02 6.33186698e-02 -8.13310683e-01 -3.77081990e-01
-8.73131335e-01 -3.37953299e-01 3.56366992e-01 1.98017031e-01
-8.24179590e-01 1.38671052e+00 2.06372008e-01 -7.49983728e-01
-3.70251298e-01 -3.71143341e-01 -3.14728349e-01 -7.72724330e-01
-3.71112168e-01 5.68141639e-01 -1.18217327e-01 -4.43760529... | [4.266066551208496, 2.6716315746307373] |
01699593-235c-40e6-bc47-1706abe6a9f2 | a-comprehensive-evaluation-of-chatgpt-s-zero | 2303.13547 | null | https://arxiv.org/abs/2303.13547v1 | https://arxiv.org/pdf/2303.13547v1.pdf | A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability | This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted exp... | ['Philip S. Yu', 'Lijie Wen', 'Xuming Hu', 'Aiwei Liu'] | 2023-03-12 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-3.54516625e-01 1.17939897e-01 -1.22256301e-01 -3.26676130e-01
-1.06732607e+00 -6.24075651e-01 9.08337653e-01 4.08357605e-02
-2.38310024e-02 5.77186406e-01 4.59025234e-01 -6.36775374e-01
6.15961328e-02 -7.87402749e-01 -4.68275189e-01 -2.28340104e-01
-2.79426515e-01 8.16103578e-01 2.23342896e-01 -5.93765914... | [11.885993003845215, 8.286605834960938] |
158bd532-24ac-4966-b771-61cefd6357e5 | target-adaptive-cnn-based-pansharpening | 1709.06054 | null | http://arxiv.org/abs/1709.06054v3 | http://arxiv.org/pdf/1709.06054v3.pdf | Target-adaptive CNN-based pansharpening | We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight networ... | ['Sergio Vitale', 'Giuseppe Scarpa', 'Davide Cozzolino'] | 2017-09-18 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.40421176e-01 -2.17162684e-01 4.48513292e-02 -3.33931774e-01
-5.32558501e-01 -2.51041383e-01 2.11864322e-01 -9.98118743e-02
-6.50617599e-01 2.77300388e-01 -2.42506042e-01 -4.32649642e-01
-1.94155827e-01 -1.25321627e+00 -9.66568172e-01 -7.64573872e-01
-6.80656806e-02 -1.29840925e-01 4.32444841e-01 -4.79220808... | [9.916291236877441, -1.796412467956543] |
31be16b0-59cb-4cc2-904d-466f4a8b0e44 | artgraph-towards-an-artistic-knowledge-graph | 2105.15028 | null | https://arxiv.org/abs/2105.15028v2 | https://arxiv.org/pdf/2105.15028v2.pdf | Integrating Contextual Knowledge to Visual Features for Fine Art Classification | Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata and textual comments. A knowledge graph that integrates a rich body of i... | ['Gennaro Vessio', 'Giovanni Sansaro', 'Giovanna Castellano'] | 2021-05-31 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [-1.27703369e-01 -1.01824552e-01 -3.28648895e-01 -2.31650203e-01
-3.20991711e-03 -6.13486469e-01 7.73959994e-01 3.80920768e-01
-7.86220431e-02 6.80826008e-01 1.73309475e-01 1.71863988e-01
-3.32171440e-01 -1.38262045e+00 -4.67977494e-01 -4.07203317e-01
4.05956388e-01 5.60184658e-01 2.11886883e-01 -1.17435917... | [11.237228393554688, 0.4804358184337616] |
e079e46c-8574-4175-9204-19291aa2d2da | federated-transfer-ordered-personalized | 2301.04829 | null | https://arxiv.org/abs/2301.04829v2 | https://arxiv.org/pdf/2301.04829v2.pdf | Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application | Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model parameters trained on local data. Although FL has been successfully applied to various domains, including driver monitoring applications (DMAs... | ['Ziran Wang', 'Lu Su', 'Liangqi Yuan'] | 2023-01-12 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-3.67634177e-01 -1.89319879e-01 -6.25725865e-01 -5.88012934e-01
-6.48222268e-01 -5.17965317e-01 6.71052039e-01 -3.59713078e-01
-3.10112834e-01 7.06744134e-01 -2.33266726e-02 -6.95062578e-01
-2.67292351e-01 -6.20327115e-01 -6.07221961e-01 -8.67248893e-01
-9.53944400e-03 3.00274700e-01 6.52365983e-01 6.55166656... | [5.844934940338135, 6.4198174476623535] |
a5e8e6e8-5679-43e8-a012-7461212a400e | pystachio-python-single-molecule-tracking | 2103.10164 | null | https://arxiv.org/abs/2103.10164v3 | https://arxiv.org/pdf/2103.10164v3.pdf | PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy | As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microcopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some ... | ['Mark C Leake', 'Adam J M Wollman', 'Ed J Higgins', 'Jack W Shepherd'] | 2021-03-18 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.87413865e-01 -7.79566109e-01 3.97602677e-01 -6.33572638e-02
-8.39624465e-01 -1.12980223e+00 3.06284696e-01 3.96776080e-01
-9.32915092e-01 1.00069416e+00 -5.05582213e-01 -4.53196347e-01
1.05755664e-01 -4.74499822e-01 -5.57871282e-01 -1.06309199e+00
-8.66104513e-02 7.69228935e-01 4.96011347e-01 2.30785578... | [13.940164566040039, -3.093432664871216] |
1369fb58-2ee0-4752-9441-df7242fbf395 | wlv-rit-at-semeval-2021-task-5-a-neural | 2104.04630 | null | https://arxiv.org/abs/2104.04630v3 | https://arxiv.org/pdf/2104.04630v3.pdf | WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans | In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While v... | ['Alexander Ororbia', 'Marcos Zampieri', 'Diptanu Sarkar', 'Tharindu Ranasinghe'] | 2021-04-09 | null | https://aclanthology.org/2021.semeval-1.111 | https://aclanthology.org/2021.semeval-1.111.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-6.22748509e-02 -3.05218622e-02 1.84110589e-02 -9.28780138e-02
-1.30714321e+00 -5.45721352e-01 3.48885119e-01 4.83222395e-01
-6.54452682e-01 4.63267893e-01 6.54480398e-01 2.28346027e-02
3.45218092e-01 -5.37820756e-01 -4.58006173e-01 -1.27591461e-01
-5.02891131e-02 1.46779060e-01 -1.61853917e-02 -5.94202220... | [8.908026695251465, 10.625113487243652] |
069c49a2-efba-44b0-bbc2-2a9d721c772a | a-benchmark-of-nested-named-entity | 2302.10204 | null | https://arxiv.org/abs/2302.10204v1 | https://arxiv.org/pdf/2302.10204v1.pdf | A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents | Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities often are nested. For example, a postal address might contain a street name and a number. This work compares three nested NER ap... | ['Edwin Carlinet', 'Bertrand Duménieu', 'J Chazalon', 'Nathalie Abadie', 'Solenn Tual'] | 2023-02-20 | null | null | null | null | ['unsupervised-pre-training', 'nested-named-entity-recognition'] | ['methodology', 'natural-language-processing'] | [-2.01651022e-01 3.29937786e-01 1.61156863e-01 -4.42204863e-01
-1.03179634e+00 -9.82032120e-01 8.02381039e-01 6.10472083e-01
-1.02330554e+00 8.60674798e-01 5.59951723e-01 -3.21522415e-01
-2.04528525e-01 -8.25441778e-01 -5.90655684e-01 -1.42465666e-01
-2.10799739e-01 7.67457128e-01 5.21715105e-01 -4.18858588... | [9.698198318481445, 9.494241714477539] |
fc1f87b3-0b49-4aac-a526-5dc89a15c3e1 | weakly-supervised-knowledge-transfer-with | 2303.05148 | null | https://arxiv.org/abs/2303.05148v1 | https://arxiv.org/pdf/2303.05148v1.pdf | Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection | Training object detection models usually requires instance-level annotations, such as the positions and labels of all objects present in each image. Such supervision is unfortunately not always available and, more often, only image-level information is provided, also known as weak supervision. Recent works have address... | ['Edward De Brouwer', 'Yves Moreau', 'Adam Arany', 'Martijn Oldenhof'] | 2023-03-09 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 3.31191570e-01 4.23845738e-01 -4.09968436e-01 -5.73270023e-01
-5.74023902e-01 -7.43642569e-01 7.47321069e-01 2.66179740e-01
-4.42168921e-01 7.33380854e-01 -2.88935304e-01 -4.90255862e-01
7.61241466e-02 -7.92236865e-01 -1.15338957e+00 -3.66429001e-01
1.20437369e-01 4.42825139e-01 9.28823590e-01 -1.05051054... | [9.424728393554688, 1.0603950023651123] |
8941654b-864a-4a22-85e6-feca0dadb83d | can-chatgpt-reproduce-human-generated-labels | 2304.10145 | null | https://arxiv.org/abs/2304.10145v2 | https://arxiv.org/pdf/2304.10145v2.pdf | Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks | The release of ChatGPT has uncovered a range of possibilities whereby large language models (LLMs) can substitute human intelligence. In this paper, we seek to understand whether ChatGPT has the potential to reproduce human-generated label annotations in social computing tasks. Such an achievement could significantly r... | ['Gareth Tyson', 'Pan Hui', 'Ehsan-Ul Haq', 'Peixian Zhang', 'Yiming Zhu'] | 2023-04-20 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-5.79822250e-02 5.84904015e-01 -2.26764694e-01 -4.68921036e-01
-7.58422971e-01 -8.22176158e-01 8.39541674e-01 3.48573536e-01
-5.69771886e-01 6.18081391e-01 1.77167863e-01 -3.37522656e-01
5.27717233e-01 -2.64003932e-01 -2.16611326e-02 -4.27546024e-01
1.19984940e-01 3.75718474e-01 2.50975907e-01 -3.19809884... | [9.161910057067871, 10.097925186157227] |
6fd3efca-065d-400f-a967-2d3519bbb3c3 | hierarchical-reinforcement-learning-with-4 | 2206.12718 | null | https://arxiv.org/abs/2206.12718v1 | https://arxiv.org/pdf/2206.12718v1.pdf | Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation | Many real-world applications can be formulated as multi-agent cooperation problems, such as network packet routing and coordination of autonomous vehicles. The emergence of deep reinforcement learning (DRL) provides a promising approach for multi-agent cooperation through the interaction of the agents and environments.... | ['Huafeng Xu', 'Divya Saxena', 'Shan Jiang', 'Jiannong Cao', 'Zhixuan Liang'] | 2022-06-25 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-4.79232848e-01 1.74743887e-02 -2.39737898e-01 4.36637998e-02
-5.77482045e-01 -3.30030084e-01 5.00972867e-01 3.07137966e-01
-9.42457616e-01 1.13249338e+00 -4.22143638e-01 -2.29054585e-01
-4.57692325e-01 -8.21851671e-01 -5.44515491e-01 -1.02487552e+00
-3.15778553e-01 5.79719007e-01 6.12091362e-01 -4.68289822... | [3.76753830909729, 1.9860918521881104] |
fc4a0467-0d57-4457-8962-1c5701b33d13 | rehearsal-free-online-continual-learning-for | 2306.10860 | null | https://arxiv.org/abs/2306.10860v1 | https://arxiv.org/pdf/2306.10860v1.pdf | Rehearsal-Free Online Continual Learning for Automatic Speech Recognition | Fine-tuning an Automatic Speech Recognition (ASR) model to new domains results in degradation on original domains, referred to as Catastrophic Forgetting (CF). Continual Learning (CL) attempts to train ASR models without suffering from CF. While in ASR, offline CL is usually considered, online CL is a more realistic bu... | ['Hugo Van hamme', 'Steven Vander Eeckt'] | 2023-06-19 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 3.41690779e-01 -1.02946617e-01 1.29984215e-01 -3.02988023e-01
-7.99010873e-01 -4.61964399e-01 8.21933091e-01 -1.85904354e-02
-6.43406570e-01 7.52174556e-01 3.72469097e-01 -4.31453824e-01
1.93209067e-01 -3.24585199e-01 -6.25013769e-01 -4.64785755e-01
2.97435373e-01 4.80514646e-01 4.58400279e-01 -2.84026712... | [14.429802894592285, 6.695429801940918] |
a12260bc-9485-4d1f-8480-73def9700a86 | learning-6-dof-grasping-interaction-via-deep | 1708.07303 | null | http://arxiv.org/abs/1708.07303v4 | http://arxiv.org/pdf/1708.07303v4.pdf | Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations | This paper focuses on the problem of learning 6-DOF grasping with a parallel
jaw gripper in simulation. We propose the notion of a geometry-aware
representation in grasping based on the assumption that knowledge of 3D
geometry is at the heart of interaction. Our key idea is constraining and
regularizing grasping intera... | ['James Davidson', 'Yunfei Bai', 'Xinchen Yan', 'Mohi Khansari', 'Honglak Lee', 'Abhinav Gupta', 'Jasmine Hsu', 'Arkanath Pathak'] | 2017-08-24 | null | null | null | null | ['3d-shape-modeling'] | ['computer-vision'] | [ 1.03907183e-01 3.72678041e-01 5.46809845e-02 -4.79744077e-01
-5.00052154e-01 -6.60371602e-01 3.23386699e-01 3.06817647e-02
1.02029018e-01 2.67337292e-01 3.89272809e-01 -5.26186042e-02
-3.20549816e-01 -7.98229754e-01 -1.55598855e+00 -5.46298862e-01
-3.53860766e-01 7.58552909e-01 -1.54432103e-01 -2.68592447... | [5.763444900512695, -0.8376744389533997] |
1661f468-4c88-4f87-b77a-035006949cab | sat-improving-semi-supervised-text | 2210.12653 | null | https://arxiv.org/abs/2210.12653v1 | https://arxiv.org/pdf/2210.12653v1.pdf | SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training | Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning. This work presents a Simple instance-Adaptive self-Training method (SAT) for semi-supervised text classification. SAT first generates two augmented views for each unlabeled data and then ... | ['Soujanya Poria', 'Wei Han', 'Hui Chen'] | 2022-10-23 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 4.36947405e-01 4.67475653e-01 -7.36803114e-01 -8.30836952e-01
-8.45892489e-01 -5.43421984e-01 7.04986334e-01 1.82948485e-01
-2.44158596e-01 7.16165304e-01 3.33539784e-01 -3.05509150e-01
4.68035042e-01 -2.56228715e-01 -4.14920926e-01 -4.59678829e-01
4.02305722e-01 9.71063077e-01 1.04039431e-01 1.23696744... | [9.716093063354492, 4.002109527587891] |
