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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
766ce80f-70c4-400e-8fa7-a48a88e5e2af | towards-unbiased-training-in-federated-open | 2305.00771 | null | https://arxiv.org/abs/2305.00771v1 | https://arxiv.org/pdf/2305.00771v1.pdf | Towards Unbiased Training in Federated Open-world Semi-supervised Learning | Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data. However, existing works for FedSSL rely on a closed-world assumption that all local training data and global ... | ['Wenchao Xu', 'Song Guo', 'Xiaosong Ma', 'Jie Zhang'] | 2023-05-01 | null | null | null | null | ['open-world-semi-supervised-learning'] | ['computer-vision'] | [-2.23523811e-01 1.29796207e-01 -4.14602429e-01 -6.84919953e-01
-9.10923064e-01 -6.39538705e-01 2.63876855e-01 -1.39523476e-01
-2.21084997e-01 1.14283729e+00 -2.24973172e-01 -3.39340940e-02
-1.90954298e-01 -6.74659550e-01 -9.08439755e-01 -1.08091044e+00
1.44248605e-01 9.59957004e-01 2.42680371e-01 2.93215841... | [5.864028453826904, 6.317831516265869] |
9655b080-7d04-4354-b05d-ffe7add71268 | towards-coherent-and-engaging-spoken-dialog | 1904.13015 | null | https://arxiv.org/abs/1904.13015v4 | https://arxiv.org/pdf/1904.13015v4.pdf | Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators | Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood~(MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a syste... | ['Dilek Hakkani-Tur', 'Behnam Hedayatnia', 'Chandra Khatri', 'Tagyoung Chung', 'Raefer Gabriel', 'Anu Venkatesh', 'Alessandra Cervone', 'Rahul Goel', 'Sanghyun Yi'] | 2019-04-30 | towards-coherent-and-engaging-spoken-dialog-1 | https://aclanthology.org/W19-8608 | https://aclanthology.org/W19-8608.pdf | ws-2019-10 | ['open-domain-dialog'] | ['natural-language-processing'] | [-8.25183839e-02 4.64804918e-01 8.65773484e-02 -9.89979744e-01
-1.08408928e+00 -7.66888022e-01 6.14651501e-01 7.23146647e-02
-4.78777975e-01 8.59461904e-01 7.75937557e-01 -1.71541318e-01
2.44451061e-01 -5.93543053e-01 -2.12620929e-01 -1.20439872e-01
4.44237679e-01 8.39350522e-01 1.51261106e-01 -1.05863261... | [12.812966346740723, 8.034542083740234] |
340c45ad-f317-4602-9c28-a8b48dbaf37a | enriching-the-webnlg-corpus | null | null | https://aclanthology.org/W18-6521 | https://aclanthology.org/W18-6521.pdf | Enriching the WebNLG corpus | This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a,b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation. We also produce a silver-standard German translation of the corpu... | ['er', 'Thiago Castro Ferreira', 'Emiel Krahmer', 'S Wubben', 'Diego Moussallem'] | 2018-11-01 | null | null | null | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.34922186e-01 9.23277140e-01 -3.57152104e-01 -1.62870273e-01
-1.09659088e+00 -9.25719798e-01 1.08987641e+00 3.69675130e-01
-5.52220225e-01 1.41747832e+00 8.70683491e-01 -5.12199640e-01
3.33461203e-02 -5.45738935e-01 -2.81781256e-01 -1.12588204e-01
-6.42406419e-02 6.89592838e-01 2.32648328e-02 -4.94932145... | [11.14443588256836, 9.178787231445312] |
a46f7afe-d017-421b-8d71-4530f751671e | vulnerability-detection-using-two-stage-deep | 2305.09673 | null | https://arxiv.org/abs/2305.09673v1 | https://arxiv.org/pdf/2305.09673v1.pdf | Vulnerability Detection Using Two-Stage Deep Learning Models | Application security is an essential part of developing modern software, as lots of attacks depend on vulnerabilities in software. The number of attacks is increasing globally due to technological advancements. Companies must include security in every stage of developing, testing, and deploying their software in order ... | ['Khloud Al Jallad', 'Mohammad Hammade', 'Mohamed Mjd Alhafi'] | 2023-05-08 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.23054467e-01 -2.66508073e-01 5.29102236e-02 -2.18017429e-01
-2.39355445e-01 -7.51720071e-01 1.65869907e-01 4.28410470e-01
-4.94287908e-02 2.73706943e-01 -3.74104708e-01 -1.09325624e+00
9.22838449e-02 -1.11565149e+00 -3.94958556e-01 -3.17655504e-01
-1.85665935e-01 -1.43553108e-01 8.31325352e-01 -3.51564467... | [7.0513691902160645, 7.779970645904541] |
250fa77e-76cf-44a9-be30-83151fcd2dc8 | unsupervised-speech-representation-pooling | 2304.03940 | null | https://arxiv.org/abs/2304.03940v1 | https://arxiv.org/pdf/2304.03940v1.pdf | Unsupervised Speech Representation Pooling Using Vector Quantization | With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remains; the length of speech representations is inherently variable. The naive average pooling is often u... | ['Hyung-Min Park', 'Hyunjun Heo', 'Kwanghee Choi', 'Jeongkyun Park'] | 2023-04-08 | null | null | null | null | ['intent-classification', 'keyword-spotting', 'speaker-identification'] | ['natural-language-processing', 'speech', 'speech'] | [ 2.34290704e-01 1.00816973e-01 -3.17026943e-01 -5.30325532e-01
-1.13168418e+00 -5.76308608e-01 5.82439005e-01 2.60548621e-01
-3.82312149e-01 4.91477877e-01 8.89622688e-01 -1.04676224e-01
3.11016887e-01 -3.64009589e-01 -3.79481822e-01 -5.22363007e-01
8.85869414e-02 -9.86199081e-02 2.60084588e-03 -3.74393724... | [14.284493446350098, 6.540535926818848] |
70afb66f-376e-475e-8152-30308af8874b | cooperative-exploration-for-multi-agent-deep | 2107.11444 | null | https://arxiv.org/abs/2107.11444v1 | https://arxiv.org/pdf/2107.11444v1.pdf | Cooperative Exploration for Multi-Agent Deep Reinforcement Learning | Exploration is critical for good results in deep reinforcement learning and has attracted much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. Very recently, exploration methods that consider cooperation among multiple agents have been developed. ... | ['Alexander G. Schwing', 'Raymond A. Yeh', 'Unnat Jain', 'Iou-Jen Liu'] | 2021-07-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.21818376e-01 -2.70710170e-01 -3.02822769e-01 1.89063832e-01
-9.74722266e-01 -3.37705672e-01 8.62063408e-01 3.21158826e-01
-7.13711739e-01 9.86251473e-01 4.09751862e-01 1.23795420e-01
-2.05156416e-01 -6.45453513e-01 -5.94943762e-01 -8.90317738e-01
-4.74629313e-01 8.86233985e-01 1.35849742e-02 -5.00253379... | [3.8549513816833496, 1.8322325944900513] |
e682c084-6f06-4cc9-8ee9-fbe3ddfa2c07 | weakly-supervised-dense-video-captioning | 1704.01502 | null | http://arxiv.org/abs/1704.01502v1 | http://arxiv.org/pdf/1704.01502v1.pdf | Weakly Supervised Dense Video Captioning | This paper focuses on a novel and challenging vision task, dense video
captioning, which aims to automatically describe a video clip with multiple
informative and diverse caption sentences. The proposed method is trained
without explicit annotation of fine-grained sentence to video region-sequence
correspondence, but i... | ['xiangyang xue', 'Yu-Gang Jiang', 'Zhou Su', 'Zhiqiang Shen', 'Jianguo Li', 'Yurong Chen', 'Minjun Li'] | 2017-04-05 | weakly-supervised-dense-video-captioning-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Shen_Weakly_Supervised_Dense_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Shen_Weakly_Supervised_Dense_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-video-captioning'] | ['computer-vision'] | [ 7.26640224e-01 1.13881744e-01 -5.22217572e-01 -6.21966898e-01
-1.41844428e+00 -5.98108351e-01 5.69031835e-01 -1.82953089e-01
-3.60504448e-01 1.14398706e+00 4.99259204e-01 1.65049732e-01
5.47691166e-01 -1.54985175e-01 -1.30207253e+00 -6.55571818e-01
2.17521608e-01 5.43052912e-01 2.24916548e-01 7.97266066... | [10.497861862182617, 0.7026985883712769] |
c3cc5c95-43f9-49ad-8683-cae3458b5278 | modeling-t1-resting-state-mri-variants-using | 2306.12435 | null | https://arxiv.org/abs/2306.12435v1 | https://arxiv.org/pdf/2306.12435v1.pdf | Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCD | Obsessive-compulsive disorder (OCD) presents itself as a highly debilitating disorder. The disorder has common associations with the prefrontal cortex and the glutamate receptor known as Metabotropic Glutamate Receptor 5 (mGluR5). This receptor has been observed to demonstrate higher levels of signaling from positron e... | ['Tarun Eswar'] | 2023-06-15 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.19826938e-02 -1.90496013e-01 -1.05189621e-01 -2.80364096e-01
-5.19794166e-01 -1.71937183e-01 2.14432284e-01 1.50773361e-01
-6.13314807e-01 5.48266113e-01 -2.93917004e-02 -3.41614962e-01
-5.51350355e-01 -5.90250790e-01 -2.86013991e-01 -4.34617043e-01
-7.61623263e-01 4.60427135e-01 -4.36373949e-01 9.26806852... | [12.58899211883545, 3.161959648132324] |
8938d360-3f76-41c8-91d9-80ee296a8dad | manifold-regularized-slow-feature-analysis | 1706.03015 | null | http://arxiv.org/abs/1706.03015v1 | http://arxiv.org/pdf/1706.03015v1.pdf | Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition | Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features f... | ['DaCheng Tao', 'Xiangmin Xu', 'Xiaofen Xing', 'Jie Miao'] | 2017-06-09 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 3.64474088e-01 -8.97101760e-01 -2.50359386e-01 -3.60626310e-01
-2.97374517e-01 -3.46885175e-01 6.61005020e-01 -4.87431705e-01
2.93639414e-02 3.31038266e-01 9.81647596e-02 5.71908429e-02
-5.88877559e-01 -8.12619328e-01 -5.19767642e-01 -1.33009696e+00
-3.18800151e-01 -2.28099868e-01 4.46477830e-01 -1.48293763... | [12.15897274017334, 0.42385780811309814] |
cf4910c5-9adf-49d6-a465-39e25f931f79 | vehicle-re-identification-exploring-feature | 1911.05541 | null | https://arxiv.org/abs/1911.05541v3 | https://arxiv.org/pdf/1911.05541v3.pdf | Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks | This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and yea... | ['Keiko V. O. Fonseca', 'Rayson Laroca', 'Rodrigo Minetto', 'David Menotti', 'Icaro O. de Oliveira'] | 2019-11-13 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-9.86560900e-03 -7.22163856e-01 -6.47962540e-02 -3.47789675e-01
-1.05042887e+00 -1.06799138e+00 6.04246318e-01 -1.17430575e-01
-4.01805073e-01 2.43086666e-01 -3.90905768e-01 -5.38047701e-02
4.18108076e-01 -7.78162658e-01 -1.25404894e+00 -5.55360377e-01
1.80336311e-02 3.01922113e-01 3.46391588e-01 -8.43747780... | [9.842771530151367, -4.896456241607666] |
c6d881c6-8566-4873-ad58-02a0e4e1408b | referential-communication-in-heterogeneous | 2302.08913 | null | https://arxiv.org/abs/2302.08913v2 | https://arxiv.org/pdf/2302.08913v2.pdf | Referential communication in heterogeneous communities of pre-trained visual deep networks | As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this dire... | ['Marco Baroni', 'Roberto Dessì', 'Francesca Franzon', 'Matéo Mahaut'] | 2023-02-04 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 3.40692282e-01 4.44130272e-01 2.07093701e-01 -5.05569540e-02
1.57560766e-01 -7.28960216e-01 1.16293526e+00 2.21847191e-01
-4.19430166e-01 7.10032165e-01 -1.97364632e-02 1.25209585e-01
-5.16445190e-02 -9.95554626e-01 -9.87442434e-01 -1.02746391e+00
-2.64196575e-01 7.49309182e-01 3.77967238e-01 -4.72654074... | [4.521557331085205, 1.616342306137085] |
a7749f9f-e0f1-4cc5-ab0c-1488b1803eba | x-model-improving-data-efficiency-in-deep-1 | 2110.04572 | null | https://arxiv.org/abs/2110.04572v1 | https://arxiv.org/pdf/2110.04572v1.pdf | X-model: Improving Data Efficiency in Deep Learning with A Minimax Model | To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due ... | ['Mingsheng Long', 'Jianmin Wang', 'Xinyang Chen', 'Ximei Wang'] | 2021-10-09 | x-model-improving-data-efficiency-in-deep | https://openreview.net/forum?id=P3Bh01hBYTH | https://openreview.net/pdf?id=P3Bh01hBYTH | iclr-2022-4 | ['value-prediction', 'age-estimation', 'age-estimation'] | ['computer-code', 'computer-vision', 'miscellaneous'] | [ 1.03485659e-01 4.56439070e-02 -4.43613112e-01 -7.67135501e-01
-6.76074505e-01 -2.92609781e-01 3.08744818e-01 1.32021636e-01
-4.93710577e-01 6.45710707e-01 6.35085702e-02 -2.07099617e-01
-2.90421605e-01 -6.26506805e-01 -7.02492654e-01 -7.89332688e-01
2.21212164e-01 3.20652574e-01 -2.74529994e-01 2.87217110... | [9.369244575500488, 3.798638105392456] |
