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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
43f85bf5-b0e5-40fc-b99a-27f6d614bf55 | 3d-petct-tumor-lesion-segmentation-via-gcn | 2302.12571 | null | https://arxiv.org/abs/2302.12571v1 | https://arxiv.org/pdf/2302.12571v1.pdf | 3D PETCT Tumor Lesion Segmentation via GCN Refinement | Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods oft... | ['Yueyang Teng', 'YuDong Yao', 'Qingqing Fang', 'Hengzhi Xue'] | 2023-02-24 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 4.49578501e-02 3.17971230e-01 -2.99366087e-01 -3.78986239e-01
-6.91765666e-01 -6.42675385e-02 1.71125144e-01 2.89436072e-01
-5.70623219e-01 6.87998593e-01 -2.10152835e-01 -3.31062227e-01
-6.92904145e-02 -1.03310156e+00 -3.80042464e-01 -1.00226593e+00
6.65108114e-02 6.65830493e-01 5.87144792e-01 1.56113744... | [14.588037490844727, -2.4886281490325928] |
b775f4c2-af67-4bf6-8a9f-ee66858e052c | consistent-classification-of-translation-1 | null | null | https://aclanthology.org/W17-0807 | https://aclanthology.org/W17-0807.pdf | Consistent Classification of Translation Revisions: A Case Study of English-Japanese Student Translations | Consistency is a crucial requirement in text annotation. It is especially important in educational applications, as lack of consistency directly affects learners{'} motivation and learning performance. This paper presents a quality assessment scheme for English-to-Japanese translations produced by learner translators a... | ['Atsushi Fujita', 'Anthony Hartley', 'Kyo Kageura', 'Kikuko Tanabe', 'Mayuka Yamamoto', 'Chiho Toyoshima'] | 2017-04-01 | null | null | null | ws-2017-4 | ['text-annotation'] | ['natural-language-processing'] | [ 1.22059703e-01 2.73370653e-01 -2.11643368e-01 -3.39272708e-01
-1.21575999e+00 -8.11593950e-01 2.65183568e-01 5.65953493e-01
-7.69157529e-01 1.04493475e+00 3.31011593e-01 -9.19808745e-01
-4.14133549e-01 -5.22781014e-01 -6.15488768e-01 -1.10889457e-01
9.87550974e-01 6.24238968e-01 2.77553797e-01 -3.55189294... | [11.263469696044922, 9.555848121643066] |
6baa326e-8f21-4dd3-bec7-7fa43bdf37e4 | revisiting-ipa-based-cross-lingual-text-to | 2110.07187 | null | https://arxiv.org/abs/2110.07187v2 | https://arxiv.org/pdf/2110.07187v2.pdf | Revisiting IPA-based Cross-lingual Text-to-speech | International Phonetic Alphabet (IPA) has been widely used in cross-lingual text-to-speech (TTS) to achieve cross-lingual voice cloning (CL VC). However, IPA itself has been understudied in cross-lingual TTS. In this paper, we report some empirical findings of building a cross-lingual TTS model using IPA as inputs. Exp... | ['Xinyuan Yu', 'Yang Zhang', 'Haoyue Zhan', 'Yue Lin', 'Haitong Zhang'] | 2021-10-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [-3.36371422e-01 -4.40943569e-01 -1.87971756e-01 -3.79581720e-01
-1.32538664e+00 -8.47354352e-01 2.91886747e-01 -4.07441914e-01
-2.66196996e-01 2.88896769e-01 4.12971973e-01 -9.33305144e-01
4.81282711e-01 -2.33886242e-01 -7.34294116e-01 -4.61654752e-01
3.15518051e-01 2.30106071e-01 1.78441092e-01 -3.96423507... | [14.614263534545898, 6.773756504058838] |
a2f2d8e5-c966-4d03-a11a-ea5e5d815c42 | rethinking-the-editing-of-generative | 2305.09454 | null | https://arxiv.org/abs/2305.09454v1 | https://arxiv.org/pdf/2305.09454v1.pdf | Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction | While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Previous researc... | ['Xuyang Li', 'Qi Li', 'Zhenghong Yu', 'Haoran Jiang', 'Yuhan Cao'] | 2023-03-07 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 5.07243693e-01 5.07235453e-02 -1.09882340e-01 -5.94072282e-01
-3.67619306e-01 -5.40212691e-01 4.59099889e-01 -4.58305150e-01
-2.03798100e-01 6.36582196e-01 9.16357189e-02 3.24000269e-01
1.06211260e-01 -1.08116663e+00 -8.35752189e-01 -6.98411465e-01
2.64881432e-01 2.93350965e-01 4.33796458e-03 -2.21235141... | [11.736138343811035, -0.45144644379615784] |
30ad301d-d49c-4d01-b2e0-ce97f1ded628 | a-dataset-for-building-code-mixed-goal | 1806.05997 | null | http://arxiv.org/abs/1806.05997v1 | http://arxiv.org/pdf/1806.05997v1.pdf | A Dataset for Building Code-Mixed Goal Oriented Conversation Systems | There is an increasing demand for goal-oriented conversation systems which
can assist users in various day-to-day activities such as booking tickets,
restaurant reservations, shopping, etc. Most of the existing datasets for
building such conversation systems focus on monolingual conversations and there
is hardly any wo... | ['Suman Banerjee', 'Mitesh M. Khapra', 'Siddhartha Arora', 'Nikita Moghe'] | 2018-06-15 | a-dataset-for-building-code-mixed-goal-2 | https://aclanthology.org/C18-1319 | https://aclanthology.org/C18-1319.pdf | coling-2018-8 | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-4.78752702e-01 -5.58755398e-02 -1.17605999e-01 -6.94611788e-01
-1.04426277e+00 -8.49968255e-01 7.24576890e-01 3.90477409e-03
-3.16605419e-01 1.01827466e+00 6.29723847e-01 -8.81130934e-01
2.37597436e-01 -5.64302206e-01 -1.61556646e-01 -3.21040779e-01
1.36852577e-01 1.04437816e+00 -2.35685706e-02 -1.22455537... | [12.570096015930176, 8.221664428710938] |
46b9fc8a-7f75-4f17-98cb-52d048ed5cba | automated-top-view-registration-of-broadcast | 1703.01437 | null | http://arxiv.org/abs/1703.01437v1 | http://arxiv.org/pdf/1703.01437v1.pdf | Automated Top View Registration of Broadcast Football Videos | In this paper, we propose a novel method to register football broadcast video
frames on the static top view model of the playing surface. The proposed method
is fully automatic in contrast to the current state of the art which requires
manual initialization of point correspondences between the image and the static
mode... | ['Vineet Gandhi', 'C. V. Jawahar', 'Rahul Anand Sharma', 'Bharath Bhat'] | 2017-03-04 | null | null | null | null | ['bird-view-synthesis', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.19960654e-01 -3.23930621e-01 2.77762264e-02 -9.21006724e-02
-7.71774530e-01 -6.93594217e-01 6.00666344e-01 2.84500774e-02