3833bc64-55ca-401a-b9b4-f73769118f6c | classification-of-perceived-human-stress | 1905.06384 | null | https://arxiv.org/abs/1905.06384v1 | https://arxiv.org/pdf/1905.06384v1.pdf | Classification of Perceived Human Stress using Physiological Signals | In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, fe... | ['Aamir Arsalan', 'Ulas Bagci', 'Syed Muhammad Anwar', 'Muhammad Majid'] | 2019-05-13 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 2.80257612e-01 -3.83529484e-01 2.49208227e-01 -5.72043657e-01
5.63994646e-02 -1.61012277e-01 -5.94499670e-02 3.40598702e-01
-6.81160688e-01 1.04432023e+00 -5.34606678e-03 -4.05418724e-02
-4.52804007e-02 -1.42801896e-01 5.59685007e-02 -5.84085524e-01
-2.98181087e-01 -8.13666582e-01 -3.21482927e-01 1.95866581... | [13.506820678710938, 3.083127737045288] |
dafe45f1-2288-48ae-a072-2e6a6cc14b19 | deep-spatial-transformation-for-pose-guided | 2008.12606 | null | https://arxiv.org/abs/2008.12606v1 | https://arxiv.org/pdf/2008.12606v1.pdf | Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation | Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable globa... | ['Thomas H. Li', 'Shan Liu', 'Ge Li', 'Yurui Ren'] | 2020-08-27 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 1.58899084e-01 -3.90836261e-02 4.34361696e-02 -3.04332048e-01
-5.35511434e-01 -4.69128698e-01 6.70182586e-01 -7.39601672e-01
-3.61476503e-02 7.62875319e-01 4.14583594e-01 2.56584048e-01
1.24181472e-01 -7.71606743e-01 -9.44587588e-01 -5.75742841e-01
2.95895606e-01 -4.81638759e-02 -1.58117488e-01 -8.10559988... | [11.064483642578125, -0.8715901374816895] |
d81aca74-9fff-4f90-96c7-26e98b67183e | coarse-to-fine-video-retrieval-before-moment | 2110.07201 | null | https://arxiv.org/abs/2110.07201v1 | https://arxiv.org/pdf/2110.07201v1.pdf | Coarse to Fine: Video Retrieval before Moment Localization | The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed. However, late fusion methods like cosine similarity alignment are unable to make full use of the information from both query texts and videos. In thi... | ['Jingyu Liu', 'Huanyu Liu', 'Zijian Gao'] | 2021-10-14 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-8.50567371e-02 -9.85193908e-01 -3.55902523e-01 -2.09973797e-01
-9.72313583e-01 -2.36837387e-01 9.38883007e-01 1.55371815e-01
-4.95534062e-01 3.11687499e-01 2.75915742e-01 6.45958781e-02
-3.13723296e-01 -4.21355158e-01 3.19361617e-03 -5.54910541e-01
6.48597702e-02 -3.17963064e-02 4.83040750e-01 -3.96907240... | [10.28172492980957, 0.7766464948654175] |
aed53665-9748-48e7-887e-85d63f1cb17a | a-multilingual-study-of-multi-sentence | 2004.04468 | null | https://arxiv.org/abs/2004.04468v1 | https://arxiv.org/pdf/2004.04468v1.pdf | A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming | Multi-Sentence Compression (MSC) aims to generate a short sentence with the key information from a cluster of similar sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes an Integer Linear Programming... | ['Andréa Carneiro Linhares', 'Juan-Manuel Torres-Moreno', 'Stéphane Huet', 'Elvys Linhares Pontes', 'Thiago G. da Silva'] | 2020-04-09 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 5.04346073e-01 4.82537478e-01 -5.44407181e-02 -3.23788017e-01
-1.28117883e+00 -6.80251062e-01 5.81529438e-01 8.44850183e-01
-3.84375155e-01 1.13370335e+00 8.16264331e-01 -1.53012633e-01
-2.50726342e-01 -7.59370983e-01 -5.52707732e-01 -2.91932583e-01
-4.42312062e-02 7.66922355e-01 1.25125289e-01 -4.29239839... | [12.374893188476562, 9.518216133117676] |
93f37658-2376-4e9c-a179-de305e317599 | causal-discovery-from-subsampled-time-series-1 | 2305.05276 | null | https://arxiv.org/abs/2305.05276v2 | https://arxiv.org/pdf/2305.05276v2.pdf | Causal Discovery from Subsampled Time Series with Proxy Variables | Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the frequency of measurement is much lower than that of causal influence. To overcome this problem, numerous methods have been proposed, yet eith... | ['Yizhou Wang', 'Lingjing Hu', 'Xinwei Sun', 'Mingzhou Liu'] | 2023-05-09 | null | null | null | null | ['causal-discovery', 'causal-identification'] | ['knowledge-base', 'reasoning'] | [ 2.36500368e-01 1.62125036e-01 -6.97557628e-01 -1.46136463e-01
-4.29324389e-01 -3.59409660e-01 4.97389704e-01 -2.26761356e-01
1.50586531e-01 1.21787608e+00 5.69877088e-01 -2.66804069e-01
-5.25848329e-01 -8.72526169e-01 -7.55758405e-01 -7.31474221e-01
-3.12638670e-01 1.31443307e-01 -1.88337728e-01 2.26697356... | [7.803852558135986, 5.2190327644348145] |
c380bb17-cb30-4306-bab9-74e848a5b58d | anticipating-the-unseen-discrepancy-for | 2209.04725 | null | https://arxiv.org/abs/2209.04725v1 | https://arxiv.org/pdf/2209.04725v1.pdf | Anticipating the Unseen Discrepancy for Vision and Language Navigation | Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes it challenging for the agent to generalize well. Previous studies propose data augmentation methods to mitigate the data bias explicitly or i... | ['William Yang Wang', 'Xin Eric Wang', 'Wenda Xu', 'Weixi Feng', 'Ping Nie', 'Huiliang Zhang', 'Yujie Lu'] | 2022-09-10 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [-3.92812230e-02 -1.53561220e-01 -1.22837834e-01 -6.79353356e-01
-6.44927740e-01 -5.65119386e-01 8.24802518e-01 -2.88965344e-01
-7.60136306e-01 6.96113110e-01 1.14615910e-01 -4.60693359e-01
2.14560464e-01 -4.65562522e-01 -1.11957943e+00 -6.15557730e-01
3.89142595e-02 4.92169410e-01 2.05483750e-01 -2.63331324... | [4.426268577575684, 0.6249600052833557] |
15be64bb-be2f-4756-a1d2-a67309862415 | discovering-governing-equations-from-partial | 2201.05136 | null | https://arxiv.org/abs/2201.05136v1 | https://arxiv.org/pdf/2201.05136v1.pdf | Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders | A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem provides conditions for when it is possible to augment these partial measurements with time delayed information, resulting in an attractor that... | ['Steven L. Brunton', 'J. Nathan Kutz', 'Kathleen Champion', 'Joseph Bakarji'] | 2022-01-13 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-9.53439549e-02 5.51769994e-02 8.98897350e-02 -6.82862625e-02
-1.58101141e-01 -8.44804049e-01 8.16899300e-01 -2.23088205e-01
-1.15306512e-01 7.11852551e-01 -2.94453725e-02 -1.24226071e-01
-3.24533463e-01 -4.53051716e-01 -8.03936362e-01 -1.10512686e+00
-3.31623495e-01 6.00428283e-01 -4.95346487e-01 -2.66299337... | [6.531064510345459, 3.510784387588501] |
6bf33bf0-e0ef-4752-9160-0565ed8ee68a | bilingunet-image-segmentation-by-modulating | 2003.12739 | null | https://arxiv.org/abs/2003.12739v3 | https://arxiv.org/pdf/2003.12739v3.pdf | Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters | How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual attention over high-level visual features, may not be optimal. We hypoth... | ['Deniz Yuret', 'Aykut Erdem', 'Erkut Erdem', 'İlker Kesen', 'Ozan Arkan Can'] | 2020-03-28 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 1.49679527e-01 -1.31874248e-01 7.29878172e-02 -5.05531549e-01
-5.47128856e-01 -5.19354284e-01 6.82363451e-01 3.76569510e-01
-7.47320712e-01 8.74746889e-02 3.48721534e-01 -5.23814261e-01
4.30786520e-01 -7.75359988e-01 -9.30302918e-01 -3.58129412e-01
4.51620907e-01 1.19750807e-02 4.59667265e-01 -2.72227079... | [10.4484224319458, 1.5655351877212524] |
788121cb-3abc-4420-a659-25fd42ff41c6 | demfi-deep-joint-deblurring-and-multi-frame | 2111.09985 | null | https://arxiv.org/abs/2111.09985v1 | https://arxiv.org/pdf/2111.09985v1.pdf | DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting | In this paper, we propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boostin... | ['Munchurl Kim', 'Jihyong Oh'] | 2021-11-19 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 2.88141407e-02 -6.32878542e-01 -6.32689893e-02 -2.10224767e-04
-8.60397279e-01 -2.15547502e-01 5.44575572e-01 -7.02349126e-01
-2.28446513e-01 8.25823069e-01 6.21894002e-01 -1.88915715e-01
3.63625400e-02 -5.68898201e-01 -7.63350904e-01 -7.85565078e-01
6.57392293e-03 -3.98633778e-01 2.40581676e-01 -1.26737803... | [11.444059371948242, -2.4156103134155273] |
932ca3b9-c76f-4436-aea5-3764d4733782 | biasing-mcts-with-features-for-general-games | 1903.08942 | null | http://arxiv.org/abs/1903.08942v1 | http://arxiv.org/pdf/1903.08942v1.pdf | Biasing MCTS with Features for General Games | This paper proposes using a linear function approximator, rather than a deep
neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for
general games. This is unlikely to match the potential raw playing strength of
DNNs, but has advantages in terms of generality, interpretability and resources
(time and ... | ['Cameron Browne', 'Éric Piette', 'Dennis J. N. J. Soemers'] | 2019-03-21 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.28573030e-01 9.28140283e-02 -2.15838358e-01 -3.54341388e-01
-7.49603331e-01 -6.09182596e-01 8.34396422e-01 -2.26133943e-01
-8.56857717e-01 1.02618015e+00 -8.31354316e-03 -4.45669174e-01
-4.39711630e-01 -1.30115533e+00 -7.36697257e-01 -7.90888011e-01
-4.10714373e-02 8.00391316e-01 7.50335515e-01 -3.87736320... | [3.523505210876465, 1.5096849203109741] |
6377d684-8c11-41d0-bfde-e801ada163a7 | a-review-of-deep-learning-techniques-for | 2201.02503 | null | https://arxiv.org/abs/2201.02503v1 | https://arxiv.org/pdf/2201.02503v1.pdf | A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets | Markerless motion capture has become an active field of research in computer vision in recent years. Its extensive applications are known in a great variety of fields, including computer animation, human motion analysis, biomedical research, virtual reality, and sports science. Estimating human posture has recently gai... | ['Russell Butler', 'Doan Duy Vo'] | 2022-01-07 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 7.45928064e-02 -2.52620876e-01 -2.49173880e-01 -6.69171140e-02
-3.07311296e-01 -1.86799884e-01 4.79436427e-01 -4.77691323e-01
-6.06269121e-01 5.36807716e-01 2.14271113e-01 1.00121677e-01
3.34910750e-01 -6.18264139e-01 -6.43949986e-01 -6.13783896e-01
-1.02001384e-01 5.16008079e-01 6.21964157e-01 -3.35095644... | [7.220550537109375, -0.6902354955673218] |
08a12caa-b520-43d1-be3b-1ab78cf44770 | pp-yoloe-r-an-efficient-anchor-free-rotated | 2211.02386 | null | https://arxiv.org/abs/2211.02386v1 | https://arxiv.org/pdf/2211.02386v1.pdf | PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector | Arbitrary-oriented object detection is a fundamental task in visual scenes involving aerial images and scene text. In this report, we present PP-YOLOE-R, an efficient anchor-free rotated object detector based on PP-YOLOE. We introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision with marginal ext... | ['dianhai yu', 'Xiaoguang Hu', 'Yi Liu', 'Qingqing Dang', 'Guanzhong Wang', 'Xinxin Wang'] | 2022-11-04 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.98446357e-01 -4.36789453e-01 -4.86617945e-02 1.70307279e-01
-9.60989594e-01 -5.69173694e-01 3.22384946e-02 -2.04365119e-01
-4.74185258e-01 1.22657962e-01 -5.62410474e-01 -4.44672823e-01
9.70626250e-02 -5.57176292e-01 -8.34175766e-01 -4.50407237e-01
-4.11003113e-01 -7.67755881e-02 8.72236967e-01 -2.98878700... | [8.703290939331055, -0.6552004218101501] |
bff3e118-6f95-4c27-ab83-06c42945e28c | pastiche-detection-based-on-stopword-rankings | null | null | https://aclanthology.org/W12-0411 | https://aclanthology.org/W12-0411.pdf | Pastiche Detection Based on Stopword Rankings. Exposing Impersonators of a Romanian Writer | null | ['Maria-Octavia Sulea', 'Vlad Niculae', 'Liviu P. Dinu'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.425676345825195, 3.56643009185791] |
2496b946-d881-43bc-aded-64e46e471285 | benchmarking-deep-reinforcement-learning-for | 1604.06778 | null | http://arxiv.org/abs/1604.06778v3 | http://arxiv.org/pdf/1604.06778v3.pdf | Benchmarking Deep Reinforcement Learning for Continuous Control | Recently, researchers have made significant progress combining the advances
in deep learning for learning feature representations with reinforcement
learning. Some notable examples include training agents to play Atari games
based on raw pixel data and to acquire advanced manipulation skills using raw
sensory inputs. H... | ['Pieter Abbeel', 'John Schulman', 'Yan Duan', 'Rein Houthooft', 'Xi Chen'] | 2016-04-22 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 2.17099171e-02 -2.41383076e-01 -1.51800647e-01 -5.17459102e-02
-3.81745875e-01 -5.49803674e-01 6.77369058e-01 4.63479199e-02
-6.77383363e-01 1.09823060e+00 -1.03734501e-01 -1.71900213e-01
-3.59498829e-01 -5.52957416e-01 -7.56599963e-01 -5.80025554e-01
-5.65096796e-01 4.14891481e-01 1.00310609e-01 -6.53646290... | [4.324071407318115, 1.2546911239624023] |
2b4c90a8-bfab-4995-a523-5d7089135b80 | bidirectional-self-training-with-multiple | 2204.07730 | null | https://arxiv.org/abs/2204.07730v2 | https://arxiv.org/pdf/2204.07730v2.pdf | Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation | A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them. Under this self-training paradigm, some competitive methods have sought to the latent-space information, which establishes the feature centroids (a.k.a prototypes) ... | ['Jun Xiao', 'Yi Yang', 'Zheyang Li', 'Li Zhang', 'Yawei Luo', 'Yulei Lu'] | 2022-04-16 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 1.00161590e-01 2.76621103e-01 -2.93605119e-01 -4.89248395e-01