b7d50e79-4228-445e-830e-d99338ed4ca8 | compositionality-and-capacity-in-emergent | null | null | https://aclanthology.org/2020.repl4nlp-1.5 | https://aclanthology.org/2020.repl4nlp-1.5.pdf | Compositionality and Capacity in Emergent Languages | Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. ... | ['Kyunghyun Cho', 'Abhinav Gupta', 'Jakob Foerster', 'Andrew Dai', 'Cinjon Resnick'] | 2020-07-01 | null | null | null | ws-2020-7 | ['systematic-generalization'] | ['reasoning'] | [ 1.03317656e-01 3.72325718e-01 -2.91979045e-01 -6.63556233e-02
-2.05893651e-01 -6.06652558e-01 8.97321582e-01 1.86146006e-01
-4.31933403e-01 8.61174762e-01 5.10675192e-01 -7.47321486e-01
-4.26809281e-01 -7.21845686e-01 -9.56886172e-01 -7.58909702e-01
-4.85497773e-01 1.96938351e-01 1.02740012e-01 -3.86565626... | [4.119128704071045, 1.7591767311096191] |
2f274a2a-5f9b-447d-adb4-c5eb9f19c64a | reorganizing-educational-institutional-domain | 2306.10300 | null | https://arxiv.org/abs/2306.10300v1 | https://arxiv.org/pdf/2306.10300v1.pdf | Reorganizing Educational Institutional Domain using Faceted Ontological Principles | The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ont... | ['Sayon Roy', 'Debashis Naskar', 'Subhashis Das'] | 2023-06-17 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-2.74196178e-01 1.95532832e-02 -5.24063647e-01 -2.83062249e-01
-1.39638956e-03 -6.81872129e-01 6.97569847e-01 6.46570623e-01
-5.57728827e-01 9.01840746e-01 4.36686784e-01 -6.78214550e-01
-1.05266738e+00 -1.12372494e+00 6.36746213e-02 -9.91514549e-02
7.38553181e-02 6.88852489e-01 3.63379270e-01 -7.76536286... | [9.365297317504883, 8.226693153381348] |
10b7826b-3aa3-4c14-b662-5f3f5bff8919 | dual-attention-network-for-heart-rate-and | 2111.00390 | null | https://arxiv.org/abs/2111.00390v1 | https://arxiv.org/pdf/2111.00390v1.pdf | Dual Attention Network for Heart Rate and Respiratory Rate Estimation | Heart rate and respiratory rate measurement is a vital step for diagnosing many diseases. Non-contact camera based physiological measurement is more accessible and convenient in Telehealth nowadays than contact instruments such as fingertip oximeters since non-contact methods reduce risk of infection. However, remote p... | ['Niranjan Avadhanam', 'Braeden Syrnyk', 'Yuzhuo Ren'] | 2021-10-31 | null | null | null | null | ['respiratory-rate-estimation'] | ['medical'] | [ 8.22053701e-02 -2.20932394e-01 -4.28191014e-02 -3.92781198e-01
-1.66009441e-01 -1.54591978e-01 -2.38115340e-01 -3.25536877e-01
-5.08829236e-01 8.79850328e-01 1.56179026e-01 -1.72609299e-01
2.73329914e-01 -3.17307204e-01 -1.29347295e-01 -6.82285428e-01
3.60254019e-01 -4.27110225e-01 -4.45076138e-01 4.62878495... | [13.908846855163574, 2.8615787029266357] |
2e21a03d-bb92-4d9d-95cd-ba022363aac4 | comparative-validation-of-ai-and-non-ai | 2207.11534 | null | https://arxiv.org/abs/2207.11534v1 | https://arxiv.org/pdf/2207.11534v1.pdf | Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes | Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with th... | ['Kyung-Su Kim', 'Jong Hyeon Ahn', 'Jin Whan Cho', 'Jinyoung Youn', 'Myung Jin Chung', 'Chae Yeon Lim', 'Jisoo Lee', 'Juyoung Hahm', 'Joomee Song'] | 2022-07-23 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [-2.27314904e-01 2.98700333e-02 -1.00535275e-02 -3.17664713e-01
-6.74187183e-01 -3.74153882e-01 2.14132398e-01 -1.20814107e-02
-8.91414106e-01 8.84878814e-01 9.25638601e-02 -3.62830102e-01
-9.24578588e-03 -4.10871685e-01 -6.62236884e-02 -6.38298512e-01
-4.96295482e-01 1.00324821e+00 4.35150564e-01 3.79591405... | [14.106085777282715, -1.9726788997650146] |
9af422e6-db3d-4380-bcb8-e6508dfa35a6 | cross-domain-named-entity-recognition-via-1 | null | null | https://aclanthology.org/2022.findings-acl.210 | https://aclanthology.org/2022.findings-acl.210.pdf | Cross-domain Named Entity Recognition via Graph Matching | Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge ... | ['Qianli Ma', 'Haibin Chen', 'Junhao Zheng'] | null | null | null | null | findings-acl-2022-5 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 2.20939949e-01 6.40711561e-02 -3.99724901e-01 -3.52442592e-01
-8.22765887e-01 -7.43555367e-01 5.72671533e-01 3.33631247e-01
-5.53844988e-01 8.23287189e-01 2.60871738e-01 1.72422752e-02
-1.20060958e-01 -1.17627585e+00 -3.90022397e-01 -4.24837232e-01
3.38027149e-01 7.51448214e-01 4.54378515e-01 -3.42685401... | [9.743542671203613, 9.53580379486084] |
a74ad78b-3cb4-4915-9a00-4b9ff8210e9a | av-sam-segment-anything-model-meets-audio | 2305.01836 | null | https://arxiv.org/abs/2305.01836v1 | https://arxiv.org/pdf/2305.01836v1.pdf | AV-SAM: Segment Anything Model Meets Audio-Visual Localization and Segmentation | Segment Anything Model (SAM) has recently shown its powerful effectiveness in visual segmentation tasks. However, there is less exploration concerning how SAM works on audio-visual tasks, such as visual sound localization and segmentation. In this work, we propose a simple yet effective audio-visual localization and se... | ['Yapeng Tian', 'Shentong Mo'] | 2023-05-03 | null | null | null | null | ['visual-localization', 'object-localization'] | ['computer-vision', 'computer-vision'] | [ 4.13805157e-01 -2.87947953e-01 -3.64962593e-02 -2.68850625e-01
-1.29525447e+00 -6.13240600e-01 3.25586885e-01 -1.52902395e-01
-1.81363255e-01 1.18044719e-01 1.25395179e-01 -5.73664121e-02
4.11204129e-01 -2.56824553e-01 -7.79142797e-01 -5.04777491e-01
2.88811237e-01 6.80824667e-02 5.63601077e-01 3.46016258... | [14.819860458374023, 4.770596981048584] |
f40e7159-29e7-494d-bccf-9ec7ef84bbc4 | independent-motion-detection-with-event | 1706.08713 | null | http://arxiv.org/abs/1706.08713v2 | http://arxiv.org/pdf/1706.08713v2.pdf | Independent Motion Detection with Event-driven Cameras | Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for ... | ['Elias Mueggler', 'Arren Glover', 'Valentina Vasco', 'Lorenzo Natale', 'Davide Scaramuzza', 'Chiara Bartolozzi'] | 2017-06-27 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.44452888e-01 -5.63262880e-01 3.00460398e-01 -2.16380224e-01
-5.07174194e-01 -6.31141961e-01 3.19991022e-01 1.62458315e-01
-1.07525861e+00 5.05005896e-01 -4.35530424e-01 4.05986845e-01
2.34545887e-01 -1.59389585e-01 -9.32779014e-01 -1.00040901e+00
-2.10334942e-01 2.61513352e-01 7.40035594e-01 3.87757719... | [8.641633987426758, -1.3055164813995361] |
2789a891-e91d-46ea-b52b-8988caa07557 | a-comparison-of-graph-neural-networks-for | 2303.12812 | null | https://arxiv.org/abs/2303.12812v1 | https://arxiv.org/pdf/2303.12812v1.pdf | A Comparison of Graph Neural Networks for Malware Classification | Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge. Function call graphs consist of a set of ... | ['Mark Stamp', 'Katerina Potika', 'Vrinda Malhotra'] | 2023-03-22 | null | null | null | null | ['graph-classification', 'malware-classification'] | ['graphs', 'miscellaneous'] | [ 4.76583332e-01 -2.88817197e-01 -6.54170096e-01 -1.99953094e-01
-1.45096630e-01 -9.64696348e-01 6.33060515e-01 3.54912937e-01
-3.07003129e-02 4.65366580e-02 -2.15562340e-02 -1.13587010e+00
-2.88963765e-02 -1.03896403e+00 -4.75409359e-01 -1.26148298e-01
-4.27882880e-01 2.30507419e-01 2.91012347e-01 -2.27663875... | [14.392561912536621, 9.668303489685059] |
a24b740b-cf64-4843-9718-bb48b666bdc5 | efficient-and-robust-bayesian-selection-of | 2306.00357 | null | https://arxiv.org/abs/2306.00357v1 | https://arxiv.org/pdf/2306.00357v1.pdf | Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization | We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics. By leveraging Bayesian optimization (BO) with a surrogate model, our approach enables efficient hyperparameter selection wit... | ['Anna Ma', 'Hengrui Luo', 'Yin-Ting Liao'] | 2023-06-01 | null | null | null | null | ['dimensionality-reduction', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-1.03675477e-01 -4.20960188e-01 -4.05823022e-01 -1.48995265e-01
-9.77728605e-01 -6.50208175e-01 5.01579285e-01 1.43172845e-01
-3.63648951e-01 9.99289691e-01 1.44122303e-01 -1.87052742e-01
-9.50676739e-01 -7.11390316e-01 -2.78618373e-02 -1.01603889e+00
-2.22696573e-01 6.78844392e-01 5.39240167e-02 -6.28907904... | [6.651214122772217, 3.9826323986053467] |
7ad6b644-e228-49c1-b9d2-be51e069dfac | sliderunner-a-tool-for-massive-cell | 1802.02347 | null | http://arxiv.org/abs/1802.02347v1 | http://arxiv.org/pdf/1802.02347v1.pdf | SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images | Large-scale image data such as digital whole-slide histology images pose a
challenging task at annotation software solutions. Today, a number of good
solutions with varying scopes exist. For cell annotation, however, we find that
many do not match the prerequisites for fast annotations. Especially in the
field of mitos... | ['Christof Bertram', 'Robert Klopfleisch', 'Marc Aubreville', 'Andreas Maier'] | 2018-02-07 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [-3.87134552e-02 2.26303458e-01 1.16443366e-01 -2.58267313e-01
-1.10713828e+00 -1.02887511e+00 1.88912414e-02 8.09227824e-01
-4.42595303e-01 7.28022933e-01 -2.48940215e-01 -5.10670245e-01
6.66937158e-02 -3.79706025e-01 -1.61455899e-01 -1.00029385e+00
1.24810047e-01 8.92346680e-01 7.52759397e-01 9.02638808... | [14.998684883117676, -3.132917642593384] |
3922659e-15d6-4a9d-b174-09cf7a891f0b | ebm-life-cycle-mcmc-strategies-for-synthesis-1 | 2205.12243 | null | https://arxiv.org/abs/2205.12243v1 | https://arxiv.org/pdf/2205.12243v1.pdf | EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling | This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories. MCMC trajectories of different lengths correspond to models with different purposes. Our experiments cover three different trajectory magnitudes and learning outcomes: 1) shortrun sampli... | ['Song-Chun Zhu', 'Mubarak Shah', 'Yuan Du', 'Chu Chen', 'Jonathan Mitchell', 'Mitch Hill'] | 2022-05-24 | ebm-life-cycle-mcmc-strategies-for-synthesis | https://openreview.net/forum?id=psQ6wcNXjS1 | https://openreview.net/pdf?id=psQ6wcNXjS1 | null | ['adversarial-defense'] | ['adversarial'] | [-1.11589760e-01 -1.42854035e-01 -2.74287015e-01 -3.12046647e-01
-1.15077507e+00 -7.20991254e-01 9.70782518e-01 -6.44197941e-01
-6.81109965e-01 6.25572026e-01 -1.56760827e-01 -7.59092867e-01
-2.03094035e-02 -5.41893244e-01 -7.64594853e-01 -7.33044326e-01
-3.93314451e-01 7.55439878e-01 2.02924371e-01 1.24100156... | [5.684556007385254, 7.816071510314941] |
063b9d10-fda0-4041-9e39-74400b9037af | image-classification-with-small-datasets | null | null | https://ieeexplore.ieee.org/abstract/document/9770050 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9770050 | Image Classification With Small Datasets: Overview and Benchmark | Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring
reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common
benchmark to... | ['Joachim Denzler', 'Luca Iocchi', 'Björn Barz', 'Lorenzo Brigato'] | 2022-05-05 | null | null | null | ieee-access-2022-5 | ['small-data'] | ['computer-vision'] | [ 4.63918537e-01 -2.94768065e-01 -4.75600988e-01 -4.46370661e-01
-9.92342532e-01 -6.09431922e-01 6.82765365e-01 2.29825582e-02
-5.14869869e-01 7.62527406e-01 1.64668001e-02 -5.08863069e-02
-1.84314176e-01 -5.28909564e-01 -2.27639839e-01 -7.18696475e-01
-2.90483475e-01 5.27466759e-02 8.38413462e-02 -1.78024858... | [9.694745063781738, 2.5850939750671387] |
ed78c40f-c8d4-4452-866d-8d714d2ecee3 | residual-sparsity-connection-learning-for | 2206.07687 | null | https://arxiv.org/abs/2206.07687v3 | https://arxiv.org/pdf/2206.07687v3.pdf | Structured Sparsity Learning for Efficient Video Super-Resolution | The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruni... | ['Luc van Gool', 'Wenming Yang', 'Yapeng Tian', 'Yitong Wang', 'Yulun Zhang', 'Jingwen He', 'Bin Xia'] | 2022-06-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution'] | ['computer-vision'] | [ 4.68707651e-01 -6.04390427e-02 -5.18026233e-01 -2.56797373e-01
