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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cac73f74-0d4d-4f44-a510-983f8453196d | on-the-advantages-of-multiple-stereo-vision | 2105.12691 | null | https://arxiv.org/abs/2105.12691v1 | https://arxiv.org/pdf/2105.12691v1.pdf | On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation | In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navigation. The system leverages state-of-the-art receding horizon exploration techniques for Next-Best-View (NBV) planning with 3D and semantic ... | ['Erdal Kayacan', 'Martim Brandão', 'Jonas Le Fevre', 'Jakob Grimm Hansen', 'Rui Pimentel de Figueiredo'] | 2021-05-26 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [-2.24596914e-02 -7.85394758e-02 4.54469591e-01 -2.84977466e-01
-3.41681719e-01 -1.19838345e+00 4.68993902e-01 -1.87766448e-01
-4.58040506e-01 5.77583194e-01 -2.34006032e-01 -3.78779531e-01
-8.16531539e-01 -8.81336689e-01 -5.28701603e-01 -3.40780586e-01
-3.90462577e-01 8.56918633e-01 8.38165998e-01 -1.09587884... | [7.347779750823975, -1.901630163192749] |
1beb54cd-710f-40bf-b45a-845cea9e936e | evaluating-the-text-to-sql-capabilities-of-1 | 2204.00498 | null | https://arxiv.org/abs/2204.00498v1 | https://arxiv.org/pdf/2204.00498v1.pdf | Evaluating the Text-to-SQL Capabilities of Large Language Models | We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a smal... | ['Dzmitry Bahdanau', 'Raymond Li', 'Nitarshan Rajkumar'] | 2022-03-15 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-6.21415079e-01 -1.01846218e-01 -9.04360235e-01 -4.68051553e-01
-1.11700523e+00 -7.89831996e-01 8.36117089e-01 1.25847280e-01
-1.01824112e-01 3.50733161e-01 4.69213307e-01 -8.20769966e-01
-2.43724406e-01 -6.92411780e-01 -9.27182853e-01 2.68401176e-01
-1.76481470e-01 5.42299688e-01 5.52412570e-01 -5.61274171... | [9.697219848632812, 7.863587379455566] |
fe79086d-1e63-4138-8f57-83fd9547bef1 | adaptive-services-function-chain | 2304.12853 | null | https://arxiv.org/abs/2304.12853v1 | https://arxiv.org/pdf/2304.12853v1.pdf | Adaptive Services Function Chain Orchestration For Digital Health Twin Use Cases: Heuristic-boosted Q-Learning Approach | Digital Twin (DT) is a prominent technology to utilise and deploy within the healthcare sector. Yet, the main challenges facing such applications are: Strict health data-sharing policies, high-performance network requirements, and possible infrastructure resource limitations. In this paper, we address all the challenge... | ['Paola Grosso', 'Arie Taal', 'Li Zhong', 'Jamila Alsayed Kassem'] | 2023-04-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [-3.20334822e-01 5.30255213e-02 -3.97338748e-01 -2.71644026e-01
6.35793030e-01 -6.23733282e-01 7.31364638e-02 2.46622413e-01
-1.36714414e-01 5.31471252e-01 -1.49578124e-01 -6.46349192e-01
-9.20931518e-01 -8.10150921e-01 3.43609244e-01 -7.47060299e-01
-2.19865561e-01 1.02014124e+00 4.34089839e-01 -1.71513498... | [5.861939907073975, 1.7572933435440063] |
3ad151b1-00d1-4c36-8af3-73368d7428b9 | quantum-natural-language-processing-based | 2305.19383 | null | https://arxiv.org/abs/2305.19383v1 | https://arxiv.org/pdf/2305.19383v1.pdf | Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit | Sentiment classification is one the best use case of classical natural language processing (NLP) where we can witness its power in various daily life domains such as banking, business and marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural languag... | ['Luis Miguel Pozo Coronado', 'Sai Nandan Morapakula', 'Srinjoy Ganguly'] | 2023-05-30 | null | null | null | null | ['marketing', 'sentiment-analysis'] | ['miscellaneous', 'natural-language-processing'] | [ 1.00005746e-01 -9.33567435e-02 1.05518013e-01 -4.74946320e-01
-9.03092921e-01 -6.38768792e-01 6.29708469e-01 4.17144597e-01
-8.04033995e-01 1.03361320e+00 -2.51045138e-01 -6.38708174e-01
2.55126655e-01 -1.05826795e+00 -4.35683489e-01 -7.99914241e-01
1.71309654e-02 5.06652117e-01 4.25127475e-03 -9.42841768... | [5.584643840789795, 4.9522905349731445] |
9797412f-a73b-4fcf-8a2c-5d351dc67ff8 | rotation-invariant-graph-neural-networks | 2106.09575 | null | https://arxiv.org/abs/2106.09575v1 | https://arxiv.org/pdf/2106.09575v1.pdf | Rotation Invariant Graph Neural Networks using Spin Convolutions | Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques based on first principles, such as Density Functional Theory (DFT), are limited in their practical use due to their high computational expe... | ['C. Lawrence Zitnick', 'Zachary Ulissi', 'Anuroop Sriram', 'Aditya Grover', 'Abhishek Das', 'Adeesh Kolluru', 'Muhammed Shuaibi'] | 2021-06-17 | null | null | null | null | ['initial-structure-to-relaxed-energy-is2re'] | ['graphs'] | [ 3.00210208e-01 -2.28439048e-01 -3.35160255e-01 -1.10222675e-01
-3.28466326e-01 -4.27696943e-01 6.30746722e-01 4.30295855e-01
-4.09402966e-01 1.11855710e+00 2.10742261e-02 -6.44833684e-01
7.66312778e-02 -1.01696002e+00 -9.42466795e-01 -1.02615082e+00
-2.60963500e-01 5.20644546e-01 -1.12638548e-01 -3.50586116... | [5.306546688079834, 5.401623249053955] |
e84068f5-33e1-4197-9365-1b6d1a9aef1a | policy-learning-for-many-outcomes-of-interest | 2212.06312 | null | https://arxiv.org/abs/2212.06312v1 | https://arxiv.org/pdf/2212.06312v1.pdf | Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation | Methods for learning optimal policies use causal machine learning models to create human-interpretable rules for making choices around the allocation of different policy interventions. However, in realistic policy-making contexts, decision-makers often care about trade-offs between outcomes, not just singlemindedly max... | ['Patrick Rehill'] | 2022-12-13 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 6.84469864e-02 3.15575540e-01 -8.70675981e-01 -2.81329185e-01
-1.06876910e+00 -4.46646631e-01 5.50261021e-01 4.50031072e-01
-7.20192730e-01 1.16234469e+00 6.57319963e-01 -1.02352428e+00
-8.92983437e-01 -6.63496077e-01 -4.26676780e-01 -7.36725569e-01
-1.25370175e-01 1.07358134e+00 -2.99922854e-01 3.04077029... | [4.4045023918151855, 2.823850154876709] |
433b0234-ec21-4830-b3bd-8611e5af9854 | demucs-deep-extractor-for-music-sources-with | 1909.01174 | null | https://arxiv.org/abs/1909.01174v1 | https://arxiv.org/pdf/1909.01174v1.pdf | Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed | We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments. State-of-the-art approaches predict soft masks over mixture spectrograms while methods working on the waveform are lagging behind as measured on the standard MusDB benchmark. Ou... | ['Francis Bach', 'Léon Bottou', 'Alexandre Défossez', 'Nicolas Usunier'] | 2019-09-03 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 4.62540239e-01 -8.45227540e-02 4.32557054e-02 -3.04632895e-02
-1.60522127e+00 -1.05771446e+00 3.30937386e-01 -2.90060520e-01
-8.06963667e-02 5.75978398e-01 5.10061979e-01 9.45538804e-02
-2.82682329e-01 -1.56828582e-01 -7.98449755e-01 -8.44228625e-01
-2.73073047e-01 1.69285685e-01 1.69025689e-01 -3.49468887... | [15.51541519165039, 5.562489032745361] |
1d92f690-e1c3-4793-a5ed-7673b84bcb2d | theres-a-time-and-place-for-reasoning-beyond-1 | null | null | https://aclanthology.org/2022.acl-long.81 | https://aclanthology.org/2022.acl-long.81.pdf | There’s a Time and Place for Reasoning Beyond the Image | Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture. For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understa... | ['Dan Roth', 'Carl Vondrick', 'Ishaan Chandratreya', 'Ben Zhou', 'Xingyu Fu'] | null | null | null | null | acl-2022-5 | ['image-clustering'] | ['computer-vision'] | [-1.98838606e-01 1.61926553e-01 -2.51632392e-01 -4.20006543e-01
-9.48993564e-01 -5.94276011e-01 8.97297919e-01 2.25580841e-01
-4.53339398e-01 5.99557161e-01 7.72200763e-01 -5.56008108e-02
2.23936029e-02 -6.62535369e-01 -1.02469766e+00 -4.73302901e-01
3.02994490e-01 4.96720344e-01 4.48325992e-01 -1.36532590... | [10.693341255187988, 1.264917016029358] |
964bde9e-aec2-49c9-8d3b-ca845ac801e1 | a-stacked-dcnn-to-predict-the-rul-of-a | null | null | http://papers.phmsociety.org/index.php/phmconf/article/view/3110 | http://papers.phmsociety.org/index.php/phmconf/article/download/3110/1838 | A stacked DCNN to predict the RUL of a turbofan engine | This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to ex... | ['Joaquín Borrego-Díaz', 'Juan Galán-Páez', 'David Solís-Martín'] | 2021-11-24 | null | null | null | annual-conference-pf-the-phm-society-2021-11 | ['remaining-useful-lifetime-estimation'] | ['time-series'] | [-8.29327293e-03 -2.27412969e-01 1.64289534e-01 -4.74752992e-01
-2.11850017e-01 -6.75808564e-02 4.19323146e-01 3.33499573e-02
-3.84992421e-01 9.19803977e-01 -6.56506270e-02 -4.14897174e-01
-1.10538208e+00 -8.09131324e-01 -4.02012259e-01 -8.90564322e-01
-4.04321164e-01 6.17849648e-01 4.75473814e-02 -1.39671803... | [6.710403919219971, 2.4483323097229004] |
473bf1a9-680d-4e33-a613-9ca84e7fa525 | s-nerf-neural-radiance-fields-for-street | 2303.00749 | null | https://arxiv.org/abs/2303.00749v1 | https://arxiv.org/pdf/2303.00749v1.pdf | S-NeRF: Neural Radiance Fields for Street Views | Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, th... | ['Li Zhang', 'Feihu Zhang', 'Wenye Li', 'Junge Zhang', 'Ziyang Xie'] | 2023-03-01 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 1.75394729e-01 -1.92791849e-01 2.68848509e-01 -5.34102261e-01
-8.50696266e-01 -6.03447914e-01 5.59468508e-01 -9.64106619e-01
5.82206808e-02 6.27436221e-01 1.79932237e-01 -9.16067362e-02
2.91593254e-01 -8.95395398e-01 -1.25387144e+00 -7.11660862e-01
5.57780623e-01 6.76530749e-02 5.36571503e-01 -2.96118468... | [8.894304275512695, -2.511805772781372] |
278d3086-8b8b-4c1f-bea7-ad2d4e0016f8 | safe-real-world-reinforcement-learning-for | 2209.11789 | null | https://arxiv.org/abs/2209.11789v2 | https://arxiv.org/pdf/2209.11789v2.pdf | SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning | Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), sea... | ['Hugues Thomas', 'Jian Zhang', 'Ali Farhadi', 'Hubert Tsai', 'Mario Srouji'] | 2022-09-23 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.60942838e-01 3.48284930e-01 -1.76188305e-01 -1.37932468e-02
-6.71809316e-01 -3.31377625e-01 5.04720330e-01 3.56619805e-01
-1.03449166e+00 9.08505797e-01 -2.26828545e-01 -7.42190480e-01
-3.93823832e-01 -9.28194106e-01 -1.02146256e+00 -5.89713097e-01
-4.77965444e-01 8.65671158e-01 8.22575212e-01 -8.89522731... | [5.030693054199219, 1.336256742477417] |
1eb974ae-8a3a-4a09-a5a8-aa44e4a18ff6 | s-t-a-r-track-latent-motion-models-for-end-to | 2306.17602 | null | https://arxiv.org/abs/2306.17602v1 | https://arxiv.org/pdf/2306.17602v1.pdf | S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations | Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches incorporate the geometric effect of object- and ego motion between frames with a geometric motion model. Inspired by this, we propose S.T.A.R... | ['Hendrik P. A. Lensch', 'Markus Enzweiler', 'Richard Schulz', 'Lukas Schneider', 'Niklas Hanselmann', 'Simon Doll'] | 2023-06-30 | null | null | null | null | ['object-tracking', '3d-object-tracking'] | ['computer-vision', 'computer-vision'] | [-4.05549049e-01 -4.22513783e-01 -2.68373907e-01 -3.47695351e-02
-5.76767147e-01 -8.57947111e-01 8.10139000e-01 -2.04186976e-01
-2.93654382e-01 1.52634621e-01 2.39427447e-01 5.74304722e-02
1.47236586e-01 -5.77031136e-01 -9.00508523e-01 -6.91691458e-01
1.37786224e-01 5.30780435e-01 7.50060320e-01 1.10239439... | [6.352200984954834, -2.0902817249298096] |
fa6f17a1-9dac-4b77-a48e-13ac5819f645 | adaptive-dithering-using-curved-markov | 2001.06983 | null | https://arxiv.org/abs/2001.06983v1 | https://arxiv.org/pdf/2001.06983v1.pdf | Adaptive Dithering Using Curved Markov-Gaussian Noise in the Quantized Domain for Mapping SDR to HDR Image | High Dynamic Range (HDR) imaging is gaining increased attention due to its realistic content, for not only regular displays but also smartphones. Before sufficient HDR content is distributed, HDR visualization still relies mostly on converting Standard Dynamic Range (SDR) content. SDR images are often quantized, or bit... | ['Guan-Ming Su', 'Irene Cheng', 'Subhayan Mukherjee'] | 2020-01-20 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.55028045e-01 -5.31338632e-01 2.13892316e-03 -3.12668383e-02
-4.98791814e-01 -5.91255784e-01 1.20213479e-01 -2.64243949e-02
-3.09761554e-01 6.49290919e-01 1.45929635e-01 -4.43460435e-01
6.75240383e-02 -7.25690365e-01 -2.59172469e-01 -7.22661912e-01
3.49530093e-02 -2.85471916e-01 5.22062778e-01 -2.03759506... | [10.87425708770752, -2.3818745613098145] |