-5.67238331e-01 3.67001176e-01 -8.77508372e-02 2.65994996e-01
-8.26954693e-02 -6.04751766e-01 -8.23671281e-01 -3.07305634e-01
6.64664358e-02 4.81846809e-01 7.87280262e-01 -5.50691605... | [7.971292495727539, -1.6502262353897095] |
1560807c-c883-46a8-bd45-fad08c9db5b5 | saliency-guided-mutual-learning-network-for | 2305.07180 | null | https://arxiv.org/abs/2305.07180v1 | https://arxiv.org/pdf/2305.07180v1.pdf | Saliency-Guided Mutual Learning Network for Few-shot Fine-grained Visual Recognition | Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature focus on addressing this challenge through global-based or local-based representation approaches. The former employs global feature representations for recognization, which may lac... | ['Tong Zhang', 'Xinrong Gong', 'C. L. Philip Chen', 'Haiqi Liu'] | 2023-05-12 | null | null | null | null | ['fine-grained-visual-recognition', 'saliency-detection'] | ['computer-vision', 'computer-vision'] | [ 4.16626364e-01 -1.38722286e-01 -6.21476054e-01 -2.65778631e-01
-8.50559413e-01 -3.10312480e-01 6.29215837e-01 2.76037902e-01
2.23993305e-02 3.16174150e-01 4.79397506e-01 8.75500739e-02
-1.03420764e-01 -6.11861408e-01 -5.20421505e-01 -7.93973565e-01
2.41971642e-01 -1.60515234e-01 4.37351644e-01 1.12690642... | [9.705199241638184, 1.950963020324707] |
c7175464-f7d3-4358-9ecb-b95e095bf46a | retrieval-augmented-chest-x-ray-report | 2305.03660 | null | https://arxiv.org/abs/2305.03660v1 | https://arxiv.org/pdf/2305.03660v1.pdf | Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models | We propose Retrieval Augmented Generation (RAG) as an approach for automated radiology report writing that leverages multimodally aligned embeddings from a contrastively pretrained vision language model for retrieval of relevant candidate radiology text for an input radiology image and a general domain generative model... | ['Tanuja Ganu', 'Ranjit Manuel', 'Gopinath Ganapathy', 'Mercy Ranjit'] | 2023-05-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 2.04562470e-01 9.39602017e-01 2.35131681e-02 -1.98379025e-01
-1.61137283e+00 -6.10220075e-01 6.26995146e-01 4.36087191e-01
-3.11086714e-01 5.32545865e-01 9.16514337e-01 -5.92371881e-01
-2.99625307e-01 -4.80107754e-01 -4.14742112e-01 -2.54338413e-01
-6.01560576e-03 7.54728019e-01 -2.12368906e-01 -3.39181662... | [15.050920486450195, -1.3866506814956665] |
d5686e23-26c7-4233-a2b2-6de49a7c1f21 | a-similarity-preserving-network-trained-on | null | null | http://papers.nips.cc/paper/9566-a-similarity-preserving-network-trained-on-transformed-images-recapitulates-salient-features-of-the-fly-motion-detection-circuit | http://papers.nips.cc/paper/9566-a-similarity-preserving-network-trained-on-transformed-images-recapitulates-salient-features-of-the-fly-motion-detection-circuit.pdf | A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit | Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning... | ['Yanis Bahroun', 'Anirvan Sengupta', 'Dmitri Chklovskii'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['motion-detection'] | ['computer-vision'] | [ 5.25224626e-01 1.05514526e-01 -1.17913134e-01 -2.51519054e-01
3.14978715e-05 -4.50334221e-01 8.42671514e-01 -1.33044809e-01
-8.07359278e-01 5.04453361e-01 1.41259506e-01 -1.68394744e-02
-1.60168305e-01 -5.75058937e-01 -9.29370165e-01 -9.88227785e-01
9.10330340e-02 1.04291186e-01 3.91851008e-01 2.50622332... | [8.945442199707031, -0.3936833143234253] |
02e7e4c1-a561-4595-9657-7d9514f522c6 | deep-bv-a-fully-automated-system-for-brain | 1811.03601 | null | http://arxiv.org/abs/1811.03601v1 | http://arxiv.org/pdf/1811.03601v1.pdf | Deep BV: A Fully Automated System for Brain Ventricle Localization and Segmentation in 3D Ultrasound Images of Embryonic Mice | Volumetric analysis of brain ventricle (BV) structure is a key tool in the
study of central nervous system development in embryonic mice. High-frequency
ultrasound (HFU) is the only non-invasive, real-time modality available for
rapid volumetric imaging of embryos in utero. However, manual segmentation of
the BV from H... | ['Jeffrey Ketterling', 'Orlando Aristizabal', 'Jack Langerman', 'Yao Wang', 'Nitin Nair', 'Jonathan Mamou', 'Ziming Qiu', 'Daniel H. Turnbull'] | 2018-11-05 | null | null | null | null | ['brain-ventricle-localization-and-segmentation'] | ['medical'] | [-4.12648842e-02 2.05950871e-01 4.28637594e-01 -1.91840410e-01
-5.13885260e-01 -6.36729658e-01 1.17561929e-01 3.31179887e-01
-6.60314441e-01 4.83101428e-01 -7.54756927e-01 -2.98302114e-01
4.58378851e-01 -7.95862615e-01 -6.87260807e-01 -6.95752919e-01
-1.91345453e-01 7.32472360e-01 5.99966466e-01 1.99372128... | [14.331670761108398, -2.6734108924865723] |
29e2f54e-571a-4919-bd7b-c4760bae2415 | maximal-multiverse-learning-for-promoting | null | null | https://aclanthology.org/2021.eacl-main.14 | https://aclanthology.org/2021.eacl-main.14.pdf | Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models | Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task. We present a method that leverages the second phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogona... | ['Lior Wolf', 'Itzik Malkiel'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['unsupervised-pre-training'] | ['methodology'] | [ 2.49103814e-01 1.36790037e-01 -3.47919852e-01 -6.19230747e-01
-9.21556175e-01 -7.87618697e-01 7.24381387e-01 2.75463432e-01
-7.17391193e-01 7.18538523e-01 -4.70629835e-04 -6.71039581e-01
-1.98688242e-03 -4.75949705e-01 -5.02850235e-01 -5.48159778e-01
-1.15163058e-01 6.11829042e-01 3.09302062e-01 -2.37121172... | [9.463397979736328, 3.643970251083374] |
5bc9aa61-6855-454f-9366-711769dc6f34 | from-images-to-sentences-through-scene | 1511.03292 | null | http://arxiv.org/abs/1511.03292v1 | http://arxiv.org/pdf/1511.03292v1.pdf | From Images to Sentences through Scene Description Graphs using Commonsense Reasoning and Knowledge | In this paper we propose the construction of linguistic descriptions of
images. This is achieved through the extraction of scene description graphs
(SDGs) from visual scenes using an automatically constructed knowledge base.