-8.34923267e-01 -8.75999749e-01 6.28209770e-01 -3.32498431e-01
-3.74966502e-01 6.96079791e-01 4.04141657e-02 -2.39591494e-01
-1.13516062e-01 -5.95429778e-01 -4.67585951e-01 -1.00272918e+00
3.91788185e-01 8.58444512e-01 4.47855234e-01 4.87112962... | [9.629524230957031, 1.3837283849716187] |
24c4842e-e2e1-4f9e-844a-3945a8715569 | scalable-and-robust-self-learning-for-skill | 2204.07135 | null | https://arxiv.org/abs/2204.07135v1 | https://arxiv.org/pdf/2204.07135v1.pdf | Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems | Skill routing is an important component in large-scale conversational systems. In contrast to traditional rule-based skill routing, state-of-the-art systems use a model-based approach to enable natural conversations. To provide supervision signal required to train such models, ideas such as human annotation, replicatio... | ['Sungjin Lee', 'Jin-Myung Won', 'Sarthak Ahuja', 'Jinseok Nam', 'Mohammad Kachuee'] | 2022-04-14 | null | https://aclanthology.org/2022.naacl-industry.1 | https://aclanthology.org/2022.naacl-industry.1.pdf | naacl-acl-2022-7 | ['self-learning'] | ['natural-language-processing'] | [ 1.79806367e-01 3.09858024e-01 -1.73562303e-01 -4.31208640e-01
-6.49059951e-01 -7.73791254e-01 5.15694201e-01 1.27153739e-01
-4.07633275e-01 1.07921612e+00 2.00440705e-01 -5.98240793e-01
-3.09678495e-01 -4.48354423e-01 -4.20720816e-01 -4.18881774e-01
1.15536377e-01 9.72387552e-01 6.54287636e-01 -5.35913110... | [12.904173851013184, 7.9771013259887695] |
6f4ebd2b-9ac8-43a0-8490-3b886121a1d5 | amark-automated-marking-and-processing | 2005.14115 | null | https://arxiv.org/abs/2005.14115v2 | https://arxiv.org/pdf/2005.14115v2.pdf | Amark: Automated Marking and Processing Techniques for Ambulatory ECG Data | We describe techniques and specifications of MATLAB software to process ambulatory electrocardiogram (ECG) data. Through template-based beat identification and simple pattern recognition models on the intervals between regular heart beats, we filter noisy sections of waveform and ectopic beats. Our end-to-end process c... | ['Richard P. Sloan', 'Sharath Koorathota'] | 2020-05-28 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 6.13340080e-01 -3.92903715e-01 3.23981076e-01 -4.83014971e-01
-4.90477622e-01 -6.69080317e-01 -2.97283351e-01 5.44076622e-01
-2.41759673e-01 8.79626989e-01 8.43266398e-02 -4.85786557e-01
-5.13584554e-01 -2.87205487e-01 3.52965683e-01 -3.43469560e-01
-6.35536134e-01 2.81238824e-01 -2.42758512e-01 3.31256352... | [14.215584754943848, 3.212907552719116] |
1adfcf79-c7e6-4898-bf08-39c164840537 | multistream-gaze-estimation-with-anatomical | 2206.09256 | null | https://arxiv.org/abs/2206.09256v1 | https://arxiv.org/pdf/2206.09256v1.pdf | Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning | We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. ... | ['Ali Etemad', 'Paul Hungler', 'Zunayed Mahmud'] | 2022-06-18 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 2.46744737e-01 1.97953761e-01 1.25700638e-01 -3.41427892e-01
-2.52384424e-01 -4.48042989e-01 3.53400737e-01 -5.57922184e-01
-4.76021916e-01 5.25566339e-01 -7.05822334e-02 -1.78082988e-01
1.48246229e-01 -3.31069440e-01 -9.71799612e-01 -7.59083450e-01
2.66263515e-01 -7.88890868e-02 2.39890501e-01 -1.14084274... | [14.134645462036133, 0.048441071063280106] |
d527cfab-e3b0-4914-88fa-70bfefe82760 | mian-xiang-fa-lu-wen-ben-de-shi-ti-guan-xi | null | null | https://aclanthology.org/2021.ccl-1.53 | https://aclanthology.org/2021.ccl-1.53.pdf | 面向法律文本的实体关系联合抽取算法(Joint Entity and Relation Extraction for Legal Texts) | “法律文本中包含的丰富信息可以通过结构化的实体关系三元组进行表示,便于法律知识的存储和查询。传统的流水线方法在自动抽取三元组时执行了大量冗余计算,造成了误差传播。而现有的联合学习方法无法适用于有大量重叠关系的法律文本,也并未关注语法结构信息对文本表示的增强,因此本文提出一种面向法律文本的实体关系联合抽取模型。该模型首先通过ON-LSTM注入语法信息,然后引入多头注意力机制分解重叠关系。相较于流水线和其他联合学习方法本文模型抽取效果最佳,在涉毒类法律文本数据集上抽取结果的F1值达到78.7%。” | ['Hongfei Lin', 'Liang Yang', 'Yuanyuan Sun', 'Ping Yang', 'Xiang Zhou', 'Wenhui Song'] | null | null | null | null | ccl-2021-8 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-8.54476511e-01 -7.03434706e-01 4.45536911e-01 3.20582211e-01
-1.85502898e-02 -5.75404227e-01 -6.64204210e-02 1.11477530e+00
-4.05275136e-01 4.21618193e-01 6.71756923e-01 -8.22299197e-02
-1.63218990e-01 -9.87018108e-01 -6.02135420e-01 -1.22195983e+00
-5.74167430e-01 1.47820282e+00 5.86456537e-01 -7.13670492... | [-3.316117763519287, 6.907679080963135] |
23f89d57-1c3c-4c4f-bc0b-eaa7aa0c0b46 | itkd-interchange-transfer-based-knowledge | 2205.15531 | null | https://arxiv.org/abs/2205.15531v2 | https://arxiv.org/pdf/2205.15531v2.pdf | itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection | Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprisi... | ['Wonjun Hwang', 'Geonwoo Baek', 'Junyong Choi', 'Hyeon Cho'] | 2022-05-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cho_itKD_Interchange_Transfer-Based_Knowledge_Distillation_for_3D_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cho_itKD_Interchange_Transfer-Based_Knowledge_Distillation_for_3D_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['cloud-detection'] | ['computer-vision'] | [-1.86630577e-01 -3.91421467e-03 -1.78869385e-02 -4.32996154e-01
-6.50632739e-01 -2.13114068e-01 5.83761990e-01 -1.71098113e-01
-5.12356281e-01 8.47916156e-02 -1.45141870e-01 -2.23673999e-01
-4.72841449e-02 -9.01477754e-01 -1.33028555e+00 -7.12990761e-01
9.08409879e-02 4.41494703e-01 4.17762667e-01 1.29293770... | [8.009734153747559, -3.312575340270996] |
3509e086-53bb-4208-a170-0f65238e4723 | hyperbolic-disentangled-representation-for | 2112.09215 | null | https://arxiv.org/abs/2112.09215v1 | https://arxiv.org/pdf/2112.09215v1.pdf | Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction | Automatic identification of salient aspects from user reviews is especially useful for opinion analysis. There has been significant progress in utilizing weakly supervised approaches, which require only a small set of seed words for training aspect classifiers. However, there is always room for improvement. First, no w... | ['Lun-Wei Ku', 'Ming-Yao Li', 'Chang-You Tai'] | 2021-12-16 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [-8.03666413e-02 3.75374496e-01 -7.28440166e-01 -3.88580173e-01
-8.41249466e-01 -8.15743327e-01 7.73845792e-01 3.79612893e-01
-1.92441382e-02 2.51119494e-01 7.00087786e-01 -3.92409742e-01
2.48702675e-01 -6.35693610e-01 -2.68540476e-02 -5.95026791e-01
1.51473433e-01 3.46966147e-01 -1.88351005e-01 -2.83912241... | [11.419685363769531, 6.708292484283447] |
94ae86d1-93e8-4e9c-b49e-ce6b87d1c38f | highlight-detection-with-pairwise-deep | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Yao_Highlight_Detection_With_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yao_Highlight_Detection_With_CVPR_2016_paper.pdf | Highlight Detection With Pairwise Deep Ranking for First-Person Video Summarization | The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos. Browsing such long unstructured videos is time-consuming and tedious. This paper studies the discovery of moments of user's major or special interest (i.e., highlights) in a video, ... | ['Ting Yao', 'Yong Rui', 'Tao Mei'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['highlight-detection'] | ['computer-vision'] | [ 4.29096580e-01 -3.81287813e-01 -2.61279911e-01 -1.81919619e-01
-9.78617072e-01 -5.40497601e-01 4.90259945e-01 3.44596088e-01
-3.56554896e-01 5.61126292e-01 5.07778168e-01 3.65072608e-01
5.31620719e-02 -3.00269872e-01 -8.58607054e-01 -6.78406656e-01
-5.33205450e-01 -3.07716250e-01 3.45435977e-01 8.62038061... | [10.220973014831543, 0.43379276990890503] |
af4189eb-ce93-4424-b38e-5f5e94c37a9f | efficient-3-d-near-field-mimo-sar-imaging-for | 2305.02064 | null | https://arxiv.org/abs/2305.02064v1 | https://arxiv.org/pdf/2305.02064v1.pdf | Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries | In this article, we introduce a novel algorithm for efficient near-field synthetic aperture radar (SAR) imaging for irregular scanning geometries. With the emergence of fifth-generation (5G) millimeter-wave (mmWave) devices, near-field SAR imaging is no longer confined to laboratory environments. Recent advances in pos... | ['Murat Torlak', 'Josiah Smith'] | 2023-05-03 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 7.14907825e-01 -2.70463526e-01 2.95636952e-01 -4.32276547e-01
-6.67031169e-01 -6.86369121e-01 1.89445809e-01 -8.73760462e-01
1.11483589e-01 6.66238964e-01 1.99598186e-02 -6.02530420e-01
-9.86484110e-01 -8.05645764e-01 -2.24725470e-01 -7.41169214e-01
-2.41092518e-02 2.82528788e-01 -2.30475307e-01 -8.66356492... | [6.707255840301514, 0.9779950976371765] |
1e9fdf77-f0b9-45c2-9ded-81f500134908 | hypernym-discovery-via-a-recurrent-mapping | null | null | https://aclanthology.org/2021.findings-acl.257 | https://aclanthology.org/2021.findings-acl.257.pdf | Hypernym Discovery via a Recurrent Mapping Model | null | ['Yongyi Mao', 'Junfan Chen', 'Fanshuang Kong', 'Richong Zhang', 'Yuhang Bai'] | null | null | null | null | findings-acl-2021-8 | ['hypernym-discovery'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.343100547790527, 3.7300987243652344] |
7de07004-b4e8-417d-81c3-902fee2060de | learning-semantics-aware-distance-map-with | 1905.12898 | null | https://arxiv.org/abs/1905.12898v2 | https://arxiv.org/pdf/1905.12898v2.pdf | Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation | In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps. The sem-dist map is a ... | ['Ziheng Zhang', 'Ling Xie', 'Shenghua Gao', 'Anpei Chen', 'Jingyi Yu'] | 2019-05-30 | null | null | null | null | ['amodal-instance-segmentation'] | ['computer-vision'] | [-3.62439901e-02 3.32650810e-01 -1.05264060e-01 -6.39182448e-01
-5.22680104e-01 -6.67714596e-01 5.27264953e-01 3.81391525e-01
-1.47869498e-01 2.20602557e-01 -7.53879696e-02 -2.01577768e-02
1.29365742e-01 -1.03280532e+00 -7.83458233e-01 -6.04626536e-01
3.61035764e-02 5.74052751e-01 5.67712724e-01 -9.73182023... | [9.69814395904541, 0.47055062651634216] |
defec607-5152-4ce0-ab82-37e882d46a04 | m3pt-a-multi-modal-model-for-poi-tagging | 2306.10079 | null | https://arxiv.org/abs/2306.10079v1 | https://arxiv.org/pdf/2306.10079v1.pdf | M3PT: A Multi-Modal Model for POI Tagging | POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in sub... | ['Shenghua Ni', 'Baohua Wu', 'Xiang Xu', 'Yanghua Xiao', 'Jingping Liu', 'Deqing Yang', 'Guanzhou Han', 'Jingsong Yang'] | 2023-06-16 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 5.14898673e-02 -2.25011364e-01 -4.43811417e-01 -8.07253085e-03
-8.14746737e-01 -3.30502391e-01 8.13520074e-01 8.40453207e-02
-4.43611056e-01 4.33459401e-01 6.45896912e-01 1.92126513e-01
-1.45806074e-01 -7.91748464e-01 -4.70899075e-01 -6.41735494e-01
1.79250538e-01 1.21875994e-01 6.21509731e-01 4.35684510... | [10.66134262084961, 1.382787823677063] |
59b3d1bc-b16b-409f-b216-2f8d478e6ba4 | convolutional-relational-machine-for-group | 1904.03308 | null | http://arxiv.org/abs/1904.03308v1 | http://arxiv.org/pdf/1904.03308v1.pdf | Convolutional Relational Machine for Group Activity Recognition | We present an end-to-end deep Convolutional Neural Network called
Convolutional Relational Machine (CRM) for recognizing group activities that
utilizes the information in spatial relations between individual persons in
image or video. It learns to produce an intermediate spatial representation
(activity map) based on i... | ['Mina Ghadimi Atigh', 'Ahmad Nickabadi', 'Sina Mokhtarzadeh Azar', 'Alexandre Alahi'] | 2019-04-05 | convolutional-relational-machine-for-group-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Azar_Convolutional_Relational_Machine_for_Group_Activity_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Azar_Convolutional_Relational_Machine_for_Group_Activity_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['group-activity-recognition'] | ['computer-vision'] | [ 1.18587285e-01 2.73726016e-01 -3.79378796e-01 -4.58028436e-01
-1.26478836e-01 -9.11608785e-02 7.05694914e-01 6.54482916e-02
-5.47329426e-01 5.64017236e-01 7.06753135e-01 1.16665207e-01
-4.36042041e-01 -1.00973165e+00 -9.66370344e-01 -2.98630148e-01
-4.47941035e-01 2.48153836e-01 3.01665723e-01 -2.34131321... | [8.096292495727539, 0.5572760105133057] |
76b0c9c6-9491-4118-8754-78dda0437780 | futuristic-methods-in-virus-genome-evolution | 1902.09148 | null | http://arxiv.org/abs/1902.09148v1 | http://arxiv.org/pdf/1902.09148v1.pdf | Futuristic methods in virus genome evolution using the Third-Generation DNA sequencing and artificial neural networks | The Third-Generation in DNA sequencing has emerged in the last few years
using new technologies that allow the production of long-read sequences.