88578db4-55be-450e-97a4-621963fd6f04 | mukea-multimodal-knowledge-extraction-and | 2203.09138 | null | https://arxiv.org/abs/2203.09138v1 | https://arxiv.org/pdf/2203.09138v1.pdf | MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answering | Knowledge-based visual question answering requires the ability of associating external knowledge for open-ended cross-modal scene understanding. One limitation of existing solutions is that they capture relevant knowledge from text-only knowledge bases, which merely contain facts expressed by first-order predicates or ... | ['Qi Wu', 'Mingxin Cui', 'Yue Hu', 'Bang Liu', 'Jing Yu', 'Yang Ding'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ding_MuKEA_Multimodal_Knowledge_Extraction_and_Accumulation_for_Knowledge-Based_Visual_Question_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ding_MuKEA_Multimodal_Knowledge_Extraction_and_Accumulation_for_Knowledge-Based_Visual_Question_CVPR_2022_paper.pdf | cvpr-2022-1 | ['implicit-relations'] | ['natural-language-processing'] | [-1.35222495e-01 1.40563682e-01 -1.93202257e-01 -4.16683644e-01
-8.59818578e-01 -7.38710880e-01 4.93450195e-01 1.23949453e-01
-1.88785598e-01 7.03559220e-01 3.10732126e-01 -2.74073780e-01
-2.49733344e-01 -8.22799742e-01 -8.76399636e-01 -3.60680670e-01
4.76536214e-01 5.91088355e-01 3.34872991e-01 -3.62072051... | [10.672405242919922, 1.7253015041351318] |
48dc6089-2d21-47c8-a558-dcba837b6f91 | overview-of-the-arabic-sentiment-analysis | 2109.14456 | null | https://arxiv.org/abs/2109.14456v1 | https://arxiv.org/pdf/2109.14456v1.pdf | Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST | This paper provides an overview of the Arabic Sentiment Analysis Challenge organized by King Abdullah University of Science and Technology (KAUST). The task in this challenge is to develop machine learning models to classify a given tweet into one of the three categories Positive, Negative, or Neutral. From our recentl... | ['Xiangliang Zhang', 'Inji Ibrahim Jaber', 'Manal Kalkatawi', 'Zuhair Khayyat', 'Basma Alharbi', 'Manal Alshehri', 'Hind Alamro'] | 2021-09-29 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-6.20560050e-01 -1.60401419e-01 -2.33306259e-01 -5.14790177e-01
-9.39397871e-01 -1.05104172e+00 7.24132180e-01 7.98283279e-01
-5.62288046e-01 5.19291699e-01 2.02364385e-01 -2.49765128e-01
2.88603961e-01 -8.10254216e-01 -3.66188705e-01 -4.99117613e-01
-8.91726166e-02 6.31688356e-01 -6.14622980e-02 -1.07664025... | [11.17921257019043, 6.91127872467041] |
1f1574cc-8c60-44b6-ae2d-ed260fa370f9 | towards-counterfactual-image-manipulation-via | 2207.02812 | null | https://arxiv.org/abs/2207.02812v3 | https://arxiv.org/pdf/2207.02812v3.pdf | Towards Counterfactual Image Manipulation via CLIP | Leveraging StyleGAN's expressivity and its disentangled latent codes, existing methods can achieve realistic editing of different visual attributes such as age and gender of facial images. An intriguing yet challenging problem arises: Can generative models achieve counterfactual editing against their learnt priors? Due... | ['Chunyan Miao', 'Xian-Sheng Hua', 'Xuansong Xie', 'Miaomiao Cui', 'Shijian Lu', 'Jiahui Zhang', 'Rongliang Wu', 'Fangneng Zhan', 'Yingchen Yu'] | 2022-07-06 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.46359247e-01 2.06016466e-01 -1.89094886e-01 -6.64377034e-01
-5.74596822e-01 -7.14133203e-01 9.66957450e-01 -7.08932042e-01
-1.93163127e-01 7.71177530e-01 5.86369693e-01 -5.44338068e-03
-1.36939272e-01 -6.74384832e-01 -9.23620880e-01 -7.04917312e-01
7.23590255e-02 1.02547176e-01 -5.53569496e-01 -1.19156398... | [11.877861022949219, -0.26424431800842285] |
7b24b5b5-af7d-48ec-bc96-95a041449451 | ptgb-pre-train-graph-neural-networks-for | 2305.14376 | null | https://arxiv.org/abs/2305.14376v1 | https://arxiv.org/pdf/2305.14376v1.pdf | PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis | The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder d... | ['Carl Yang', 'Hejie Cui', 'Yi Yang'] | 2023-05-20 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 1.33206472e-01 2.17921764e-01 -5.98814152e-02 -4.05267656e-01
2.31391471e-02 -3.04907888e-01 5.69112360e-01 2.95946807e-01
-4.02280658e-01 3.97676021e-01 2.41115302e-01 -1.22203611e-01
-4.16959316e-01 -8.77035797e-01 -5.32984018e-01 -3.97340506e-01
-3.59625667e-01 5.84973693e-01 3.71674806e-01 -3.05768605... | [12.405475616455078, 3.3552911281585693] |
4a1f42fc-1f5a-4325-9509-b9314c0d2fe5 | cope-end-to-end-trainable-constant-runtime | 2208.08807 | null | https://arxiv.org/abs/2208.08807v2 | https://arxiv.org/pdf/2208.08807v2.pdf | COPE: End-to-end trainable Constant Runtime Object Pose Estimation | State-of-the-art object pose estimation handles multiple instances in a test image by using multi-model formulations: detection as a first stage and then separately trained networks per object for 2D-3D geometric correspondence prediction as a second stage. Poses are subsequently estimated using the Perspective-n-Point... | ['Markus Vincze', 'Timothy Patten', 'Stefan Thalhammer'] | 2022-08-18 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 1.91199586e-01 1.44263059e-01 1.60246819e-01 -2.95616955e-01
-1.26806891e+00 -5.62502861e-01 5.72497606e-01 2.05493063e-01
-4.97750074e-01 1.38747655e-02 -5.88634074e-01 3.05256248e-02
-1.55657172e-01 -4.35732573e-01 -1.19994748e+00 -4.79520798e-01
-1.52087286e-01 1.41622221e+00 8.02109778e-01 1.54635474... | [7.567980766296387, -2.6578783988952637] |
ce4312f4-51ed-4c7f-b708-af920ecc887a | beyond-pretrained-features-noisy-image | 2302.01056 | null | https://arxiv.org/abs/2302.01056v2 | https://arxiv.org/pdf/2302.01056v2.pdf | Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense | Recent advancements in masked image modeling (MIM) have made it a prevailing framework for self-supervised visual representation learning. The MIM pretrained models, like most deep neural network methods, are still vulnerable to adversarial attacks, limiting their practical application, and this issue has received litt... | ['Chang Xu', 'Bohyung Han', 'Daochang Liu', 'Zunzhi You'] | 2023-02-02 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.81382108e-01 2.39363518e-02 -2.61976793e-02 -1.84693411e-01
-6.72289133e-01 -8.42160761e-01 7.72537410e-01 -4.54642564e-01
-3.18615675e-01 4.79945183e-01 1.85784608e-01 -3.95513147e-01
1.74072236e-01 -6.46006882e-01 -8.24447036e-01 -1.09175897e+00
6.31801179e-03 -6.02905691e-01 7.85195827e-02 -3.14533144... | [5.545076370239258, 7.932552337646484] |
1326be5c-dee6-4027-be4a-e025e1c86ee3 | tensor-based-multi-view-block-diagonal | null | null | https://ieeexplore.ieee.org/document/9428106 | https://ieeexplore.ieee.org/document/9428106 | Tensor-Based Multi-View Block-Diagonal Structure Diffusion for Clustering Incomplete Multi-View Data | In this paper, we propose a novel incomplete multi-view clustering method, in which a tensor nuclear norm regularizer elegantly diffuses the information of multi-view block-diagonal structure across different views. By exploring the membership between observed and missing samples and that between missing ones in each i... | ['En Zhu', 'Wei zhang', 'Xiao Zheng', 'Xinwang Liu', 'Chang Tang', 'Zhenglai Li'] | 2021-06-09 | null | null | null | ieee-international-conference-on-multimedia | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-2.40004048e-01 -4.13631380e-01 -3.18826407e-01 -2.44053170e-01
-5.60591221e-01 -4.96226460e-01 3.12951773e-01 -5.57616413e-01
1.28496125e-01 3.93904597e-01 5.68959534e-01 3.40889305e-01
-5.83867967e-01 -1.70845404e-01 -2.54010916e-01 -1.22126245e+00
3.28459799e-01 2.74522692e-01 -4.23792869e-01 1.50745586... | [8.262006759643555, 4.622869491577148] |
06b329cf-a8f3-4f2f-9862-c95a09cb0e9a | on-device-scalable-image-based-localization | 1802.03510 | null | http://arxiv.org/abs/1802.03510v2 | http://arxiv.org/pdf/1802.03510v2.pdf | On-device Scalable Image-based Localization via Prioritized Cascade Search and Fast One-Many RANSAC | We present the design of an entire on-device system for large-scale urban
localization using images. The proposed design integrates compact image
retrieval and 2D-3D correspondence search to estimate the location in extensive
city regions. Our design is GPS agnostic and does not require network
connection. In order to ... | ['Ngai-Man Cheung', 'Tuan-Anh Bui', 'Thanh-Toan Do', 'Anh-Dzung Doan', 'Dang-Khoa Le Tan', 'Ngoc-Trung Tran', 'Mengxuan Tan'] | 2018-02-10 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-4.70299989e-01 -6.00178063e-01 -3.02072793e-01 -2.30948702e-01
-1.38848782e+00 -7.44148374e-01 5.43120086e-01 7.68132657e-02
-3.18139523e-01 2.45652080e-01 1.35762721e-01 -4.01875943e-01
9.56655815e-02 -1.01269734e+00 -7.77150810e-01 -3.83264989e-01
1.21866316e-01 5.26981831e-01 6.46090150e-01 -3.26312155... | [7.694272518157959, -2.194432020187378] |
f1eb1e7f-398b-4b0f-af5f-d6fd5710e667 | indoor-group-activity-recognition-using-multi | 2101.10857 | null | https://arxiv.org/abs/2101.10857v1 | https://arxiv.org/pdf/2101.10857v1.pdf | Indoor Group Activity Recognition using Multi-Layered HMMs | Discovery and recognition of Group Activities (GA) based on imagery data processing have significant applications in persistent surveillance systems, which play an important role in some Internet services. The process is involved with analysis of sequential imagery data with spatiotemporal associations. Discretion of v... | ['Vinayak Elangovan'] | 2021-01-23 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 4.93086249e-01 -1.61132082e-01 -1.35835215e-01 -4.43732023e-01
-1.31988212e-01 -4.15344298e-01 8.20897281e-01 3.98854524e-01
-2.32095063e-01 1.94728404e-01 2.61059999e-01 -1.20946459e-01
-3.42888802e-01 -7.21008778e-01 -3.22417378e-01 -7.65583456e-01
-8.22211683e-01 2.77999967e-01 8.91823113e-01 -4.62627597... | [8.226293563842773, 0.39561864733695984] |
a0f9bd0f-e33a-4c90-aa25-d00966f75391 | multiple-instance-active-learning-for-object | 2104.02324 | null | https://arxiv.org/abs/2104.02324v1 | https://arxiv.org/pdf/2104.02324v1.pdf | Multiple instance active learning for object detection | Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing ins... | ['Qixiang Ye', 'Xiangyang Ji', 'Songcen Xu', 'Jianzhuang Liu', 'Mengying Fu', 'Fang Wan', 'Tianning Yuan'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf | cvpr-2021-1 | ['active-object-detection'] | ['computer-vision'] | [ 3.58445287e-01 5.10524213e-01 -8.91915262e-01 -6.93594396e-01
-1.92382479e+00 -4.69343424e-01 6.68988049e-01 2.38587573e-01
-3.39689642e-01 5.44018328e-01 -1.41426288e-02 6.34752661e-02
-4.49173152e-02 -6.19054496e-01 -8.52518320e-01 -8.95487428e-01
-5.98953441e-02 6.26058519e-01 1.93963125e-01 5.55275083... | [9.273008346557617, 1.305159330368042] |
0207a487-738b-458d-aabc-74a06338e609 | neural-machine-translation-with-heterogeneous | null | null | https://aclanthology.org/2021.emnlp-main.256 | https://aclanthology.org/2021.emnlp-main.256.pdf | Neural Machine Translation with Heterogeneous Topic Knowledge Embeddings | Neural Machine Translation (NMT) has shown a strong ability to utilize local context to disambiguate the meaning of words. However, it remains a challenge for NMT to leverage broader context information like topics. In this paper, we propose heterogeneous ways of embedding topic information at the sentence level into a... | ['Qun Liu', 'Meng Zhang', 'Wei Peng', 'Weixuan Wang'] | null | null | null | null | emnlp-2021-11 | ['topic-models'] | ['natural-language-processing'] | [ 1.95206940e-01 2.12649614e-01 -7.69502759e-01 -4.71472085e-01
-1.23450613e+00 -4.18542355e-01 9.44199264e-01 -7.44862184e-02
-2.15734705e-01 9.79709685e-01 8.73531282e-01 -5.95770419e-01
2.09969729e-01 -6.21734262e-01 -7.51141727e-01 -6.00891292e-01
3.74669671e-01 5.49379945e-01 -3.14841926e-01 -1.28559992... | [11.615428924560547, 10.076553344726562] |
c4dc7396-6220-4773-b2c3-47a06a97b4cd | co-existence-of-micro-pico-and-atto-cells-in | 1906.08212 | null | http://arxiv.org/abs/1906.08212v2 | http://arxiv.org/pdf/1906.08212v2.pdf | Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication | Interference between cells or users can have a significant impact on the
quality of optical wireless communication (OWC) links. This paper studies the
co-existence of infrared based Micro cells with Visible light communication
based Pico and Atto Cells for downlink communication. The signal to noise ratio
(SNR) of each... | [] | 2019-10-15 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [-1.98199272e-01 1.71842426e-01 1.63654327e-01 5.10244608e-01
1.78915471e-01 -5.58473647e-01 4.60159808e-01 -2.17116252e-01
-5.91096818e-01 1.49282312e+00 -4.59234454e-02 -3.32499921e-01
1.78228930e-01 -8.40175509e-01 8.61332044e-02 -1.45048702e+00
-1.87651575e-01 8.57473612e-02 1.18992202e-01 -9.63771716... | [6.254836559295654, 1.2200568914413452] |