SDGs are constructed using both vision and reasoning. Specifically, commonsense
reasoning is ap... | ['Somak Aditya', 'Cornelia Fermuller', 'Chitta Baral', 'Yiannis Aloimonos', 'Yezhou Yang'] | 2015-11-10 | null | null | null | null | ['image-sentence-alignment'] | ['natural-language-processing'] | [ 3.16103190e-01 2.50484616e-01 2.14474201e-01 -6.40759408e-01
-6.71007633e-01 -7.64572620e-01 1.10807729e+00 3.70763630e-01
-7.00135589e-01 6.43307686e-01 4.50663894e-01 -1.22478753e-01
-8.37948397e-02 -5.00829697e-01 -8.67404819e-01 -1.89573228e-01
4.45594758e-01 3.52674037e-01 3.87097061e-01 -4.09323066... | [10.811554908752441, 1.266785740852356] |
cfd332bc-64f8-472b-bfe9-fa779565854d | wildfire-detection-via-transfer-learning-a | 2306.12276 | null | https://arxiv.org/abs/2306.12276v1 | https://arxiv.org/pdf/2306.12276v1.pdf | Wildfire Detection Via Transfer Learning: A Survey | This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookout towers. The neural network models are pre-trained on ImageNet-1K and fine-tuned on a custom wildfire dataset. The performance of these mod... | ['A. Enis Cetin', 'Hongyi Pan', 'Tianxiao Ye', 'Yifei Zhao', 'Emadeldeen Hamdan', 'Ziliang Hong'] | 2023-06-21 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 2.98351049e-01 -5.64857602e-01 -1.23552263e-01 -3.45940083e-01
-2.17500359e-01 -6.79867744e-01 4.60557520e-01 -1.87169522e-01
-8.75875235e-01 4.67023313e-01 2.04988331e-01 -6.13251328e-01
-2.38968194e-01 -1.15138686e+00 -3.74162376e-01 -6.47810817e-01
-7.65187621e-01 -6.67672306e-02 3.96263063e-01 -3.69852781... | [9.261537551879883, -1.300600290298462] |
4c3cd0e4-ccc7-4e7c-81c7-8fa836e918e1 | deep-hyperedges-a-framework-for-transductive | 1910.02633 | null | https://arxiv.org/abs/1910.02633v1 | https://arxiv.org/pdf/1910.02633v1.pdf | Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs | From social networks to protein complexes to disease genomes to visual data, hypergraphs are everywhere. However, the scope of research studying deep learning on hypergraphs is still quite sparse and nascent, as there has not yet existed an effective, unified framework for using hyperedge and vertex embeddings jointly ... | ['Josh Payne'] | 2019-10-07 | null | null | null | null | ['hypergraph-embedding', 'hyperedge-classification'] | ['graphs', 'graphs'] | [ 3.43694955e-01 3.39852840e-01 -2.26128444e-01 -1.87806070e-01
-2.32412100e-01 -7.81203449e-01 7.60400593e-01 1.28179476e-01
-2.45191120e-02 6.85910821e-01 1.81346387e-01 -6.44096196e-01
-4.06068683e-01 -1.03803360e+00 -7.90686369e-01 -7.23028898e-01
-4.24457282e-01 8.25164080e-01 8.60527828e-02 -1.95620686... | [6.958550930023193, 6.238804817199707] |
c5a732b3-ffe9-4709-9327-ef6f81432509 | tart-a-plug-and-play-transformer-module-for | 2306.07536 | null | https://arxiv.org/abs/2306.07536v1 | https://arxiv.org/pdf/2306.07536v1.pdf | TART: A plug-and-play Transformer module for task-agnostic reasoning | Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional adaptation approaches, such as fine-tuning, modify the underlying models for each specific task. In-context learning, however, consistently... | ['Christopher Ré', 'Christopher De Sa', 'Avanika Narayan', 'Kush Bhatia'] | 2023-06-13 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.64739454e-01 2.66913325e-01 -1.46668896e-01 -3.53482872e-01
-1.07895744e+00 -6.43982828e-01 8.02526474e-01 -1.63460538e-01
-3.22290570e-01 6.43098652e-01 9.61999968e-02 -8.33845794e-01
-1.66508317e-01 -7.39941001e-01 -9.14050519e-01 -2.38003030e-01
3.97015154e-01 8.61544847e-01 3.07193995e-01 -3.60117704... | [10.20919418334961, 7.88517951965332] |
0f170e26-f7d4-46c2-b9c8-d24a291ca3c9 | person-re-identification-based-on-res2net | 1910.04061 | null | https://arxiv.org/abs/1910.04061v2 | https://arxiv.org/pdf/1910.04061v2.pdf | Improved Res2Net model for Person re-identification | Person re-identification has become a very popular research topic in the computer vision community owing to its numerous applications and growing importance in visual surveillance. Person re-identification remains challenging due to occlusion, illumination and significant intra-class variations across different cameras... | ['Hyo Jong Lee', 'Zongjing Cao'] | 2019-10-08 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [ 1.15994904e-02 -6.84227109e-01 1.51001751e-01 -4.60950613e-01
-4.61917907e-01 -3.69596153e-01 6.75434709e-01 7.87596256e-02
-9.75523770e-01 8.56440842e-01 1.43076986e-01 2.60130793e-01
2.51906663e-01 -4.37285602e-01 -4.69351172e-01 -5.53897500e-01
2.88744509e-01 3.17227900e-01 1.79458678e-01 1.14989579... | [14.695645332336426, 0.9311878681182861] |
c441cd5d-3d8a-437b-ba2b-5d211f01a0a7 | the-spike-gating-flow-a-hierarchical | 2206.01910 | null | https://arxiv.org/abs/2206.01910v2 | https://arxiv.org/pdf/2206.01910v2.pdf | The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition | Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in the emerging industrial fields such as robotic visions and automobiles. However, current deep learning faces major challenges for such applications because of the huge computational cost and the inefficient l... | ['Yuan Xie', 'Junwen Luo', 'C. -J. Richard Shi', 'Xiaoan Wang', 'Jiansong Zhang', 'Fangbo Tao', 'Tie XU', 'Qiaosha Zou', 'Yanhong Wang', 'Zihao Zhao'] | 2022-06-04 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.92036700e-01 -2.96251625e-01 -5.89190759e-02 -8.90008669e-05
1.41425747e-02 -1.06419787e-01 6.57434702e-01 -3.98106091e-02
-7.96230316e-01 7.43379951e-01 -2.00608820e-01 -6.05859570e-02
-1.91781282e-01 -9.39863801e-01 -7.12614298e-01 -1.15591979e+00
1.29104868e-01 1.07448407e-01 1.00413203e+00 -1.92878246... | [8.22718334197998, 2.395636796951294] |
63648821-3e49-407e-b557-4e986943673c | exploring-large-scale-unlabeled-faces-to | 2303.08617 | null | https://arxiv.org/abs/2303.08617v2 | https://arxiv.org/pdf/2303.08617v2.pdf | Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition | Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in human-computer interaction, intelligent security, emotion analysis, and other fields. However, the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffe... | ['Wangyuan Zhu', 'Jichao Zhu', 'Guochen Xie', 'Gongpeng Zhao', 'Renda Li', 'Zhongpeng Cai', 'Jun Yu'] | 2023-03-15 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.97610015e-01 -1.75934732e-01 -3.07728320e-01 -1.07662308e+00
-3.67544204e-01 -2.92290717e-01 9.52118561e-02 -6.25747621e-01
-4.13400441e-01 7.76062548e-01 -4.77720588e-01 -6.49117082e-02
3.49876851e-01 -4.92654294e-01 -1.32369593e-01 -7.83361197e-01
7.32000619e-02 -4.07762080e-02 -3.29236031e-01 -2.44993284... | [13.587821960449219, 1.7268437147140503] |
9fcdb66c-d5ec-4208-a1f5-c81849bf8e10 | end-to-end-adversarial-text-to-speech | 2006.03575 | null | https://arxiv.org/abs/2006.03575v3 | https://arxiv.org/pdf/2006.03575v3.pdf | End-to-End Adversarial Text-to-Speech | Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which oper... | ['Mikołaj Bińkowski', 'Sander Dieleman', 'Jeff Donahue', 'Karen Simonyan', 'Erich Elsen'] | 2020-06-05 | null | https://openreview.net/forum?id=rsf1z-JSj87 | https://openreview.net/pdf?id=rsf1z-JSj87 | iclr-2021-1 | ['adversarial-text'] | ['adversarial'] | [ 8.12605977e-01 4.62440848e-01 1.33700877e-01 -2.85398096e-01
-1.27216554e+00 -6.78186893e-01 7.01240242e-01 -2.16534995e-02