Applications of the Third-Generation sequencing enable real-time and on-site
data production, changing the research paradigms in environmental and medical
sampling in virolog... | [] | 2019-02-25 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 6.45378590e-01 -4.33076739e-01 1.95683241e-02 -3.58513504e-01
-1.81307063e-01 -6.76535785e-01 5.19905210e-01 9.55232456e-02
-5.84322095e-01 8.68047357e-01 -1.09766841e-01 -6.40871823e-01
-2.15011343e-01 -8.11277628e-01 -8.34014773e-01 -1.14149714e+00
-2.56866962e-01 8.20506155e-01 -4.82011974e-01 -3.15400809... | [5.448887348175049, 5.468742847442627] |
0ae7446a-adc6-4c08-b828-14a80a9aa0d0 | ssncse-nlp-dravidianlangtech-eacl2021-meme | null | null | https://aclanthology.org/2021.dravidianlangtech-1.49 | https://aclanthology.org/2021.dravidianlangtech-1.49.pdf | SSNCSE_NLP@DravidianLangTech-EACL2021: Meme classification for Tamil using machine learning approach | Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes. A meme is an image or video tha... | ['Agnusimmaculate Silvia A', 'Bharathi B'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-3.22770357e-01 -2.60754582e-02 1.96995959e-01 2.59022921e-01
-1.14946395e-01 -9.24208105e-01 1.09147656e+00 6.46928549e-01
-4.16088879e-01 9.63280499e-01 4.85661626e-01 4.64405343e-02
4.46188986e-01 -8.42892230e-01 -4.01709259e-01 -5.11524677e-01
3.36359024e-01 -7.97879919e-02 2.79952884e-01 -5.90638399... | [8.56498908996582, 10.651022911071777] |
73e5a700-cb52-4ecd-85b9-02a543b4a22e | ccpl-contrastive-coherence-preserving-loss | 2207.04808 | null | https://arxiv.org/abs/2207.04808v4 | https://arxiv.org/pdf/2207.04808v4.pdf | CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer | In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which coul... | ['Xiang Bai', 'Junping Du', 'Zhen Zhu', 'Zijie Wu'] | 2022-07-11 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 3.64836454e-01 -2.19783202e-01 1.63826682e-02 -2.22826228e-01
-5.25707126e-01 -6.64090037e-01 7.79891491e-01 -4.29498821e-01
-1.12815395e-01 8.40110302e-01 9.27234888e-02 2.13113260e-02
7.74923488e-02 -6.18368626e-01 -8.80514622e-01 -9.16997075e-01
6.15418017e-01 -1.34776086e-01 2.73684710e-01 -3.18496853... | [11.413702011108398, -0.7205803990364075] |
995ac14a-8500-49c0-93e0-8137c0688d0a | joint-task-and-data-oriented-semantic | 2302.13580 | null | https://arxiv.org/abs/2302.13580v1 | https://arxiv.org/pdf/2302.13580v1.pdf | Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme | Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic com... | ['Wei zhang', 'Xiaoqi Qin', 'Chuan Huang', 'Dongxu Li', 'Jianhao Huang'] | 2023-02-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 4.66558456e-01 1.45876139e-01 -1.44085854e-01 -3.87188613e-01
-9.17798519e-01 3.13046873e-01 4.62431550e-01 -4.49852571e-02
-3.12964201e-01 6.66641057e-01 4.30221558e-01 4.42508608e-02
-5.50033748e-01 -8.16073239e-01 -5.86338639e-01 -1.09902918e+00
1.72071263e-01 3.18457246e-01 -9.61742103e-02 7.53934085... | [11.29651165008545, -1.6621114015579224] |
f8281015-87ce-43ba-a182-bdd97bfafab2 | iapucp-at-semeval-2021-task-1-stacking-fine | null | null | https://aclanthology.org/2021.semeval-1.14 | https://aclanthology.org/2021.semeval-1.14.pdf | IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity Prediction | This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages standard morphosyntactic and frequency-based features that proved helpful for Complex Word Identification (a related task), and combines them with predictions made by Transformer-based pre-t... | ['Fernando Alva-Manchego', 'Kervy Rivas Rojas'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-7.77217001e-02 6.33949637e-02 -3.08053851e-01 -2.50721723e-01
-1.08225536e+00 -6.82618141e-01 6.89535737e-01 5.26791632e-01
-8.38342488e-01 5.38040280e-01 5.66494524e-01 -4.99542505e-01
-1.38417512e-01 -5.57623684e-01 -3.21441174e-01 -1.00205310e-01
6.31125495e-02 7.29571164e-01 3.67217988e-01 -6.51517451... | [10.629138946533203, 10.318766593933105] |
4bb09040-eeaa-49ae-b081-23b249f81747 | fast-rule-based-decoding-revisiting-syntactic | 2212.08458 | null | https://arxiv.org/abs/2212.08458v1 | https://arxiv.org/pdf/2212.08458v1.pdf | Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing | Most recent studies on neural constituency parsing focus on encoder structures, while few developments are devoted to decoders. Previous research has demonstrated that probabilistic statistical methods based on syntactic rules are particularly effective in constituency parsing, whereas syntactic rules are not used duri... | ['Cong Liu', 'Liyin Xiao', 'Zhicheng Wang', 'Tianyu Shi'] | 2022-12-16 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.52748126e-01 2.70033509e-01 -1.70719415e-01 -6.51249349e-01
-1.09101272e+00 -5.93308270e-01 4.79558319e-01 1.45802617e-01
-6.22665763e-01 8.34986448e-01 4.01313573e-01 -6.35411084e-01
3.51663589e-01 -8.47563088e-01 -8.19215953e-01 -5.53072393e-01
3.00647318e-01 2.11006477e-01 2.41449445e-01 -2.26793066... | [10.413485527038574, 9.711186408996582] |
800b2a3d-b65b-4f21-8021-4f0dda164d3d | studying-very-low-resolution-recognition | 1601.04153 | null | http://arxiv.org/abs/1601.04153v2 | http://arxiv.org/pdf/1601.04153v2.pdf | Studying Very Low Resolution Recognition Using Deep Networks | Visual recognition research often assumes a sufficient resolution of the
region of interest (ROI). That is usually violated in practice, inspiring us to
explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI
in a VLRR problem can be smaller than $16 \times 16$ pixels, and is challenging
to be re... | ['Shiyu Chang', 'Zhangyang Wang', 'Yingzhen Yang', 'Thomas S. Huang', 'Ding Liu'] | 2016-01-16 | studying-very-low-resolution-recognition-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Studying_Very_Low_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Studying_Very_Low_CVPR_2016_paper.pdf | cvpr-2016-6 | ['font-recognition'] | ['computer-vision'] | [ 3.24191153e-01 -1.57573849e-01 -4.31362726e-02 -3.04754019e-01
-1.02394223e+00 -2.06585839e-01 4.26331282e-01 -4.93457258e-01
-2.67885476e-01 8.50743890e-01 -7.64007568e-02 5.97984232e-02
-3.40955615e-01 -3.35352898e-01 -6.06439412e-01 -1.03902340e+00
3.28644544e-01 -3.43181677e-02 -2.98975915e-01 -1.74862165... | [12.888928413391113, 0.11093834042549133] |
cd531114-c346-4721-a0a7-68f3865acbf8 | learning-correspondence-from-the-cycle | 1903.07593 | null | http://arxiv.org/abs/1903.07593v2 | http://arxiv.org/pdf/1903.07593v2.pdf | Learning Correspondence from the Cycle-Consistency of Time | We introduce a self-supervised method for learning visual correspondence from
unlabeled video. The main idea is to use cycle-consistency in time as free
supervisory signal for learning visual representations from scratch. At
training time, our model learns a feature map representation to be useful for
performing cycle-... | ['Xiaolong Wang', 'Alexei A. Efros', 'Allan Jabri'] | 2019-03-18 | learning-correspondence-from-the-cycle-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Learning_Correspondence_From_the_Cycle-Consistency_of_Time_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Learning_Correspondence_From_the_Cycle-Consistency_of_Time_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [-1.61432996e-01 -3.08454245e-01 -8.49438429e-01 -4.18356538e-01
-5.50071716e-01 -8.09476852e-01 5.71517050e-01 -6.28597960e-02
-2.85402298e-01 6.30063951e-01 1.83175638e-01 5.34206219e-02
8.27144533e-02 -4.07743365e-01 -9.55156922e-01 -3.55219841e-01
-2.66001880e-01 3.72739017e-01 4.56681460e-01 1.46484882... | [8.951781272888184, -0.21573621034622192] |
143cad07-63a2-4b62-ab93-cfb3cf891d35 | deep-monocular-3d-human-pose-estimation-via | 2104.03520 | null | https://arxiv.org/abs/2104.03520v1 | https://arxiv.org/pdf/2104.03520v1.pdf | Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting | The 3D pose estimation from a single image is a challenging problem due to depth ambiguity. One type of the previous methods lifts 2D joints, obtained by resorting to external 2D pose detectors, to the 3D space. However, this type of approaches discards the contextual information of images which are strong cues for 3D ... | ['Yuan Chang', 'Fangneng Zhan', 'Changgong Zhang'] | 2021-04-08 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-7.99043626e-02 -4.12305519e-02 -2.40212828e-01 -2.47869581e-01
-6.18537486e-01 -2.98160821e-01 1.99514642e-01 -4.39432383e-01
-6.40124738e-01 4.46586639e-01 1.01728149e-01 -9.62819234e-02
1.15241699e-01 -6.05266631e-01 -7.49396324e-01 -5.79947352e-01
1.43652380e-01 4.42187309e-01 2.86982089e-01 -2.80411810... | [6.99766731262207, -1.0312323570251465] |
f6307c28-0761-4b32-92a4-bc56d860708b | faxplainac-a-fact-checking-tool-based-on | 2110.10144 | null | https://arxiv.org/abs/2110.10144v1 | https://arxiv.org/pdf/2110.10144v1.pdf | FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop | Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the information (support or refute the claim) based on secondary sources of information. However, existing approaches do not consider the problem o... | ['Avishek Anand', 'Koustav Rudra', 'Zijian Zhang'] | 2021-09-12 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-1.53434217e-01 5.91294050e-01 -3.58101517e-01 -3.68028194e-01
-7.38303840e-01 -8.89528334e-01 6.82984293e-01 1.03297341e+00
-5.20207547e-02 8.98189306e-01 8.99105966e-02 -8.19447517e-01
5.35616688e-02 -8.14940393e-01 -1.01712000e+00 -1.40090525e-01
4.16167557e-01 6.16150796e-01 6.41341209e-01 1.44097030... | [9.010374069213867, 9.423772811889648] |
be007b95-8049-46fb-8c57-35d881edc8b0 | analysis-of-hand-segmentation-in-the-wild | 1803.03317 | null | http://arxiv.org/abs/1803.03317v2 | http://arxiv.org/pdf/1803.03317v2.pdf | Analysis of Hand Segmentation in the Wild | A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first-person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands... | ['Ali Borji', 'Aisha Urooj Khan'] | 2018-03-08 | analysis-of-hand-segmentation-in-the-wild-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Urooj_Analysis_of_Hand_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Urooj_Analysis_of_Hand_CVPR_2018_paper.pdf | cvpr-2018-6 | ['hand-segmentation', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.25549072e-01 1.33259073e-01 -4.48011085e-02 -2.59888470e-01
-4.95989203e-01 -7.78285146e-01 2.28747606e-01 -6.19036078e-01
-5.44631004e-01 7.06073761e-01 6.53649122e-02 2.75793314e-01
-5.68762422e-02 -3.13246936e-01 -7.56806910e-01 -7.54791439e-01
2.47647017e-01 9.17926848e-01 6.19014978e-01 1.24585852... | [6.648462772369385, -0.6505990028381348] |
cb83ede5-8a1b-46c8-8ef8-aac3c1383f55 | datasets-for-data-driven-reinforcement | 2004.07219 | null | https://arxiv.org/abs/2004.07219v4 | https://arxiv.org/pdf/2004.07219v4.pdf | D4RL: Datasets for Deep Data-Driven Reinforcement Learning | The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy is learned from a static dataset, is compelling as progress enables RL methods to take advantage of large, previously-collected datasets, much like how the rise of large datasets has fueled results in supervised learning. Howe... | ['Sergey Levine', 'George Tucker', 'Ofir Nachum', 'Aviral Kumar', 'Justin Fu'] | 2020-04-15 | null | https://openreview.net/forum?id=px0-N3_KjA | https://openreview.net/pdf?id=px0-N3_KjA | null | ['d4rl'] | ['robots'] | [ 4.85788435e-02 -1.47801870e-02 -5.23871183e-01 -1.10753149e-01
-9.00200963e-01 -1.04944658e+00 8.32140923e-01 -7.50792250e-02