70a93b38-4318-4aec-b77d-8617c5ac91ab | i2edit-towards-multi-turn-interactive-image | 2303.11108 | null | https://arxiv.org/abs/2303.11108v2 | https://arxiv.org/pdf/2303.11108v2.pdf | I2Edit: Towards Multi-turn Interactive Image Editing via Dialogue | Although there have been considerable research efforts on controllable facial image editing, the desirable interactive setting where the users can interact with the system to adjust their requirements dynamically hasn't been well explored. This paper focuses on facial image editing via dialogue and introduces a new ben... | ['Zhaofeng He', 'Hailin Shi', 'Yibo Hu', 'Peipei Li', 'Zekun Li', 'Xing Cui'] | 2023-03-20 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 7.31414318e-01 2.46629655e-01 1.12415567e-01 -7.45211482e-01
-3.97276551e-01 -6.22359276e-01 5.87036848e-01 -2.97462851e-01
-3.23040366e-01 3.65817547e-01 -7.11922422e-02 1.76940728e-02
1.49059579e-01 -4.39061642e-01 -3.83157849e-01 -3.78648192e-01
3.84249777e-01 4.36089694e-01 1.23069800e-01 -4.53202307... | [12.52230167388916, -0.29158467054367065] |
531afa72-ca8d-44ee-b58c-753209f5360b | on-the-utility-and-protection-of-optimization | 2209.03175 | null | https://arxiv.org/abs/2209.03175v1 | https://arxiv.org/pdf/2209.03175v1.pdf | On the utility and protection of optimization with differential privacy and classic regularization techniques | Nowadays, owners and developers of deep learning models must consider stringent privacy-preservation rules of their training data, usually crowd-sourced and retaining sensitive information. The most widely adopted method to enforce privacy guarantees of a deep learning model nowadays relies on optimization techniques e... | ['Matteo Matteucci', 'Eugenio Lomurno'] | 2022-09-07 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 6.07859418e-02 2.44073480e-01 -1.79952383e-02 -6.68464541e-01
-9.43388879e-01 -8.57805669e-01 4.33879077e-01 4.00951989e-02
-7.44101763e-01 8.76567304e-01 -1.23289280e-01 -4.53766435e-01
-1.17806539e-01 -5.50270319e-01 -9.12791312e-01 -1.03400660e+00
1.81826994e-01 8.08352903e-02 -1.26002878e-01 2.44155034... | [5.93593692779541, 6.826268196105957] |
036e6ac4-ceaa-4b5d-bd29-3b2f5f1e31dc | any-shot-sequential-anomaly-detection-in | 2004.02072 | null | https://arxiv.org/abs/2004.02072v1 | https://arxiv.org/pdf/2004.02072v1.pdf | Any-Shot Sequential Anomaly Detection in Surveillance Videos | Anomaly detection in surveillance videos has been recently gaining attention. Even though the performance of state-of-the-art methods on publicly available data sets has been competitive, they demand a massive amount of training data. Also, they lack a concrete approach for continuously updating the trained model once ... | ['Yasin Yilmaz', 'Keval Doshi'] | 2020-04-05 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.40803272e-01 -1.07717298e-01 -3.57306272e-01 -3.05206150e-01
-6.75232291e-01 -2.66398877e-01 4.34520900e-01 1.13906279e-01
-3.02612126e-01 4.34629619e-01 -2.38940760e-01 -2.78378129e-01
-1.45294995e-03 -7.07317710e-01 -7.47453928e-01 -7.08642721e-01
-3.86072040e-01 -7.91960657e-02 7.52610147e-01 5.53547032... | [7.877026081085205, 1.571636438369751] |
c01825e7-f7c4-43ba-bccd-378f8248ed1b | mitigating-gender-bias-in-captioning-systems | 2006.08315 | null | https://arxiv.org/abs/2006.08315v7 | https://arxiv.org/pdf/2006.08315v7.pdf | Mitigating Gender Bias in Captioning Systems | Image captioning has made substantial progress with huge supporting image collections sourced from the web. However, recent studies have pointed out that captioning datasets, such as COCO, contain gender bias found in web corpora. As a result, learning models could heavily rely on the learned priors and image context f... | ['Na Zou', 'Yuening Li', 'Ruixiang Tang', 'Xia Hu', 'Zirui Liu', 'Mengnan Du'] | 2020-06-15 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 1.42391354e-01 4.23595250e-01 -3.75511348e-01 -8.61243248e-01
-5.31281531e-01 -5.32605648e-01 5.95926046e-01 -9.56722647e-02
-2.87438005e-01 6.20895624e-01 1.12517752e-01 -2.97319461e-02
5.34984291e-01 -5.84408224e-01 -1.06047893e+00 -5.89490652e-01
5.27688980e-01 7.28320479e-01 -2.35732317e-01 -1.62161782... | [10.960569381713867, 1.2547354698181152] |
13250c41-09a0-417c-90bd-449606a2e3df | modularized-interaction-network-for-named | null | null | https://aclanthology.org/2021.acl-long.17 | https://aclanthology.org/2021.acl-long.17.pdf | Modularized Interaction Network for Named Entity Recognition | Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks. The sequence labeling-based NER models do not perform well in recognizing long entities as they focus only on word-level information, while the segment-based NER models which focus on pro... | ['Meihuizi Jia', 'Guoxiu He', 'Jing Xu', 'Dandan song', 'Lejian Liao', 'Siu Cheung Hui', 'Zheng Wang', 'Fei Li'] | 2021-08-01 | null | null | null | acl-2021-5 | ['type-prediction', 'boundary-detection'] | ['computer-code', 'computer-vision'] | [-7.13026151e-02 -5.19616008e-02 -1.69601068e-01 -4.53209370e-01
-3.54981601e-01 -4.97599870e-01 2.55929470e-01 5.01976073e-01
-7.46627212e-01 6.90819442e-01 4.15227711e-01 -2.92326361e-01
7.09441826e-02 -7.93854356e-01 -2.62323529e-01 -3.45522165e-01
2.94888820e-02 1.78504944e-01 5.60082018e-01 -1.99503884... | [9.716283798217773, 9.596170425415039] |
d9645a1f-0b5f-4455-88d9-b0fc8b6c7efc | accdoa-activity-coupled-cartesian-direction | 2010.15306 | null | https://arxiv.org/abs/2010.15306v2 | https://arxiv.org/pdf/2010.15306v2.pdf | ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection | Neural-network (NN)-based methods show high performance in sound event localization and detection (SELD). Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target. The two-branch representation with a single network has to decide how to balance the ... | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Naoya Takahashi', 'Yuichiro Koyama', 'Kazuki Shimada'] | 2020-10-29 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-1.30960420e-01 -3.11351895e-01 2.63877779e-01 -1.43381998e-01
-7.95798898e-01 -2.89621025e-01 4.06404138e-01 3.48198861e-01
-6.04007363e-01 2.81654030e-01 5.26146106e-02 -3.17077219e-01
-5.78980148e-01 -6.36443973e-01 -2.98752040e-01 -7.47963071e-01
-3.68838668e-01 2.93345541e-01 7.32470930e-01 6.45116568... | [15.173649787902832, 5.237888336181641] |
69799feb-118d-47ea-9e4f-19875da9ec16 | glamr-global-occlusion-aware-human-mesh | 2112.01524 | null | https://arxiv.org/abs/2112.01524v2 | https://arxiv.org/pdf/2112.01524v2.pdf | GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras | We present an approach for 3D global human mesh recovery from monocular videos recorded with dynamic cameras. Our approach is robust to severe and long-term occlusions and tracks human bodies even when they go outside the camera's field of view. To achieve this, we first propose a deep generative motion infiller, which... | ['Jan Kautz', 'Kris Kitani', 'Pavlo Molchanov', 'Umar Iqbal', 'Ye Yuan'] | 2021-12-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yuan_GLAMR_Global_Occlusion-Aware_Human_Mesh_Recovery_With_Dynamic_Cameras_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yuan_GLAMR_Global_Occlusion-Aware_Human_Mesh_Recovery_With_Dynamic_Cameras_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-mesh-recovery'] | ['computer-vision'] | [ 3.29575166e-02 -1.29942000e-01 1.01459716e-02 7.48455599e-02
-5.46414554e-01 -5.55774033e-01 3.66558641e-01 -4.43405926e-01
-2.41917193e-01 5.22784293e-01 3.28155190e-01 4.76049900e-01
1.56630784e-01 -6.00860655e-01 -1.11639953e+00 -3.98101121e-01
1.14409111e-01 7.11183131e-01 2.99616337e-01 1.44267157... | [7.257015705108643, -0.9475335478782654] |
4c420d32-74d6-42d7-ad44-0e0298212ccb | an-end-to-end-pipeline-for-3d-slide-wise | 2305.11968 | null | https://arxiv.org/abs/2305.11968v1 | https://arxiv.org/pdf/2305.11968v1.pdf | An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration | Tissue examination and quantification in a 3D context on serial section whole slide images (WSIs) were laborintensive and time-consuming tasks. Our previous study proposed a novel registration-based method (Map3D) to automatically align WSIs to the same physical space, reducing the human efforts of screening serial sec... | ['Yuankai Huo', 'Ruining Deng', 'Peize Li'] | 2023-05-19 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 1.57858625e-01 -1.71516284e-01 7.26235136e-02 -2.59810805e-01
-1.42685628e+00 -8.61335814e-01 5.85236214e-02 6.34916365e-01
-3.79381746e-01 3.64678502e-01 -1.77869603e-01 -4.32562321e-01
-3.09816182e-01 -5.49456477e-01 -4.79776114e-01 -7.67098844e-01
-5.32579012e-02 7.90958405e-01 5.99339187e-01 1.85026824... | [14.952105522155762, -3.0435307025909424] |
c0aa1dae-5fed-4c1a-83ae-61a1c465e364 | a-human-eye-based-text-color-scheme | 2010.07510 | null | https://arxiv.org/abs/2010.07510v2 | https://arxiv.org/pdf/2010.07510v2.pdf | A Human Eye-based Text Color Scheme Generation Method for Image Synthesis | Synthetic data used for scene text detection and recognition tasks have proven effective. However, there are still two problems: First, the color schemes used for text coloring in the existing methods are relatively fixed color key-value pairs learned from real datasets. The dirty data in real datasets may cause the pr... | ['Xiang Yu Luo', 'Guan Jie Huang', 'Shao Wei Wang'] | 2020-10-15 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.49399191e-01 -6.58672273e-01 3.91174316e-01 -9.29401815e-02
-1.94546074e-01 -7.17633128e-01 4.37252313e-01 -5.97214326e-03
-3.31858426e-01 6.10606492e-01 -1.19154036e-01 -1.03485100e-01
4.40478325e-01 -8.45769703e-01 -5.19400120e-01 -8.10725331e-01
3.76840293e-01 2.01126158e-01 9.28562582e-01 -2.11646203... | [11.847118377685547, 1.9478427171707153] |
bbf1041d-1aad-4503-ac8b-46b3433afebd | bda-sketret-bi-level-domain-adaptation-for | 2201.06570 | null | https://arxiv.org/abs/2201.06570v1 | https://arxiv.org/pdf/2201.06570v1.pdf | BDA-SketRet: Bi-Level Domain Adaptation for Zero-Shot SBIR | The efficacy of zero-shot sketch-based image retrieval (ZS-SBIR) models is governed by two challenges. The immense distributions-gap between the sketches and the images requires a proper domain alignment. Moreover, the fine-grained nature of the task and the high intra-class variance of many categories necessitates a c... | ['Zeynep Akata', 'Anjan Dutta', 'Biplab Banerjee', 'Ruchika Chavan', 'Ushasi Chaudhuri'] | 2022-01-17 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.84951514e-01 -4.00557876e-01 -1.38998404e-01 -2.08405241e-01
-6.74126387e-01 -6.45489097e-01 8.65918040e-01 -3.99100542e-01
-3.12123708e-02 3.94884408e-01 1.93304732e-01 3.37833315e-01
-5.88336766e-01 -6.50130153e-01 -6.14394724e-01 -7.41148829e-01
3.41974974e-01 1.01482227e-01 1.02597915e-01 -2.40801886... | [11.602238655090332, 0.7169002890586853] |
ebaeb61b-ba93-40b3-ae82-99a0aeede933 | sana-a-large-scale-multi-genre-multi-dialect | null | null | https://aclanthology.org/L14-1702 | https://aclanthology.org/L14-1702.pdf | SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis | The computational treatment of subjectivity and sentiment in natural language is usually significantly improved by applying features exploiting lexical resources where entries are tagged with semantic orientation (e.g., positive, negative values). In spite of the fair amount of work on Arabic sentiment analysis over th... | ['Muhammad Abdul-Mageed', 'Mona Diab'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.52966809e-01 2.48670757e-01 -2.74313420e-01 -4.67028916e-01
-4.05877829e-01 -9.42754209e-01 7.01804340e-01 6.51940405e-01
-1.89341664e-01 7.53193080e-01 5.93164980e-01 -2.56662309e-01
-2.84948833e-02 -8.31648409e-01 -1.90261364e-01 -2.01375857e-01
4.29851376e-02 3.45561266e-01 -8.25536400e-02 -1.05137360... | [11.100010871887207, 6.919246673583984] |
61642c05-7886-4d61-8ccf-a309690f25e5 | clusterllm-large-language-models-as-a-guide | 2305.14871 | null | https://arxiv.org/abs/2305.14871v1 | https://arxiv.org/pdf/2305.14871v1.pdf | ClusterLLM: Large Language Models as a Guide for Text Clustering | We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. Compared with traditional unsupervised methods that builds upon "small" embedders, ClusterLLM exhibits two intriguing advantages: (1) it enjoys the emergent capability of L... | ['Jingbo Shang', 'Zihan Wang', 'Yuwei Zhang'] | 2023-05-24 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-5.43318391e-01 -5.26521727e-02 -2.77344227e-01 -5.09439230e-01
-1.13701820e+00 -8.04168761e-01 1.96574524e-01 8.02648604e-01
-2.22886086e-01 1.35718584e-01 3.94245446e-01 -5.26142597e-01
-3.66756499e-01 -4.73348141e-01 -5.91196775e-01 -5.98370492e-01