-1.70311511e-01 5.62539160e-01 2.20094323e-01 -2.64769375e-01
3.99145842e-01 -5.59261262e-01 -9.31231320e-01 -5.78639090e-01
7.54616864e-04 2.48159051e-01 -7.24350661e-02 -8.50723386... | [15.449807167053223, 6.098127365112305] |
a1d0b92d-e774-4288-8828-e42d86ce7007 | a-two-stream-amr-enhanced-model-for-document-1 | 2205.00241 | null | https://arxiv.org/abs/2205.00241v1 | https://arxiv.org/pdf/2205.00241v1.pdf | A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction | Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and argument... | ['Zhifang Sui', 'Baobao Chang', 'Shuang Zeng', 'Tianyu Liu', 'Peiyi Wang', 'Runxin Xu'] | 2022-04-30 | null | https://aclanthology.org/2022.naacl-main.370 | https://aclanthology.org/2022.naacl-main.370.pdf | naacl-2022-7 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 3.11457187e-01 1.79120582e-02 -2.51397461e-01 -4.06048864e-01
-1.01911294e+00 -6.39741182e-01 7.95401692e-01 7.25199103e-01
-5.19398570e-01 6.86171174e-01 7.38340914e-01 -2.36417606e-01
-2.31649265e-01 -7.92186081e-01 -6.23376608e-01 -4.49950248e-01
-1.74264945e-02 3.24917920e-02 5.05063295e-01 -2.16572881... | [9.090896606445312, 9.168861389160156] |
859aab68-b994-420c-946a-8a6ce1f70593 | promptpose-language-prompt-helps-animal-pose | 2206.11752 | null | https://arxiv.org/abs/2206.11752v3 | https://arxiv.org/pdf/2206.11752v3.pdf | CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose | Animal pose estimation is challenging for existing image-based methods because of limited training data and large intra- and inter-species variances. Motivated by the progress of visual-language research, we propose that pre-trained language models (e.g., CLIP) can facilitate animal pose estimation by providing rich pr... | ['DaCheng Tao', 'Jing Zhang', 'Yufei Xu', 'Zhe Chen', 'Wen Wang', 'Xu Zhang'] | 2022-06-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_CLAMP_Prompt-Based_Contrastive_Learning_for_Connecting_Language_and_Animal_Pose_CVPR_2023_paper.pdf | cvpr-2023-1 | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.03666365e-01 -3.16955775e-01 -2.08266139e-01 -4.63571578e-01
-5.35772085e-01 -4.67772752e-01 6.10054314e-01 2.35494286e-01
-8.17220390e-01 4.45857793e-01 7.27787763e-02 3.59637141e-01
-2.36497447e-02 -4.90275681e-01 -1.15946949e+00 -4.57836509e-01
-1.45999059e-01 2.08682910e-01 5.26951015e-01 -2.33135670... | [7.6684980392456055, -0.9397859573364258] |
adffc92c-e6f0-4750-8b09-ba458b7d83bc | high-dimensional-and-permutation-invariant | 2306.03933 | null | https://arxiv.org/abs/2306.03933v1 | https://arxiv.org/pdf/2306.03933v1.pdf | High-dimensional and Permutation Invariant Anomaly Detection | Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficu... | ['Benjamin Nachman', 'Vinicius Mikuni'] | 2023-06-06 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 7.63075352e-02 -2.90185124e-01 -2.12679263e-02 -2.25048900e-01
-6.08666658e-01 -6.21021926e-01 9.90886390e-01 2.49434114e-01
-2.96606660e-01 9.26006138e-01 -1.24026306e-01 -4.63651925e-01
-6.51174009e-01 -8.88658941e-01 -5.48449993e-01 -8.95040572e-01
-3.34058374e-01 8.36751878e-01 4.71708953e-01 4.73705888... | [7.333451747894287, 3.951925277709961] |
0afdccc9-f1e7-4488-96ba-8a521417a9ce | 3d-saliency-guided-deep-quality-predictor-for | null | null | https://www.sciencedirect.com/science/article/pii/S0925231222000029 | https://www.researchgate.net/publication/357645676_3D_Saliency_guided_Deep_Quality_predictor_for_No-Reference_Stereoscopic_Images | 3D Saliency guided Deep Quality predictor for No-Reference Stereoscopic Images | The use of 3D technologies is growing rapidly, and stereoscopic imaging is usually used to display the 3D contents. However, compression, transmission and other necessary treatments may reduce the quality of these images. Stereo Image Quality Assessment (SIQA) has attracted more attention to ensure good viewing experie... | ['Zianou Ahmed seghir', 'Fella Hachouf', 'Aladine Chetouani', 'Oussama Messai'] | 2022-01-06 | null | null | null | journal-2022-1 | ['image-quality-estimation', 'blind-image-quality-assessment', 'stereoscopic-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.33807242e-01 -2.89241165e-01 -2.33609732e-02 -2.29189113e-01
-8.61102104e-01 -2.04537749e-01 3.68959159e-01 -4.44725715e-02
-2.95995146e-01 5.71666241e-01 4.08636272e-01 2.72803791e-02
-1.28632009e-01 -6.09272301e-01 -5.51172853e-01 -7.52488911e-01
-1.04052350e-01 -8.03329498e-02 4.93552446e-01 -2.73680210... | [11.789329528808594, -1.9554492235183716] |
819876b3-6017-4dbf-bf6e-aa2eb763c417 | are-negative-samples-necessary-in-entity | 2108.05278 | null | https://arxiv.org/abs/2108.05278v2 | https://arxiv.org/pdf/2108.05278v2.pdf | Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness | Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-scale datasets. We summarize three issues leading to such high time-space complexity in existing EA ... | ['Man Lan', 'Yuanbin Wu', 'Wenting Wang', 'Xin Mao'] | 2021-08-11 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.22891821e-01 1.05468892e-01 -3.98195654e-01 -1.63190737e-01
-8.15533698e-01 -2.42394656e-01 1.02596171e-01 3.73988837e-01
-3.67095083e-01 8.94367576e-01 1.60568982e-01 -2.54299134e-01
-2.18289807e-01 -8.56464863e-01 -7.47336924e-01 -4.12586182e-01
-2.39500985e-01 4.81906533e-01 5.67536414e-01 -2.41724521... | [8.74822998046875, 7.973048210144043] |
fbb7fa08-4afb-463b-9e38-e91ab00678b4 | one-class-kernel-spectral-regression | 1807.01085 | null | http://arxiv.org/abs/1807.01085v6 | http://arxiv.org/pdf/1807.01085v6.pdf | One-Class Kernel Spectral Regression | The paper introduces a new efficient nonlinear one-class classifier
formulated as the Rayleigh quotient criterion optimisation. The method,
operating in a reproducing kernel Hilbert space, minimises the scatter of
target distribution along an optimal projection direction while at the same
time keeping projections of po... | ['Shervin Rahimzadeh Arashloo', 'Josef Kittler'] | 2018-07-03 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 4.05199081e-01 3.18779141e-01 1.94948286e-01 -1.04785904e-01
-4.28154469e-01 -3.82752448e-01 5.71636140e-01 3.74087125e-01
-6.59843862e-01 5.40566325e-01 -2.36493155e-01 -3.23029995e-01
-5.77265799e-01 -7.42611349e-01 -3.20570409e-01 -1.16589773e+00
-3.52769911e-01 5.26968420e-01 1.41574740e-01 -1.95293069... | [7.823635578155518, 4.101921558380127] |
337caf5e-c329-47c6-b459-25168005dfff | multi-modal-egocentric-activity-recognition | 1807.00612 | null | https://arxiv.org/abs/1807.00612v3 | https://arxiv.org/pdf/1807.00612v3.pdf | Multi-modal Egocentric Activity Recognition using Audio-Visual Features | Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each fea... | ['Alptekin Temizel', 'Peter Jančovič', 'Fatih Özkan', 'Mehmet Ali Arabaci', 'Elif Surer'] | 2018-07-02 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 3.87889892e-02 -5.50571978e-01 -8.87808483e-03 -2.14062199e-01
-8.57091188e-01 -1.46009997e-01 7.39253819e-01 1.82420149e-01
-6.62068665e-01 6.51135147e-01 7.96074867e-01 6.37750983e-01
-4.74766254e-01 -2.27682605e-01 -3.16582412e-01 -1.11970615e+00
-2.24266067e-01 -1.34183735e-01 2.35127985e-01 1.25928074... | [7.976809978485107, 0.375964492559433] |