-7.14933932e-01 1.15371454e+00 2.87481964e-01 -2.51147985e-01
-1.68396056e-01 -1.68213546e-01 -8.17295730e-01 -6.25750065e-01
-5.54696679e-01 5.87210953e-01 -1.65865928e-01 -3.12936872... | [4.0568671226501465, 1.6971025466918945] |
f04fcda6-2b38-4437-a97e-8ff406105a6a | harmonizing-base-and-novel-classes-a-class | 2303.13724 | null | https://arxiv.org/abs/2303.13724v1 | https://arxiv.org/pdf/2303.13724v1.pdf | Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation | Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome this limitation, the task of generalized few-shot semantic segmentation (GFSSeg) has been introduced, aiming to predict segmentation masks for... | ['Guosheng Lin', 'Jun Cheng', 'Chuan-Sheng Foo', 'Yuming Fang', 'Yang Zhao', 'Zhonghua Wu', 'Weide Liu'] | 2023-03-24 | null | null | null | null | ['generalized-few-shot-semantic-segmentation'] | ['computer-vision'] | [ 2.78674662e-01 2.30366737e-02 -2.48439431e-01 -5.19172370e-01
-6.48228586e-01 -1.23976439e-01 4.87222314e-01 4.61267710e-01
-4.99231905e-01 5.75099230e-01 -3.88452083e-01 3.63578975e-01
9.65207666e-02 -7.45415866e-01 -5.66071093e-01 -7.42244422e-01
3.63628209e-01 5.67158759e-01 1.21631825e+00 -5.00699468... | [9.522047996520996, 1.5550899505615234] |
bd6549b0-d9cf-44fd-86b7-2b62a2b6256e | layoutdiffuse-adapting-foundational-diffusion | 2302.08908 | null | https://arxiv.org/abs/2302.08908v1 | https://arxiv.org/pdf/2302.08908v1.pdf | LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation | Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational diffusion model pretrained on large-scale image or text-image datasets for layout-to-image generation. By adopting a novel neural adaptor bas... | ['Mu Li', 'Tianjun Xiao', 'Tong He', 'Xingjian Shi', 'Xiao Liang', 'Jiaxin Cheng'] | 2023-02-16 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 3.79134357e-01 2.45087624e-01 2.65476257e-01 -2.48783290e-01
-6.83943391e-01 -5.97546935e-01 7.12421358e-01 -5.12755275e-01
1.16937652e-01 6.62120163e-01 4.25685912e-01 -3.95377070e-01
2.02096984e-01 -1.00973654e+00 -1.10065746e+00 -3.99507225e-01
5.22690773e-01 3.78303856e-01 -6.11689687e-02 -4.47182357... | [11.471221923828125, -0.30291691422462463] |
c7d2b3ca-49f7-4990-8bcb-1a7384a99653 | diffg-rl-leveraging-difference-between-state | 2211.16002 | null | https://arxiv.org/abs/2211.16002v1 | https://arxiv.org/pdf/2211.16002v1.pdf | DiffG-RL: Leveraging Difference between State and Common Sense | Taking into account background knowledge as the context has always been an important part of solving tasks that involve natural language. One representative example of such tasks is text-based games, where players need to make decisions based on both description text previously shown in the game, and their own backgrou... | ['Michiaki Tatsubori', 'Daiki Kimura', 'Tsunehiko Tanaka'] | 2022-11-29 | null | null | null | null | ['text-based-games', 'common-sense-reasoning'] | ['playing-games', 'reasoning'] | [ 2.40224168e-01 2.95125157e-01 -9.18178260e-02 -2.07191601e-01
-5.99334061e-01 -8.43469799e-01 7.73716509e-01 5.66058636e-01
-6.43928170e-01 4.57247317e-01 6.61126852e-01 -4.31133598e-01
7.47196898e-02 -1.12232685e+00 -5.69675088e-01 -1.82922661e-01
2.48279467e-01 4.08050060e-01 6.40977919e-01 -7.10900486... | [3.8051981925964355, 1.322052240371704] |
b7be3329-9443-4c0a-a596-f44f3b192d07 | sensor-fusion-using-backward-shortcut | 1912.06879 | null | https://arxiv.org/abs/1912.06879v2 | https://arxiv.org/pdf/1912.06879v2.pdf | Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data | Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital beds and trained sleep technicians. To assist in the diagnosis process, automated detection methods are... | ['Tom Dhaene', 'Tom Van Steenkiste', 'Dirk Deschrijver'] | 2019-12-14 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 2.65835106e-01 1.05355911e-01 -1.13377152e-02 -6.39546752e-01
-5.19110620e-01 2.61177551e-02 6.67474279e-03 2.10977510e-01
-6.56421244e-01 7.43109226e-01 2.15851828e-01 2.47669592e-02
-1.48123115e-01 -5.76727629e-01 -1.54613748e-01 -7.91611969e-01
2.20703751e-01 2.80771911e-01 2.83037931e-01 -7.42242336... | [13.643396377563477, 3.4449214935302734] |
86d7c9b8-e5f1-42aa-978d-e94518073391 | prompt-tuning-pushes-farther-contrastive | 2307.01595 | null | https://arxiv.org/abs/2307.01595v1 | https://arxiv.org/pdf/2307.01595v1.pdf | Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases | As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the origin... | ['Ying Wang', 'Xin Wang', 'Mengnan Du', 'Yingji Li'] | 2023-07-04 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 6.25243336e-02 2.11580843e-01 -6.74301088e-01 -3.30053866e-01
-5.21567583e-01 -4.20111626e-01 9.38419938e-01 4.01062332e-02
-4.94635850e-01 1.01609254e+00 7.34772265e-01 -5.26827812e-01
2.97246128e-01 -9.51942861e-01 -8.06477010e-01 -5.41463137e-01
2.60117561e-01 3.69467765e-01 -2.19755933e-01 -4.49589193... | [10.289628982543945, 7.693171977996826] |
40444cad-9774-4e06-b2d9-134324fe1935 | large-language-models-in-the-workplace-a-case | 2303.07142 | null | https://arxiv.org/abs/2303.07142v3 | https://arxiv.org/pdf/2303.07142v3.pdf | Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification | This case study investigates the task of job classification in a real-world setting, where the goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position. We explore multiple approaches to text classification, including supervised approaches such as traditional mo... | ['Thomas Brightwell', 'Guillaume Soulié', 'Frederick Naylor', 'Alexandru Ciceu', 'Benjamin Clavié'] | 2023-03-13 | null | null | null | null | ['job-classification'] | ['natural-language-processing'] | [ 7.37294555e-02 1.47744664e-04 -5.49352884e-01 -3.62909436e-01
-7.18004704e-01 -3.61541986e-01 7.39453077e-01 6.96821392e-01
-5.75758457e-01 2.79022217e-01 1.72729939e-01 -1.23890007e+00
-2.43302122e-01 -7.28642583e-01 -2.79718906e-01 -1.64906666e-01
5.69901109e-01 5.57832181e-01 -1.68788563e-02 -4.87154603... | [10.939555168151855, 8.516921043395996] |
1d5d7cb5-c95b-4dd5-bc19-964585829ad4 | cartoonrenderer-an-instance-based-multi-style | 1911.06102 | null | https://arxiv.org/abs/1911.06102v1 | https://arxiv.org/pdf/1911.06102v1.pdf | CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator | Instance based photo cartoonization is one of the challenging image stylization tasks which aim at transforming realistic photos into cartoon style images while preserving the semantic contents of the photos. State-of-the-art Deep Neural Networks (DNNs) methods still fail to produce satisfactory results with input phot... | ['Bingbing Ni', 'Muchun Chen', 'Chaoyue Song', 'Yugang Chen'] | 2019-11-14 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 4.74159300e-01 -3.85489245e-03 2.04845294e-01 -2.22663030e-01
-2.33751178e-01 -5.40823936e-01 7.07702756e-01 -6.14326000e-01
-2.41283420e-02 8.33209276e-01 2.10462749e-01 1.61991507e-01
3.21029276e-01 -1.10655665e+00 -9.86428380e-01 -6.33610368e-01
7.85811484e-01 2.10710704e-01 -1.15027212e-01 -4.68274295... | [11.623879432678223, -0.7245042324066162] |
d65d64be-33d1-4433-9b4b-bcd439df8d78 | multi-predict-few-shot-predictors-for | 2306.02459 | null | https://arxiv.org/abs/2306.02459v1 | https://arxiv.org/pdf/2306.02459v1.pdf | Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search | Many hardware-aware neural architecture search (NAS) methods have been developed to optimize the topology of neural networks (NN) with the joint objectives of higher accuracy and lower latency. Recently, both accuracy and latency predictors have been used in NAS with great success, achieving high sample efficiency and ... | ['Mohamed S. Abdelfattah', 'Yash Akhauri'] | 2023-06-04 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.05351441e-01 -5.77908218e-01 -4.31920469e-01 -6.44154072e-01
-9.94056582e-01 -3.38431418e-01 4.42743972e-02 -2.34824624e-02
-7.20449507e-01 5.61468422e-01 -3.37326348e-01 -5.35049558e-01
-3.00733209e-01 -6.03175163e-01 -9.81457829e-01 -3.90798092e-01
-5.65037914e-02 4.80834812e-01 5.93732953e-01 6.81065097... | [8.508374214172363, 2.994194984436035] |
8ba294d2-49e0-43ad-987f-b48c8d2e6a88 | edge-aware-guidance-fusion-network-for-rgb | 2112.05144 | null | https://arxiv.org/abs/2112.05144v1 | https://arxiv.org/pdf/2112.05144v1.pdf | Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing | RGB thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high level features. In addition, these methods simply fuse the features from RGB and thermal mo... | ['Yaguan Qian', 'Caie Xu', 'Shaohua Dong', 'WuJie Zhou'] | 2021-12-09 | null | null | null | null | ['scene-parsing', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.57436925e-01 -2.06360593e-02 5.85694760e-02 -7.72847295e-01
-9.25307333e-01 -2.84146369e-01 3.17353398e-01 4.63547534e-04
-4.09463197e-01 2.35321954e-01 1.06522210e-01 -1.14992835e-01
-7.02693090e-02 -8.79155934e-01 -7.59752870e-01 -7.74488747e-01
4.96665061e-01 -1.30421817e-01 3.48737866e-01 -1.46419778... | [9.463968276977539, -1.0915910005569458] |
31bfafca-f245-44ef-8fd3-e59d98686e8b | ground-plane-matters-picking-up-ground-plane | 2211.01556 | null | https://arxiv.org/abs/2211.01556v1 | https://arxiv.org/pdf/2211.01556v1.pdf | Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection | The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD). However, it has been neglected by most mainstream methods. In this paper, we identify two key factors that limit the applicability of ground plane prior: the projection point localization issue and the ground plane tilt ... | ['Guiguang Ding', 'Kai Ni', 'Jungong Han', 'Yuchen Guo', 'Hui Chen', 'Xinhao Xu', 'Fan Yang'] | 2022-11-03 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-8.63306075e-02 6.75324127e-02 -3.32106054e-01 -9.14843157e-02
-4.20849741e-01 -3.39935750e-01 2.19838411e-01 -1.22790448e-01
-1.19027101e-01 1.98549822e-01 -2.73412883e-01 -2.67973185e-01
-4.69255522e-02 -8.36808741e-01 -8.67081642e-01 -6.82373703e-01
3.00530523e-01 4.10456449e-01 6.44029319e-01 1.48712676... | [7.916460990905762, -2.481415033340454] |
188ebfe2-d007-4956-a862-715f5098a544 | dense-hybrid-proposal-modulation-for-lane | 2304.14874 | null | https://arxiv.org/abs/2304.14874v1 | https://arxiv.org/pdf/2304.14874v1.pdf | Dense Hybrid Proposal Modulation for Lane Detection | In this paper, we present a dense hybrid proposal modulation (DHPM) method for lane detection. Most existing methods perform sparse supervision on a subset of high-scoring proposals, while other proposals fail to obtain effective shape and location guidance, resulting in poor overall quality. To address this, we densel... | ['Haibin Yan', 'Jiwen Lu', 'Linqing Zhao', 'Yuejian Wu'] | 2023-04-28 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.28193334e-01 1.32443190e-01 -5.66192925e-01 -6.61262870e-01
-9.48456526e-01 -4.61293280e-01 6.24081790e-01 1.97778672e-01
-1.90124691e-01 4.89747167e-01 2.93656349e-01 -1.72091588e-01
-3.60422060e-02 -8.01967502e-01 -7.18725383e-01 -6.55473888e-01
2.06440836e-01 4.55982506e-01 7.37839758e-01 -2.32937075... | [7.957812309265137, -1.6384923458099365] |