-2.23201662e-01 9.79639411e-01 4.04190689e-01 -2.22885236... | [10.684284210205078, 6.833302974700928] |
31d30994-868a-454c-92d5-5a5fb5dff117 | msrf-net-a-multi-scale-residual-fusion | 2105.07451 | null | https://arxiv.org/abs/2105.07451v2 | https://arxiv.org/pdf/2105.07451v2.pdf | MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation | Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi... | ['Pål Halvorsen', 'Sharib Ali', 'Michael A. Riegler', 'Dag Johansen', 'Håvard D. Johansen', 'Umapada Pal', 'Sukalpa Chanda', 'Debesh Jha', 'Abhishek Srivastava'] | 2021-05-16 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.42174774e-01 -2.02778414e-01 -1.07696958e-01 -4.14084107e-01
-8.58824193e-01 -3.28667372e-01 6.27116486e-02 1.91896155e-01
-6.28845155e-01 7.48239517e-01 -5.70630990e-02 -8.04568753e-02
-2.23348662e-01 -7.95675039e-01 -5.28743207e-01 -7.97401071e-01
1.12357482e-01 1.55832618e-01 5.91956735e-01 -5.07229706... | [14.698726654052734, -2.6127583980560303] |
f6fdebbc-fea9-4559-8aa6-d2ee0f8a1ab8 | conversational-exploratory-search-via | 1709.05298 | null | https://arxiv.org/abs/1709.05298v1 | https://arxiv.org/pdf/1709.05298v1.pdf | Conversational Exploratory Search via Interactive Storytelling | Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search a... | ['Maarten de Rijke', 'Ilya Markov', 'Svitlana Vakulenko'] | 2017-09-15 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.28793851e-01 4.26383138e-01 -6.27169199e-03 -2.65818506e-01
-1.21995427e-01 -9.18242693e-01 9.77459908e-01 5.30738175e-01
-2.94152796e-01 4.10231620e-01 6.85613453e-01 -8.62624407e-01
-3.81656885e-01 -5.46308517e-01 3.07596087e-01 4.35879640e-02
2.06155166e-01 4.06231523e-01 1.14638291e-01 -5.97999990... | [12.271883010864258, 7.815826416015625] |
43ce6a09-269e-48d3-a97e-b854c3fb8669 | deep-tree-ensembles-for-multi-output | 2011.02829 | null | https://arxiv.org/abs/2011.02829v2 | https://arxiv.org/pdf/2011.02829v2.pdf | Deep tree-ensembles for multi-output prediction | Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their optimal performance depends on massive amounts of training data and the tuning of an... | ['Celine Vens', 'Konstantinos Pliakos', 'Felipe Kenji Nakano'] | 2020-11-03 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 6.19172096e-01 -2.61553109e-01 -5.69041431e-01 -5.47949553e-01
-5.43367326e-01 -4.63458858e-02 5.49909651e-01 2.32963115e-01
-3.73779088e-01 8.27168703e-01 -1.80855189e-02 -1.34418353e-01
-2.13077277e-01 -9.28842962e-01 -5.06087840e-01 -9.40957308e-01
2.79553890e-01 3.95328969e-01 1.26411185e-01 2.20521599... | [9.270298957824707, 4.291067600250244] |
ed6a02ee-da89-4037-ae16-a38344960b36 | error-in-variables-modelling-for-operator | 2204.10909 | null | https://arxiv.org/abs/2204.10909v2 | https://arxiv.org/pdf/2204.10909v2.pdf | Error-in-variables modelling for operator learning | Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dep... | ['Mamikon Gulian', 'Myoungkyu Lee', 'Indu Manickam', 'Ravi G. Patel'] | 2022-04-22 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 8.92314836e-02 -1.27163038e-01 3.64396483e-01 3.06020111e-01
-6.43435359e-01 -2.49972522e-01 2.59309709e-01 -5.16306385e-02
-4.41097856e-01 9.78015721e-01 -5.40166534e-02 -1.97537586e-01
-4.90260273e-01 -5.44728339e-01 -8.80365014e-01 -1.10488200e+00
-5.13027549e-01 4.70191240e-01 -1.22457176e-01 -4.39722806... | [6.585322856903076, 3.6106417179107666] |
292b32a7-c246-46df-b567-a6fcf8bf2096 | learning-based-point-cloud-registration-for | 2203.15309 | null | https://arxiv.org/abs/2203.15309v2 | https://arxiv.org/pdf/2203.15309v2.pdf | MatchNorm: Learning-based Point Cloud Registration for 6D Object Pose Estimation in the Real World | In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches to addressing this task have shown great success on synthetic datasets, we have observed them to fail in the presence of real-world data. We thus analyze the causes of these failures, wh... | ['Mathieu Salzmann', 'Yu Guo', 'Lizhou Wang', 'Zheng Dang'] | 2022-03-29 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 5.44664077e-02 -2.26588458e-01 9.69608128e-02 -3.58468413e-01
-9.79429424e-01 -5.54718971e-01 9.53083694e-01 1.77289620e-01
-3.86482924e-01 2.31217563e-01 -2.76299715e-01 -1.86862841e-01
-2.38513887e-01 -5.63894629e-01 -8.22788239e-01 -4.93041009e-01
-2.57641286e-01 9.98533607e-01 5.85691333e-01 -2.03023434... | [7.700311660766602, -2.841219186782837] |
0955d98e-056a-42fd-ab6e-323b230f7395 | tackling-online-abuse-a-survey-of-automated | 1908.06024 | null | https://arxiv.org/abs/1908.06024v2 | https://arxiv.org/pdf/1908.06024v2.pdf | Tackling Online Abuse: A Survey of Automated Abuse Detection Methods | Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there... | ['Pushkar Mishra', 'Ekaterina Shutova', 'Helen Yannakoudakis'] | 2019-08-13 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-4.60749045e-02 -7.24484846e-02 -4.50245470e-01 -4.08384711e-01
-2.10999325e-01 -8.07855427e-01 2.26224229e-01 4.76571739e-01
-5.68541169e-01 9.16531086e-01 1.72126532e-01 -2.59631693e-01
1.07739614e-02 -5.58090568e-01 -6.39523044e-02 -2.70330422e-02
-2.19733477e-01 5.11933491e-03 -3.40850979e-01 -2.07518488... | [8.72620964050293, 10.378969192504883] |
8bd42390-c727-44e3-b7c6-fdd66f50616d | breaking-common-sense-whoops-a-vision-and | 2303.07274 | null | https://arxiv.org/abs/2303.07274v2 | https://arxiv.org/pdf/2303.07274v2.pdf | Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images | Weird, unusual, and uncanny images pique the curiosity of observers because they challenge commonsense. For example, an image released during the 2022 world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo playing chess, which playfully violates our expectation that their competition should occur ... | ['Roy Schwartz', 'Gabriel Stanovsky', 'Yuval Elovici', 'Ludwig Schmidt', 'Jack Hessel', 'Yonatan Bitton', 'Nitzan Bitton-Guetta'] | 2023-03-13 | null | null | null | null | ['explanation-generation', 'common-sense-reasoning', 'visual-commonsense-reasoning'] | ['natural-language-processing', 'reasoning', 'reasoning'] | [ 2.66351819e-01 3.82988960e-01 1.52238622e-01 -2.01517493e-01
-4.53099996e-01 -7.72195339e-01 7.23897874e-01 -9.90328938e-02
-1.57002792e-01 6.65255070e-01 3.45895857e-01 -5.38156807e-01
2.01986089e-01 -5.10849655e-01 -1.07161999e+00 -1.59267247e-01
5.16729355e-01 5.30992091e-01 1.16832303e-02 -7.21967578... | [10.780535697937012, 1.8131250143051147] |
d5de4298-f5f5-4eb5-a4a0-9d970992dcd9 | chitransformer-towards-reliable-stereo-from-1 | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.pdf | Chitransformer: Towards Reliable Stereo From Cues | Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenges and can achieve satisfactory results with the extracted monocular cues, the lack of stereoscopic relationship renders the monocular pr... | ['Shihao Ji', 'Qing Su'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.75512058e-01 5.50203770e-02 -6.16178960e-02 -3.26909125e-01
-3.72872204e-01 -5.54243267e-01 6.30971611e-01 -5.10517955e-01
-3.33644152e-01 6.85095310e-01 2.53560632e-01 4.23905589e-02
-5.33306971e-04 -5.00461042e-01 -6.64225698e-01 -9.17466283e-01
5.42731404e-01 2.49131098e-02 6.28726006e-01 1.06026260... | [8.766242980957031, -2.4149832725524902] |
7f4259d2-313f-48a3-a2db-c98adf08c204 | weakly-supervised-affordance-detection | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Sawatzky_Weakly_Supervised_Affordance_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Sawatzky_Weakly_Supervised_Affordance_CVPR_2017_paper.pdf | Weakly Supervised Affordance Detection | Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances,... | ['Johann Sawatzky', 'Abhilash Srikantha', 'Juergen Gall'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['affordance-detection'] | ['computer-vision'] | [ 4.47523445e-02 1.93499073e-01 -5.94496787e-01 -5.44184983e-01
-3.52923542e-01 -4.60520893e-01 5.31390131e-01 3.69629264e-01
-7.04994082e-01 6.47304237e-01 3.26938093e-01 -1.45037860e-01
-7.52628893e-02 -4.39793676e-01 -1.15092862e+00 -3.29894602e-01
-1.48225725e-01 2.41138294e-01 5.86767137e-01 -1.69460267... | [5.164822578430176, -0.11426082998514175] |
08ee943a-a144-4814-be87-1476f56900d1 | sa-cnn-application-to-text-categorization | 2303.07153 | null | https://arxiv.org/abs/2303.07153v1 | https://arxiv.org/pdf/2303.07153v1.pdf | SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization | Convolutional neural networks (CNNs) are a representative class of deep learning algorithms including convolutional computation that perform translation-invariant classification of input data based on their hierarchical architecture. However, classical convolutional neural network learning methods use the steepest desc... | ['Yueying Cao', 'Zihao Guo'] | 2023-03-13 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 3.40662301e-01 -5.50293103e-02 -3.57800305e-01 -4.98467177e-01
-3.27335089e-01 -6.23127043e-01 3.40665638e-01 3.45518976e-03
-8.33463371e-01 4.29641247e-01 2.64479360e-03 -6.44263029e-01
-2.76582897e-01 -7.04313576e-01 -5.90641022e-01 -6.14475965e-01
1.03884377e-01 5.21804392e-01 8.39093849e-02 -1.45647034... | [8.602965354919434, 3.3916993141174316] |
25676aa9-4782-48d3-a0ff-0e267537a15f | large-raw-emotional-dataset-with-aggregation | 2212.12266 | null | https://arxiv.org/abs/2212.12266v1 | https://arxiv.org/pdf/2212.12266v1.pdf | Large Raw Emotional Dataset with Aggregation Mechanism | We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russian speech and their transcripts. Therefore it is the biggest open bi-modal data collection for SER task nowadays. It is annotated using a c... | ['Fyodor Minkin', 'Nikita Savushkin', 'Oleg Kutuzov', 'Nikolay Karpov', 'Artem Sokolov', 'Vladimir Kondratenko'] | 2022-12-23 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-2.23941877e-01 3.89022112e-01 5.03374338e-01 -5.60939550e-01
-1.01138985e+00 -4.25430775e-01 5.86058915e-01 1.10241853e-01
-6.71623766e-01 8.16017389e-01 5.29392540e-01 6.45885617e-02
3.48297298e-01 -5.14824949e-02 -1.79813415e-01 -6.08152568e-01
-1.84818029e-01 7.71598756e-01 5.66454325e-03 -5.29658854... | [13.582880973815918, 5.8543806076049805] |
3cb32a2e-1b98-48b7-8191-50a92dea6df2 | cross-domain-recommender-systems-via | 2306.13887 | null | https://arxiv.org/abs/2306.13887v2 | https://arxiv.org/pdf/2306.13887v2.pdf | Cross-domain Recommender Systems via Multimodal Domain Adaptation | Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a critical issue kno... | ['Venkateswara Rao Kagita', 'Vikas Kumar', 'Ramya Kamani'] | 2023-06-24 | null | null | null | null | ['entity-alignment', 'transfer-learning', 'collaborative-filtering', 'entity-alignment'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-1.13263436e-01 -4.09851342e-01 -4.87337947e-01 -3.90982300e-01
-3.98775995e-01 -6.57700717e-01 5.80932558e-01 4.21391189e-01
-1.61048606e-01 5.85571527e-01 5.66126525e-01 2.68854171e-01
-2.38091409e-01 -9.62050974e-01 -2.66380876e-01 -5.65522730e-01
2.32044145e-01 2.81470239e-01 2.30871841e-01 -3.15349489... | [10.12230396270752, 5.527244567871094] |
ca06bfd7-4f53-4f13-abeb-96d968598cd2 | data-augmentation-for-skin-lesion-using-self | 1910.11960 | null | https://arxiv.org/abs/1910.11960v1 | https://arxiv.org/pdf/1910.11960v1.pdf | Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network | Deep Neural Networks (DNNs) show a significant impact on medical imaging. One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. This problem hinders the training of robust and well-generalizing models. Data Augmentation addresses... | ['Yousef Bassyouni Mahdy', 'Mamdouh Farouk Mohamed', 'Ibrahim Saad Ali'] | 2019-10-25 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 6.44766092e-01 4.38816130e-01 -1.32228762e-01 -3.89788508e-01
-9.50227857e-01 -1.03931360e-01 5.45374930e-01 -7.50458315e-02
-2.61418790e-01 1.03375840e+00 3.34999591e-01 -3.01460195e-02
2.26271182e-01 -1.00345314e+00 -7.01427341e-01 -1.04248428e+00
4.37603742e-01 1.14881568e-01 -8.70028585e-02 -2.96235681... | [14.198902130126953, -1.990584135055542] |
ec7a83d6-940c-47f5-b290-0a2ebbec288e | radiologist-level-stroke-classification-on | 2003.14287 | null | https://arxiv.org/abs/2003.14287v1 | https://arxiv.org/pdf/2003.14287v1.pdf | Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net | Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN architecture for the stroke identification problem using non-contrast CT. We applied the proposed DL model to historical patient data and al... | ['Vladimir Kokh', 'Dmitry Umerenkov', 'Alex Tuzhilin', 'Manvel Avetisian'] | 2020-03-31 | null | null | null | null | ['stroke-classification'] | ['methodology'] | [-2.65682697e-01 -1.81792319e-01 -1.19985245e-01 -4.37008649e-01