7acb243b-99de-4cbd-92e0-bc7c1f281ff7 | neural-best-buddies-sparse-cross-domain | 1805.04140 | null | http://arxiv.org/abs/1805.04140v2 | http://arxiv.org/pdf/1805.04140v2.pdf | Neural Best-Buddies: Sparse Cross-Domain Correspondence | Correspondence between images is a fundamental problem in computer vision,
with a variety of graphics applications. This paper presents a novel method for
sparse cross-domain correspondence. Our method is designed for pairs of images
where the main objects of interest may belong to different semantic categories
and dif... | ['Daniel Cohen-Or', 'Mingyi Shi', 'Jing Liao', 'Kfir Aberman', 'Dani Lischinski', 'Baoquan Chen'] | 2018-05-10 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 3.00448537e-01 -3.63499187e-02 2.12145343e-01 -3.67232144e-01
-3.85797888e-01 -4.56508577e-01 5.90796828e-01 5.36223590e-01
-2.94906348e-01 3.99326771e-01 1.50049962e-02 2.34445766e-01
-1.10741839e-01 -8.54600549e-01 -7.80402720e-01 -5.39391160e-01
1.55343086e-01 4.59057182e-01 7.22049952e-01 -4.41232294... | [8.430933952331543, -1.9154328107833862] |
550562f2-25a7-45d5-bc65-474ff9c6af9c | robust-semi-supervised-learning-for | 2303.09930 | null | https://arxiv.org/abs/2303.09930v1 | https://arxiv.org/pdf/2303.09930v1.pdf | Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring | Semi-supervised learning (semi-SL) is a promising alternative to supervised learning for medical image analysis when obtaining good quality supervision for medical imaging is difficult. However, semi-SL assumes that the underlying distribution of unaudited data matches that of the few labeled samples, which is often vi... | ['Amit Sethi', 'Shashikant Khade', 'Abhijit PATIL', 'Varsha S', 'Nikhil Cherian Kurian'] | 2023-03-17 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 4.62894380e-01 2.13882148e-01 -4.89264816e-01 -5.28289199e-01
-1.31429207e+00 -4.78962868e-01 2.68379360e-01 5.39968967e-01
-5.29084086e-01 6.33459568e-01 -1.24429323e-01 -3.79344881e-01
-1.17164738e-01 -3.92736971e-01 -7.00915635e-01 -1.07395554e+00
1.71152145e-01 7.86297381e-01 3.21007818e-01 4.47168499... | [15.067413330078125, -2.787741184234619] |
b0e5ef59-639b-459b-b11a-144199e456dd | defending-against-adversarial-attack-in-ecg | 2203.09487 | null | https://arxiv.org/abs/2203.09487v1 | https://arxiv.org/pdf/2203.09487v1.pdf | Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training | In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of t... | ['Shenda Hong', 'Tong Liu', 'Weilun Xu', 'Zhaoji Fu', 'Shijia Geng', 'Jiahao Shao'] | 2022-03-14 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.28026292e-01 -8.77713785e-02 2.32037783e-01 -2.63818473e-01
-5.14410138e-01 -6.47702992e-01 8.28825310e-03 -8.90953243e-02
-3.50027263e-01 7.12958515e-01 1.04627192e-01 -5.09165466e-01
4.78765368e-02 -8.53585541e-01 -3.94055128e-01 -7.51221418e-01
-3.46281588e-01 -1.62421122e-01 -8.00468177e-02 -4.43090469... | [14.302435874938965, 3.1592235565185547] |
835de342-8e08-4ce7-8f24-8685d77f5742 | learning-structural-information-for-syntax | null | null | https://aclanthology.org/2022.findings-naacl.160 | https://aclanthology.org/2022.findings-naacl.160.pdf | Learning Structural Information for Syntax-Controlled Paraphrase Generation | Syntax-controlled paraphrase generation aims to produce paraphrase conform to given syntactic patterns. To address this task, recent works have started to use parse trees (or syntactic templates) to guide generation.A constituency parse tree contains abundant structural information, such as parent-child relation, sibli... | ['Yufeng Chen', 'Jinan Xu', 'Yao Meng', 'Yujie Zhang', 'Deyi Xiong', 'Chenglin Bai', 'Erguang Yang'] | null | null | null | null | findings-naacl-2022-7 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.90817761e-01 1.10468566e-02 -2.96845406e-01 -6.47786617e-01
-6.29741669e-01 -4.43155318e-01 3.62730056e-01 1.14050500e-01
-2.04992041e-01 4.64597970e-01 7.46547997e-01 -3.03270221e-01
1.40560493e-01 -1.00449228e+00 -7.74668932e-01 -2.68876523e-01
6.22931540e-01 3.16268384e-01 1.02017701e-01 -5.01493871... | [11.630940437316895, 9.315764427185059] |
c7420447-0014-42a9-af59-65489e1e4122 | pseudo-value-based-deep-neural-networks-for | 2207.05291 | null | https://arxiv.org/abs/2207.05291v1 | https://arxiv.org/pdf/2207.05291v1.pdf | Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis | Multi-state survival analysis (MSA) uses multi-state models for the analysis of time-to-event data. In medical applications, MSA can provide insights about the complex disease progression in patients. A key challenge in MSA is the accurate subject-specific prediction of multi-state model quantities such as transition p... | ['Sanjay Purushotham', 'Md Mahmudur Rahman'] | 2022-07-12 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-7.91528299e-02 -3.14957201e-01 -5.50538957e-01 -7.03274846e-01
-1.20800626e+00 1.02111794e-01 2.46083990e-01 2.90937930e-01
-1.37183875e-01 1.20439231e+00 1.81451559e-01 -7.27736652e-01
-2.60890454e-01 -6.78129613e-01 -5.09106100e-01 -8.14529479e-01
-3.10013205e-01 6.72902703e-01 -2.98851520e-01 1.05904276... | [7.801756381988525, 5.567692756652832] |
b560115a-cedd-42de-9e79-a6a1c4ec80ac | 100-things-you-always-wanted-to-know-about-1 | null | null | https://aclanthology.org/P18-5001 | https://aclanthology.org/P18-5001.pdf | 100 Things You Always Wanted to Know about Semantics \& Pragmatics But Were Afraid to Ask | Meaning is a fundamental concept in Natural Language Processing (NLP), given its aim to build systems that mean what they say to you, and understand what you say to them. In order for NLP to scale beyond partial, task-specific solutions, it must be informed by what is known about how humans use language to express and ... | ['Emily M. Bender'] | 2018-07-01 | null | null | null | acl-2018-7 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.47551554e-01 5.87242067e-01 -1.42803714e-01 -8.07152331e-01
-4.45845723e-01 -7.81012774e-01 5.86635113e-01 4.28332627e-01
-4.58757460e-01 7.00369000e-01 6.42148972e-01 -8.20822060e-01
-1.71597242e-01 -3.92511994e-01 -2.06728503e-02 -1.10215031e-01
2.14036137e-01 3.26229423e-01 9.78391021e-02 -6.00529134... | [10.40298843383789, 8.691010475158691] |
99dcee22-7701-46a1-b400-02b3fae7dd42 | robust-controlled-table-to-text-generation | null | null | https://openreview.net/forum?id=VBZCrsaUpsM | https://openreview.net/pdf?id=VBZCrsaUpsM | Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning | Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 5.46176016e-01 6.03007138e-01 -1.88986391e-01 -9.38735902e-02
-5.78892827e-01 -7.71224797e-01 6.52489364e-01 3.05432111e-01
2.21713781e-01 8.24481428e-01 5.88096380e-01 -5.08212090e-01
2.56796330e-01 -1.34597921e+00 -1.01015842e+00 -3.50173354e-01
1.58861473e-01 7.25214243e-01 2.27898583e-01 -6.83813393... | [10.855363845825195, 8.48784351348877] |
59c27a33-ff68-4d82-944c-753f69ea2517 | a-distributional-view-on-multi-objective | 2005.07513 | null | https://arxiv.org/abs/2005.07513v1 | https://arxiv.org/pdf/2005.07513v1.pdf | A Distributional View on Multi-Objective Policy Optimization | Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-o... | ['Nicolas Heess', 'Leonard Hasenclever', 'Sandy H. Huang', 'Martin Riedmiller', 'Abbas Abdolmaleki', 'Murilo F. Martins', 'Raia Hadsell', 'Michael Neunert', 'H. Francis Song', 'Martina Zambelli'] | 2020-05-15 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 1.82599932e-01 -1.72262222e-01 -2.32029364e-01 -2.06290156e-01