-1.99056491e-01 -4.48732786e-02 2.47070760e-01 -6.72987998e-01
-5.39745837e-02 7.44453311e-01 4.05756980e-01 -1.70559227e-01
-2.59621471e-01 -8.02846372e-01 -6.98930323e-01 -6.38118267e-01
2.33819172e-01 -5.01368344e-01 4.15395111e-01 -2.02999830... | [10.948111534118652, -1.6935508251190186] |
97cc5dbb-5303-442a-8bd5-0e7227d6f2d9 | rodnet-a-real-time-radar-object-detection | 2102.05150 | null | https://arxiv.org/abs/2102.05150v1 | https://arxiv.org/pdf/2102.05150v1.pdf | RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization | Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used sensors, radar has usually been considered as a robust and cost-effective solution even in adverse driving scenarios, e.g., weak/strong lighti... | ['Hui Liu', 'Guanbin Xing', 'Jenq-Neng Hwang', 'Yudong Li', 'Zhongyu Jiang', 'Yizhou Wang'] | 2021-02-09 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 2.38252819e-01 -2.56560475e-01 1.62384614e-01 -7.06695795e-01
-7.69660056e-01 -5.13081312e-01 6.76098764e-01 -4.32667792e-01
-7.32885301e-01 7.27878094e-01 -4.70816165e-01 -2.24686444e-01
-2.69466013e-01 -9.75571632e-01 -5.94665170e-01 -9.14662302e-01
1.58820078e-01 3.62533122e-01 3.56282115e-01 -9.26936045... | [7.861734390258789, -1.4113441705703735] |
c96a21ca-eac9-4273-b070-e308364eadc6 | g-nm-a-group-of-numerical-time-series | 2306.11667 | null | https://arxiv.org/abs/2306.11667v3 | https://arxiv.org/pdf/2306.11667v3.pdf | G-NM: A Group of Numerical Time Series Prediction Models | In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G-NM). This inclusive set comprises traditional models such as Autoregressive Integrated Moving Average ... | ['Juyoung Yun'] | 2023-06-20 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.00288324e-01 -5.58760643e-01 -7.57412091e-02 -3.13309819e-01
-1.42755821e-01 -4.89258707e-01 8.70695353e-01 -4.92850766e-02
8.38258341e-02 6.43386781e-01 3.99452895e-01 -1.02137482e+00
-2.06128776e-01 -9.05184567e-01 -3.13094735e-01 -5.02490938e-01
-9.66074169e-01 -1.24959841e-01 -2.62997270e-01 -7.22750604... | [6.538689136505127, 3.006308078765869] |
49f4c8bf-6ada-4de4-9c62-1a83f1cbee38 | deconstructing-data-reconstruction-multiclass | 2307.01827 | null | https://arxiv.org/abs/2307.01827v1 | https://arxiv.org/pdf/2307.01827v1.pdf | Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses | Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently, Haim et al. (2022) proposed a scheme to reconstruct training samples from multilayer perceptron binary classifiers, effectively demonstrating that a large portion o... | ['Michal Irani', 'Yaniv Nikankin', 'Yakir Oz', 'Gal Vardi', 'Gilad Yehudai', 'Niv Haim', 'Gon Buzaglo'] | 2023-07-04 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 4.73854840e-01 1.21121086e-01 -2.08028480e-01 -4.81607437e-01
-1.29024088e-01 -1.96019962e-01 4.05551404e-01 7.67472535e-02
-6.40431404e-01 9.05204177e-01 1.28347753e-02 -3.32331330e-01
-2.88272649e-01 -9.74411547e-01 -1.09973240e+00 -6.71213448e-01
2.16473475e-01 -8.87493640e-02 6.08448833e-02 -1.07856117... | [8.562278747558594, 3.450573205947876] |
fdb7d3d2-a2a2-4ad6-becf-82fa77f79b9f | learning-to-generate-scene-graph-from-head-to | 2206.11653 | null | https://arxiv.org/abs/2206.11653v1 | https://arxiv.org/pdf/2206.11653v1.pdf | Learning To Generate Scene Graph from Head to Tail | Scene Graph Generation (SGG) represents objects and their interactions with a graph structure. Recently, many works are devoted to solving the imbalanced problem in SGG. However, underestimating the head predicates in the whole training process, they wreck the features of head predicates that provide general features f... | ['Lianli Gao', 'Jingkuan Song', 'Pengpeng Zeng', 'Yuyu Guo', 'Xinyu Lyu', 'Chaofan Zheng'] | 2022-06-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.41771221e-01 5.25062084e-01 -7.80481175e-02 -3.38018507e-01
-4.73361343e-01 -1.61454096e-01 5.56978226e-01 2.74498910e-01
3.97021957e-02 4.65689063e-01 2.55934656e-01 1.46672487e-01
-1.53725773e-01 -1.15979815e+00 -9.23596919e-01 -6.98448300e-01
2.60366976e-01 6.49946690e-01 5.02391338e-01 -3.01945537... | [10.28231143951416, 1.760029911994934] |
11f2c075-fa34-4aeb-a480-c4633cc84e35 | draw-a-recurrent-neural-network-for-image | 1502.04623 | null | http://arxiv.org/abs/1502.04623v2 | http://arxiv.org/pdf/1502.04623v2.pdf | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of com... | ['Danilo Jimenez Rezende', 'Ivo Danihelka', 'Daan Wierstra', 'Karol Gregor', 'Alex Graves'] | 2015-02-16 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 1.57224000e-01 4.08718556e-01 4.37371969e-01 1.87933147e-02
-1.90280467e-01 -4.57157582e-01 1.09805596e+00 -7.39654541e-01
-3.30871582e-01 7.35469520e-01 2.34798998e-01 -3.18185121e-01
1.48952246e-01 -1.05337799e+00 -9.05637562e-01 -7.75402427e-01
3.93294156e-01 5.94007850e-01 -5.87177686e-02 -3.19571227... | [11.354050636291504, -0.1903604418039322] |
be2b5c18-0034-4cf0-b8e3-ad80fa853377 | efficient-feature-compression-for-edge-cloud | 2211.09897 | null | https://arxiv.org/abs/2211.09897v1 | https://arxiv.org/pdf/2211.09897v1.pdf | Efficient Feature Compression for Edge-Cloud Systems | Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder a... | ['Fengqing Zhu', 'Zhihao Duan'] | 2022-11-17 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 3.25065583e-01 -6.94247723e-01 -6.03848577e-01 -4.89873976e-01
-5.90133071e-01 -3.34670961e-01 1.12233624e-01 5.73932305e-02
-3.50260824e-01 1.09975830e-01 -4.87170629e-02 -6.11056387e-01
-7.07148910e-02 -8.93578768e-01 -3.23322088e-01 -4.09860253e-01
-3.98761928e-02 -1.90492675e-01 1.84066325e-01 9.04726051... | [8.433586120605469, 2.945391893386841] |
8a4bf9b8-bd43-440e-a908-d07c97e4683b | matryoshka-networks-predicting-3d-geometry | 1804.10975 | null | http://arxiv.org/abs/1804.10975v1 | http://arxiv.org/pdf/1804.10975v1.pdf | Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers | In this paper, we develop novel, efficient 2D encodings for 3D geometry,
which enable reconstructing full 3D shapes from a single image at high
resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction
problem. To that end, we first develop a simple baseline network that predicts
entire voxel tubes... | ['Stephan R. Richter', 'Stefan Roth'] | 2018-04-29 | matryoshka-networks-predicting-3d-geometry-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Richter_Matryoshka_Networks_Predicting_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Richter_Matryoshka_Networks_Predicting_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.07868710e-01 4.16583359e-01 2.60964990e-01 -3.26653957e-01
-6.38355076e-01 -7.14463472e-01 6.33587480e-01 4.34368737e-02
9.50459465e-02 2.63559103e-01 2.70372152e-01 -2.45551378e-01
-9.22944173e-02 -1.31685996e+00 -9.36684847e-01 -1.91588938e-01
-3.15637946e-01 1.21727514e+00 4.26394552e-01 -9.02246088... | [8.646559715270996, -3.627495050430298] |
149d8e55-3999-4d4e-ba38-038f4154d32d | dagan-depth-aware-generative-adversarial | 2305.06225 | null | https://arxiv.org/abs/2305.06225v1 | https://arxiv.org/pdf/2305.06225v1.pdf | DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation | Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial geometry, such as pixel-wise depth, plays a critical role in constructing accurate 3D facial structures and suppressing complex background no... | ['Dan Xu', 'Li Shen', 'Fa-Ting Hong'] | 2023-05-10 | null | null | null | null | ['talking-head-generation', 'video-generation'] | ['computer-vision', 'computer-vision'] | [-1.58085227e-01 2.16917902e-01 5.68098724e-02 -6.39888942e-01
-9.89643574e-01 -3.29967111e-01 4.86629516e-01 -9.10141766e-01
1.63772210e-01 4.23937142e-01 4.98198062e-01 1.81801334e-01
2.30524823e-01 -6.30338192e-01 -9.20319080e-01 -8.57592404e-01
3.34890932e-01 2.58587718e-01 -2.58917421e-01 -2.57922053... | [12.960790634155273, -0.2743467390537262] |
86f46857-640c-4c0c-bded-d3d8f352862d | pane-gnn-unifying-positive-and-negative-edges | 2306.04095 | null | https://arxiv.org/abs/2306.04095v2 | https://arxiv.org/pdf/2306.04095v2.pdf | PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation | Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growing interest in leveraging graph neural networks (GNNs) for recommender systems, capitalizing on advancements in graph representation learnin... | ['Kun Gai', 'Na Mou', 'Yang song', 'Kai Zheng', 'Cheng Wu', 'Jingcao Xu', 'Chaokun Wang', 'Ziyang Liu'] | 2023-06-07 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [-2.54024584e-02 -1.56239625e-02 -5.53112566e-01 -3.38927239e-01
1.81717902e-01 -4.81339991e-01 5.83552182e-01 2.26205871e-01
-1.32601425e-01 1.81639969e-01 7.96187043e-01 -3.97557527e-01
-4.05069739e-01 -1.06250405e+00 -3.00907731e-01 -4.21650022e-01
-2.03337923e-01 3.95374037e-02 9.11952555e-02 -9.09821689... | [10.209988594055176, 5.6084675788879395] |
0ff1f5b4-41c2-4fca-809d-f3614b68f9a0 | a-better-choice-entire-space-datasets-for | 2212.09052 | null | https://arxiv.org/abs/2212.09052v1 | https://arxiv.org/pdf/2212.09052v1.pdf | A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction | Aspect sentiment triplet extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences. Since the initial datasets used to evaluate models on ASTE had flaws, several studies later corrected the initial datasets and released new versions of the datasets independently. As a result, dif... | ['Sheng-hua Zhong', 'Fang Wang', 'Yuncong Li'] | 2022-12-18 | null | null | null | null | ['extract-aspect', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.02229454e-01 -1.93223283e-01 -2.69742310e-01 -6.53538883e-01
-4.96375442e-01 -7.27849603e-01 5.48816562e-01 7.98816606e-02
-2.91851789e-01 4.74803150e-01 3.09758931e-01 -2.84809798e-01
-1.27862198e-02 -7.04918265e-01 -3.60451072e-01 -4.04873878e-01
5.16792417e-01 1.44888178e-01 2.61610568e-01 -6.96377575... | [11.485114097595215, 6.649343490600586] |
3e41ecfc-004c-4f81-b8cc-0928efd94fcb | victr-video-conditioned-text-representations | 2304.02560 | null | https://arxiv.org/abs/2304.02560v1 | https://arxiv.org/pdf/2304.02560v1.pdf | VicTR: Video-conditioned Text Representations for Activity Recognition | Vision-Language models have shown strong performance in the image-domain -- even in zero-shot settings, thanks to the availability of large amount of pretraining data (i.e., paired image-text examples). However for videos, such paired data is not as abundant. Thus, video-text models are usually designed by adapting pre... | ['Michael S. Ryoo', 'Arsha Nagrani', 'Anurag Arnab', 'Kumara Kahatapitiya'] | 2023-04-05 | null | null | null | null | ['zero-shot-action-recognition', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 2.25560471e-01 -3.09138656e-01 -3.39422554e-01 -3.04219157e-01
-5.40959239e-01 -3.78959358e-01 8.24539244e-01 -7.72580504e-02
-8.18317354e-01 4.18847919e-01 5.23835361e-01 -1.14453778e-01
4.00242478e-01 -5.10899425e-01 -1.14262617e+00 -7.71883130e-01
1.63658053e-01 2.35191956e-01 1.95279121e-01 -2.83721481... | [10.002355575561523, 0.8942254781723022] |
691c6b12-ae65-4843-a3ab-c381d14f7295 | ccs-explorer-relevance-prediction-extractive | 2211.00201 | null | https://arxiv.org/abs/2211.00201v2 | https://arxiv.org/pdf/2211.00201v2.pdf | CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies | Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of ... | ['Cassie S. Mitchell', 'Sarah Bi', 'Albert J. Lee', 'Davi Nakajima An', 'Irfan Al-Hussaini'] | 2022-11-01 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 1.99349463e-01 -1.38051892e-02 -6.08032942e-01 -1.60469785e-01
-1.53275979e+00 -4.90859687e-01 2.62949497e-01 1.13466918e+00
-4.88093823e-01 8.60925615e-01 6.96582735e-01 -6.92411959e-01
-2.04125538e-01 -5.73626876e-01 -5.10924160e-01 -1.50281087e-01