-8.82339060e-01 -4.24117476e-01 3.04878443e-01 1.76908076e-02
-1.04638302e+00 9.15027201e-01 4.00277436e-01 -8.21396589e-01
-2.73561738e-02 -8.35713029e-01 -3.77564698e-01 -4.17987257e-01
-4.53784585e-01 8.68876815e-01 4.52638090e-01 2.62271106... | [14.327607154846191, -2.092195749282837] |
13d95f5e-c502-40ca-b72c-717f83fa4d1f | cross-field-transformer-for-diabetic | 2211.14552 | null | https://arxiv.org/abs/2211.14552v2 | https://arxiv.org/pdf/2211.14552v2.pdf | Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images | Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologi... | ['Rui Feng', 'Wenwen Xue', 'Lina Lu', 'Haidong Zou', 'Yuejie Zhang', 'Rui-Wei Zhao', 'Fan Xiao', 'Jilan Xu', 'Junlin Hou'] | 2022-11-26 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-2.22338557e-01 -3.90665203e-01 -2.83756088e-02 -5.88332355e-01
-5.03945231e-01 -3.60557616e-01 1.49605647e-01 -4.08344090e-01
-1.09418325e-01 7.40431428e-01 4.42649186e-01 -2.76685625e-01
-3.97147954e-01 -6.81388140e-01 -3.51141781e-01 -7.22411752e-01
4.15993571e-01 -2.04999462e-01 2.11130187e-01 -2.02389464... | [15.784130096435547, -3.9609992504119873] |
a56b20d5-ea8b-4583-90a3-6ca18fc54b27 | mega-tts-zero-shot-text-to-speech-at-scale | 2306.03509 | null | https://arxiv.org/abs/2306.03509v1 | https://arxiv.org/pdf/2306.03509v1.pdf | Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias | Scaling text-to-speech to a large and wild dataset has been proven to be highly effective in achieving timbre and speech style generalization, particularly in zero-shot TTS. However, previous works usually encode speech into latent using audio codec and use autoregressive language models or diffusion models to generate... | ['Zhou Zhao', 'Zejun Ma', 'Xiang Yin', 'Chunfeng Wang', 'Rongjie Huang', 'Shengpeng Ji', 'Qian Yang', 'Chen Zhang', 'Jinglin Liu', 'Zhenhui Ye', 'Yi Ren', 'Ziyue Jiang'] | 2023-06-06 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [-1.27084926e-01 -8.99486151e-03 1.40648186e-02 -4.51206386e-01
-1.10042250e+00 -6.59945428e-01 3.80479604e-01 -4.17979866e-01
1.34718232e-02 3.94824415e-01 4.62085307e-01 -2.69497544e-01
4.71387893e-01 -6.24108493e-01 -7.34535754e-01 -9.00426209e-01
1.77621752e-01 2.76439130e-01 -2.83350539e-03 -2.95770884... | [14.98084545135498, 6.462255001068115] |
5f062c20-bec9-4749-9ec6-12a5f8c0d81c | towards-precise-weakly-supervised-object | 2304.14114 | null | https://arxiv.org/abs/2304.14114v2 | https://arxiv.org/pdf/2304.14114v2.pdf | Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information | Weakly supervised object detection (WSOD) aims at learning precise object detectors with only image-level tags. In spite of intensive research on deep learning (DL) approaches over the past few years, there is still a significant performance gap between WSOD and fully supervised object detection. In fact, most existing... | ['ChiMan Vong', 'Qi Lai'] | 2023-04-27 | null | null | null | null | ['weakly-supervised-object-detection'] | ['computer-vision'] | [-1.67992674e-02 6.28790259e-03 -3.88336003e-01 -3.49038601e-01
-5.46242177e-01 -2.27286965e-01 7.36341953e-01 2.45389596e-01
-4.87247944e-01 3.49986583e-01 -6.98379725e-02 -4.16074879e-02
9.31960531e-03 -5.69327712e-01 -5.99622548e-01 -8.18969548e-01
-2.77368294e-04 5.05898744e-02 8.21464181e-01 -1.34862721... | [9.33964729309082, 1.1931759119033813] |
721d4cb9-94f3-4d9e-b7af-7b59054fa859 | towards-an-lstm-based-predictive-framework | 1907.09395 | null | https://arxiv.org/abs/1907.09395v2 | https://arxiv.org/pdf/1907.09395v2.pdf | Mining Temporal Evolution of Knowledge Graph and Genealogical Features for Literature-based Discovery Prediction | Literature-based knowledge discovery process identifies the important but implicit relations among information embedded in published literature. Existing techniques from Information Retrieval and Natural Language Processing attempt to identify the hidden or unpublished connections between information concepts within pu... | ['Fahim Faisal', 'Nazim Choudhury', 'Matloob Khushi'] | 2019-07-22 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [-3.95326048e-01 -1.38612073e-02 -7.42260873e-01 2.12737963e-01
2.88630128e-01 -5.45197070e-01 6.98329926e-01 6.32709026e-01
-3.17005247e-01 9.99286652e-01 2.77335018e-01 -6.11771643e-01
-1.08657277e+00 -1.15059686e+00 -5.06904304e-01 -3.88062775e-01
-6.98172867e-01 1.69494241e-01 7.32406648e-03 1.54464245... | [9.469139099121094, 8.184218406677246] |
e9bc7c1f-62fd-4dc3-b684-89700ef92fb5 | mobilevig-graph-based-sparse-attention-for | 2307.00395 | null | https://arxiv.org/abs/2307.00395v1 | https://arxiv.org/pdf/2307.00395v1.pdf | MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications | Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfortunately, for mobile applications, ViGs are computationally expensive due to the overhead of represent... | ['Radu Marculescu', 'William Avery', 'Mustafa Munir'] | 2023-07-01 | null | null | null | null | ['instance-segmentation', 'graph-attention'] | ['computer-vision', 'graphs'] | [-2.19494164e-01 2.16395289e-01 -4.97396111e-01 3.57218832e-02
-3.36843163e-01 -3.85846406e-01 1.98140010e-01 -4.27633196e-01
-3.71799290e-01 3.17623824e-01 -2.39359379e-01 -9.55669701e-01
3.45912963e-01 -8.25800359e-01 -1.06737232e+00 -2.01796085e-01
1.39649175e-02 3.58019292e-01 2.37120405e-01 -4.41219583... | [9.287571907043457, 1.3897967338562012] |
f5b62a30-8d4c-4f15-9342-a8a7eeee5122 | trustworthy-deep-learning-for-medical-image | 2305.17456 | null | https://arxiv.org/abs/2305.17456v1 | https://arxiv.org/pdf/2305.17456v1.pdf | Trustworthy Deep Learning for Medical Image Segmentation | Despite the recent success of deep learning methods at achieving new state-of-the-art accuracy for medical image segmentation, some major limitations are still restricting their deployment into clinics. One major limitation of deep learning-based segmentation methods is their lack of robustness to variability in the im... | ['Lucas Fidon'] | 2023-05-27 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.94833621e-01 2.51360893e-01 -2.79364526e-01 -5.38174748e-01
-8.83025885e-01 -5.53417981e-01 -7.94382244e-02 4.47509348e-01
-6.45999372e-01 6.80449724e-01 -3.15149039e-01 -4.89189118e-01
-4.42701671e-03 -5.68182290e-01 -5.29668570e-01 -6.60225630e-01
7.64380917e-02 7.58542180e-01 2.06308201e-01 1.34349838... | [14.603623390197754, -2.3876285552978516] |
1c6ef58e-0a1e-41ce-8b75-11fd2a7b037c | personalized-federated-learning-under-mixture | 2305.01068 | null | https://arxiv.org/abs/2305.01068v1 | https://arxiv.org/pdf/2305.01068v1.pdf | Personalized Federated Learning under Mixture of Distributions | The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept ... | ['Wei Cheng', 'Haifeng Chen', 'Dawei Zhou', 'Quanquan Gu', 'Yanchi Liu', 'Wenchao Yu', 'Shuaicheng Zhang', 'Yue Wu'] | 2023-05-01 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.86943078e-01 -3.26996803e-01 -3.48201036e-01 -6.41295433e-01
-7.81182230e-01 -6.54067397e-01 4.10943896e-01 2.14727465e-02
-2.50916362e-01 6.48532450e-01 -3.68076377e-03 -3.19344550e-01
-7.37449676e-02 -5.34417331e-01 -6.88966155e-01 -8.12749267e-01
-1.41483620e-01 3.60477418e-01 -2.03261096e-02 5.83447158... | [5.808900833129883, 6.320919513702393] |
7d318165-712d-4064-9937-66c13070dbbb | show-me-your-face-and-i-ll-tell-you-how-you | 2206.14009 | null | https://arxiv.org/abs/2206.14009v1 | https://arxiv.org/pdf/2206.14009v1.pdf | Show Me Your Face, And I'll Tell You How You Speak | When we speak, the prosody and content of the speech can be inferred from the movement of our lips. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate speech given only the lip movements of a speaker where we focus on learning accurate lip to speech mappings for multiple speakers i... | ['Timon Ulrich', 'Lotfy Abdel Khaliq', 'Christen Millerdurai'] | 2022-06-28 | null | null | null | null | ['lip-to-speech-synthesis'] | ['computer-vision'] | [ 7.79844895e-02 3.88887644e-01 -6.39167786e-01 -4.78641719e-01
-9.78789687e-01 -5.58392704e-01 7.13478625e-01 -6.08759284e-01
5.29952012e-02 7.45471358e-01 8.34675193e-01 3.63484509e-02
5.80534756e-01 -4.35278118e-02 -4.78816360e-01 -6.07194066e-01
5.61235428e-01 2.98777997e-01 -4.74637806e-01 7.38187656... | [14.325013160705566, 4.9457688331604] |
144b69e1-2e1a-4e9e-ac5b-0dfe5d9562cb | multi-camera-multiple-3d-object-tracking-on | 2204.09151 | null | https://arxiv.org/abs/2204.09151v1 | https://arxiv.org/pdf/2204.09151v1.pdf | Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles | The development of autonomous vehicles provides an opportunity to have a complete set of camera sensors capturing the environment around the car. Thus, it is important for object detection and tracking to address new challenges, such as achieving consistent results across views of cameras. To address these challenges, ... | ['Khoa Luu', 'Xuan-Bac Nguyen', 'Ngan Le', 'Chi Nhan Duong', 'Kha Gia Quach', 'Pha Nguyen'] | 2022-04-19 | null | null | null | null | ['3d-object-tracking'] | ['computer-vision'] | [-2.67088473e-01 -3.31877559e-01 -4.04813915e-01 -1.58086419e-02
-3.39073002e-01 -5.70899069e-01 6.88110709e-01 1.00082727e-02
-3.33361700e-02 3.41653824e-01 -3.02848190e-01 -1.23430192e-01
2.95762531e-03 -5.63564777e-01 -8.22816432e-01 -4.27520454e-01
-1.25835538e-02 4.94826376e-01 1.10428894e+00 -5.13972752... | [6.636973857879639, -2.1424431800842285] |
a632eb23-8800-4ee9-92f6-6978ed2feded | a-complex-network-based-graph-embedding | 2209.04884 | null | https://arxiv.org/abs/2209.04884v1 | https://arxiv.org/pdf/2209.04884v1.pdf | A Complex Network based Graph Embedding Method for Link Prediction | Graph embedding methods aim at finding useful graph representations by mapping nodes to a low-dimensional vector space. It is a task with important downstream applications, such as link prediction, graph reconstruction, data visualization, node classification, and language modeling. In recent years, the field of graph ... | ['Hafida Benhidour', 'Said Kerrache'] | 2022-09-11 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-1.59500867e-01 2.09841266e-01 -4.15283918e-01 3.07411095e-03
-7.41594806e-02 -4.67658669e-01 7.26223826e-01 7.30326533e-01
-2.27246925e-01 3.08519810e-01 2.32300967e-01 -4.13548887e-01
-3.96287978e-01 -1.00025058e+00 -2.61402637e-01 -5.06064892e-01
-5.82484186e-01 3.86063069e-01 1.12693891e-01 -3.41321081... | [7.0891432762146, 6.01719331741333] |
a972da5b-cc8e-4a9c-b42d-5c1ba0100690 | image-based-virtual-try-on-system-with | 2302.14197 | null | https://arxiv.org/abs/2302.14197v1 | https://arxiv.org/pdf/2302.14197v1.pdf | Image-Based Virtual Try-on System With Clothing-Size Adjustment | The conventional image-based virtual try-on method cannot generate fitting images that correspond to the clothing size because the system cannot accurately reflect the body information of a person. In this study, an image-based virtual try-on system that could adjust the clothing size was proposed. The size information... | ['Nobuo Funabiki', 'Koki Nakai', 'Minoru Kuribayashi'] | 2023-02-27 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [-6.32426143e-02 -8.36216211e-02 3.65841001e-01 -1.42949253e-01
4.87468213e-01 -4.44465846e-01 -2.81445801e-01 2.03221709e-01
-3.84467572e-01 1.34028584e-01 -2.16320544e-01 3.25583629e-02
1.88009337e-01 -9.55118239e-01 -3.67780983e-01 -1.83276758e-01
6.13730967e-01 4.08179909e-01 5.72582424e-01 -3.46601158... | [11.35093879699707, -1.1047451496124268] |
df8af7d0-047e-428b-973c-df78291100d4 | abusive-and-threatening-language-detection-in | 2111.14830 | null | https://arxiv.org/abs/2111.14830v1 | https://arxiv.org/pdf/2111.14830v1.pdf | Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach | Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. They also employ moderators who can check the posts violating moderation policies and take appropriate action. Academicians in the abusive lang... | ['Punyajoy Saha', 'Somnath Banerjee', 'Mithun Das'] | 2021-11-27 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [-6.75750494e-01 -8.64014477e-02 -3.14229488e-01 -3.55050229e-02
-7.71610618e-01 -8.54831100e-01 6.75209522e-01 3.38702559e-01
-3.95033896e-01 8.27939987e-01 4.52878475e-01 -2.98220336e-01
2.56988913e-01 -5.90448439e-01 -1.68907136e-01 -4.96201664e-01
3.79696578e-01 1.43700778e-01 8.32450092e-02 -8.77258778... | [8.827747344970703, 10.554503440856934] |
6072ef16-4bba-4b02-af8a-1400e96bf5ed | continuous-implicit-authentication-for-mobile | 1705.06715 | null | http://arxiv.org/abs/1705.06715v1 | http://arxiv.org/pdf/1705.06715v1.pdf | Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System | As mobile devices have become indispensable in modern life, mobile security
is becoming much more important. Traditional password or PIN-like