5bf25565-d15c-45d1-9c66-522ba3588e18 | computer-vision-application-for-improved | 2207.01323 | null | https://arxiv.org/abs/2207.01323v1 | https://arxiv.org/pdf/2207.01323v1.pdf | Computer vision application for improved product traceability in the granite manufacturing industry | The traceability of granite blocks consists in identifying each block with a finite number of color bands which represent a numerical code. This code has to be read several times throughout the manufacturing process, but its accuracy is subject to human errors, leading to cause faults in the traceability system. A comp... | ['Antonio Recaman', 'Maria Araujo', 'Javier Martinez', 'Xurxo Rigueira'] | 2022-07-04 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 3.61055493e-01 -2.66256094e-01 3.79075587e-01 -4.88947555e-02
-6.34394065e-02 -6.46603644e-01 3.86330992e-01 4.15132701e-01
-2.86158204e-01 3.49302769e-01 -8.46136510e-01 -4.74322647e-01
-3.49431396e-01 -1.07832420e+00 -2.25634381e-01 -7.08587825e-01
3.78958315e-01 4.25508440e-01 1.50103301e-01 -3.05282623... | [9.433064460754395, -1.597640037536621] |
20bb5cdb-bdb2-4bfa-a360-d14c420e44d3 | glt-t-global-local-transformer-voting-for-3d | 2211.10927 | null | https://arxiv.org/abs/2211.10927v1 | https://arxiv.org/pdf/2211.10927v1.pdf | GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds | Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated eq... | ['Jing Zhang', 'Mingyu Gao', 'Yuxiang Yang', 'Zhiwei He', 'Jiahao Nie'] | 2022-11-20 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.19197059e-01 -2.38693818e-01 -5.62344193e-01 -3.64372700e-01
-7.09885597e-01 -3.94063860e-01 6.57356024e-01 -1.07522354e-01
-2.88644493e-01 3.49528223e-01 -1.05909025e-02 -1.56526402e-01
1.22618735e-01 -7.27453709e-01 -6.03476584e-01 -8.31609607e-01
4.07961786e-01 4.90036070e-01 7.24759340e-01 -6.34534657... | [6.561954498291016, -2.3013381958007812] |
bab38a03-173b-47f9-9eaf-194d2816d7b6 | osp2b-one-stage-point-to-box-network-for-3d | 2304.11584 | null | https://arxiv.org/abs/2304.11584v2 | https://arxiv.org/pdf/2304.11584v2.pdf | OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking | Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. T... | ['Jing Zhang', 'Mingyu Gao', 'Zhengyi Bao', 'Yuxiang Yang', 'Zhiwei He', 'Jiahao Nie'] | 2023-04-23 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-3.74136776e-01 -3.05513412e-01 -4.78809029e-01 -3.13562304e-01
-7.95417130e-01 -4.44556475e-01 3.50004673e-01 -1.70006201e-01
-3.71621490e-01 2.75991470e-01 -1.02319047e-01 4.79896627e-02
-8.04997981e-02 -3.99137974e-01 -6.05732262e-01 -7.59368062e-01
-2.36047313e-01 5.71267784e-01 8.30168188e-01 -1.21702708... | [6.43184232711792, -2.237067937850952] |
d712b037-9812-48b9-8700-7650f385aa55 | nnembs-at-semeval-2017-task-4-neural-twitter | null | null | https://aclanthology.org/S17-2102 | https://aclanthology.org/S17-2102.pdf | NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings | Recently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models. We collect several word embedding sets ... | ['Ming Zhang', 'Yangqiu Song', 'Yichun Yin'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['learning-word-embeddings'] | ['methodology'] | [-2.18405500e-01 -3.09609741e-01 -2.42049247e-01 -4.16510403e-01
-4.60356295e-01 -5.04600883e-01 6.65308535e-01 1.19343303e-01
-9.39570308e-01 6.62963569e-01 5.49857378e-01 -1.85412839e-01
2.72192024e-02 -8.37695062e-01 -5.70291817e-01 -6.85596764e-01
1.37423247e-01 1.92525722e-02 3.92474048e-02 -5.74482024... | [10.540263175964355, 8.331510543823242] |
4222b83d-ba58-4fb3-b05c-cafe75484386 | scalable-k-means-clustering-via-lightweight | 1702.08248 | null | http://arxiv.org/abs/1702.08248v2 | http://arxiv.org/pdf/1702.08248v2.pdf | Scalable k-Means Clustering via Lightweight Coresets | Coresets are compact representations of data sets such that models trained on
a coreset are provably competitive with models trained on the full data set. As
such, they have been successfully used to scale up clustering models to massive
data sets. While existing approaches generally only allow for multiplicative
appro... | ['Andreas Krause', 'Olivier Bachem', 'Mario Lucic'] | 2017-02-27 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 3.14869165e-01 3.02151352e-01 -4.44742948e-01 -4.15840894e-01
-1.12352598e+00 -5.04706323e-01 5.60956776e-01 7.21398890e-01
-2.46657684e-01 2.89345235e-01 4.56780970e-01 3.25108357e-02
-5.00078321e-01 -6.46364033e-01 -7.89746106e-01 -7.93241262e-01
-2.16746330e-01 1.01237845e+00 6.68802261e-02 3.10356617... | [6.810621738433838, 5.068139553070068] |
5f009c08-8561-4386-97d0-fe92bc9b2a8b | flexible-channel-dimensions-for | 2306.08021 | null | https://arxiv.org/abs/2306.08021v1 | https://arxiv.org/pdf/2306.08021v1.pdf | Flexible Channel Dimensions for Differentiable Architecture Search | Finding optimal channel dimensions (i.e., the number of filters in DNN layers) is essential to design DNNs that perform well under computational resource constraints. Recent work in neural architecture search aims at automating the optimization of the DNN model implementation. However, existing neural architecture sear... | ['Pascal Frossard', 'Nikolaos Dimitriadis', 'Ahmet Caner Yüzügüler'] | 2023-06-13 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 5.57452887e-02 -1.59167245e-01 4.00923267e-02 -3.44225496e-01
-4.40448970e-01 -6.46720111e-01 1.80430725e-01 -1.16493538e-01
-7.13597178e-01 5.51833868e-01 -2.76700139e-01 -6.23617887e-01
-3.01021218e-01 -8.20544362e-01 -6.23802543e-01 -5.77512145e-01
2.68598318e-01 5.56100190e-01 -7.79897021e-03 5.53825274... | [8.462272644042969, 3.104220390319824] |
10be9c2b-19e6-4c36-8cb5-031091227740 | a-cnn-transformer-deep-learning-model-for | 2211.13005 | null | https://arxiv.org/abs/2211.13005v1 | https://arxiv.org/pdf/2211.13005v1.pdf | A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device | This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The... | ['Xilin Liu', 'Zongyan Yao'] | 2022-11-20 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [-2.55871832e-01 -3.38227659e-01 1.09086268e-01 -5.24475873e-01
-1.22739293e-01 -8.74751732e-02 -2.92306572e-01 -1.31308630e-01
-7.32626319e-01 9.15023565e-01 -2.43586704e-01 -2.51565963e-01
-3.56522501e-02 -6.03009760e-01 -3.54148477e-01 -6.47739887e-01
-3.52042615e-01 -1.60375997e-01 1.85283899e-01 1.37227625... | [13.475996017456055, 3.5056540966033936] |
b92fdb20-1391-431d-9147-d9844aa39e81 | make-your-video-customized-video-generation | 2306.00943 | null | https://arxiv.org/abs/2306.00943v1 | https://arxiv.org/pdf/2306.00943v1.pdf | Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance | Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient in conveying the overall scene context, it may be insufficient to control precise... | ['Tien-Tsin Wong', 'Ying Shan', 'Xintao Wang', 'Xiaodong Cun', 'Haoxin Chen', 'Hanyuan Liu', 'Yingqing He', 'Yong Zhang', 'Yuechen Zhang', 'Yuxin Liu', 'Menghan Xia', 'Jinbo Xing'] | 2023-06-01 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 4.66443181e-01 -9.84533429e-02 -1.53616369e-01 -2.79520243e-01
-7.79830039e-01 -4.09400553e-01 9.18876946e-01 -2.93912262e-01
-7.07165748e-02 7.48296797e-01 6.94162190e-01 -1.48355499e-01
3.25550050e-01 -6.15117788e-01 -6.34073734e-01 -7.30918765e-01
3.13178688e-01 -1.64499655e-01 1.46040931e-01 -1.79826230... | [10.867264747619629, -0.5807967782020569] |
e7448fd6-6b70-46e4-a4b0-63f15cd6f936 | deeprecon-joint-2d-cardiac-segmentation-and | 2206.07163 | null | https://arxiv.org/abs/2206.07163v1 | https://arxiv.org/pdf/2206.07163v1.pdf | DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method | Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns. However, due to the low through-plane resolution of cine MR and high inter-subject variance, accurately segmenting cardiac images and re... | ['Dimitris Metaxas', 'Leon Axel', 'Subhi Al Aref', 'Mikael Kanski', 'Qilong Zhangli', 'Meng Ye', 'Khalid Sawalha', 'Di Liu', 'Mu Zhou', 'Zhennan Yan', 'Qi Chang'] | 2022-06-14 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.39222541e-01 1.61088705e-01 -5.08508720e-02 -4.78184015e-01
-1.03073478e+00 -9.12490666e-01 1.86363190e-01 -1.20407425e-01
-2.45936438e-02 4.55389172e-01 4.26870346e-01 -4.23647836e-02
-2.58435130e-01 -5.18413544e-01 -4.29740220e-01 -7.18796909e-01
-3.05666506e-01 8.40075910e-01 1.47759646e-01 4.04940248... | [13.979771614074707, -2.389449119567871] |
29e09ea3-7594-442f-bdc8-f1a3461edc9c | region-prediction-for-efficient-robot | 2303.00295 | null | https://arxiv.org/abs/2303.00295v1 | https://arxiv.org/pdf/2303.00295v1.pdf | Region Prediction for Efficient Robot Localization on Large Maps | Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure detection. In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with... | ['Davide Maltoni', 'Matteo Scucchia'] | 2023-03-01 | null | null | null | null | ['simultaneous-localization-and-mapping', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 1.55546933e-01 2.05937531e-02 -1.62566245e-01 -4.81575310e-01
-5.17872870e-01 -5.77831864e-01 6.87512338e-01 5.82891822e-01
-7.09897041e-01 8.17330420e-01 -3.02716315e-01 -2.19483107e-01
-3.51904958e-01 -7.95927048e-01 -9.27304089e-01 -6.00456595e-01
-6.35556698e-01 8.89821827e-01 6.11357570e-01 -1.66183114... | [7.356849193572998, -2.0206503868103027] |
55b5a8c5-8daf-4f16-8224-339e7527c6d0 | gait-recognition-using-3-d-human-body-shape | 2212.09042 | null | https://arxiv.org/abs/2212.09042v1 | https://arxiv.org/pdf/2212.09042v1.pdf | Gait Recognition Using 3-D Human Body Shape Inference | Gait recognition, which identifies individuals based on their walking patterns, is an important biometric technique since it can be observed from a distance and does not require the subject's cooperation. Recognizing a person's gait is difficult because of the appearance variants in human silhouette sequences produced ... | ['Ram Nevatia', 'Zhaoheng Zheng', 'Haidong Zhu'] | 2022-12-18 | null | null | null | null | ['gait-recognition', 'gait-identification'] | ['computer-vision', 'computer-vision'] | [ 1.10138267e-01 -4.32144135e-01 -3.57635058e-02 -3.84974360e-01
-2.89700598e-01 -6.12971008e-01 3.37280899e-01 -3.88784796e-01
-3.13806385e-01 6.25868738e-01 1.70578286e-01 4.06867057e-01
2.60492086e-01 -5.75779676e-01 -4.63048190e-01 -6.43401563e-01
-3.27449322e-01 7.22303987e-01 2.45610595e-01 -2.72079319... | [14.279719352722168, 1.382308006286621] |
0baa8371-ba8d-4f42-ad85-482e3f1e46e5 | boningknife-joint-entity-mention-detection | 2107.09429 | null | https://arxiv.org/abs/2107.09429v1 | https://arxiv.org/pdf/2107.09429v1.pdf | BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge | While named entity recognition (NER) is a key task in natural language processing, most approaches only target flat entities, ignoring nested structures which are common in many scenarios. Most existing nested NER methods traverse all sub-sequences which is both expensive and inefficient, and also don't well consider b... | ['Börje F. Karlsson', 'Chengxi Zhang', 'WEILE CHEN', 'Guoxin Wang', 'Huiqiang Jiang'] | 2021-07-20 | null | null | null | null | ['nested-named-entity-recognition', 'nested-mention-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-3.92607480e-01 2.43243590e-01 -2.79897362e-01 -4.57790256e-01
-8.08445513e-01 -7.59990811e-01 3.35344225e-01 5.44590592e-01
-8.32306385e-01 7.76759267e-01 5.84978282e-01 -3.49761158e-01
2.72951901e-01 -1.04398489e+00 -7.53164589e-01 -2.37219393e-01
-6.98041767e-02 3.07632983e-01 3.20712835e-01 -9.77445990... | [9.570478439331055, 9.440321922302246] |