-2.60134697e-01 4.91924375e-01 -1.15521237e-01 1.37854889... | [8.579818725585938, 8.643009185791016] |
1443b6d2-7f5c-45b7-aba6-8e3680d7fe83 | privacy-preserving-visual-localization-with | 2212.03177 | null | https://arxiv.org/abs/2212.03177v2 | https://arxiv.org/pdf/2212.03177v2.pdf | Privacy-Preserving Visual Localization with Event Cameras | We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algor... | ['Jian Wang', 'Sizhuo Ma', 'Gurunandan Krishnan', 'Weston A. Welge', 'Ramzi Zahreddine', 'Yicheng Wu', 'Young Min Kim', 'Junho Kim'] | 2022-12-04 | null | null | null | null | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 3.71998906e-01 -1.34207204e-01 -1.51841894e-01 -4.98699278e-01
-8.92702162e-01 -9.76279318e-01 4.18502480e-01 3.02523792e-01
-4.73401815e-01 4.63720709e-01 1.72326401e-01 -2.72130817e-01
2.14505643e-01 -5.98454833e-01 -1.00764501e+00 -5.41924000e-01
1.80334598e-02 -5.46552598e-01 3.22973102e-01 4.54770356... | [12.653803825378418, 0.7846998572349548] |
5779ca74-a085-47cb-87df-08797e4ac6e4 | adaptive-data-free-quantization | 2303.06869 | null | https://arxiv.org/abs/2303.06869v3 | https://arxiv.org/pdf/2303.06869v3.pdf | Adaptive Data-Free Quantization | Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e.... | ['Meng Wang', 'Richang Hong', 'Yang Wang', 'Biao Qian'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.14430778e-03 4.96240795e-01 -3.49678874e-01 -3.67580093e-02
-8.45707357e-01 -6.65889680e-01 2.21580669e-01 -2.23741949e-01
-3.84852290e-01 1.02615309e+00 -1.33447334e-01 -2.45459005e-01
-4.06497359e-01 -1.10382390e+00 -6.26103640e-01 -1.02422214e+00
-7.99658597e-02 2.35914975e-01 1.95179015e-01 -4.59220409... | [8.722103118896484, 2.9669289588928223] |
15894e26-3790-461d-812d-432b4edaf949 | efficient-dialog-policy-learning-via-positive | 1810.01371 | null | https://arxiv.org/abs/1810.01371v3 | https://arxiv.org/pdf/1810.01371v3.pdf | Efficient Dialog Policy Learning via Positive Memory Retention | This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typically requires a large amount of samples. However, the collection of the required data in form of conversations between chat-bots and human a... | ['Rui Zhao', 'Volker Tresp'] | 2018-10-02 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.20558822e-01 1.85906500e-01 -2.26350158e-01 -1.21092321e-02
-4.85790223e-01 -4.37198460e-01 6.80615962e-01 -2.44322106e-01
-1.00156093e+00 1.19241965e+00 -1.58784389e-01 -6.92661822e-01
3.70264828e-01 -8.01659167e-01 -5.68471909e-01 -6.17269456e-01
-4.89199273e-02 1.08013618e+00 3.71667713e-01 -4.72269684... | [13.11587142944336, 8.102787017822266] |
0ea70d73-3922-43bd-a258-62dd1d43ca61 | augnet-end-to-end-unsupervised-visual | 2106.06250 | null | https://arxiv.org/abs/2106.06250v1 | https://arxiv.org/pdf/2106.06250v1.pdf | AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation | Most of the achievements in artificial intelligence so far were accomplished by supervised learning which requires numerous annotated training data and thus costs innumerable manpower for labeling. Unsupervised learning is one of the effective solutions to overcome such difficulties. In our work, we propose AugNet, a n... | ['Zhecheng Wang', 'Liufang Guo', 'Zhuang Li', 'Bitao Yang', 'Haonan Lu', 'Zhanguo Chang', 'Mingxiang Chen'] | 2021-06-11 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-1.21826030e-01 -5.14514804e-01 -2.56746590e-01 -7.74429202e-01
-1.02038026e+00 -4.76577699e-01 7.46918499e-01 3.18358153e-01
-7.72745311e-01 3.39694679e-01 -2.10982323e-01 2.23368481e-01
-2.78773010e-01 -7.23950505e-01 -4.01227117e-01 -8.67014289e-01
9.64265615e-02 5.37402928e-01 -1.40053883e-01 1.78411722... | [10.64659595489502, 0.7371737957000732] |
9b65f53a-85b6-428a-94a3-7a672d7424d0 | pedestrian-detection-with-high-resolution | 2305.18008 | null | https://arxiv.org/abs/2305.18008v1 | https://arxiv.org/pdf/2305.18008v1.pdf | Pedestrian detection with high-resolution event camera | Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image qu... | ['Tomasz Kryjak', 'Piotr Wzorek'] | 2023-05-29 | null | null | null | null | ['self-driving-cars', 'pedestrian-detection', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.75776356e-01 -4.76023853e-01 1.53197318e-01 -2.40799770e-01
-2.29448020e-01 -2.72495925e-01 7.78364897e-01 1.00892395e-01
-8.40272903e-01 7.34350085e-01 -2.19625793e-02 -2.41967458e-02
9.79101807e-02 -7.72524714e-01 -7.64518976e-01 -5.57870626e-01
-2.11088836e-01 -2.70223860e-02 7.68436491e-01 -5.41617051... | [8.516554832458496, -1.0768749713897705] |
52cf6298-2092-42d4-8f1a-32325970b207 | projection-domain-self-supervision-for | 2212.07431 | null | https://arxiv.org/abs/2212.07431v2 | https://arxiv.org/pdf/2212.07431v2.pdf | Simulator-Based Self-Supervision for Learned 3D Tomography Reconstruction | We propose a deep learning method for 3D volumetric reconstruction in low-dose helical cone-beam computed tomography. Prior machine learning approaches require reference reconstructions computed by another algorithm for training. In contrast, we train our model in a fully self-supervised manner using only noisy 2D X-ra... | ['Jaakko Lehtinen', 'Miika Aittala', 'Tero Karras', 'Samuli Laine', 'Onni Kosomaa'] | 2022-12-14 | null | null | null | null | ['3d-volumetric-reconstruction'] | ['computer-vision'] | [ 1.76282391e-01 1.99290320e-01 -4.37362790e-02 -5.94233751e-01
-1.39621913e+00 -3.12879890e-01 3.68785203e-01 1.64037019e-01
-5.70593715e-01 7.29963720e-01 5.49715102e-01 -6.26677454e-01
-1.80238470e-01 -8.87923121e-01 -7.46559262e-01 -4.89258587e-01
-2.62265563e-01 7.69977450e-01 1.76072091e-01 4.53298278... | [13.463834762573242, -2.613274097442627] |
795bbcdf-03a7-4353-855e-82f199c5b932 | a-fast-topological-approach-for-predicting | 2305.06523 | null | https://arxiv.org/abs/2305.06523v1 | https://arxiv.org/pdf/2305.06523v1.pdf | A fast topological approach for predicting anomalies in time-varying graphs | Large time-varying graphs are increasingly common in financial, social and biological settings. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, a persistence diagram (PD) from topological... | ['Ekaterina Smirnova', 'Cuneyt Akcora', 'Omid Khormali', 'Hasani Pathirana', 'Umar Islambekov'] | 2023-05-11 | null | null | null | null | ['topological-data-analysis', 'change-point-detection'] | ['graphs', 'time-series'] | [ 8.17043483e-02 -3.12311053e-01 -1.61118098e-02 6.84618950e-02
-1.93866208e-01 -9.45406914e-01 6.03778243e-01 8.06498230e-01
-1.09041005e-01 6.53809905e-01 -1.76526546e-01 -4.61516947e-01
-7.56344974e-01 -1.12855363e+00 -5.21796703e-01 -8.88763666e-01
-9.15688396e-01 2.36912340e-01 2.91794300e-01 -3.29746544... | [7.390061855316162, 4.383115291595459] |
5cb13570-34a9-4a7b-a03f-dc2d67e153e0 | learning-monocular-depth-estimation-infusing | 1904.04144 | null | http://arxiv.org/abs/1904.04144v1 | http://arxiv.org/pdf/1904.04144v1.pdf | Learning monocular depth estimation infusing traditional stereo knowledge | Depth estimation from a single image represents a fascinating, yet
challenging problem with countless applications. Recent works proved that this
task could be learned without direct supervision from ground truth labels
leveraging image synthesis on sequences or stereo pairs. Focusing on this
second case, in this paper... | ['Filippo Aleotti', 'Stefano Mattoccia', 'Matteo Poggi', 'Fabio Tosi'] | 2019-04-08 | learning-monocular-depth-estimation-infusing-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tosi_Learning_Monocular_Depth_Estimation_Infusing_Traditional_Stereo_Knowledge_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tosi_Learning_Monocular_Depth_Estimation_Infusing_Traditional_Stereo_Knowledge_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 3.06945384e-01 3.53159696e-01 -1.03415839e-01 -3.74632299e-01
-6.55624866e-01 -4.97962415e-01 7.23420858e-01 -2.05711678e-01
-4.47545588e-01 7.98255801e-01 1.80005029e-01 -2.34987661e-02
2.30567038e-01 -7.89533675e-01 -1.04650950e+00 -6.34255350e-01
3.50625247e-01 3.14735085e-01 2.12418228e-01 -3.39880846... | [8.686433792114258, -2.425529956817627] |
d91f9d23-11ff-4488-b310-ad3c68bfd159 | a-synthesis-based-approach-for-thermal-to | 2108.09558 | null | https://arxiv.org/abs/2108.09558v3 | https://arxiv.org/pdf/2108.09558v3.pdf | A Synthesis-Based Approach for Thermal-to-Visible Face Verification | In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners. However, such systems are ineffective in low-light and nighttime conditions. Thermal face imagery, which captures body heat emissions, effectively augments the visible spectrum, captur... | ['Rama Chellappa', 'Vishal M. Patel', 'Thirimachos Bourlai', 'Carlos D. Castillo', 'Joshua Gleason', 'Neehar Peri'] | 2021-08-21 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 4.55150545e-01 -6.45599782e-01 1.39552772e-01 -6.32916987e-01
-1.05014062e+00 -5.90205431e-01 3.62368017e-01 -7.72886574e-01
-2.43767984e-02 4.71932322e-01 -1.56533971e-01 -1.08748944e-02
7.59779587e-02 -3.01182926e-01 -4.76965904e-01 -9.98277187e-01
3.25954854e-01 -1.29712731e-01 -5.30504048e-01 -1.39879972... | [13.039947509765625, 0.3307240903377533] |
fe5ab75e-46ce-45a0-8b31-75b1b7616942 | quality-and-cost-trade-offs-in-passage-re | 2111.09927 | null | https://arxiv.org/abs/2111.09927v1 | https://arxiv.org/pdf/2111.09927v1.pdf | Quality and Cost Trade-offs in Passage Re-ranking Task | Deep learning models named transformers achieved state-of-the-art results in a vast majority of NLP tasks at the cost of increased computational complexity and high memory consumption. Using the transformer model in real-time inference becomes a major challenge when implemented in production, because it requires expens... | ['Marina Chernyshevich', 'Nikolay Bushkov', 'Andrei Khobnia', 'Pavel Goncharov', 'Raman Makouski', 'Vsevolod Mitskevich', 'Pavel Podberezko'] | 2021-11-18 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 6.42118379e-02 7.92969167e-02 1.54913336e-01 -3.88015330e-01
-1.21617436e+00 -6.22072458e-01 7.95500696e-01 4.28025514e-01
-8.41922700e-01 6.15476370e-01 -1.23778567e-01 -4.75706667e-01
-4.71306264e-01 -8.24576735e-01 -9.40764368e-01 -5.91319561e-01
-8.40623528e-02 1.17917216e+00 4.39318478e-01 -2.25715920... | [11.252175331115723, 7.579833984375] |
075c7d81-f7c1-4093-9bd4-ffca840cfc83 | fault-detection-scheme-for-grid-forming | 2209.12457 | null | https://arxiv.org/abs/2209.12457v3 | https://arxiv.org/pdf/2209.12457v3.pdf | Fault Detection for Grid-Forming Inverters in Islanded Droop-Controlled AC Microgrids | In this paper, we develop an observer-based fault detection mechanism for grid-forming inverters operating in islanded droop-controlled AC microgrids. The detection scheme uses linear matrix inequalities as constraints with $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ optimization to achieve sensitivity to faults and robustn... | ['Charalambos Konstantinou', 'Yu Zhang', 'Andres Intriago', 'Gabriel Intriago'] | 2022-09-26 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-0.05266977 0.09146027 0.13207304 0.02838328 -0.30355775 -0.72432107
0.12056362 0.14847738 0.0207152 1.1174926 -0.55391955 -0.23592952
-0.6782334 -0.70334226 -0.75798994 -1.0629413 -0.26380846 0.11460908
0.18239771 -0.48980764 0.07985595 0.4644221 -1.4110032 -0.7956857
1.249943 1.2998526 -0.1... | [5.673067569732666, 2.597625970840454] |
70200d6b-7cd2-43b1-a0d2-906cce9ac204 | mimicking-a-pathologist-dual-attention-model | 2302.09682 | null | https://arxiv.org/abs/2302.09682v1 | https://arxiv.org/pdf/2302.09682v1.pdf | Mimicking a Pathologist: Dual Attention Model for Scoring of Gigapixel Histology Images | Some major challenges associated with the automated processing of whole slide images (WSIs) includes their sheer size, different magnification levels and high resolution. Utilizing these images directly in AI frameworks is computationally expensive due to memory constraints, while downsampling WSIs incurs information l... | ['Nasir M. Rajpoot', 'Talha Qaiser', 'Raja Muhammad Saad Bashir', 'Ruqayya Awan', 'Manahil Raza'] | 2023-02-19 | null | null | null | null | ['whole-slide-images', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 8.30066502e-01 4.85299319e-01 4.51282412e-02 -5.72059341e-02