point-of-entry security measures score low on usability and are vulnerable to
brute force and other types of attacks. In order to improve mobile security, an
adaptive neuro-fuzzy... | ['Suleiman Y. Yerima', 'Sakir Sezer', 'Feng Yao', 'BooJoong Kang'] | 2017-05-18 | null | null | null | null | ['mobile-security'] | ['miscellaneous'] | [ 4.30509374e-02 -1.53500766e-01 6.88524991e-02 8.06814805e-02
2.18116418e-01 -6.80967987e-01 4.09240544e-01 4.46669191e-01
-7.91725338e-01 7.09308565e-01 -5.91034412e-01 -7.49310672e-01
-5.39392650e-01 -6.23671651e-01 -2.42326204e-02 -3.53315651e-01
-2.31157154e-01 -6.27538040e-02 2.11827844e-01 -4.31192786... | [13.542731285095215, 1.3425278663635254] |
7dae8d8e-535c-448e-b785-695356856316 | sgl-pt-a-strong-graph-learner-with-graph | 2302.12449 | null | https://arxiv.org/abs/2302.12449v1 | https://arxiv.org/pdf/2302.12449v1.pdf | SGL-PT: A Strong Graph Learner with Graph Prompt Tuning | Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre... | ['Siliang Tang', 'Jianhao Guo', 'Yun Zhu'] | 2023-02-24 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.93222386e-01 4.34663922e-01 -2.81338602e-01 -5.03618300e-01
-4.24046904e-01 -5.47842741e-01 7.87630260e-01 8.09873790e-02
-2.82172054e-01 5.99512815e-01 3.53278726e-01 -3.93364668e-01
-5.49551323e-02 -9.60036039e-01 -9.04169559e-01 -4.76704895e-01
1.93519622e-01 3.85285020e-01 2.49892268e-02 -5.34182310... | [9.956206321716309, 8.12522029876709] |
7dc24e7e-de66-4f7d-8e05-265d459bf07f | graf-generative-radiance-fields-for-3d-aware | 2007.02442 | null | https://arxiv.org/abs/2007.02442v4 | https://arxiv.org/pdf/2007.02442v4.pdf | GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis | While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, they do not provide precise control over camera viewpoint or object pose. To address this problem, several recent approaches leverage intermedi... | ['Michael Niemeyer', 'Katja Schwarz', 'Yiyi Liao', 'Andreas Geiger'] | 2020-07-05 | null | http://proceedings.neurips.cc/paper/2020/hash/e92e1b476bb5262d793fd40931e0ed53-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e92e1b476bb5262d793fd40931e0ed53-Paper.pdf | neurips-2020-12 | ['scene-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 6.14465952e-01 -7.03448206e-02 1.55675247e-01 4.73931469e-02
-7.83962131e-01 -1.06914210e+00 9.52280998e-01 -4.44102734e-01
1.74485862e-01 7.05084085e-01 8.27966556e-02 -1.10327981e-01
-2.63265092e-02 -1.10248041e+00 -9.56155837e-01 -8.70613635e-01
3.02317113e-01 3.21141452e-01 -1.83073338e-02 -1.86206400... | [9.313028335571289, -3.194117307662964] |
815603ef-e69a-461d-96b9-5aceac187ffe | a-transformer-based-contrastive-learning | 2204.02803 | null | https://arxiv.org/abs/2204.02803v1 | https://arxiv.org/pdf/2204.02803v1.pdf | A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition | Sign language recognition from sequences of monocular images or 2D poses is a challenging field, not only due to the difficulty to infer 3D information from 2D data, but also due to the temporal relationship between the sequences of information. Additionally, the wide variety of signs and the constant need to add new o... | ['Jampierre Rocha', 'Márcio Dahia', 'Esdras Costa', 'Silvan Ferreira'] | 2022-04-05 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 1.54911175e-01 -5.63951552e-01 -3.03943545e-01 -2.47377738e-01
-2.59710044e-01 -5.02710760e-01 6.91266954e-01 -7.13813961e-01
-2.97221243e-01 6.09047949e-01 2.33851418e-01 1.21281169e-01
-9.97262746e-02 -4.10039783e-01 -5.20686150e-01 -6.56439066e-01
-4.46004346e-02 3.16034615e-01 5.12432754e-01 -2.90771723... | [9.064603805541992, -6.3711981773376465] |
3ef0d588-dd0e-41a4-a20c-faf20bc1496f | rethinking-gradient-operator-for-exposing-ai | 2205.00767 | null | https://arxiv.org/abs/2205.00767v1 | https://arxiv.org/pdf/2205.00767v1.pdf | Rethinking Gradient Operator for Exposing AI-enabled Face Forgeries | For image forensics, convolutional neural networks (CNNs) tend to learn content features rather than subtle manipulation traces, which limits forensic performance. Existing methods predominantly solve the above challenges by following a general pipeline, that is, subtracting the original pixel value from the predicted ... | ['Ming Xia', 'Dengyong Zhang', 'Gaobo Yang', 'Zhiqing Guo'] | 2022-05-02 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 1.0285518e-02 -3.8612968e-01 6.6623330e-02 -2.4824123e-01
-2.5050735e-01 -3.5540524e-01 2.1692738e-01 -2.8505310e-01
-3.6307770e-01 1.8623536e-02 -5.3078663e-02 -3.7454224e-01
1.3639842e-01 -8.9406675e-01 -7.8653896e-01 -6.9605917e-01
7.8618256e-03 -3.5661694e-01 2.4768420e-01 -5.1473353e-02
5.2292609e-01... | [12.410929679870605, 0.957928478717804] |
76bf607e-c259-4b80-803c-7f38581069b9 | detecting-frames-in-news-headlines-and-lead | null | null | https://aclanthology.org/2021.findings-emnlp.339 | https://aclanthology.org/2021.findings-emnlp.339.pdf | Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage | News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called “frames” in communica... | ['Derry Tanti Wijaya', 'Prakash Ishwar', 'Margrit Betke', 'Hengchang Hu', 'Sha Lai', 'Boqi Chen', 'Mona Jalal', 'Edward Edberg Halim', 'Fabian Zhafransyah', 'Taufiq Husada Daryanto', 'Lei Guo', 'Isidora Tourni'] | null | null | null | null | findings-emnlp-2021-11 | ['multimodal-text-and-image-classification', 'news-classification', 'news-annotation'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 6.02687597e-01 2.73317188e-01 -6.16780102e-01 -4.07240033e-01
-7.65664339e-01 -5.94573200e-01 1.20963502e+00 9.67020750e-01
-4.31432843e-01 5.16335130e-01 1.48807180e+00 -1.35068446e-01
8.88456702e-02 -7.55540490e-01 -6.33492410e-01 -2.33287469e-01
2.14143530e-01 -7.86595270e-02 3.53847304e-03 -2.26765946... | [8.73755168914795, 9.890167236328125] |
e092c8ab-67b7-4d57-ac9e-99c3c36da380 | reading-chinese-in-natural-scenes-with-a-bag | 2210.02576 | null | https://arxiv.org/abs/2210.02576v1 | https://arxiv.org/pdf/2210.02576v1.pdf | Reading Chinese in Natural Scenes with a Bag-of-Radicals Prior | Scene text recognition (STR) on Latin datasets has been extensively studied in recent years, and state-of-the-art (SOTA) models often reach high accuracy. However, the performance on non-Latin transcripts, such as Chinese, is not satisfactory. In this paper, we collect six open-source Chinese STR datasets and evaluate ... | ['Wang Yunhong', 'Chen Jiaxin', 'Liu Qingjie', 'Liu Yongbin'] | 2022-10-05 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.36318421e-02 -6.68935716e-01 -1.71400785e-01 -3.60680282e-01
-9.72199976e-01 -4.04102087e-01 6.51326835e-01 -1.67212367e-01
-5.26990831e-01 3.64582688e-02 6.45708263e-01 1.37533452e-02
6.33275807e-01 -5.40563822e-01 -7.13509142e-01 -7.32556283e-01
6.21637583e-01 1.68854788e-01 1.24371171e-01 -2.80555159... | [11.767202377319336, 2.0599093437194824] |
7c881c29-ace5-41fe-ac15-d15839c232f7 | the-sweet-home-speech-and-multimodal-corpus | null | null | https://aclanthology.org/L14-1125 | https://aclanthology.org/L14-1125.pdf | The Sweet-Home speech and multimodal corpus for home automation interaction | Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes and Home Automation. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. ... | ['Fran{\\c{c}}ois Portet', 'Brigitte Meillon', 'Pedro Chahuara', 'Nicolas Bonnefond', 'Michel Vacher', 'Benjamin Lecouteux'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['distant-speech-recognition'] | ['speech'] | [ 4.14400101e-01 4.48634118e-01 3.79677057e-01 -3.23649079e-01
-8.34176540e-01 -3.68866950e-01 8.32363546e-01 -2.50430077e-01
-6.88304484e-01 1.27304912e+00 1.11455476e+00 6.45036250e-02
-1.96655318e-01 -4.92688745e-01 3.91742885e-02 -7.08825290e-01
-1.80661947e-01 3.66520137e-01 3.37503329e-02 -1.81758463... | [7.121118068695068, 0.5146183967590332] |
3186814f-32c2-4aee-bdfb-a9b91b67fa4a | towards-unpaired-depth-enhancement-and-super | 2105.12038 | null | https://arxiv.org/abs/2105.12038v4 | https://arxiv.org/pdf/2105.12038v4.pdf | Unpaired Depth Super-Resolution in the Wild | Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. Acquisition of real-w... | ['Evgeny Burnaev', 'Denis Zorin', 'Alexander Filippov', 'Alexey Artemov', 'Oleg Voynov', 'Nikita Drobyshev', 'Maxim Kan', 'Aleksandr Safin'] | 2021-05-25 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 1.06045902e+00 1.11943446e-01 1.57237187e-01 -4.29670155e-01
-1.39372289e+00 -1.90959081e-01 4.67407376e-01 -1.46431047e-02
-1.08451739e-01 9.59836781e-01 1.59604445e-01 3.52562308e-01
-1.47237912e-01 -1.24663258e+00 -8.72960925e-01 -6.47448719e-01
1.73539951e-01 6.01592004e-01 6.98479891e-01 -2.92009920... | [9.526644706726074, -2.4783198833465576] |
ea77395a-2af0-43ac-931f-6f8fea8cbd77 | socialgym-2-0-simulator-for-multi-agent | 2303.05584 | null | https://arxiv.org/abs/2303.05584v1 | https://arxiv.org/pdf/2303.05584v1.pdf | SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces | We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots wi... | ['Joydeep Biswas', 'Jarrett Holtz', 'Rohan Chandra', 'Zayne Sprague'] | 2023-03-09 | null | null | null | null | ['social-navigation', 'robot-navigation'] | ['robots', 'robots'] | [-8.11155021e-01 3.96996699e-02 3.71391289e-02 -2.43788719e-01
-4.46022332e-01 -6.95608795e-01 5.31395853e-01 3.40720385e-01
-8.23684275e-01 1.02324522e+00 -1.83076069e-01 -6.15127444e-01
-3.27648848e-01 -9.61315274e-01 -6.01589203e-01 -4.42586750e-01
-7.42414474e-01 9.01698411e-01 5.76999664e-01 -9.62480426... | [4.627251625061035, 1.121848464012146] |
afb7aa7d-76bd-4359-99b1-b85f8b76bdb7 | a-convnet-for-the-2020s | 2201.03545 | null | https://arxiv.org/abs/2201.03545v2 | https://arxiv.org/pdf/2201.03545v2.pdf | A ConvNet for the 2020s | The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semanti... | ['Saining Xie', 'Trevor Darrell', 'Christoph Feichtenhofer', 'Chao-yuan Wu', 'Hanzi Mao', 'Zhuang Liu'] | 2022-01-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_A_ConvNet_for_the_2020s_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_A_ConvNet_for_the_2020s_CVPR_2022_paper.pdf | cvpr-2022-1 | ['real-time-object-detection'] | ['computer-vision'] | [ 8.56948346e-02 7.45704994e-02 1.36790618e-01 -1.59373343e-01
-3.04416902e-02 -5.31184852e-01 9.66342270e-01 -3.54594409e-01
-6.42467320e-01 2.20600933e-01 -2.88151130e-02 -3.97873104e-01
-2.00207774e-02 -7.08760440e-01 -5.65901816e-01 -6.76460385e-01
3.05548310e-01 1.77717939e-01 5.56784570e-01 -3.60669076... | [9.412857055664062, 1.5904340744018555] |
a833d4fb-34c1-47ab-8697-b4ee289b9110 | domain-adaptation-for-question-answering-via | 2209.04998 | null | https://arxiv.org/abs/2209.04998v2 | https://arxiv.org/pdf/2209.04998v2.pdf | Domain Adaptation for Question Answering via Question Classification | Question answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems are trained in a source domain but deployed in a different target domain. In this work, we in... | ['Dong Wang', 'Lanyu Shang', 'Ziyi Kou', 'Huimin Zeng', 'Zhenrui Yue'] | 2022-09-12 | null | https://aclanthology.org/2022.coling-1.153 | https://aclanthology.org/2022.coling-1.153.pdf | coling-2022-10 | ['classification'] | ['methodology'] | [ 4.19610262e-01 8.58971551e-02 -6.99137850e-03 -7.92947352e-01
-1.57150364e+00 -9.86783862e-01 3.64624739e-01 1.95455506e-01
-2.87009597e-01 8.30390811e-01 1.68678939e-01 -3.84884477e-01
1.14459349e-02 -6.49766207e-01 -6.41463220e-01 -3.61432672e-01
7.19475448e-01 8.23450446e-01 4.97297198e-01 -5.50396740... | [11.264833450317383, 8.06272029876709] |
7764481e-1f8c-448b-b63f-a4ff40567025 | acr-pose-adversarial-canonical-representation | 2111.10524 | null | https://arxiv.org/abs/2111.10524v1 | https://arxiv.org/pdf/2111.10524v1.pdf | ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation | Recently, category-level 6D object pose estimation has achieved significant improvements with the development of reconstructing canonical 3D representations. However, the reconstruction quality of existing methods is still far from excellent. In this paper, we propose a novel Adversarial Canonical Representation Recons... | ['Jun He', 'Hongyan Liu', 'Kejian Wu', 'Zhicheng Wang', 'Jian Xu', 'Zhengbo Song', 'Zhaoxin Fan'] | 2021-11-20 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 2.59849668e-01 2.09987149e-01 6.07191436e-02 -3.09809148e-01
-1.03356373e+00 -4.31801498e-01 5.56449234e-01 -5.34431815e-01
-1.00867143e-02 3.92667323e-01 8.55186805e-02 1.20561630e-01
2.37239286e-01 -6.98457658e-01 -1.21608138e+00 -6.65209353e-01
1.07272260e-01 4.67184156e-01 2.07530022e-01 -5.34529425... | [8.526199340820312, -3.2519333362579346] |