-8.09881330e-01 -8.53935421e-01 3.30373257e-01 6.97175562e-02
-8.07394445e-01 1.08185089e+00 3.75933796e-02 -9.62104946e-02
-7.39923298e-01 -5.57557344e-01 -6.02231205e-01 -7.04563141e-01
-1.66170642e-01 8.60208392e-01 1.11097872e-01 -2.01161385... | [4.3005266189575195, 2.2826032638549805] |
afae981d-b2a8-4d40-85ea-94ccab2fe7dc | a-unified-software-hardware-scalable | 2201.02262 | null | https://arxiv.org/abs/2201.02262v1 | https://arxiv.org/pdf/2201.02262v1.pdf | A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models | The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In t... | ['Andres Upegui', 'Quentin Berthet', 'Joachim Schmidt', 'Lyes Khacef', 'Benoit Miramond', 'Laurent Rodriguez', 'Artem R. Muliukov'] | 2022-01-06 | null | null | null | null | ['multimodal-association'] | ['time-series'] | [-1.68149862e-02 2.64916658e-01 3.53191346e-01 -2.86455490e-02
4.15247560e-01 -3.72192800e-01 6.34230554e-01 5.59772372e-01
-6.55436635e-01 5.54302096e-01 -4.37442623e-02 1.79829493e-01
-4.05565321e-01 -9.54486966e-01 -4.62429762e-01 -8.25333118e-01
-3.07035148e-01 7.29584932e-01 4.66660351e-01 -3.55494767... | [8.107510566711426, 2.722280263900757] |
6d95c742-ff22-41d4-a096-8e5b9d70eb26 | visual-scene-graphs-for-audio-source | 2109.11955 | null | https://arxiv.org/abs/2109.11955v1 | https://arxiv.org/pdf/2109.11955v1.pdf | Visual Scene Graphs for Audio Source Separation | State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sourc... | ['Anoop Cherian', 'Narendra Ahuja', 'Jonathan Le Roux', 'Moitreya Chatterjee'] | 2021-09-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chatterjee_Visual_Scene_Graphs_for_Audio_Source_Separation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chatterjee_Visual_Scene_Graphs_for_Audio_Source_Separation_ICCV_2021_paper.pdf | iccv-2021-1 | ['audio-source-separation'] | ['audio'] | [ 3.44511390e-01 -3.26762706e-01 2.24404544e-01 1.31353771e-03
-1.05407453e+00 -8.52001607e-01 2.38966063e-01 1.22489311e-01
9.49226022e-02 5.10431081e-02 6.72193646e-01 1.54029444e-01
-6.35765493e-02 -2.24390998e-01 -7.77170300e-01 -7.06034064e-01
-1.58097729e-01 1.12759555e-02 1.86565772e-01 5.76683693... | [14.8845796585083, 4.983665943145752] |
828aa182-e48c-40da-8566-864ddd2d4fb3 | robust-contact-state-estimation-in-humanoid | 2208.00278 | null | https://arxiv.org/abs/2208.00278v1 | https://arxiv.org/pdf/2208.00278v1.pdf | Robust Contact State Estimation in Humanoid Walking Gaits | In this article, we propose a deep learning framework that provides a unified approach to the problem of leg contact detection in humanoid robot walking gaits. Our formulation accomplishes to accurately and robustly estimate the contact state probability for each leg (i.e., stable or slip/no contact). The proposed fram... | ['Panos Trahanias', 'Dimitrios Kanoulas', 'Michael Maravgakis', 'Stylianos Piperakis'] | 2022-07-30 | null | null | null | null | ['contact-detection'] | ['robots'] | [-9.83062834e-02 1.43742725e-01 -3.57541591e-01 1.36183664e-01
-3.77753645e-01 -1.18466839e-01 3.24273437e-01 -3.00728589e-01
-4.45044041e-01 9.88577425e-01 -3.72240096e-01 1.68154851e-01
-1.53452620e-01 -7.65036225e-01 -8.58797133e-01 -4.59426522e-01
-5.25387287e-01 7.77453244e-01 5.63212752e-01 -7.40321219... | [4.820591926574707, 1.0969507694244385] |
79bc318f-354e-4825-baaa-397940cdb88d | metropolis-hastings-algorithm-in-joint | 2305.19936 | null | https://arxiv.org/abs/2305.19936v1 | https://arxiv.org/pdf/2305.19936v1.pdf | Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study | In this study, we explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies investigate how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we have focused on a joint attention... | ['Akira Taniguchi', 'Yosinobu Hagiwara', 'Tadahiro Taniguchi', 'Ryota Okumura'] | 2023-05-31 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.27817959e-01 3.45327646e-01 -5.40418401e-02 -2.48532206e-01
4.81689662e-01 -3.67169410e-01 8.71494651e-01 2.60384288e-02
-9.71771359e-01 6.39411807e-01 -4.38577048e-02 -4.21514601e-01
-2.32571974e-01 -7.92683840e-01 -3.82987171e-01 -3.87887836e-01
7.69960657e-02 8.93937647e-01 -1.19374819e-01 -2.62777824... | [9.73060417175293, 7.580016136169434] |
3b6ae408-4f66-4fcc-a8df-dfdd1d6d376a | a-constraints-fusion-induced-symmetric | 2302.12114 | null | https://arxiv.org/abs/2302.12114v1 | https://arxiv.org/pdf/2302.12114v1.pdf | A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection | Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretabilit... | ['Xin Luo', 'ZhiGang Liu'] | 2023-02-23 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 2.42604852e-01 -1.33120403e-01 -2.55767405e-01 1.52266055e-01
1.70476399e-02 -5.16415119e-01 3.49725544e-01 -8.71148407e-02
1.97301626e-01 2.26539060e-01 1.27489626e-01 -2.17351824e-01
-4.72325146e-01 -7.97064722e-01 -2.13013515e-01 -8.53816152e-01
-3.82053167e-01 3.62534106e-01 1.36621416e-01 -1.85973555... | [7.302908420562744, 5.684795379638672] |
edc09155-b3a5-4f3d-bf36-6c7a20e55e63 | adversarial-attacks-on-binary-image-1 | 2010.11782 | null | https://arxiv.org/abs/2010.11782v1 | https://arxiv.org/pdf/2010.11782v1.pdf | Adversarial Attacks on Binary Image Recognition Systems | We initiate the study of adversarial attacks on models for binary (i.e. black and white) image classification. Although there has been a great deal of work on attacking models for colored and grayscale images, little is known about attacks on models for binary images. Models trained to classify binary images are used i... | ['Richard Wang', 'Yaron Singer', 'Alexander Rilee', 'Kojin Oshiba', 'Harrison Chase', 'Eric Balkanski'] | 2020-10-22 | adversarial-attacks-on-binary-image | https://openreview.net/forum?id=xCm8kiWRiBT | https://openreview.net/pdf?id=xCm8kiWRiBT | null | ['license-plate-recognition'] | ['computer-vision'] | [ 4.14524257e-01 -2.56934106e-01 -8.96152705e-02 -3.19472492e-01
-7.32602954e-01 -1.28096402e+00 5.66608071e-01 -1.75991744e-01
-3.64721835e-01 4.86676574e-01 -7.40404546e-01 -1.14212179e+00
3.14238161e-01 -1.10392141e+00 -7.11560190e-01 -6.31408691e-01
1.69238418e-01 1.73377231e-01 1.82343632e-01 -3.55027884... | [5.689977645874023, 7.893373012542725] |
06bc89e9-4938-4b33-8a2b-f11155f778cf | multimodal-emotion-recognition-for-one-minute | 1805.01060 | null | http://arxiv.org/abs/1805.01060v1 | http://arxiv.org/pdf/1805.01060v1.pdf | Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge | The continuous dimensional emotion modelled by arousal and valence can depict
complex changes of emotions. In this paper, we present our works on arousal and
valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal
representations are first extracted from videos using a variety of acoustic,
video ... | ['Chenjie Cao', 'Ziqi Zheng', 'Xingwei Chen', 'Guoqiang Xu'] | 2018-05-03 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-0.08267318 -0.06546824 0.03238434 -0.8378961 -0.7631837 -0.6141438
0.63907504 0.17994802 -0.22246373 0.6405761 0.42627928 0.5692184
0.17172682 -0.08262662 -0.25538564 -0.5394707 -0.4206246 -0.2639759
-0.4146924 -0.47473273 -0.05885255 0.18294613 -1.9048896 0.58836734
0.363532 1.8060606 -0.436... | [13.347736358642578, 5.095846176147461] |