-1.44988716e+00 -3.53314638e-01 2.21742064e-01 4.15735483e-01
-5.59703946e-01 5.50972283e-01 -6.17225049e-03 -3.09912384e-01
-1.59355327e-01 -7.90840447e-01 -9.05403912e-01 -1.10360515e+00
1.12137914e-01 4.09558862e-01 2.89230347e-01 1.27383918... | [15.088176727294922, -2.953338146209717] |
ea318b4f-62d0-490a-8fb2-61257bf36918 | speaker-specific-thresholding-for-robust | 2306.00952 | null | https://arxiv.org/abs/2306.00952v1 | https://arxiv.org/pdf/2306.00952v1.pdf | Speaker-specific Thresholding for Robust Imposter Identification in Unseen Speaker Recognition | Speaker identification systems are deployed in diverse environments, often different from the lab conditions on which they are trained and tested. In this paper, first, we show the problem of generalization using fixed thresholds computed using the equal error rate metric. Secondly, we introduce a novel and generalizab... | ['Susmita Ghose', 'Sparsh Sinha', 'Ashutosh Chaubey'] | 2023-06-01 | null | null | null | null | ['speaker-recognition', 'speaker-identification'] | ['speech', 'speech'] | [ 3.15462917e-01 -2.26739287e-01 2.60099620e-01 -5.75426817e-01
-1.01048005e+00 -7.82298207e-01 3.05284500e-01 -1.82430208e-01
-5.83417296e-01 6.24651492e-01 -8.95455629e-02 -3.16889226e-01
4.78956588e-02 -3.78771983e-02 -5.64399660e-01 -6.99549913e-01
-3.86559255e-02 2.74244964e-01 6.13603704e-02 -9.23168957... | [14.378290176391602, 6.088435173034668] |
235d148d-7a52-46e9-93e8-b8edac34d549 | improved-conformalized-quantile-regression | 2207.02808 | null | https://arxiv.org/abs/2207.02808v8 | https://arxiv.org/pdf/2207.02808v8.pdf | Improved conformalized quantile regression | Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression. That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage. In this way, we get adaptive pred... | ['José Moreira', 'Ana Maria Tomé', 'Martim Sousa'] | 2022-07-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.12350872e-02 7.23451227e-02 -3.32857698e-01 -6.95724785e-01
-1.16723132e+00 -8.57154250e-01 4.65278625e-01 2.78062761e-01
-1.84092641e-01 1.06194401e+00 1.23524167e-01 -4.18269902e-01
-6.61164224e-01 -1.02983677e+00 -7.29637980e-01 -6.45496905e-01
-1.16174847e-01 7.29272187e-01 1.34845689e-01 1.25911206... | [7.754391193389893, 4.406935691833496] |
f4e1e4e5-7ad7-4dfc-8668-c2e6245f45d3 | aestheticnet-reducing-bias-in-facial-data | null | null | https://openreview.net/forum?id=Eot1M5o2Zy | https://openreview.net/pdf?id=Eot1M5o2Zy | AestheticNet: Reducing bias in facial data sets under ethical considerations | Facial Beauty Prediction (FBP) aims to develop a machine that can automatically evaluate facial attractiveness. Usually, these results were highly correlated with human ratings, and therefore also reflected human bias in annotations. Everyone will have biases that are usually subconscious and not easy to notice. Uncons... | ['Josef Kittler', 'Matthias Rätsch', 'Patrik Huber', 'Tobias Gerlach', 'Thomas Weber', 'Muhammad Awais Tanvir Rana', 'Michael Danner'] | 2021-09-29 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-3.55373509e-02 5.51996112e-01 1.09101757e-01 -8.20860624e-01
3.41774434e-01 -8.88675973e-02 2.89778590e-01 -2.91388392e-01
-4.91962463e-01 8.43607903e-01 2.51421724e-02 9.87253785e-02
2.12761909e-01 -8.45076203e-01 -3.06079954e-01 -7.37193346e-01
4.71142560e-01 3.29883724e-01 -4.32981521e-01 -6.18759930... | [13.183624267578125, 1.2454473972320557] |
79e39d3f-519c-497e-9446-e73179da5960 | accurate-and-diverse-sampling-of-sequences | 1806.07772 | null | http://arxiv.org/abs/1806.07772v2 | http://arxiv.org/pdf/1806.07772v2.pdf | Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective | For autonomous agents to successfully operate in the real world, anticipation
of future events and states of their environment is a key competence. This
problem has been formalized as a sequence extrapolation problem, where a number
of observations are used to predict the sequence into the future. Real-world
scenarios ... | ['Apratim Bhattacharyya', 'Mario Fritz', 'Bernt Schiele'] | 2018-06-20 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 3.44117850e-01 -5.21110334e-02 -1.94575816e-01 -6.26769066e-01
-7.29587317e-01 -5.79366863e-01 9.32999909e-01 -1.35545939e-01
-2.22566873e-01 1.13561869e+00 2.46510133e-01 -3.11363459e-01
1.93593711e-01 -4.24510777e-01 -7.25868106e-01 -5.51002800e-01
-3.87991101e-01 7.48938560e-01 3.18841040e-01 -3.22083831... | [8.35218620300293, 0.29622235894203186] |
53fd9a94-70bf-458c-929b-f81481286d35 | attentive-multi-view-deep-subspace-clustering | 2112.12506 | null | https://arxiv.org/abs/2112.12506v1 | https://arxiv.org/pdf/2112.12506v1.pdf | Attentive Multi-View Deep Subspace Clustering Net | In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism. Unlike most multi-view subspace learning method... | ['Xin Zuo', 'Jian-wei Liu', 'Run-kun Lu'] | 2021-12-23 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.38702643e-01 -5.94862163e-01 -3.14332157e-01 -2.93947995e-01
-6.46404266e-01 -6.60293341e-01 5.61646819e-01 -6.14415884e-01
-2.34004967e-02 2.48609960e-01 9.19053137e-01 2.05111086e-01
-4.92741674e-01 -2.92809337e-01 -4.45631951e-01 -9.97470140e-01
2.30888948e-01 5.38349092e-01 -2.09913120e-01 1.21565968... | [8.309614181518555, 4.592662811279297] |
9441f8fb-5e40-41ea-ae42-0be2859d9682 | desam-decoupling-segment-anything-model-for | 2306.00499 | null | https://arxiv.org/abs/2306.00499v1 | https://arxiv.org/pdf/2306.00499v1.pdf | DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation | Deep learning based automatic medical image segmentation models often suffer from domain shift, where the models trained on a source domain do not generalize well to other unseen domains. As a vision foundation model with powerful generalization capabilities, Segment Anything Model (SAM) shows potential for improving t... | ['Xin Gao', 'Dingdu Hu', 'Wei Xia', 'Yifan Gao'] | 2023-06-01 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 5.65285563e-01 3.43847305e-01 -2.55131155e-01 -5.30566871e-01
-1.22237575e+00 -6.79313779e-01 3.74326140e-01 1.66333228e-01
-5.98946989e-01 5.95868647e-01 1.98509514e-01 -2.81107128e-01
3.25457036e-01 -6.05851889e-01 -7.51906991e-01 -6.78679764e-01
2.95523107e-01 4.01933551e-01 3.57609361e-01 -5.13222143... | [14.642570495605469, -2.2372984886169434] |
c78befc3-320e-4f17-939a-51f64497e8cc | highres-net-recursive-fusion-for-multi-frame | 2002.06460 | null | https://arxiv.org/abs/2002.06460v1 | https://arxiv.org/pdf/2002.06460v1.pdf | HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery | Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.... | ['Vincent Michalski', 'Julien Cornebise', 'Israel Goytom', 'Yoshua Bengio', 'Samira E. Kahou', 'Michel Deudon', 'Kris Sankaran', 'Zhichao Lin', 'Md Rifat Arefin', 'Alfredo Kalaitzis'] | 2020-02-15 | null | null | null | null | ['de-aliasing', 'multi-frame-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 0.6323837 0.1988844 0.20600392 -0.49250332 -1.5286572 -0.41399863
0.91087896 -0.5716709 -0.3070333 0.910336 0.62662995 0.20549022
-0.23061453 -1.0863004 -0.86098975 -0.8343503 -0.3204141 0.272782
-0.17617343 -0.77845323 -0.22379336 0.69967717 -1.4655521 0.4908565
0.6273735 0.6687561 0.201... | [10.337820053100586, -1.9057570695877075] |
11ee0f62-d55a-4af9-9249-3e5776d86a78 | encoding-high-level-visual-attributes-in | 1909.05926 | null | https://arxiv.org/abs/1909.05926v5 | https://arxiv.org/pdf/1909.05926v5.pdf | Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses | Convolutional neural network based systems have largely failed to be adopted in many high-risk application areas, including healthcare, military, security, transportation, finance, and legal, due to their highly uninterpretable "black-box" nature. Towards solving this deficiency, we teach a novel multi-task capsule net... | ['Ulas Bagci', 'Drew Torigian', 'Rodney LaLonde'] | 2019-09-12 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-9.86038372e-02 9.66703951e-01 -5.78788400e-01 -7.63088703e-01
-7.25175560e-01 -6.40068591e-01 -4.61898893e-02 3.84508729e-01
-8.08424223e-03 4.51832861e-01 5.40528059e-01 -7.59934723e-01
-2.33969346e-01 -5.42790830e-01 -8.46125841e-01 -2.96537101e-01
-3.19981366e-01 9.28458214e-01 -2.87818581e-01 4.56888080... | [15.196866989135742, -2.323049783706665] |
7e02985f-e9fc-43ee-bc0b-76b4b00badce | photometric-lidar-and-rgb-d-bundle-adjustment | 2303.16878 | null | https://arxiv.org/abs/2303.16878v1 | https://arxiv.org/pdf/2303.16878v1.pdf | Photometric LiDAR and RGB-D Bundle Adjustment | The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those tak... | ['Giorgio Grisetti', 'Omar Salem', 'Leonardo Brizi', 'Emanuele Giacomini', 'Luca Di Giammarino'] | 2023-03-29 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.98436524e-02 -4.62215394e-01 1.74063370e-01 -5.62282979e-01
-8.29476416e-01 -6.24119759e-01 6.51765466e-01 3.41642886e-01
-8.63784909e-01 7.84950376e-01 -3.28007191e-01 -2.30275348e-01
-8.12086016e-02 -9.10143673e-01 -8.63096237e-01 -5.21047771e-01
2.59612892e-02 9.71105039e-01 5.89719355e-01 -4.39956605... | [7.382189750671387, -2.1984920501708984] |
36a186b9-c577-438f-b4ab-ab22388feda8 | deep-reinforcement-learning-based-text | null | null | https://aclanthology.org/D19-1240 | https://aclanthology.org/D19-1240.pdf | Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference | User-generated textual data is rich in content and has been used in many user behavioral modeling tasks. However, it could also leak user private-attribute information that they may not want to disclose such as age and location. User{'}s privacy concerns mandate data publishers to protect privacy. One effective way is ... | ['Huan Liu', 'Ghazaleh Beigi', 'Ahmadreza Mosallanezhad'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['text-anonymization'] | ['natural-language-processing'] | [ 3.36268663e-01 3.40476185e-01 -5.03230274e-01 -7.05634594e-01
-9.71381783e-01 -6.84131026e-01 3.16302985e-01 5.64927876e-01
-6.21446609e-01 7.65275478e-01 6.45347238e-01 -1.26878858e-01
-2.88564358e-02 -9.28151846e-01 -7.89625108e-01 -5.56005299e-01
8.61560777e-02 3.03401560e-01 -5.86353481e-01 6.54133335... | [6.024016380310059, 7.035678386688232] |
0102446c-47dc-45e1-9761-9eba8f937cc6 | automatic-sentence-classifier-using-sentence | null | null | https://aclanthology.org/U12-1018 | https://aclanthology.org/U12-1018.pdf | Automatic sentence classifier using sentence ordering features for Event Based Medicine: Shared task system description | null | ['ana', 'Sp Gella', 'Long Duong Thanh'] | 2012-12-01 | automatic-sentence-classifier-using-sentence-1 | https://aclanthology.org/U12-1018 | https://aclanthology.org/U12-1018.pdf | alta-2012-12 | ['sentence-ordering'] | ['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... | [-7.362677097320557, 3.7075397968292236] |
c5aedf4e-6613-45b3-a16e-a07a06e01447 | learnable-skeleton-aware-3d-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.pdf | Learnable Skeleton-Aware 3D Point Cloud Sampling | Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the above-mentioned task-specific sampling methods usually fail to explore the geometri... | ['DaCheng Tao', 'Baosheng Yu', 'Cheng Wen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-classification'] | ['computer-vision'] | [ 8.34892318e-02 -1.52306288e-01 -1.99731439e-01 -6.04011357e-01
-1.30792403e+00 -4.42589104e-01 4.23561066e-01 1.90935314e-01
-2.46838838e-01 3.03192049e-01 -1.99659556e-01 -2.31871083e-02
-1.15304112e-01 -7.66046166e-01 -1.19072473e+00 -6.94517910e-01