693f1dd6-41ad-4fb7-9b21-e9defce1e75e | graph-collaborative-reasoning | 2112.13705 | null | https://arxiv.org/abs/2112.13705v2 | https://arxiv.org/pdf/2112.13705v2.pdf | Graph Collaborative Reasoning | Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering. However, most of the graph-structured data in practice suffers from incompleteness, and thus link prediction becomes an important research pro... | ['Yongfeng Zhang', 'He Zhu', 'Shuchang Liu', 'Shaoyun Shi', 'Yunqi Li', 'Hanxiong Chen'] | 2021-12-27 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-2.23453179e-01 4.18215513e-01 -6.64073288e-01 -3.90679538e-01
2.39566699e-01 -2.56392926e-01 3.93051356e-01 4.84341115e-01
3.72580513e-02 4.14659679e-01 7.75545761e-02 -7.52285123e-01
-5.50758958e-01 -1.53988266e+00 -7.22193480e-01 -1.49075314e-03
-1.19126312e-01 4.81598884e-01 5.97486258e-01 -4.30709213... | [8.957550048828125, 7.779364109039307] |
8f052b9e-a5a5-4164-9ece-918548d5debb | 3d-human-pose-estimation-with-spatial-and | 2103.10455 | null | https://arxiv.org/abs/2103.10455v3 | https://arxiv.org/pdf/2103.10455v3.pdf | 3D Human Pose Estimation with Spatial and Temporal Transformers | Transformer architectures have become the model of choice in natural language processing and are now being introduced into computer vision tasks such as image classification, object detection, and semantic segmentation. However, in the field of human pose estimation, convolutional architectures still remain dominant. I... | ['Zhengming Ding', 'Chen Chen', 'Taojiannan Yang', 'Matias Mendieta', 'Sijie Zhu', 'Ce Zheng'] | 2021-03-18 | 3d-human-pose-estimation-with-spatial-and-1 | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_3D_Human_Pose_Estimation_With_Spatial_and_Temporal_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_3D_Human_Pose_Estimation_With_Spatial_and_Temporal_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-3.25878322e-01 -1.66204885e-01 1.87302724e-01 -4.05345708e-01
-5.93786538e-01 -4.13006157e-01 4.33488846e-01 -1.41764477e-01
-7.01386511e-01 1.43382221e-01 1.79914951e-01 8.29595476e-02
1.56668007e-01 -3.61040384e-01 -6.98368728e-01 -2.94036478e-01
-1.32992074e-01 6.88031554e-01 4.28366542e-01 -1.99676886... | [7.1348185539245605, -0.7319750189781189] |
1b1441ba-86e3-4ed1-9f9f-983b0dc7b80a | premise-based-multimodal-reasoning-a-human | 2105.07122 | null | https://arxiv.org/abs/2105.07122v3 | https://arxiv.org/pdf/2105.07122v3.pdf | Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues | It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query. In this work, we take a sober look at such an unconditional formulation in the sense that no prior knowledge is speci... | ['Zhongyu Wei', 'Sujian Li', 'Weidong Zhan', 'Zuifang Sui', 'Tianyu Liu', 'Lin Xu', 'Haoran Meng', 'Shoujie Tong', 'Tian Feng', 'Heming Xia', 'Ziwei Qin', 'Qingxiu Dong'] | 2021-05-15 | null | https://aclanthology.org/2022.acl-long.66 | https://aclanthology.org/2022.acl-long.66.pdf | acl-2022-5 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 5.44256151e-01 4.22991812e-02 6.91659749e-02 -4.03704971e-01
-1.02579594e+00 -7.45489478e-01 8.93717349e-01 -2.68599272e-01
-5.46224654e-01 6.48051679e-01 5.56188934e-02 -5.03335655e-01
2.06049979e-01 -6.01035774e-01 -9.39681232e-01 -4.67974752e-01
7.98557401e-01 4.66913491e-01 3.13830823e-01 -3.26907903... | [10.750039100646973, 1.6436680555343628] |
edff9c71-0f1b-40e8-985d-998497fcec01 | 2d-3d-facial-expression-recognition-via-1 | 2201.12506 | null | https://arxiv.org/abs/2201.12506v1 | https://arxiv.org/pdf/2201.12506v1.pdf | 2D+3D facial expression recognition via embedded tensor manifold regularization | In this paper, a novel approach via embedded tensor manifold regularization for 2D+3D facial expression recognition (FERETMR) is proposed. Firstly, 3D tensors are constructed from 2D face images and 3D face shape models to keep the structural information and correlations. To maintain the local structure (geometric info... | ['Jun Wan', 'Yi Jin', 'Gaoyun An', 'Ziyan Luo', 'Qiuqi Ruan', 'Yunfang Fu'] | 2022-01-29 | null | null | null | null | ['3d-facial-expression-recognition', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [-3.00211996e-01 -1.11098453e-01 -2.11206526e-01 -2.88833499e-01
-3.28231990e-01 -7.42057115e-02 5.40493522e-03 -5.40393829e-01
-9.20836441e-03 3.21836233e-01 1.64590001e-01 -1.04621053e-01
-6.02362871e-01 -1.64484382e-01 -3.99673402e-01 -1.06411505e+00
-3.98963124e-01 1.77999567e-02 -6.59761369e-01 -3.07362020... | [7.610286235809326, 4.405937194824219] |
d2e8025b-d5e2-466c-b924-d4b5889730d1 | vtcc-nlp-at-nl4opt-competition-subtask-1-an | 2212.07219 | null | https://arxiv.org/abs/2212.07219v1 | https://arxiv.org/pdf/2212.07219v1.pdf | VTCC-NLP at NL4Opt competition subtask 1: An Ensemble Pre-trained language models for Named Entity Recognition | We propose a combined three pre-trained language models (XLM-R, BART, and DeBERTa-V3) as an empower of contextualized embedding for named entity recognition. Our model achieves a 92.9% F1 score on the test set and ranks 5th on the leaderboard at NL4Opt competition subtask 1. | ['Xuan-Dung Doan'] | 2022-12-14 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-2.65009612e-01 5.07100642e-01 -4.26444054e-01 -4.56375003e-01
-9.98416245e-01 -6.14465952e-01 7.14687884e-01 1.53072670e-01
-9.69648778e-01 7.41438746e-01 8.29951167e-01 -5.58253169e-01
1.48819372e-01 -2.25595847e-01 -5.20066977e-01 5.28447218e-02
-1.72196835e-01 4.74363387e-01 -3.02834392e-01 -1.32555634... | [9.78378963470459, 9.621247291564941] |
183ebc6d-fa0a-4f23-b2e8-6063b2d48e86 | multivariate-probabilistic-forecasting-of | 2205.13826 | null | https://arxiv.org/abs/2205.13826v4 | https://arxiv.org/pdf/2205.13826v4.pdf | Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows | Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) ... | ['Manuel Dahmen', 'Alexander Mitsos', 'Dirk Witthaut', 'Eike Cramer'] | 2022-05-27 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-4.79770064e-01 -2.71649331e-01 -7.33881593e-02 -1.22529380e-01
-5.41094482e-01 -7.06620455e-01 8.05616260e-01 -1.44114261e-02
5.94251975e-02 1.08491015e+00 1.01861067e-01 -4.50624228e-01
-6.27400279e-01 -1.07312107e+00 -4.98400718e-01 -8.82381737e-01
-1.57799631e-01 7.33153880e-01 -3.65120918e-01 2.41847727... | [6.062414646148682, 3.0027811527252197] |
4aea09bd-be47-43af-9d66-bb125467aa39 | pmal-open-set-recognition-via-robust | 2203.08569 | null | https://arxiv.org/abs/2203.08569v1 | https://arxiv.org/pdf/2203.08569v1.pdf | PMAL: Open Set Recognition via Robust Prototype Mining | Open Set Recognition (OSR) has been an emerging topic. Besides recognizing predefined classes, the system needs to reject the unknowns. Prototype learning is a potential manner to handle the problem, as its ability to improve intra-class compactness of representations is much needed in discrimination between the known ... | ['Yi Niu', 'Zhanzhan Cheng', 'Hao Li', 'Yunxu Xu', 'Jing Lu'] | 2022-03-16 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.51278049e-01 -8.21464520e-04 -2.66177684e-01 -4.24548715e-01
-4.81578082e-01 -1.82999447e-01 3.23492616e-01 1.66636452e-01
-1.90159515e-01 5.87071300e-01 -1.81919962e-01 -6.76622540e-02
-8.19975615e-01 -7.28352606e-01 -3.64377946e-01 -8.70568037e-01
2.63953526e-02 4.67823058e-01 8.52680132e-02 -9.51861590... | [9.654389381408691, 3.035294771194458] |
b2a3eb66-a5aa-475e-9123-db6f3f41a3b0 | se-ssd-self-ensembling-single-stage-object | 2104.09804 | null | https://arxiv.org/abs/2104.09804v1 | https://arxiv.org/pdf/2104.09804v1.pdf | SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud | We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specif... | ['Chi-Wing Fu', 'Li Jiang', 'Weiliang Tang', 'Wu Zheng'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zheng_SE-SSD_Self-Ensembling_Single-Stage_Object_Detector_From_Point_Cloud_CVPR_2021_paper.pdf | cvpr-2021-1 | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-1.37571439e-01 1.93166226e-01 5.95895462e-02 -5.30160904e-01
-8.62958252e-01 -4.80898768e-01 4.71183032e-01 -7.37078935e-02
-2.66606331e-01 2.02446785e-02 -5.85790396e-01 -3.37373286e-01
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2.94516832e-01 6.88445330e-01 8.80984247e-01 -3.67601030... | [7.832198619842529, -2.432886838912964] |
313abe1e-c5b8-4096-9c80-6218808a65b4 | achieving-real-time-object-detection-on | 2106.14943 | null | https://arxiv.org/abs/2106.14943v1 | https://arxiv.org/pdf/2106.14943v1.pdf | Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search | Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a compiler-aware neural pruning search framework to achieve high-speed inferen... | ['Xue Lin', 'Yanzhi Wang', 'Bin Ren', 'Yuxuan Cai', 'Geng Yuan', 'Wei Niu', 'Pu Zhao'] | 2021-06-28 | null | null | null | null | ['compiler-optimization', 'real-time-object-detection'] | ['computer-code', 'computer-vision'] | [-2.59100169e-01 -1.30667239e-01 -7.16383755e-01 -4.42591310e-01
-5.45805991e-01 -3.66946697e-01 5.04659712e-01 1.25605181e-01
-5.07789850e-01 1.74879320e-02 -9.29932833e-01 -1.17461407e+00
3.80775422e-01 -9.28141594e-01 -8.36281598e-01 -2.64967054e-01
5.62259182e-02 5.67999005e-01 1.02152216e+00 -1.81198031... | [8.22231388092041, -1.1965583562850952] |
e5ecdb79-4569-479b-9252-632b09bfeaf3 | lasso-based-feature-selection-for-malaria | 1511.01284 | null | http://arxiv.org/abs/1511.01284v1 | http://arxiv.org/pdf/1511.01284v1.pdf | Lasso based feature selection for malaria risk exposure prediction | In life sciences, the experts generally use empirical knowledge to recode
variables, choose interactions and perform selection by classical approach. The
aim of this work is to perform automatic learning algorithm for variables
selection which can lead to know if experts can be help in they decision or
simply replaced ... | ['Noël Fonton', 'Bienvenue Kouwayè', 'Fabrice Rossi'] | 2015-11-04 | null | null | null | null | ['malaria-risk-exposure-prediction'] | ['medical'] | [ 1.39619395e-01 1.02420291e-02 -4.55173552e-01 -4.05100286e-01
-2.01773003e-01 -3.54090750e-01 3.64632308e-01 2.30846450e-01
-3.42365116e-01 1.51737833e+00 -1.61706492e-01 -2.51502752e-01
-5.63676357e-01 -7.12625384e-01 -4.66941565e-01 -7.38060772e-01
-1.01431780e-01 8.68582368e-01 -2.10019141e-01 -4.84367646... | [7.817060947418213, 4.8235955238342285] |
e7c0e292-82d5-4a5f-9309-d76a8f330b6f | multi-modal-entity-alignment-in-hyperbolic | 2106.03619 | null | https://arxiv.org/abs/2106.03619v1 | https://arxiv.org/pdf/2106.03619v1.pdf | Multi-modal Entity Alignment in Hyperbolic Space | Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However, MMKGs usually suffer from low coverage and incompleteness. To mitigate this problem, a viable appro... | ['Li Liu', 'Xiang Zhao', 'Weixin Zeng', 'Jiuyang Tang', 'Hao Guo'] | 2021-06-07 | null | null | null | null | ['multi-modal-entity-alignment'] | ['knowledge-base'] | [-2.53468335e-01 4.43657845e-01 -2.34022979e-02 -9.66374725e-02
-3.83660406e-01 -4.07031536e-01 4.97786343e-01 2.81308472e-01
-1.44615084e-01 3.90712053e-01 5.10640323e-01 1.64374575e-01
-4.13309038e-01 -1.09049547e+00 -6.31059408e-01 -6.28261626e-01
2.59453446e-01 2.06913605e-01 1.35073319e-01 -2.01650977... | [8.687518119812012, 7.731289386749268] |
25a6f550-eae7-47f1-9ded-ccedf60cbe9c | complementary-pseudo-multimodal-feature-for | 2303.13194 | null | https://arxiv.org/abs/2303.13194v1 | https://arxiv.org/pdf/2303.13194v1.pdf | Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection | Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descriptions with powerful pre-trained 2D neural networks. To this end, this study proposes Complementary Pseudo Multimodal Feature (CPMF) that inc... | ['Weiming Shen', 'Xiaohao Xu', 'Yunkang Cao'] | 2023-03-23 | null | null | null | null | ['3d-anomaly-detection-and-segmentation', 'depth-anomaly-detection-and-segmentation'] | ['methodology', 'methodology'] | [-8.19336921e-02 -1.93229243e-01 4.58343811e-02 -1.49862692e-01