96473f1f-5819-4246-a348-7d55b6d0a44c | an-unsupervised-domain-adaptive-approach-for | 2203.03568 | null | https://arxiv.org/abs/2203.03568v1 | https://arxiv.org/pdf/2203.03568v1.pdf | An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions | Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that fuse vision and range data for 2D object detection have thrived in recent years, the corresponding modalities can degrade in adverse weathe... | ['Bin Yang', 'Karim Guirguis', 'Mario Döbler', 'Pavithran Pandiyan', 'Robert A. Marsden', 'George Eskandar'] | 2022-03-07 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 7.45973229e-01 -3.05911124e-01 -2.15332896e-01 -6.38165057e-01
-8.28148723e-01 -7.85681307e-01 7.32902348e-01 -1.15493998e-01
-4.70573723e-01 6.65336728e-01 1.19660255e-02 -1.90165550e-01
-6.24817498e-02 -5.89989960e-01 -6.27050698e-01 -8.42371941e-01
4.65060860e-01 2.39173889e-01 5.49051225e-01 -2.11009473... | [8.297542572021484, -2.19661283493042] |
97a0b32b-e192-4a7c-a8f1-10e89944c6f6 | star-boosting-low-resource-event-extraction | 2305.15090 | null | https://arxiv.org/abs/2305.15090v1 | https://arxiv.org/pdf/2305.15090v1.pdf | STAR: Boosting Low-Resource Event Extraction by Structure-to-Text Data Generation with Large Language Models | Structure prediction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies, thus they still heavily rely on task-specific training data to obtain reasonable performance. Due to the high cost of human annotation, low-resource event extraction, which requires m... | ['Wei Wang', 'Nanyun Peng', 'P. Jeffrey Brantingham', 'Po-Nien Kung', 'Xiaoxuan Wang', 'Mingyu Derek Ma'] | 2023-05-24 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.38390282e-01 2.75194377e-01 -6.57064468e-02 -3.91831994e-01
-1.32004082e+00 -7.25321770e-01 5.73396981e-01 5.34545124e-01
-5.10006666e-01 8.42518270e-01 5.31942725e-01 -2.28421345e-01
1.16411313e-01 -8.39806139e-01 -7.71670520e-01 4.94113900e-02
1.39749840e-01 5.17268240e-01 2.80362546e-01 -1.46585479... | [9.439888954162598, 9.016218185424805] |
dd870455-40c9-482f-aaa7-0a52512da21c | learning-to-agree-on-vision-attention-for | 2302.02117 | null | https://arxiv.org/abs/2302.02117v2 | https://arxiv.org/pdf/2302.02117v2.pdf | Learning to Agree on Vision Attention for Visual Commonsense Reasoning | Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning. A VCR model generally aims at answering a textual question regarding an image, followed by the rationale prediction for the preceding answering process. Though these two processes are sequential a... | ['Kejie Wang', 'Mohan Kankanhalli', 'Liqiang Nie', 'Fan Liu', 'Yangyang Guo', 'Zhenyang Li'] | 2023-02-04 | null | null | null | null | ['visual-reasoning', 'visual-commonsense-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning', 'reasoning'] | [ 4.53244746e-01 1.10481717e-01 -6.88017625e-03 -2.21111789e-01
-6.67696357e-01 -4.71303374e-01 9.38124716e-01 -7.41634071e-02
-1.81552678e-01 2.65452713e-01 4.75148201e-01 -5.86317778e-01
3.53260823e-02 -5.77094793e-01 -5.27386069e-01 -5.64859629e-01
7.39764392e-01 2.76879728e-01 3.25811535e-01 -2.48936161... | [10.67404842376709, 1.7343604564666748] |
f13dc381-27ec-443c-a1bb-7889974f11a6 | iterative-greedy-matching-for-3d-human-pose | 2101.09745 | null | https://arxiv.org/abs/2101.09745v1 | https://arxiv.org/pdf/2101.09745v1.pdf | Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views | In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras. Estimating 3D human poses from multiple views has several compelling properties: human poses are estimated within a global coordinate space and multiple cameras provide an extended field of view which ... | ['Juergen Gall', 'Julian Tanke'] | 2021-01-24 | null | null | null | null | ['3d-human-pose-tracking'] | ['computer-vision'] | [-3.27591628e-01 -2.62512594e-01 -2.86655314e-03 -3.00258577e-01
-7.80268848e-01 -7.27739573e-01 4.33624059e-01 -2.26664618e-01
-5.28223932e-01 6.00564480e-01 4.50878918e-01 4.39403623e-01
1.57922417e-01 -3.35102886e-01 -6.30202830e-01 -2.27599591e-01
-4.29284610e-02 9.24776435e-01 3.34722877e-01 3.32345488... | [7.048717021942139, -0.9976739883422852] |
0d13779f-a3e2-41b8-911f-5b4e5340429e | multi-task-text-classification-using-graph | 2205.01204 | null | https://arxiv.org/abs/2205.01204v1 | https://arxiv.org/pdf/2205.01204v1.pdf | Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language | Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplor... | ['Radhika Mamidi', 'Venkata Charan Chinni', 'Lakshmi Sireesha Vakada', 'Subba Reddy Oota', 'Mounika Marreddy'] | 2022-05-02 | null | null | null | null | ['graph-reconstruction', 'xlm-r'] | ['graphs', 'natural-language-processing'] | [ 6.78367242e-02 1.02781951e-01 6.31676316e-02 -3.11894089e-01
-7.27874279e-01 -6.48410559e-01 3.65912557e-01 2.96661407e-01
-4.00097817e-01 3.19380224e-01 3.72783840e-01 -6.91282034e-01
2.90213168e-01 -6.60569310e-01 -4.87420857e-01 -4.90940988e-01
7.37158209e-02 4.66021955e-01 -4.31325048e-01 -5.33378363... | [10.78431224822998, 9.555343627929688] |
879c5240-8904-4867-b653-7d9576d1da3c | the-kriston-ai-system-for-the-voxceleb | 2209.11433 | null | https://arxiv.org/abs/2209.11433v1 | https://arxiv.org/pdf/2209.11433v1.pdf | The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022 | This technical report describes our system for track 1, 2 and 4 of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). By combining several ResNet variants, our submission for track 1 attained a minDCF of 0:090 with EER 1:401%. By further incorporating three fine-tuned pre-trained models, our submission for tr... | ['Haizhou Li', 'Ximin Li', 'Zhijian Ye', 'Guoqiang Hong', 'Qutang Cai'] | 2022-09-23 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-2.56358415e-01 4.11242068e-01 5.96687123e-02 -2.98531890e-01
-1.26354909e+00 -3.23312283e-01 7.73223877e-01 -5.00369072e-02
-5.13745904e-01 2.61287719e-01 5.43815613e-01 -2.39405766e-01
3.25389892e-01 3.75852846e-02 -1.23461351e-01 -6.43021703e-01
-2.62914687e-01 1.48993477e-01 1.99507669e-01 1.23263158... | [14.443395614624023, 6.025880813598633] |
5a4fa934-d0ac-479b-bc15-a6ca3a24299a | introduction-to-core-sets-an-updated-survey | 2011.09384 | null | https://arxiv.org/abs/2011.09384v1 | https://arxiv.org/pdf/2011.09384v1.pdf | Introduction to Core-sets: an Updated Survey | In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal i... | ['Dan Feldman'] | 2020-11-18 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-1.26188472e-01 -2.49088630e-01 -2.48598486e-01 -4.48021829e-01
-7.94200420e-01 -6.24836862e-01 -3.64645422e-01 8.99642766e-01
-5.20583570e-01 3.50312233e-01 -2.63467789e-01 6.93463087e-02
-7.90043831e-01 -1.18025076e+00 -9.83303428e-01 -6.08223915e-01
-5.96268177e-01 1.22428060e+00 2.07422987e-01 -1.58933043... | [6.6469502449035645, 4.949954032897949] |
b4f4fbad-950c-43d2-8fa4-3c97afbcd508 | parameter-efficient-deep-probabilistic | 2112.02905 | null | https://arxiv.org/abs/2112.02905v2 | https://arxiv.org/pdf/2112.02905v2.pdf | Parameter Efficient Deep Probabilistic Forecasting | Probabilistic time series forecasting is crucial in many application domains such as retail, ecommerce, finance, or biology. With the increasing availability of large volumes of data, a number of neural architectures have been proposed for this problem. In particular, Transformer-based methods achieve state-of-the-art ... | ['Maarten de Rijke', 'Sebastian Schelter', 'Olivier Sprangers'] | 2021-12-06 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-2.39372283e-01 -2.98737556e-01 8.54955167e-02 -4.30515736e-01