34fc8b85-961a-4a90-a402-e7e9646e3b70 | acnet-approaching-and-centralizing-network | 2111.12757 | null | https://arxiv.org/abs/2111.12757v4 | https://arxiv.org/pdf/2111.12757v4.pdf | ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval | The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}). The zero-shot sketch-based image retrieval (\underline{ZS-SBIR}) is more generic and practical but poses an even greater challenge because of the additional... | ['Sai-Kit Yeung', 'Ying Shan', 'Yang Yang', 'Hong Lu', 'Yang Wu', 'Ziqiang Zheng', 'Hao Ren'] | 2021-11-24 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.97005436e-01 -6.39583245e-02 -3.27615589e-01 -1.53999403e-01
-1.20252037e+00 -8.12043428e-01 9.89142597e-01 -4.77533013e-01
-5.22549562e-02 6.12351477e-01 1.18691161e-01 2.55285561e-01
-4.13802356e-01 -7.25723445e-01 -7.50822544e-01 -6.83467031e-01
3.99050534e-01 4.63845313e-01 5.13862446e-02 -4.18255031... | [11.617443084716797, 0.645307719707489] |
07c2eb57-98b6-403c-8942-8c635ad55e96 | towards-real-time-dnn-inference-on-mobile | 2004.11250 | null | https://arxiv.org/abs/2004.11250v1 | https://arxiv.org/pdf/2004.11250v1.pdf | Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization | High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage resources on these devices still pose significant challenges for real-time DNN inference executions. To address this problem, we propose a set... | ['Yanzhi Wang', 'Wei Niu', 'Pu Zhao', 'Zheng Zhan', 'Xue Lin', 'Bin Ren'] | 2020-04-22 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [ 1.99639335e-01 -2.51574039e-01 -5.32041371e-01 -4.48055208e-01
-1.12912275e-01 -4.47384238e-01 3.36571783e-01 -5.34214795e-01
-5.07295191e-01 6.62499785e-01 -2.78520644e-01 -1.02186966e+00
1.99683398e-01 -8.58058691e-01 -6.51225805e-01 -3.68713319e-01
2.85014570e-01 5.73496997e-01 4.12118077e-01 -1.66555196... | [8.46019172668457, 3.0226731300354004] |
ad871dd8-644a-4b9a-9925-e5dfc9d1d79b | learnable-front-ends-based-on-temporal | 2211.15254 | null | https://arxiv.org/abs/2211.15254v1 | https://arxiv.org/pdf/2211.15254v1.pdf | Learnable Front Ends Based on Temporal Modulation for Music Tagging | While end-to-end systems are becoming popular in auditory signal processing including automatic music tagging, models using raw audio as input needs a large amount of data and computational resources without domain knowledge. Inspired by the fact that temporal modulation is regarded as an essential component in auditor... | ['Richard M. Stern', 'Yinghao Ma'] | 2022-11-28 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.26437008e-01 -4.13760215e-01 -2.50631366e-02 -1.65639803e-01
-6.08534753e-01 -4.99235570e-01 3.04325223e-01 -3.64395455e-02
-6.58499777e-01 2.40494102e-01 4.66137111e-01 -7.90564045e-02
-4.40039515e-01 -3.87949586e-01 -2.01401010e-01 -4.48221654e-01
-3.38390052e-01 2.19056513e-02 2.37945825e-01 -4.69977558... | [15.688530921936035, 5.313606262207031] |
36fbaf60-0494-4625-b1fc-d89d7f27f95e | sarg-a-novel-semi-autoregressive-generator | 2008.01474 | null | https://arxiv.org/abs/2008.01474v3 | https://arxiv.org/pdf/2008.01474v3.pdf | SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration | Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoratio... | ['Weidong Zhang', 'Feng Li', 'Mengzuo Huang', 'Wuhe Zou'] | 2020-08-04 | null | null | null | null | ['dialogue-rewriting'] | ['natural-language-processing'] | [ 1.12071030e-01 5.36494970e-01 2.68877447e-01 -3.58493328e-01
-1.08074188e+00 -1.59605622e-01 9.45828915e-01 -3.99122775e-01
-1.86833039e-01 1.23382127e+00 8.02427053e-01 -1.26679376e-01
7.93163031e-02 -4.82638448e-01 -3.95614266e-01 -6.23497128e-01
3.54540944e-01 8.49613607e-01 -1.65036526e-02 -8.55734825... | [12.569392204284668, 8.239862442016602] |
e3561950-72cb-4745-854b-34e97898a344 | side-window-filtering | 1905.07177 | null | https://arxiv.org/abs/1905.07177v1 | https://arxiv.org/pdf/1905.07177v1.pdf | Side Window Filtering | Local windows are routinely used in computer vision and almost without exception the center of the window is aligned with the pixels being processed. We show that this conventional wisdom is not universally applicable. When a pixel is on an edge, placing the center of the window on the pixel is one of the fundamental r... | ['Guoping Qiu', 'Yuanhao Gong', 'Hui Yin'] | 2019-05-17 | side-window-filtering-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yin_Side_Window_Filtering_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yin_Side_Window_Filtering_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-smoothing', 'tone-mapping', 'point-interactive-image-colorization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.82688177e-01 -3.26336831e-01 1.78292140e-01 -6.40794560e-02
-6.91023283e-03 -2.08158493e-01 3.81900936e-01 -1.00139752e-01
-4.15195495e-01 4.48425114e-01 5.76693937e-03 -3.34610283e-01
8.32067430e-02 -8.21327865e-01 -5.52911222e-01 -9.60816324e-01
3.66226025e-02 -6.63489759e-01 6.90303087e-01 -4.26934749... | [11.013176918029785, -2.4692118167877197] |
f8c3e7a4-0cfb-4d6f-b834-6b5d75492822 | unsupervised-procedure-learning-via-joint | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Elhamifar_Unsupervised_Procedure_Learning_via_Joint_Dynamic_Summarization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Elhamifar_Unsupervised_Procedure_Learning_via_Joint_Dynamic_Summarization_ICCV_2019_paper.pdf | Unsupervised Procedure Learning via Joint Dynamic Summarization | We address the problem of unsupervised procedure learning from unconstrained instructional videos. Our goal is to produce a summary of the procedure key-steps and their ordering needed to perform a given task, as well as localization of the key-steps in videos. We develop a collaborative sequential subset selection fra... | [' Zwe Naing', 'Ehsan Elhamifar'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['procedure-learning'] | ['computer-vision'] | [ 5.77941418e-01 -2.13847026e-01 -4.50770557e-01 -2.00994670e-01
-1.01675200e+00 -1.04715574e+00 2.75074631e-01 1.58661172e-01
-4.06712025e-01 3.53440255e-01 3.46970677e-01 -2.93897420e-01
-2.15157598e-01 -1.14001907e-01 -1.26951504e+00 -8.32601309e-01
-3.06384206e-01 2.94553995e-01 4.21075262e-02 4.23130155... | [8.71296501159668, 0.6173815727233887] |
f616b498-704b-40ca-9aa8-499e0de09374 | inference-in-linear-dyadic-data-models-with | 2203.03497 | null | https://arxiv.org/abs/2203.03497v5 | https://arxiv.org/pdf/2203.03497v5.pdf | Inference in Linear Dyadic Data Models with Network Spillovers | When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares and conduct inference using ``dyadic-robust" variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same indi... | ['Ko Sugiura', 'Nathan Canen'] | 2022-03-07 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-7.91894794e-02 3.27081889e-01 -8.21149528e-01 -2.42899895e-01
-3.21538210e-01 -6.34665608e-01 7.06322193e-01 2.61781335e-01
-4.98926461e-01 1.08006036e+00 6.24570370e-01 -8.38088751e-01
-6.51597679e-01 -1.11825681e+00 -8.78042459e-01 -5.06612301e-01
-5.00454962e-01 3.31690788e-01 -2.44422525e-01 1.15410425... | [7.708118915557861, 5.231941223144531] |
54f31da5-0e17-4580-b3c7-cd09a68f3be1 | overfitting-at-semeval-2016-task-3-detecting | null | null | https://aclanthology.org/S16-1133 | https://aclanthology.org/S16-1133.pdf | Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings | null | ['Pascal Poupart', 'Hujie Wang'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['question-similarity'] | ['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... | [-7.362209320068359, 3.6909050941467285] |
167fab57-bb72-488c-bc5c-7f0259c2ae2b | automated-segmentation-and-connectivity | null | null | https://downloads.spj.sciencemag.org/bmef/2022/9783128.pdf | https://downloads.spj.sciencemag.org/bmef/2022/9783128.pdf | Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus | We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from ... | ['B. S. Manjunath', 'Jefferson Chen', 'Ashutosh Shelat', 'Vikram Iyer', 'Peter Tran', 'Christopher Nguyen', 'Judy Pham', 'Saisidharth Majeti', 'Amil Khan', 'Angela Zhang'] | 2022-01-10 | null | null | null | bme-frontiers-2022-1 | ['3d-medical-imaging-segmentation'] | ['medical'] | [-4.07499857e-02 3.43148708e-01 -8.34994838e-02 -5.70526719e-01
-2.70936817e-01 -1.97837442e-01 3.41872983e-02 3.46007913e-01
-7.19364583e-01 6.41648293e-01 2.68121034e-01 -3.24035555e-01
-2.03622743e-01 -1.04986584e+00 -3.86147618e-01 -4.82446283e-01
-6.93330169e-01 1.26923573e+00 4.99102086e-01 4.10966501... | [14.217615127563477, -2.3587467670440674] |
73c03e82-6a47-4f45-84f0-ade9865658ac | progressive-multi-scale-fusion-network-for | 2106.03941 | null | https://arxiv.org/abs/2106.03941v1 | https://arxiv.org/pdf/2106.03941v1.pdf | Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection | Salient object detection(SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks. The depth map could provide additional spatial prior and boundary cues to boost the performance. Combining the depth information with image... | ['Tania Stathaki', 'Tianhong Dai', 'Yanchu Xie', 'Guangyu Ren'] | 2021-06-07 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.16690505e-01 -3.02832425e-01 1.63888231e-01 -3.30665261e-01
-5.48476815e-01 -7.12372921e-03 5.01817405e-01 2.30538532e-01
-5.73315442e-01 5.86239636e-01 2.33288348e-01 4.06528413e-01
-1.57516617e-02 -7.19523907e-01 -5.19782424e-01 -9.20438409e-01
4.34898704e-01 -3.82817835e-01 1.05622160e+00 -6.90627247... | [9.695062637329102, -0.719302773475647] |
93a089d9-967d-4334-9dc4-2d6f5cb1bf99 | constraint-based-causal-structure-learning | null | null | http://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets | http://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets.pdf | Constraint-based Causal Structure Learning with Consistent Separating Sets | We consider constraint-based methods for causal structure learning, such as the PC algorithm or any PC-derived algorithms whose first step consists in pruning a complete graph to obtain an undirected graph skeleton, which is subsequently oriented. All constraint-based methods perform this first step of removing dispensab... | ['Honghao Li', 'Nadir Sella', 'Herve Isambert', 'Vincent Cabeli'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['tree-decomposition'] | ['graphs'] | [ 2.97285736e-01 2.85356492e-01 -7.34780371e-01 8.05665404e-02
-2.90478796e-01 -4.50955033e-01 3.52835566e-01 3.76478881e-01
1.04574084e-01 9.88214433e-01 -3.65579873e-02 -3.99826407e-01
-8.92054617e-01 -9.97175038e-01 -6.40123010e-01 -9.59743619e-01
-4.24359500e-01 5.26083350e-01 3.90992165e-01 3.24521989... | [7.771693706512451, 5.327712535858154] |
1ab0fd6b-a708-4574-983f-aaad32f8523c | lake-net-topology-aware-point-cloud | 2203.16771 | null | https://arxiv.org/abs/2203.16771v1 | https://arxiv.org/pdf/2203.16771v1.pdf | LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints | Point cloud completion aims at completing geometric and topological shapes from a partial observation. However, some topology of the original shape is missing, existing methods directly predict the location of complete points, without predicting structured and topological information of the complete shape, which leads ... | ['Lizhuang Ma', 'Yuan Xie', 'Ran Yi', 'Zhijun Gong', 'Junshu Tang'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_LAKe-Net_Topology-Aware_Point_Cloud_Completion_by_Localizing_Aligned_Keypoints_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_LAKe-Net_Topology-Aware_Point_Cloud_Completion_by_Localizing_Aligned_Keypoints_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-completion'] | ['computer-vision'] | [-5.22824414e-02 7.98745826e-02 -5.80820851e-02 2.85538007e-02
-9.18168843e-01 -5.85327387e-01 5.80448687e-01 3.07438463e-01
5.56698479e-02 1.35263607e-01 -1.32652730e-01 1.49475217e-01
-7.44994134e-02 -1.06172943e+00 -9.31526065e-01 -3.11283976e-01
1.10304601e-01 9.85461712e-01 6.46961212e-01 4.52703945... | [8.330403327941895, -3.4848885536193848] |
d3637e33-7591-4278-92a7-08f4a9ad0614 | unsupervised-rewriter-for-multi-sentence | null | null | https://aclanthology.org/P19-1216 | https://aclanthology.org/P19-1216.pdf | Unsupervised Rewriter for Multi-Sentence Compression | Multi-sentence compression (MSC) aims to generate a grammatical but reduced compression from multiple input sentences while retaining their key information. Previous dominating approach for MSC is the extraction-based word graph approach. A few variants further leveraged lexical substitution to yield more abstractive c... | ['Akiko Aizawa', 'Xiaoyu Shen', 'Yang Zhao', 'Wei Bi'] | 2019-07-01 | null | null | null | acl-2019-7 | ['sentence-compression'] | ['natural-language-processing'] | [ 6.18969619e-01 2.25464910e-01 1.85753610e-02 -2.77961135e-01