-9.14686680e-01 -4.09003764e-01 9.02625024e-01 3.04301471e-01
-1.61634728e-01 2.38244548e-01 1.42458647e-01 9.71901566e-02
9.77319255e-02 -8.37511897e-01 -7.10409343e-01 -7.63464272e-01
-5.21000549e-02 3.02812755e-01 3.53671163e-01 -2.13312060... | [7.656673908233643, 1.9371974468231201] |
23190c6b-7e9f-466c-b9a5-7082f862c7c4 | evidence-of-task-independent-person-specific | 2007.13517 | null | https://arxiv.org/abs/2007.13517v4 | https://arxiv.org/pdf/2007.13517v4.pdf | Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques | Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by... | ['Hema A. Murthy', 'Shrikanth Narayanan', 'Mriganka Sur', 'Mari Ganesh Kumar'] | 2020-07-27 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 2.65855521e-01 -4.16864723e-01 2.21703917e-01 -4.60713804e-01
-5.44470191e-01 -5.31144679e-01 4.02054757e-01 -1.22099958e-01
-5.94028831e-01 7.05528736e-01 4.43762124e-01 1.79537624e-01
-2.88290232e-01 -5.45348860e-02 -3.22951883e-01 -9.30677354e-01
-2.34868884e-01 4.73245904e-02 -5.36568344e-01 2.03597203... | [13.210680961608887, 3.2646782398223877] |
81ee57d5-4027-4577-b5e3-f6c96fb1dd17 | diverse-plausible-360-degree-image | 2203.14668 | null | https://arxiv.org/abs/2203.14668v1 | https://arxiv.org/pdf/2203.14668v1.pdf | Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation | We address the problem of generating a 360-degree image from a single image with a narrow field of view by estimating its surroundings. Previous methods suffered from overfitting to the training resolution and deterministic generation. This paper proposes a completion method using a transformer for scene modeling and n... | ['Yoshimitsu Aoki', 'Yuhi Matsuo', 'Naofumi Akimoto'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Akimoto_Diverse_Plausible_360-Degree_Image_Outpainting_for_Efficient_3DCG_Background_Creation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Akimoto_Diverse_Plausible_360-Degree_Image_Outpainting_for_Efficient_3DCG_Background_Creation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 3.88927490e-01 1.13671519e-01 5.80155015e-01 -2.56335348e-01
-5.64392865e-01 -5.68997979e-01 5.33184707e-01 -6.34011328e-01
-4.55004089e-02 5.54576099e-01 -7.80447014e-03 -2.34059975e-01
3.50951254e-01 -8.94634724e-01 -1.04302859e+00 -5.01757443e-01
3.48478734e-01 1.08472683e-01 3.64398241e-01 -1.67796940... | [9.388742446899414, -3.0922200679779053] |
c966d7e3-90f7-43a4-bdd9-f0fc3f54d49b | data-splits-and-metrics-for-method | 2204.05235 | null | https://arxiv.org/abs/2204.05235v2 | https://arxiv.org/pdf/2204.05235v2.pdf | Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets | In addition to generating data and annotations, devising sensible data splitting strategies and evaluation metrics is essential for the creation of a benchmark dataset. This practice ensures consensus on the usage of the data, homogeneous assessment, and uniform comparison of research methods on the dataset. This study... | ['Nicolas Padoy', 'Chinedu Innocent Nwoye'] | 2022-04-11 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 8.23865924e-03 3.45175317e-03 -7.29469180e-01 -3.97450298e-01
-7.52073288e-01 -5.80577791e-01 5.83460093e-01 1.85738072e-01
-6.38400316e-01 4.63827014e-01 6.74090087e-01 -3.58024389e-01
-4.80507731e-01 -4.56497997e-01 -3.54733676e-01 -4.67393786e-01
-2.50891060e-01 3.99465501e-01 -1.25930784e-02 2.60213345... | [14.08061408996582, -3.3648154735565186] |
c32246ad-fa70-412e-85b7-17a10d154251 | an-advanced-yolov3-method-for-small-object | 2212.02809 | null | https://arxiv.org/abs/2212.02809v3 | https://arxiv.org/pdf/2212.02809v3.pdf | An advanced YOLOv3 method for small object detection | Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To so... | ['Wenjie Liu', 'Jiacheng Li', 'Shiqiang Du', 'Fengjie He', 'Baokai Liu'] | 2022-12-06 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-2.27562517e-01 -2.25376531e-01 1.46347851e-01 1.80772871e-01
-1.34589016e-01 -3.29971202e-02 2.04862759e-01 -1.34508342e-01
-7.41231084e-01 2.94905156e-01 -3.07189643e-01 5.62679395e-02
3.20014328e-01 -7.51062691e-01 -5.17757893e-01 -9.88994837e-01
3.34062397e-01 -2.04710141e-01 1.10902250e+00 -2.45247304... | [8.674844741821289, -0.6441714763641357] |
824cc224-183a-409b-beab-3dda5769978a | repeatability-is-not-enough-learning-affine | 1711.06704 | null | http://arxiv.org/abs/1711.06704v4 | http://arxiv.org/pdf/1711.06704v4.pdf | Repeatability Is Not Enough: Learning Affine Regions via Discriminability | A method for learning local affine-covariant regions is presented. We show
that maximizing geometric repeatability does not lead to local regions, a.k.a
features,that are reliably matched and this necessitates descriptor-based
learning. We explore factors that influence such learning and registration: the
loss function... | ['Jiri Matas', 'Dmytro Mishkin', 'Filip Radenovic'] | 2017-11-17 | repeatability-is-not-enough-learning-affine-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Dmytro_Mishkin_Repeatability_Is_Not_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Dmytro_Mishkin_Repeatability_Is_Not_ECCV_2018_paper.pdf | eccv-2018-9 | ['image-matching'] | ['computer-vision'] | [-1.59470096e-01 -2.72085965e-01 -1.98514789e-01 -7.93302238e-01
-1.50548363e+00 -7.97598600e-01 7.54253924e-01 2.47068748e-01
-6.34662986e-01 2.82544851e-01 2.80383706e-01 2.30360091e-01
-3.58487815e-01 -7.22243845e-01 -9.35981691e-01 -8.27876270e-01
-4.45755161e-02 4.40654159e-01 2.25394413e-01 -3.82828936... | [8.175150871276855, -2.042151689529419] |
a43f270b-68d2-43ba-b3a4-cc9798507807 | tinydefectnet-highly-compact-deep-neural | 2111.14319 | null | https://arxiv.org/abs/2111.14319v1 | https://arxiv.org/pdf/2111.14319v1.pdf | TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection | A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-consuming and laborious, and is a significant bottleneck especially for high-throughput manufacturing scenarios. Given significant advances in t... | ['Alexander Wong', 'Francis Li', 'Gautam Bathla', 'Mahmoud Famouri', 'Mohammad Javad Shafiee'] | 2021-11-29 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-1.54328883e-01 9.54231340e-03 1.22983918e-01 -1.02502555e-01
-2.07099035e-01 -1.45463914e-01 -2.47225732e-01 4.22431499e-01
5.95121048e-02 -2.87461951e-02 -7.11150169e-01 -8.17414582e-01
-1.02366768e-01 -8.48028660e-01 -5.31820297e-01 -2.51973152e-01
-2.52016068e-01 2.63447136e-01 1.23394378e-01 -1.03642590... | [7.392809867858887, 1.9408339262008667] |
21fc37c5-f865-42a7-ad8c-08f4b85e64af | a-neural-prosody-encoder-for-end-ro-end | 2205.05590 | null | https://arxiv.org/abs/2205.05590v1 | https://arxiv.org/pdf/2205.05590v1.pdf | A neural prosody encoder for end-ro-end dialogue act classification | Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural approaches to integrate prosodic features into end-to-end (E2E) DAC m... | ['Maurizio Omologo', 'Athanasios Mouchtaris', 'Nathan Susanj', 'Grant P. Strimel', 'Markus Muller', 'Thanh Tran', 'Martin Radfar', 'Dillon Knox', 'Kai Wei'] | 2022-05-11 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 2.18809824e-02 4.44809109e-01 -3.29863355e-02 -9.36541498e-01
-6.38641357e-01 -6.25288248e-01 6.66409969e-01 1.97255820e-01
-5.11602879e-01 7.15622663e-01 8.48949075e-01 1.29880413e-01
2.79602140e-01 -5.99708498e-01 -5.30571640e-02 -4.88281161e-01
-3.23713645e-02 3.86970580e-01 8.55692849e-02 -6.09218240... | [12.94299030303955, 7.681529998779297] |
982a1433-1b94-49f8-8b4c-e2e49ec09216 | local-global-context-aware-transformer-for | 2203.09773 | null | https://arxiv.org/abs/2203.09773v1 | https://arxiv.org/pdf/2203.09773v1.pdf | Local-Global Context Aware Transformer for Language-Guided Video Segmentation | We explore the task of language-guided video segmentation (LVS). Previous algorithms mostly adopt 3D CNNs to learn video representation, struggling to capture long-term context and easily suffering from visual-linguistic misalignment. In light of this, we present Locater (local-global context aware Transformer), which ... | ['Yi Yang', 'Yawei Luo', 'Jiaxu Miao', 'Tianfei Zhou', 'Wenguan Wang', 'Chen Liang'] | 2022-03-18 | null | null | null | null | ['referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.40810364e-01 -1.89634785e-01 -5.19239724e-01 -2.34576076e-01
-9.59350705e-01 -8.14896286e-01 1.87752202e-01 -7.58527145e-02
-3.60565335e-01 1.57107204e-01 1.56336784e-01 -3.19833487e-01
4.12894785e-01 -4.06728089e-01 -9.08671737e-01 -4.57910836e-01
7.59482682e-02 2.92590767e-01 4.31177080e-01 2.62219068... | [9.531639099121094, 0.4532826542854309] |
757cc530-7f27-487d-93bb-6d6eade4a98d | vulaste-long-sequence-model-with-abstract | 2302.02345 | null | https://arxiv.org/abs/2302.02345v1 | https://arxiv.org/pdf/2302.02345v1.pdf | VuLASTE: Long Sequence Model with Abstract Syntax Tree Embedding for vulnerability Detection | In this paper, we build a model named VuLASTE, which regards vulnerability detection as a special text classification task. To solve the vocabulary explosion problem, VuLASTE uses a byte level BPE algorithm from natural language processing. In VuLASTE, a new AST path embedding is added to represent source code nesting ... | ['Huobin Tan', 'Botong Zhu'] | 2023-02-05 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-4.12772864e-01 -4.81646925e-01 -2.48573929e-01 -1.24840297e-01
-6.53110504e-01 -6.18995249e-01 5.92637844e-02 8.47006798e-01
-3.55012149e-01 2.59258747e-01 4.27260727e-01 -4.95103866e-01
2.24148855e-01 -9.51587439e-01 -5.61904013e-01 -1.60026819e-01
-2.80139707e-02 -1.41088590e-01 5.76040208e-01 -3.99871975... | [7.060922145843506, 7.771525859832764] |
278427b3-fe13-40e1-9706-12f0ac1dd135 | pointnu-net-simultaneous-multi-tissue | 2111.01557 | null | https://arxiv.org/abs/2111.01557v2 | https://arxiv.org/pdf/2111.01557v2.pdf | PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification | Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this paper, we aim to build a reliable and robust method capable ... | ['Amir Hussain', 'Jie Sun', 'Kaizhu Huang', 'Kai Yao'] | 2021-11-01 | null | null | null | null | ['multi-tissue-nucleus-segmentation'] | ['medical'] | [ 4.22892004e-01 -4.19176668e-02 -1.05529264e-01 -1.84647366e-01
-1.14274311e+00 -6.61715209e-01 4.10514563e-01 6.71158552e-01
-7.28094816e-01 5.17525792e-01 -2.12742105e-01 3.34922224e-02
-1.11788407e-01 -7.29384184e-01 -2.15264246e-01 -1.41567373e+00
1.84944883e-01 4.51100081e-01 6.51397109e-01 -3.86588089... | [14.968878746032715, -3.052016258239746] |
e34c359a-bf62-4a46-a5da-cb4adcd54d3f | reproducing-kernel-hilbert-space-mercer-s | 2106.08443 | null | https://arxiv.org/abs/2106.08443v1 | https://arxiv.org/pdf/2106.08443v1.pdf | Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey | This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels... | ['Mark Crowley', 'Fakhri Karray', 'Ali Ghodsi', 'Benyamin Ghojogh'] | 2021-06-15 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [-3.43953788e-01 -3.32318634e-01 -5.23065366e-02 -2.86113620e-01
-2.80307323e-01 -6.48216724e-01 4.11731824e-02 4.32248414e-02
-5.18381000e-01 6.10876203e-01 8.08933452e-02 -3.32237244e-01
-6.87619686e-01 -3.82246524e-01 -7.82746747e-02 -9.81214046e-01
-1.01845407e+00 -2.07818180e-01 1.25134643e-02 -1.42632559... | [7.5272626876831055, 4.044815540313721] |
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