2.79012591e-01 9.61103737e-01 3.05005103e-01 3.42300892... | [8.164220809936523, -3.419935464859009] |
db0f60a4-ac63-4ee7-98a4-9744e5743ba6 | simpson-simplifying-photo-cleanup-with-single-1 | 2305.17624 | null | https://arxiv.org/abs/2305.17624v1 | https://arxiv.org/pdf/2305.17624v1.pdf | SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network | In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor... | ['Abhinav Shrivastava', 'Sohrab Amirghodsi', 'Eli Shechtman', 'Connelly Barnes', 'Zhe Lin', 'Yuqian Zhou', 'Chuong Huynh'] | 2023-05-28 | simpson-simplifying-photo-cleanup-with-single | http://openaccess.thecvf.com//content/CVPR2023/html/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.30144173e-01 -1.26646832e-01 9.08969417e-02 -7.10094646e-02
-6.31874263e-01 -7.35139370e-01 3.35238844e-01 4.03522030e-02
-2.95445949e-01 4.45682853e-01 -9.99518186e-02 -2.31712073e-01
3.04921776e-01 -3.10787767e-01 -5.49658000e-01 -6.23762369e-01
4.21054065e-01 1.45197675e-01 6.95459127e-01 1.97621554... | [11.057190895080566, -1.0487314462661743] |
b80ba5db-3246-4f16-9d3c-a5524fbcd6a9 | the-compositional-structure-of-bayesian | 2305.06112 | null | https://arxiv.org/abs/2305.06112v1 | https://arxiv.org/pdf/2305.06112v1.pdf | The Compositional Structure of Bayesian Inference | Bayes' rule tells us how to invert a causal process in order to update our beliefs in light of new evidence. If the process is believed to have a complex compositional structure, we may observe that the inversion of the whole can be computed piecewise in terms of the component processes. We study the structure of this ... | ['Toby St Clere Smithe', 'Jules Hedges', 'Dylan Braithwaite'] | 2023-05-10 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 4.47010636e-01 5.38070798e-01 7.57513866e-02 -5.24613380e-01
1.41165584e-01 -7.14135110e-01 1.45429325e+00 -2.07988784e-01
-2.14005664e-01 2.87101179e-01 6.41793191e-01 -8.97133052e-01
-4.52414662e-01 -1.10901082e+00 -8.91925991e-01 -7.15505779e-01
-2.46667907e-01 4.61977094e-01 4.30039853e-01 -2.04743907... | [8.211933135986328, 5.849465847015381] |
69a02cfa-eb90-4e3e-954c-99ddd848f290 | towards-realistic-face-photo-sketch-synthesis | 1712.00899 | null | https://arxiv.org/abs/1712.00899v4 | https://arxiv.org/pdf/1712.00899v4.pdf | Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs | Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. W... | ['DaCheng Tao', 'Xingxin Xu', 'Meng Wang', 'Fei Gao', 'Qingming Huang', 'Jun Yu', 'Shengjie Shi'] | 2017-12-04 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 4.69991565e-01 1.20046653e-01 1.37102008e-01 -3.06515962e-01
-7.03967035e-01 -6.11359775e-01 6.22998834e-01 -1.00434768e+00
3.66530120e-01 6.78982258e-01 1.82992443e-01 1.00647025e-02
1.65315732e-01 -7.57597446e-01 -8.59829664e-01 -7.59444833e-01
5.85440993e-01 4.90167849e-02 -3.48855168e-01 -2.08409250... | [12.510843276977539, -0.19637039303779602] |
b99bc83f-2997-42d7-8e2c-9a4c47491918 | video-semantic-segmentation-with-inter-frame | 2301.03832 | null | https://arxiv.org/abs/2301.03832v1 | https://arxiv.org/pdf/2301.03832v1.pdf | Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement | Video semantic segmentation aims to generate accurate semantic maps for each video frame. To this end, many works dedicate to integrate diverse information from consecutive frames to enhance the features for prediction, where a feature alignment procedure via estimated optical flow is usually required. However, the opt... | ['Junjie Li', 'Zilei Wang', 'Jiafan Zhuang'] | 2023-01-10 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [-8.18246827e-02 -3.90181601e-01 -2.02188671e-01 -3.38942230e-01
-4.70343232e-01 -2.14558005e-01 2.62500942e-01 -1.21354004e-02
-3.99889052e-01 6.28503084e-01 6.06623553e-02 1.91309020e-01
-6.13849461e-02 -8.08205724e-01 -5.73655486e-01 -7.64088213e-01
2.33962864e-01 7.36438110e-02 6.71900451e-01 1.33463576... | [9.416841506958008, -0.3730413317680359] |
049668de-4644-4964-a809-df062e453463 | shoe-supervised-hashing-with-output | 1502.00030 | null | http://arxiv.org/abs/1502.00030v1 | http://arxiv.org/pdf/1502.00030v1.pdf | SHOE: Supervised Hashing with Output Embeddings | We present a supervised binary encoding scheme for image retrieval that
learns projections by taking into account similarity between classes obtained
from output embeddings. Our motivation is that binary hash codes learned in
this way improve both the visual quality of retrieval results and existing
supervised hashing ... | ['Larry S. Davis', 'David Doermann', 'Varun Manjunatha', 'Sravanthi Bondugula'] | 2015-01-30 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 7.93535542e-03 -2.92162418e-01 -4.08008635e-01 -7.47578979e-01
-1.04710615e+00 -5.87135255e-01 9.38983381e-01 5.71272850e-01
-8.37111712e-01 3.46531928e-01 5.23671925e-01 5.99645823e-02
-2.89458364e-01 -9.68112946e-01 -8.25591207e-01 -7.35284984e-01
-4.43474382e-01 4.90481496e-01 4.11633961e-02 1.21112347... | [10.91723346710205, 0.7594685554504395] |
b6ffd95a-4e9b-4722-95e9-5706540bf9bf | out-of-distribution-detection-for-long-tailed | 2206.15186 | null | https://arxiv.org/abs/2206.15186v1 | https://arxiv.org/pdf/2206.15186v1.pdf | Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images | Recent years have witnessed a rapid development of automated methods for skin lesion diagnosis and classification. Due to an increasing deployment of such systems in clinics, it has become important to develop a more robust system towards various Out-of-Distribution(OOD) samples (unknown skin lesions and conditions). H... | ['ZongYuan Ge', 'Paul Bonnington', 'Adrian Bowling', 'Yaniv Gal', 'Deval Mehta'] | 2022-06-30 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.35275936e-01 9.60360244e-02 -2.80612648e-01 -3.52967344e-02
-8.26596379e-01 -4.98638749e-01 5.98677456e-01 1.73762858e-01
-5.01632094e-02 5.07011771e-01 -5.60523793e-02 -2.27706119e-01
-2.51899034e-01 -6.63124800e-01 -1.05738163e-01 -8.97121429e-01
2.06475213e-01 2.38459438e-01 4.01545495e-01 1.63057476... | [15.589299201965332, -2.828965902328491] |
4dbbb53c-eb2e-4a00-98bd-5341217242f3 | toward-pareto-efficient-fairness-utility | 2201.00140 | null | https://arxiv.org/abs/2201.00140v1 | https://arxiv.org/pdf/2201.00140v1.pdf | Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning | The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint... | ['Yongfeng Zhang', 'Chu-Cheng Hsieh', 'Diane Hu', 'Saurabh Paul', 'Lucia Yu', 'Xiaoting Zhao', 'Yingqiang Ge'] | 2022-01-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-3.03497463e-01 -1.28554747e-01 -7.31492817e-01 -4.82485712e-01
-8.17652196e-02 -5.02085209e-01 2.82304645e-01 3.59169841e-02
-5.72189629e-01 9.08846557e-01 2.77538240e-01 -3.29531372e-01
-6.97488725e-01 -1.10688579e+00 -1.92946255e-01 -6.35578752e-01
1.05653875e-01 5.01878142e-01 -2.93561280e-01 -4.53587830... | [9.607507705688477, 5.585655212402344] |
137ad251-84a1-4492-b186-7b2d4a107b0a | segnet-a-deep-convolutional-encoder-decoder | 1511.00561 | null | http://arxiv.org/abs/1511.00561v3 | http://arxiv.org/pdf/1511.00561v3.pdf | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation | We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder... | ['Vijay Badrinarayanan', 'Alex Kendall', 'Roberto Cipolla'] | 2015-11-02 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.38547105e-01 2.40882382e-01 1.11435875e-01 -5.38078487e-01
-6.19937062e-01 -4.71641481e-01 5.25425732e-01 -2.10134491e-01
-6.03965342e-01 7.76846230e-01 -9.42238271e-02 -4.08124328e-01
5.40003143e-02 -1.14104593e+00 -1.07134938e+00 -5.85862815e-01
7.63186216e-02 3.27038318e-01 5.59487998e-01 6.88719272... | [9.516684532165527, 0.07881677150726318] |
72b8585c-604c-4402-88b4-c356619c5b83 | evaluating-link-prediction-accuracy-on | 1607.07330 | null | http://arxiv.org/abs/1607.07330v1 | http://arxiv.org/pdf/1607.07330v1.pdf | Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges | The task of predicting future relationships in a social network, known as
link prediction, has been studied extensively in the literature. Many link
prediction methods have been proposed, ranging from common neighbors to
probabilistic models. Recent work by Yang et al. has highlighted several
challenges in evaluating l... | ['Vijay K. Devabhaktuni', 'Ruthwik R. Junuthula', 'Kevin S. Xu'] | 2016-07-25 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [ 6.49285167e-02 3.23373377e-01 -8.78816605e-01 -2.23320439e-01
1.42973503e-02 -5.23003936e-01 5.92382133e-01 4.98010129e-01
1.15460344e-01 1.31539595e+00 -5.33043519e-02 -3.92052442e-01
-9.27093506e-01 -1.33627307e+00 -2.59234875e-01 -1.66625336e-01
-8.58184993e-01 7.29976952e-01 7.89643466e-01 -2.30696633... | [7.178003311157227, 5.897246837615967] |
1a63f018-6956-4017-b377-2360ce0f4ce6 | exact-hard-monotonic-attention-for-character | 1905.06319 | null | https://arxiv.org/abs/1905.06319v2 | https://arxiv.org/pdf/1905.06319v2.pdf | Exact Hard Monotonic Attention for Character-Level Transduction | Many common character-level, string-to-string transduction tasks, e.g. graphemeto-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, which are non-monotonic, often outperform popular monotonic models. In this wor... | ['Ryan Cotterell', 'Shijie Wu'] | 2019-05-15 | exact-hard-monotonic-attention-for-character-1 | https://aclanthology.org/P19-1148 | https://aclanthology.org/P19-1148.pdf | acl-2019-7 | ['hard-attention'] | ['methodology'] | [ 7.30469465e-01 -3.43980119e-02 -3.95113289e-01 -4.13940847e-01
-1.23309588e+00 -1.03342640e+00 4.76798713e-01 -3.58697660e-02
-3.71880144e-01 7.75940895e-01 4.96689498e-01 -8.10796797e-01
3.85116428e-01 -7.93501079e-01 -1.27194691e+00 -7.22925544e-01
2.27168366e-01 4.46201503e-01 1.16886711e-02 -4.77815151... | [11.211357116699219, 9.16483211517334] |
bd278683-9565-4b27-92a9-c084eab9aa53 | hysia-serving-dnn-based-video-to-retail | 2006.05117 | null | https://arxiv.org/abs/2006.05117v1 | https://arxiv.org/pdf/2006.05117v1.pdf | Hysia: Serving DNN-Based Video-to-Retail Applications in Cloud | Combining \underline{v}ideo streaming and online \underline{r}etailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end inf... | ['Yichao Jin', 'Yong Luo', 'Nguyen Binh Duong Ta', 'Yuanming Li', 'Qiming Ai', 'Huaizheng Zhang', 'Yonggang Wen'] | 2020-06-09 | null | null | null | null | ['video-to-shop'] | ['computer-vision'] | [-6.86127484e-01 -6.99112773e-01 -2.14319751e-01 -1.72195524e-01
-6.74156368e-01 -5.69919765e-01 7.51173869e-02 7.68736228e-02
-1.81510776e-01 -2.86208987e-02 -3.89990583e-02 -5.44705272e-01
2.86734194e-01 -7.82601893e-01 -5.44925392e-01 -1.21379055e-01
-1.25514105e-01 2.85631835e-01 8.29980671e-01 -2.04647765... | [8.967430114746094, -0.17666985094547272] |
ea5333be-b149-4d2d-b0c8-fc089bfa810c | selftext-beyond-polygon-unconstrained-text | 2011.13307 | null | https://arxiv.org/abs/2011.13307v3 | https://arxiv.org/pdf/2011.13307v3.pdf | Polygon-free: Unconstrained Scene Text Detection with Box Annotations | Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging. Unlike existing works that employ fully-supervised training with polygon annotations, this study proposes an unconstrained text detection system term... | ['Hong Zhou', 'Ping Luo', 'Wenhai Wang', 'Ruimao Zhang', 'Enze Xie', 'Weijia Wu'] | 2020-11-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.50823128e-01 3.04343432e-01 -2.04576254e-01 -7.21424669e-02
-9.23900962e-01 -3.27353626e-01 4.57655668e-01 7.29931220e-02
-4.37036514e-01 3.97173166e-01 2.30186153e-03 -2.56239265e-01
5.67990482e-01 -9.03420389e-01 -9.54883337e-01 -5.23445547e-01
4.33179140e-01 3.07519972e-01 7.16352165e-01 -1.78999305... | [12.074053764343262, 2.2812905311584473] |