-8.85977268e-01 -1.60383791e-01 5.07601917e-01 5.76497614e-01
-5.69461763e-01 8.55914712e-01 5.37326336e-01 -4.97882634e-01
1.42425597e-01 -1.06493652e+00 -5.43010473e-01 -1.74367994e-01
2.88024008e-01 2.35583395e-01 1.68357223e-01 -5.04835904... | [12.24853515625, 9.333260536193848] |
3a44f3b1-b799-44a8-86ca-61a39edf2023 | chatgpt-a-study-on-its-utility-for-ubiquitous | 2305.16837 | null | https://arxiv.org/abs/2305.16837v1 | https://arxiv.org/pdf/2305.16837v1.pdf | ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks | ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised and reinforcement learning techniques and has received widespread attention for its artic... | ['Sourav Mazumdar', 'Ranjani H. G.', 'Giriprasad Sridhara'] | 2023-05-26 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [ 2.95554614e-03 5.21441519e-01 -1.98560059e-02 -2.25587338e-01
-1.18535542e+00 -7.72593796e-01 2.82360017e-01 -1.56740602e-02
2.07708567e-01 8.03613007e-01 3.37546259e-01 -7.15897858e-01
-1.88098475e-01 -4.82161611e-01 -1.42398775e-01 2.75363717e-02
4.70739514e-01 7.44435608e-01 1.08060129e-01 -5.88553369... | [11.966883659362793, 8.211235046386719] |
3718d3ea-65c8-42e7-ad65-00fd17f40560 | softctc-unicode-x2013-semi-supervised | 2212.02135 | null | https://arxiv.org/abs/2212.02135v2 | https://arxiv.org/pdf/2212.02135v2.pdf | SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels | This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition. We propose a novel loss function $\unicode{x2013}$ SoftCTC $\unicode{x2013}$ which is an extension of CTC allowing to consider multiple transcription variants at the same time. This al... | ['Michal Kula', 'Petr Buchal', 'Karel Beneš', 'Michal Hradiš', 'Martin Kišš'] | 2022-12-05 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.57137907e-01 5.61709926e-02 1.74548626e-01 -5.38835943e-01
-1.19646060e+00 -8.33234429e-01 5.44741690e-01 2.20430531e-02
-8.43917727e-01 9.47269619e-01 -4.12597768e-02 -5.89861453e-01
9.19155255e-02 -4.28787857e-01 -8.24341536e-01 -7.99306273e-01
2.91559309e-01 5.93865633e-01 4.43751395e-01 2.20720410... | [11.86205005645752, 2.75075364112854] |
94079529-7240-4365-8db3-aa4327535c78 | dspoint-dual-scale-point-cloud-recognition | 2111.10332 | null | https://arxiv.org/abs/2111.10332v4 | https://arxiv.org/pdf/2111.10332v4.pdf | DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion | Point cloud processing is a challenging task due to its sparsity and irregularity. Prior works introduce delicate designs on either local feature aggregator or global geometric architecture, but few combine both advantages. We propose Dual-Scale Point Cloud Recognition with High-frequency Fusion (DSPoint) to extract lo... | ['Jianbo Shi', 'Kexue Fu', 'Xinben Gao', 'Ziyu Guo', 'Ziyao Zeng', 'Renrui Zhang'] | 2021-11-19 | null | null | null | null | ['3d-shape-retrieval', 'scene-segmentation', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.52852212e-02 -3.05112749e-01 3.52887005e-01 -4.73335028e-01
-9.45522428e-01 -6.15362287e-01 5.85668445e-01 1.08650230e-01
-3.26394439e-01 -1.54211500e-03 8.37941915e-02 -2.13182315e-01
-2.00873315e-01 -1.01506054e+00 -9.14589882e-01 -3.96602929e-01
-2.01142907e-01 4.29988176e-01 1.65716290e-01 -2.38549218... | [7.867537975311279, -3.6124649047851562] |
4a629735-413b-4a2d-aa7f-517a355019f6 | deep-learning-based-group-wise-registration | 2306.10611 | null | https://arxiv.org/abs/2306.10611v1 | https://arxiv.org/pdf/2306.10611v1.pdf | Deep learning-based group-wise registration for longitudinal MRI analysis in glioma | Glioma growth may be quantified with longitudinal image registration. However, the large mass-effects and tissue changes across images pose an added challenge. Here, we propose a longitudinal, learning-based, and groupwise registration method for the accurate and unbiased registration of glioma MRI. We evaluate on a da... | ['Bo Li', 'Esther Bron', 'Frans Vos', 'Roel Verhaak', 'Mathilde Kouwenhoven', 'Pim French', 'Bart Westerman', 'Pieter Wesseling', 'Marion Smits', 'Karin van Garderen', 'Claudia Chinea Hammecher'] | 2023-06-18 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [ 1.13281958e-01 4.87358682e-03 -5.64666688e-02 -5.05317628e-01
-1.12057137e+00 -4.38429654e-01 7.86730111e-01 6.00940764e-01
-7.28161752e-01 4.86284673e-01 4.15056229e-01 -1.73493728e-01
-2.64577657e-01 -3.38618845e-01 -1.13907427e-01 -9.86963987e-01
-6.51950121e-01 5.04826427e-01 2.90430754e-01 -1.46342233... | [14.000199317932129, -2.563905715942383] |
7157a9db-acf5-428b-97f6-0baf7bed0468 | team-hub-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.17 | https://aclanthology.org/2021.ltedi-1.17.pdf | TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model | This article introduces the system description of TEAM_HUB team participating in LT-EDI 2021: Hope Speech Detection. This shared task is the first task related to the desired voice detection. The data set in the shared task consists of three different languages (English, Tamil, and Malayalam). The task type is text cla... | ['Yang Bai', 'Bo Huang'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-3.59710127e-01 -2.52902687e-01 -2.01662555e-01 -4.08874661e-01
-1.22754455e+00 -4.72906530e-01 7.91783035e-01 -1.20825365e-01
-5.06064236e-01 5.57459235e-01 6.88920557e-01 -5.23332655e-01
-7.96311051e-02 -1.26523525e-02 2.21531522e-02 -2.77775496e-01
4.02852520e-02 6.90393388e-01 1.30541101e-01 -3.92274857... | [9.524809837341309, 10.670978546142578] |
9032c677-366f-41a8-a85b-ea02deda4948 | histruct-improving-extractive-text-1 | 2203.09629 | null | https://arxiv.org/abs/2203.09629v1 | https://arxiv.org/pdf/2203.09629v1.pdf | HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information | Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We pro... | ['Georg Rehm', 'Malte Ostendorff', 'Qian Ruan'] | 2022-03-17 | null | https://aclanthology.org/2022.findings-acl.102 | https://aclanthology.org/2022.findings-acl.102.pdf | findings-acl-2022-5 | ['extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.13093653e-01 4.61472690e-01 -4.48352575e-01 -7.72634670e-02
-1.12832355e+00 -6.88172579e-01 6.85986698e-01 5.06389558e-01
-4.19214368e-01 9.16484714e-01 1.19412243e+00 -2.67084092e-01
2.38558650e-01 -5.52792370e-01 -9.89378572e-01 -3.73719513e-01
3.83585423e-01 3.73889625e-01 2.58224234e-02 -1.87865078... | [12.285308837890625, 9.279956817626953] |
96a87aab-a6c3-42d4-ad8e-1a8db988e12c | ensemble-learning-for-spectral-clustering | null | null | https://ieeexplore.ieee.org/document/9338349 | https://scholar.google.com/scholar_url?url=https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/1r54GbKzRdu/pdf%3Fcasa_token%3Dx1Nb_lJqLlcAAAAA:DPz3ReM6KGDlssLERhLHXS-KoqLClMeS5iiK0o2QhAvmKpsl2Umkk2oeWpQH5xmaagT0ALi43N4&hl=en&sa=T&oi=gsb-gga&ct=res&cd=0&d=13812841914647698097&ei=drqOYL62HILUyATd4pf4D... | Ensemble Learning for Spectral Clustering | Ensemble clustering has attracted much attention in machine learning and data mining for the high performance in the task of clustering. Spectral clustering is one of the most popular clustering methods and has superior performance compared with the traditional clustering methods. Existing ensemble clustering methods u... | ['Tetsuya Sakurai', 'Akira Imakura', 'Xiucai Ye', 'Hongmin Li'] | 2020-11-20 | null | null | null | null | ['imagedocument-clustering'] | ['computer-vision'] | [-8.47344007e-03 -5.48257113e-01 8.97260606e-02 -5.90690598e-02
-5.15039146e-01 -5.26702821e-01 1.48008242e-01 8.29466805e-02
-2.27248698e-01 2.78863996e-01 6.62145540e-02 -3.29587758e-02
-6.15306497e-01 -7.05540717e-01 -2.05628186e-01 -1.29878080e+00
-1.74154863e-01 2.81409502e-01 3.58899236e-02 1.61343351... | [7.915320873260498, 4.648407936096191] |
12ae4b91-9214-49b4-88db-766646d5e5af | gocor-bringing-globally-optimized | 2009.07823 | null | https://arxiv.org/abs/2009.07823v4 | https://arxiv.org/pdf/2009.07823v4.pdf | GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network | The feature correlation layer serves as a key neural network module in numerous computer vision problems that involve dense correspondences between image pairs. It predicts a correspondence volume by evaluating dense scalar products between feature vectors extracted from pairs of locations in two images. However, this ... | ['Luc van Gool', 'Radu Timofte', 'Martin Danelljan', 'Prune Truong'] | 2020-09-16 | null | http://proceedings.neurips.cc/paper/2020/hash/a4a8a31750a23de2da88ef6a491dfd5c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a4a8a31750a23de2da88ef6a491dfd5c-Paper.pdf | neurips-2020-12 | ['geometric-matching', 'dense-pixel-correspondence-estimation'] | ['computer-vision', 'computer-vision'] | [-1.54628351e-01 -1.55471386e-02 6.55926391e-02 -2.34434202e-01
-5.95921099e-01 -4.22813654e-01 8.15369129e-01 1.19667456e-01
-5.19490778e-01 2.39607766e-01 1.80057138e-01 -9.22904070e-03
-3.78088765e-02 -6.74171269e-01 -6.59950495e-01 -4.35582697e-01
8.62870440e-02 3.77329856e-01 2.18864709e-01 -9.68596935... | [8.602540016174316, -2.1729979515075684] |
45114b57-704d-4d75-9665-e36e19726e30 | unsupervised-activity-segmentation-by-joint | 2105.13353 | null | https://arxiv.org/abs/2105.13353v6 | https://arxiv.org/pdf/2105.13353v6.pdf | Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering | We present a novel approach for unsupervised activity segmentation which uses video frame clustering as a pretext task and simultaneously performs representation learning and online clustering. This is in contrast with prior works where representation learning and clustering are often performed sequentially. We leverag... | ['Quoc-Huy Tran', 'M. Zeeshan Zia', 'Andrey Konin', 'Awais Ahmed', 'Sanjay Haresh', 'Sateesh Kumar'] | 2021-05-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kumar_Unsupervised_Action_Segmentation_by_Joint_Representation_Learning_and_Online_Clustering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kumar_Unsupervised_Action_Segmentation_by_Joint_Representation_Learning_and_Online_Clustering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-clustering'] | ['computer-vision'] | [ 2.24373534e-01 -2.43633762e-01 -6.01695955e-01 -3.99727017e-01
-8.54405999e-01 -8.69571865e-01 4.16015625e-01 3.40609580e-01
-5.78740060e-01 2.95090288e-01 2.03707442e-01 -2.29562804e-01
-1.10199740e-02 -3.79290164e-01 -7.47165084e-01 -6.83744907e-01
-3.62180680e-01 4.31468666e-01 3.56803179e-01 5.03326774... | [8.534173011779785, 0.5103325247764587] |
4c7b65f9-e78d-47b8-8ca7-793c2f797c6c | boosted-cascaded-convnets-for-multilabel | 1711.08760 | null | http://arxiv.org/abs/1711.08760v1 | http://arxiv.org/pdf/1711.08760v1.pdf | Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs | Chest X-ray is one of the most accessible medical imaging technique for
diagnosis of multiple diseases. With the availability of ChestX-ray14, which is
a massive dataset of chest X-ray images and provides annotations for 14
thoracic diseases; it is possible to train Deep Convolutional Neural Networks
(DCNN) to build Co... | ['Monika Grewal', 'Muktabh Mayank Srivastava', 'Pulkit Kumar'] | 2017-11-23 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 1.52762264e-01 1.00237548e-01 -5.19889772e-01 -6.53744340e-01
-1.00166798e+00 -1.72871232e-01 -1.02825463e-01 1.57940000e-01
-2.80684441e-01 6.84380531e-01 2.15335023e-02 -8.43223810e-01
-2.03845292e-01 -7.60348558e-01 -4.36096847e-01 -5.54626107e-01
2.75943838e-02 7.82136023e-01 2.35899806e-01 1.07093424... | [15.193033218383789, -2.0208754539489746] |
90f209cc-107d-43f0-9e88-f499e837ced3 | contrast-stylize-and-adapt-unsupervised | 2306.09098 | null | https://arxiv.org/abs/2306.09098v1 | https://arxiv.org/pdf/2306.09098v1.pdf | Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation | To overcome the domain gap between synthetic and real-world datasets, unsupervised domain adaptation methods have been proposed for semantic segmentation. Majority of the previous approaches have attempted to reduce the gap either at the pixel or feature level, disregarding the fact that the two components interact pos... | ['Stephane Lathuiliere', 'Hongtao Lu', 'Huayi Zhou', 'Subhankar Roy', 'Tianyu Li'] | 2023-06-15 | null | null | null | null | ['style-transfer', 'contrastive-learning', 'contrastive-learning', 'unsupervised-domain-adaptation'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 4.13311690e-01 1.67186171e-01 -7.69117549e-02 -5.66686213e-01
-8.52435589e-01 -5.55530190e-01 7.19291329e-01 -1.79996878e-01
-2.65587777e-01 5.57071984e-01 -1.16785608e-01 4.32642214e-02
5.23434617e-02 -6.29060447e-01 -7.02564776e-01 -5.99769473e-01
4.85354811e-01 3.38064730e-01 7.14181006e-01 -1.90536439... | [9.6831693649292, 1.2855991125106812] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.