ffc0afe3-6702-48c9-89d8-2788db94df78 | performance-analysis-of-free-space | 2211.14771 | null | https://arxiv.org/abs/2211.14771v1 | https://arxiv.org/pdf/2211.14771v1.pdf | Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications | In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to... | ['Boon Hee Soong', 'Dong In Kim', 'Zehui Xiong', 'Jiawen Kang', 'Dusit Niyato', 'Jiacheng Wang', 'Hongyang Du'] | 2022-11-27 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-7.69003034e-02 -2.75630150e-02 -9.15338397e-02 -2.22474560e-01
-5.49988449e-01 -4.02917981e-01 2.00895786e-01 -2.35556886e-01
-3.39146644e-01 5.79545677e-01 -5.23820519e-02 -5.20190299e-01
-7.90394843e-02 -9.88032401e-01 -1.87746495e-01 -9.16556835e-01
8.86251219e-03 1.19903041e-02 2.86235034e-01 -2.61253148... | [5.9942803382873535, 1.5449408292770386] |
92f9495b-9fd6-414d-9ddd-3794c1d255dd | exploiting-optical-flow-guidance-for | 2301.10048 | null | https://arxiv.org/abs/2301.10048v1 | https://arxiv.org/pdf/2301.10048v1.pdf | Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting | Transformers have been widely used for video processing owing to the multi-head self attention (MHSA) mechanism. However, the MHSA mechanism encounters an intrinsic difficulty for video inpainting, since the features associated with the corrupted regions are degraded and incur inaccurate self attention. This problem, t... | ['Dong Liu', 'Jingjing Fu', 'Jialun Peng', 'Kaidong Zhang'] | 2023-01-24 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-0.04445363 -0.38034216 -0.09133589 -0.04327165 -0.47532895 -0.24358751
0.31576014 -0.18237151 -0.31038138 0.6662223 0.6338973 0.12342111
-0.14443381 -0.7978227 -0.6795261 -0.7431895 0.08211492 -0.2731743
0.4415701 -0.15067783 0.3708195 0.51710635 -1.2856057 0.24220933
1.2471008 1.0898081 0.2... | [10.788804054260254, -1.3784856796264648] |
90babc86-0b54-484f-8070-393c0c1c4225 | spatio-temporal-instance-learning-action | 1807.02800 | null | http://arxiv.org/abs/1807.02800v2 | http://arxiv.org/pdf/1807.02800v2.pdf | Spatio-Temporal Instance Learning: Action Tubes from Class Supervision | The goal of this work is spatio-temporal action localization in videos, using
only the supervision from video-level class labels. The state-of-the-art casts
this weakly-supervised action localization regime as a Multiple Instance
Learning problem, where instances are a priori computed spatio-temporal
proposals. Rather ... | ['Pascal Mettes', 'Cees G. M. Snoek'] | 2018-07-08 | null | null | null | null | ['weakly-supervised-action-localization', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.47268403e-01 1.54639587e-01 -1.04468584e+00 -2.69848943e-01
-1.17867553e+00 -4.41309661e-01 7.51775801e-01 -2.08505243e-01
-4.90927279e-01 7.19111085e-01 4.61934984e-01 1.66255489e-01
-3.14215273e-01 -3.05662334e-01 -9.44662273e-01 -8.79558444e-01
-3.79218578e-01 3.35311621e-01 6.27934694e-01 3.84501278... | [8.5444974899292, 0.5385475158691406] |
ad6f0f2c-28ca-4b7c-a578-7f0c6e373f84 | sta-spatial-temporal-attention-for-large | 1811.04129 | null | http://arxiv.org/abs/1811.04129v1 | http://arxiv.org/pdf/1811.04129v1.pdf | STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification | In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more... | ['Yunchao Wei', 'Yang Fu', 'Xiaoyang Wang', 'Thomas Huang'] | 2018-11-09 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-6.97646141e-02 -5.14658153e-01 6.50356263e-02 -3.56041312e-01
-8.87726843e-01 -1.85947686e-01 5.16338229e-01 2.25438938e-01
-6.22338831e-01 4.99406546e-01 4.59886611e-01 5.32813668e-01
-4.30446155e-02 -4.03712600e-01 -7.38383114e-01 -6.60266042e-01
-9.80466381e-02 6.78590164e-02 1.42315263e-02 -1.39479235... | [14.67484188079834, 0.940614640712738] |
f57a8f0d-790b-4ea1-a4d0-37a931bc91dc | vision-lanauge-pre-training-by-contrastive | 2305.04474 | null | https://arxiv.org/abs/2305.04474v3 | https://arxiv.org/pdf/2305.04474v3.pdf | Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation | Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI... | ['Fei Huang', 'Jie Zhang', 'Shikun Zhang', 'Miang yan', 'Haiyang Xu', 'Wei Ye', 'Chaoya Jiang'] | 2023-05-08 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 4.43277985e-01 1.04926057e-01 -1.20099835e-01 -4.47880089e-01
-1.39302945e+00 -5.49997628e-01 7.86540449e-01 1.14985727e-01
-9.51397121e-01 6.13510489e-01 6.69875816e-02 -2.03338981e-01
-1.36439011e-01 -3.75354618e-01 -8.60925853e-01 -7.86711216e-01
-3.58892530e-02 1.50074646e-01 -5.49956970e-02 9.17122290... | [10.786683082580566, 1.5163215398788452] |
af3cd297-d150-4b4f-99a0-06b2faca48a8 | hyperspectral-unmixing-ground-truth-labeling | 1708.05125 | null | http://arxiv.org/abs/1708.05125v2 | http://arxiv.org/pdf/1708.05125v2.pdf | Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey | Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground ... | ['Feiyun Zhu'] | 2017-08-17 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.78233814e-01 -4.37697828e-01 -2.68615838e-02 -1.19258203e-01
-6.68800116e-01 -7.06818700e-01 2.48525411e-01 -2.06829309e-01
-3.92036103e-02 8.88384879e-01 -4.00533617e-01 -3.96063238e-01
-4.76441950e-01 -1.13727808e+00 -5.53905964e-01 -1.15273809e+00
-1.83493346e-01 2.17187196e-01 3.63635309e-02 -3.26401114... | [10.08197021484375, -2.0397143363952637] |
5d55b4ff-44fa-4022-9551-7b9d340fb324 | multi-graph-based-multi-scenario | 2205.02446 | null | https://arxiv.org/abs/2205.02446v1 | https://arxiv.org/pdf/2205.02446v1.pdf | Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services | Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly sugge... | ['Yue Qi', 'Da Lin', 'Rong Zeng', 'Zheng Pan', 'Yulin Wu', 'Qiuying Peng', 'Fan Zhang'] | 2022-05-05 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-5.12989834e-02 -9.72324517e-03 -6.20833337e-01 -3.18929493e-01
-3.23274583e-01 -7.68550158e-01 5.09207249e-01 1.32144630e-01
7.46614933e-02 4.73581553e-01 4.12752599e-01 -2.63607532e-01
-4.88433987e-01 -7.07190275e-01 -8.71723831e-01 -7.48903573e-01
-4.99460340e-01 4.11065251e-01 -9.77423117e-02 -4.72662508... | [10.1582670211792, 5.589545249938965] |
86504c34-30e4-4050-8852-8e6224c5ee91 | effects-of-a-mesoporous-bioactive-glass-on | 2103.08552 | null | https://arxiv.org/abs/2103.08552v1 | https://arxiv.org/pdf/2103.08552v1.pdf | Effects of a mesoporous bioactive glass on osteoblasts, osteoclasts and macrophages | A mesoporous bioactive glass (MBG) of molar composition 75SiO2-20CaO-5P2O5 (MBG-75S) has been synthetized as a potential bioceramic for bone regeneration purposes. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), nitrogen adsorption studies and transmission electron microscopy (TEM) demonstrate... | ['M. T. Portoles', 'D. Arcos', 'M. Vallet-Regi', 'M. J. Feito', 'I. Morales', 'L. Casarrubios', 'N. Gomez-Cerezo'] | 2021-03-15 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [-7.49827325e-02 2.65652776e-01 -4.63142157e-01 5.71000516e-01
-1.72167838e-01 4.82801706e-01 1.52281016e-01 7.10873485e-01
-5.18703163e-01 8.56177866e-01 1.63175300e-01 -1.21211864e-01
1.97456449e-01 -1.05402124e+00 -5.34950376e-01 -1.12200141e+00
-2.14458436e-01 8.89491916e-01 5.56035578e-01 -2.10422829... | [5.025256156921387, 4.800262928009033] |
0d93bd2c-4a23-48e7-904c-026a5c406d0a | robust-zero-shot-cross-domain-slot-filling | 1906.06870 | null | https://arxiv.org/abs/1906.06870v1 | https://arxiv.org/pdf/1906.06870v1.pdf | Robust Zero-Shot Cross-Domain Slot Filling with Example Values | Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web form... | ['Dilek Hakkani-Tur', 'Raghav Gupta', 'Darsh J Shah', 'Amir A Fayazi'] | 2019-06-17 | robust-zero-shot-cross-domain-slot-filling-1 | https://aclanthology.org/P19-1547 | https://aclanthology.org/P19-1547.pdf | acl-2019-7 | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [-7.74306385e-03 5.12671173e-01 -6.69593155e-01 -6.89381599e-01
-9.37737167e-01 -7.74270594e-01 7.35981822e-01 2.40654111e-01
-5.41938305e-01 1.04764247e+00 2.31651038e-01 -2.55222380e-01
1.48622081e-01 -8.58934641e-01 -4.63280410e-01 7.54926354e-02
4.52039689e-01 1.31339991e+00 4.17449564e-01 -7.54680395... | [12.555937767028809, 7.481194972991943] |
87f10336-e15a-437c-9297-206c26d96aec | pypop7-a-pure-python-library-for-population | 2212.05652 | null | https://arxiv.org/abs/2212.05652v2 | https://arxiv.org/pdf/2212.05652v2.pdf | PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization | In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods are becoming increasingly popular for BBO, our design goal is to provide a unified API and elegant implementations for them, particularly for high-dimensional cases. Since population-based methods... | ['Yuhui Shi', 'Qi Zhao', 'Yijun Yang', 'Mingyang Feng', 'Zhuowei Wang', 'Chang Shao', 'Guochen Zhou', 'Qiqi Duan'] | 2022-12-12 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-5.06277621e-01 -2.62051314e-01 -9.88385528e-02 -6.90298527e-02
-6.21501267e-01 -3.27313632e-01 2.60570109e-01 -1.32460609e-01
-1.77484885e-01 1.07613564e+00 2.47621566e-01 -8.38612244e-02
-3.94773424e-01 -6.80395424e-01 -5.00759780e-01 -9.62383509e-01
-2.48016626e-01 7.20843256e-01 -5.44632934e-02 -3.85681272... | [6.639104843139648, 3.960463285446167] |
2306b0ef-d6af-43b1-b95b-24093157a2fa | unsupervised-deep-keyphrase-generation | 2104.08729 | null | https://arxiv.org/abs/2104.08729v1 | https://arxiv.org/pdf/2104.08729v1.pdf | Unsupervised Deep Keyphrase Generation | Keyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated a remarkable success in this task, capable of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of a... | ['Jingbo Shang', 'Rui Meng', 'Yinghan Wang', 'Xianjie Shen'] | 2021-04-18 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.15536460e-01 1.98759452e-01 -3.16205442e-01 7.97464848e-02
-1.22205162e+00 -8.17833483e-01 1.07422554e+00 7.04292715e-01
-3.00511301e-01 9.85232770e-01 7.88826346e-01 4.03784439e-02
-2.19754409e-02 -8.25552046e-01 -8.49438846e-01 -6.51821911e-01
2.62718946e-01 4.62256044e-01 1.44611567e-01 -2.23508120... | [12.317730903625488, 8.90587043762207] |
0b275fb5-a41e-4cf7-800b-75f92c305af7 | faf-a-novel-multimodal-emotion-recognition | 2211.15425 | null | https://arxiv.org/abs/2211.15425v1 | https://arxiv.org/pdf/2211.15425v1.pdf | FAF: A novel multimodal emotion recognition approach integrating face, body and text | Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotio... | ['Lei Ma', 'Tong Zhang', 'Weiping Ding', 'Baopeng Gao', 'Qihui Yu', 'Aoyun He', 'Zhongyu Fang'] | 2022-11-20 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-4.78357300e-02 -3.03778380e-01 8.75161365e-02 -3.45247775e-01
-7.58181632e-01 -2.13218451e-01 4.19306040e-01 -3.66746746e-02
-3.59952927e-01 6.39428437e-01 3.44805390e-01 5.20788670e-01
8.40657651e-02 -3.78137469e-01 3.06490380e-02 -9.21904087e-01
1.50737539e-01 -2.23147377e-01 -5.01659632e-01 -2.83071935... | [13.236513137817383, 5.1554460525512695] |
0552b66a-98de-4a59-88c6-d502f472c821 | learn-what-is-possible-then-choose-what-is | 2304.07258 | null | https://arxiv.org/abs/2304.07258v2 | https://arxiv.org/pdf/2304.07258v2.pdf | Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games | Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogue there are many valid responses for a given context. However, only some of these responses will be ... | ['Ke Zhou', 'Benjamin Towle'] | 2023-04-14 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 2.11612001e-01 6.06136262e-01 -3.30804199e-01 -6.06790900e-01
-1.06462789e+00 -7.81742215e-01 8.72855544e-01 6.54532388e-02
-4.40618426e-01 1.02294707e+00 4.91123080e-01 -4.34675932e-01
-1.73971936e-01 -7.44618237e-01 -3.87055486e-01 -5.29498160e-01
3.07433516e-01 1.20894718e+00 2.76209652e-01 -5.66213965... | [12.895880699157715, 8.044374465942383] |
31cfe6e7-14da-4992-9a20-5128d69325df | indudonet-a-model-driven-interpretable-dual | 2112.12660 | null | https://arxiv.org/abs/2112.12660v2 | https://arxiv.org/pdf/2112.12660v2.pdf | InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images | During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based me... | ['Yefeng Zheng', 'Deyu Meng', 'Haimiao Zhang', 'Yuexiang Li', 'Hong Wang'] | 2021-12-23 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 6.29097447e-02 1.09193400e-01 5.94229437e-02 -1.20896794e-01
-7.32555211e-01 -9.69855338e-02 -1.28526930e-02 -1.23261370e-01
-9.15376693e-02 5.77736735e-01 1.73362568e-01 -3.14099312e-01
-6.93885148e-01 -7.04438686e-01 -5.13407111e-01 -9.87855971e-01
9.19560343e-02 3.64939123e-01 1.68873727e-01 5.36123030... | [13.538259506225586, -2.5361058712005615] |
83aaf916-5b91-4f6c-92ee-ca8c89bd2b70 | enhancing-dynamic-image-advertising-with | 2306.14112 | null | https://arxiv.org/abs/2306.14112v1 | https://arxiv.org/pdf/2306.14112v1.pdf | Enhancing Dynamic Image Advertising with Vision-Language Pre-training | In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval a... | ['Lin Liu', 'Shuanglong Li', 'Xiaodong Chen', 'Wei Jia', 'Yi Yang', 'Zhipeng Jin', 'Xinyu Zhao', 'Zhoufutu Wen'] | 2023-06-25 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 2.79695950e-02 -2.45605081e-01 -5.69947839e-01 -5.46103060e-01
-1.45405865e+00 -5.89044750e-01 8.98170531e-01 -1.87669873e-01
-3.02933067e-01 5.68112917e-02 1.95260584e-01 -1.93961844e-01
-1.82172582e-02 -7.72820950e-01 -7.76012838e-01 -1.20251060e-01
1.31794333e-01 6.47519350e-01 2.47690499e-01 -4.11275595... | [10.830836296081543, 1.1332567930221558] |
c10bbb5f-7123-4103-b68f-78730617fb19 | feature-selection-based-intrusion-detection | 2201.00584 | null | https://arxiv.org/abs/2201.00584v1 | https://arxiv.org/pdf/2201.00584v1.pdf | Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification | Preventing and detecting intrusions and attacks on wireless networks has become an important and serious challenge. On the other hand, due to the limited resources of wireless nodes, the use of monitoring nodes for permanent monitoring in wireless sensor networks in order to prevent and detect intrusion and attacks in ... | ['Saeedeh Shafaei Mehr', 'Aidin Molazadeh', 'Alireza Najafi Souha', 'Farid Sorouri', 'Amir Mojtahedi'] | 2022-01-03 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.20467342e-01 -1.25369325e-01 1.62671074e-01 -1.57054558e-01
6.44427359e-01 -2.37797633e-01 9.46291611e-02 5.42238891e-01
-8.56947184e-01 7.85705328e-01 -3.67202252e-01 -9.67948809e-02
-9.01905775e-01 -1.47871637e+00 2.52910368e-02 -8.96448255e-01
-4.79512721e-01 2.77132004e-01 5.71200073e-01 -4.81263518... | [5.232585906982422, 7.112142562866211] |
83c57708-d5c9-466d-ad36-4630b184ad26 | which-style-makes-me-attractive-interpretable | 2201.09689 | null | https://arxiv.org/abs/2201.09689v1 | https://arxiv.org/pdf/2201.09689v1.pdf | Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN | The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exp... | ['Xiangyang Ji', 'Yu Yang', 'JiQuan Pei', 'Qiulin Wang', 'Bo Li'] | 2022-01-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.14379901e-01 7.77054727e-01 -4.30247635e-01 -4.55752641e-01
-2.48375520e-01 -1.03419018e+00 1.01207149e+00 -9.35046375e-01
3.93422335e-01 7.90182471e-01 5.92403293e-01 -7.57667348e-02
-6.61670119e-02 -6.36001527e-01 -9.10436392e-01 -7.43574917e-01
1.29439756e-01 1.41286045e-01 -8.46420169e-01 1.12603623... | [11.975373268127441, -0.2597273886203766] |
3ba5f9ad-2121-4030-bcc5-29d6b811e1c9 | rinq-fingerprinting-recurrence-informed | 1907.05277 | null | https://arxiv.org/abs/1907.05277v2 | https://arxiv.org/pdf/1907.05277v2.pdf | RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting | Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times $T_1$ and $T_2$. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Temp... | ['Gregor Körzdörfer', 'Heiko Meyer', 'Franziska Schirrmacher', 'Elisabeth Hoppe', 'Mathias Nittka', 'Florian Thamm', 'Christopher Syben', 'Andreas Maier', 'Josef Pfeuffer'] | 2019-07-09 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.46285880e-01 -1.51938975e-01 2.62734592e-01 -5.54933190e-01
-1.00505185e+00 -1.06875718e-01 2.29192406e-01 2.56888956e-01
-9.31778848e-01 7.48884559e-01 -8.41713548e-02 -2.94986144e-02
-6.88259363e-01 -4.80727136e-01 -8.89531672e-01 -9.83908713e-01
-3.92792553e-01 4.61983263e-01 1.41996861e-01 -5.19528426... | [13.506970405578613, -2.422041654586792] |
3f997295-b34a-47d8-961d-676631d5b5d5 | np-match-when-neural-processes-meet-semi | 2207.01066 | null | https://arxiv.org/abs/2207.01066v1 | https://arxiv.org/pdf/2207.01066v1.pdf | NP-Match: When Neural Processes meet Semi-Supervised Learning | Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is ... | ['Alexandros Neophytou', 'Vladimir Pavlovic', 'Xiaolin Hu', 'Daniela Massiceti', 'Thomas Lukasiewicz', 'JianFeng Wang'] | 2022-07-03 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.94010453e-02 1.95091873e-01 -5.60871601e-01 -8.68455887e-01
-1.09962344e+00 -2.15508118e-01 4.68266070e-01 3.23137671e-01
-6.77617967e-01 8.53747368e-01 -2.18842924e-02 7.25387558e-02
1.61114156e-01 -6.80946171e-01 -9.89366949e-01 -7.91338980e-01
3.28575701e-01 6.37555599e-01 3.52945119e-01 5.65953016... | [9.549192428588867, 3.617396116256714] |
dba14620-0713-4bed-b704-0fcaf9fa5ea0 | semantic-communication-an-information | 2204.13366 | null | https://arxiv.org/abs/2204.13366v4 | https://arxiv.org/pdf/2204.13366v4.pdf | Semantic Information Recovery in Wireless Networks | Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and thus... | ['Armin Dekorsy', 'Carsten Bockelmann', 'Edgar Beck'] | 2022-04-28 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 7.45763481e-01 5.90990841e-01 -3.22750002e-01 -1.58933103e-01
-6.03274345e-01 -9.57648233e-02 6.96384788e-01 5.13567626e-01
-5.61390340e-01 7.57307231e-01 4.90807444e-01 -5.69234312e-01
-3.44558358e-01 -8.30969930e-01 -6.33789599e-01 -9.30410981e-01
-5.49076498e-01 1.20526507e-01 -2.28021353e-01 -7.90345296... | [6.463792324066162, 1.629032850265503] |
bcb5cd68-7994-4f99-921d-dea2739a27db | bertifying-sinhala-a-comprehensive-analysis-1 | null | null | https://aclanthology.org/2022.lrec-1.803 | https://aclanthology.org/2022.lrec-1.803.pdf | BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification | This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER)... | ['Sanath Jayasena', 'Surangika Ranathunga', 'Piyumal Demotte', 'Vinura Dhananjaya'] | null | null | null | null | lrec-2022-6 | ['xlm-r'] | ['natural-language-processing'] | [-3.10414016e-01 -9.20301154e-02 -2.88718879e-01 -8.48261178e-01
-1.27573192e+00 -7.32416630e-01 9.10642803e-01 4.54729199e-01
-9.23671663e-01 6.70057118e-01 5.20006955e-01 -9.35136378e-01
1.74250498e-01 -5.45081794e-01 -3.85141820e-01 -2.47410402e-01
2.42755428e-01 9.28425133e-01 1.00487553e-01 -6.64204538... | [10.94458293914795, 10.094939231872559] |
41fe5a14-d913-480e-ac57-9ae26db941d4 | stackelberg-games-for-learning-emergent | 2305.03735 | null | https://arxiv.org/abs/2305.03735v1 | https://arxiv.org/pdf/2305.03735v1.pdf | Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula | Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interact... | ['Joshua R. Smith', 'Byron Boots', 'Lillian J. Ratliff', 'Liyuan Zheng', 'Boling Yang'] | 2023-05-04 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.51807576e-01 3.69899869e-01 1.66445553e-01 3.74627978e-01
-1.38894141e-01 -6.06812060e-01 5.78044713e-01 2.15020049e-02
-6.21997774e-01 1.24528480e+00 -1.37128130e-01 -1.17652126e-01
-5.23115396e-01 -7.58998156e-01 -8.73688698e-01 -1.35687160e+00
-5.58968544e-01 7.00921714e-01 3.57600152e-01 -1.01623273... | [3.788236141204834, 1.9607884883880615] |
60cedf18-41ff-48f7-a513-fee1ae499917 | decoding-finger-flexion-from-band-specific | null | null | https://www.frontiersin.org/articles/10.3389/fnins.2012.00091/full | https://www.frontiersin.org/articles/10.3389/fnins.2012.00091/pdf | Decoding finger flexion from band-specific ECoG signals in humans | This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal... | ['Laurent Bougrain', 'Nanying Liang'] | 2012-06-28 | null | null | null | frontiers-in-neuroscience-section | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.65743351e-01 -2.11561382e-01 2.00893879e-01 -2.41633952e-01
-3.46068352e-01 -2.34442756e-01 5.01381934e-01 -3.92463446e-01
-5.20332873e-01 1.15062201e+00 4.49739188e-01 1.28005268e-02
-6.30018234e-01 -4.00437683e-01 -7.46817708e-01 -7.18188047e-01
-3.96934420e-01 -2.02550050e-02 -2.01332830e-02 2.75660045... | [12.986639022827148, 3.3973724842071533] |
afdcbe86-bc61-46b8-a889-2353c59c5e17 | exposure-fusion-for-hand-held-camera-inputs | 2304.04464 | null | https://arxiv.org/abs/2304.04464v1 | https://arxiv.org/pdf/2304.04464v1.pdf | Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch | This paper proposes a hybrid synthesis method for multi-exposure image fusion taken by hand-held cameras. Motions either due to the shaky camera or caused by dynamic scenes should be compensated before any content fusion. Any misalignment can easily cause blurring/ghosting artifacts in the fused result. Our hybrid meth... | ['Shuaicheng Liu', 'Bing Zeng', 'Guanghui Liu', 'Ru Li'] | 2023-04-10 | null | null | null | null | ['multi-exposure-image-fusion', 'superpixels'] | ['computer-vision', 'computer-vision'] | [ 3.46015543e-01 -5.63480854e-01 2.80607462e-01 -1.44088836e-02
-4.19453174e-01 -6.16147339e-01 2.98869789e-01 -3.85456622e-01
-1.91026717e-01 8.36242199e-01 3.07223678e-01 3.51906806e-01
-5.56577668e-02 -4.28646117e-01 -6.27280593e-01 -9.39630985e-01
3.44542384e-01 -2.30118051e-01 5.53503573e-01 3.31878401... | [10.868388175964355, -1.9049088954925537] |
693dcb27-654a-4274-9853-4599b54f7f97 | sma-stn-segmented-movement-attending | 2010.09342 | null | https://arxiv.org/abs/2010.09342v1 | https://arxiv.org/pdf/2010.09342v1.pdf | SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition | Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER). To handle th... | ['Yuan Zong', 'Wenming Zheng', 'Jiateng Liu'] | 2020-10-19 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 2.95432657e-01 -7.20884144e-01 -1.09987691e-01 -3.32596540e-01
-3.20331156e-01 -2.42220268e-01 2.55497515e-01 -6.83314621e-01
-1.79147571e-01 4.28619683e-01 2.19698310e-01 4.15874839e-01
1.75721142e-02 -3.01171958e-01 -5.07213414e-01 -1.10517287e+00
-1.28980592e-01 -6.57962382e-01 1.34550616e-01 -5.22601664... | [13.61573314666748, 1.694783091545105] |
fd97636b-d9d0-4426-958c-0ab5b93dcb15 | gray-level-co-occurrence-matrices | 1205.4831 | null | https://arxiv.org/abs/1205.4831v1 | https://arxiv.org/pdf/1205.4831v1.pdf | Gray Level Co-Occurrence Matrices: Generalisation and Some New Features | Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extensio... | ['Kannan Balakrishnan', 'A. Unnikrishnan', 'Bino Sebastian V'] | 2012-05-22 | null | null | null | null | ['texture-classification', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 4.77720857e-01 -7.68867254e-01 -4.65966724e-02 -1.49635136e-01
-5.77865660e-01 5.59990592e-02 4.47029799e-01 5.56813598e-01
-5.75309992e-01 2.65430868e-01 5.77155948e-02 -9.93533134e-02
-6.82438195e-01 -9.34535384e-01 1.99004620e-01 -9.14907575e-01
-4.31771547e-01 6.38362067e-03 3.29350054e-01 -3.37709904... | [10.443434715270996, -0.3559471368789673] |
2dc8c776-5862-46ef-aa95-0acf1a213efa | towards-accurate-open-set-recognition-via | 2207.10287 | null | https://arxiv.org/abs/2207.10287v1 | https://arxiv.org/pdf/2207.10287v1.pdf | Towards Accurate Open-Set Recognition via Background-Class Regularization | In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g.... | ['Jaegul Choo', 'Wonwoo Cho'] | 2022-07-21 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.40571278e-01 9.34746191e-02 -4.73585695e-01 -5.68591893e-01
-7.56722033e-01 -4.85409945e-01 1.66429132e-01 3.72775681e-02
-4.09077317e-01 8.02561641e-01 -5.02748966e-01 -4.94753450e-01
-4.08666223e-01 -7.90201485e-01 -5.48964143e-01 -7.69585490e-01
9.11361799e-02 2.33201504e-01 1.01630256e-01 3.25613081... | [9.633349418640137, 3.1200311183929443] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.