-7.44057059e-01 -5.44833839e-01 7.97769129e-01 -1.32548865e-02
-2.72601336e-01 5.40486753e-01 7.67291561e-02 -5.30900955e-01
-3.88940573e-01 -8.20557356e-01 -7.68210530e-01 -9.06181335e-01
-4.79219824e-01 2.58887112e-01 2.77209729e-01 -1.58070147... | [6.970136642456055, 3.0271527767181396] |
99adfa11-195c-482e-b01b-be4a371cce66 | points2vec-unsupervised-object-level-feature | 2102.04136 | null | https://arxiv.org/abs/2102.04136v1 | https://arxiv.org/pdf/2102.04136v1.pdf | Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds | Unsupervised representation learning techniques, such as learning word embeddings, have had a significant impact on the field of natural language processing. Similar representation learning techniques have not yet become commonplace in the context of 3D vision. This, despite the fact that the physical 3D spaces have a ... | ['Roland Siegwart', 'Julian Förster', 'Kenneth Blomqvist', 'Joël Bachmann'] | 2021-02-08 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-7.22633069e-03 9.83834341e-02 -1.99572202e-02 -5.53669572e-01
-1.06925227e-01 -6.30737126e-01 8.40034306e-01 8.93406332e-01
-3.82400513e-01 -5.12055233e-02 6.31490707e-01 -2.58096069e-01
-1.11282200e-01 -9.32908535e-01 -4.94279206e-01 -4.11229730e-01
-2.95477986e-01 4.26968366e-01 2.08770961e-01 -1.13852412... | [10.321168899536133, 2.3803248405456543] |
8fff9f12-ac4f-43b9-8614-b9c4764a292d | mia-cov19d-covid-19-detection-through-3-d | 2106.07524 | null | https://arxiv.org/abs/2106.07524v2 | https://arxiv.org/pdf/2106.07524v2.pdf | MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis | Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are... | ['Stefanos Kollias', 'Levon Soukissian', 'Anastasios Arsenos', 'Dimitrios Kollias'] | 2021-06-14 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.54774064e-01 -2.67307460e-01 -9.29984376e-02 -4.57860053e-01
-6.87931478e-01 -1.31279781e-01 5.97217791e-02 3.83625567e-01
-5.44866621e-01 6.34415030e-01 7.86170065e-02 -7.26100028e-01
-3.24543118e-01 -8.90915275e-01 -2.65286952e-01 -6.44015968e-01
-2.65520841e-01 1.28091633e+00 1.64441437e-01 2.63847142... | [15.362744331359863, -1.876570701599121] |
dc2d8a21-4a35-48d0-b663-15b6ec82819d | towards-unsupervised-speech-recognition-and | 1910.12729 | null | https://arxiv.org/abs/1910.12729v2 | https://arxiv.org/pdf/1910.12729v2.pdf | Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning | In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very close to phoneme sequences of speech utterances. This is achieved by proper temporal segmentation to make the representations phoneme-synchro... | ['Lin-shan Lee', 'Hung-Yi Lee', 'Tao Tu', 'Alexander H. Liu'] | 2019-10-28 | null | null | null | null | ['unsupervised-speech-recognition'] | ['speech'] | [ 2.10003942e-01 5.83831906e-01 -5.78095131e-02 -6.22113705e-01
-1.07760692e+00 -8.17712188e-01 4.75318253e-01 -2.66904272e-02
-1.15462482e-01 5.98214626e-01 6.03042901e-01 -4.16464716e-01
1.75568268e-01 -4.63910103e-01 -5.03240585e-01 -4.17877585e-01
-3.45452093e-02 7.64136672e-01 3.99930403e-02 -2.16719374... | [14.629858016967773, 6.644370079040527] |
dac530a2-805f-4833-90ef-dc70b9710e23 | knowledge-acquisition-and-completion-for-long | 2301.06834 | null | https://arxiv.org/abs/2301.06834v1 | https://arxiv.org/pdf/2301.06834v1.pdf | Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions using Knowledge Graph Embedding | In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users and robots. With the existing HRI pipelines, users can teach the robots some concepts to increase their knowledge base. Unfortunately, the d... | ['D. Nardi', 'V. Suriani', 'F. Argenziano', 'E. Bartoli'] | 2023-01-17 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.18519464e-01 5.86113453e-01 1.53895840e-01 -4.04069424e-01
-8.65616743e-03 -5.99470317e-01 4.69620615e-01 6.57893181e-01
-4.57911819e-01 6.24780059e-01 6.12725616e-02 3.15251164e-02
-1.76369205e-01 -8.59202802e-01 -8.98779929e-01 -6.04868717e-02
-3.91103655e-01 8.69795978e-01 4.91410255e-01 -5.59845924... | [4.517464637756348, 0.8066277503967285] |
7cc9d6e6-a012-4fb7-a6c1-ed6ca58b898c | reader-aware-multi-document-summarization-via | 1504.07324 | null | http://arxiv.org/abs/1504.07324v1 | http://arxiv.org/pdf/1504.07324v1.pdf | Reader-Aware Multi-Document Summarization via Sparse Coding | We propose a new MDS paradigm called reader-aware multi-document
summarization (RA-MDS). Specifically, a set of reader comments associated with
the news reports are also collected. The generated summaries from the reports
for the event should be salient according to not only the reports but also the
reader comments. To... | ['Piji Li', 'Hang Li', 'Yi Liao', 'Wai Lam', 'Lidong Bing'] | 2015-04-28 | null | null | null | null | ['reader-aware-summarization'] | ['natural-language-processing'] | [ 3.17650586e-01 2.08564833e-01 -1.52347043e-01 -2.57557988e-01
-1.12832749e+00 -4.41959113e-01 7.28349805e-01 7.64503419e-01
-2.31123626e-01 7.36181855e-01 1.45087016e+00 3.19756150e-01
-2.88434267e-01 -7.14539349e-01 -3.48981410e-01 -3.90907764e-01
3.12825620e-01 2.71134347e-01 2.01849323e-02 -3.54816407... | [12.585785865783691, 9.5249662399292] |
4653c094-147f-4ffe-95b5-39b4f7d00661 | hipool-modeling-long-documents-using-graph | 2305.03319 | null | https://arxiv.org/abs/2305.03319v2 | https://arxiv.org/pdf/2305.03319v2.pdf | HiPool: Modeling Long Documents Using Graph Neural Networks | Encoding long sequences in Natural Language Processing (NLP) is a challenging problem. Though recent pretraining language models achieve satisfying performances in many NLP tasks, they are still restricted by a pre-defined maximum length, making them challenging to be extended to longer sequences. So some recent works ... | ['Rex Ying', 'Dragomir Radev', 'Aosong Feng', 'Irene Li'] | 2023-05-05 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 2.94490904e-01 -1.70215949e-01 -5.22039115e-01 -3.85211319e-01
-9.64966893e-01 -6.89086199e-01 3.41772079e-01 5.24076462e-01
-6.83855891e-01 7.00311720e-01 4.89947975e-01 -5.11668563e-01
3.13965976e-01 -5.17087162e-01 -8.01877975e-01 -4.26289558e-01
-3.06431532e-01 3.40867490e-01 4.68794554e-01 -1.36352107... | [10.981687545776367, 8.614027976989746] |
8451b021-eed5-431d-b82f-758dc6baeca3 | codim-learning-with-noisy-labels-via | 2111.11652 | null | https://arxiv.org/abs/2111.11652v1 | https://arxiv.org/pdf/2111.11652v1.pdf | CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning | Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strat... | ['Xiao Han', 'Dimitris Samaras', 'Wei Yang', 'Junzhou Huang', 'Tian Shen', 'Kaiwen Xiao', 'Zixuan Liu', 'Xin Zhang'] | 2021-11-23 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 4.00321960e-01 1.49617836e-01 -3.39956760e-01 -6.28281593e-01
-1.33734667e+00 -7.52304375e-01 7.16598332e-01 2.98516899e-01
-4.73417610e-01 6.14588678e-01 1.40056051e-02 2.22567972e-02
-1.82212874e-01 -1.48812458e-01 -3.64739746e-01 -7.85521328e-01
3.78907546e-02 6.55648947e-01 9.08612534e-02 2.04326794... | [9.459659576416016, 3.9394540786743164] |
eb6a2827-e10a-44d7-8828-656fb976119d | clustering-by-maximizing-mutual-information | 2107.11635 | null | https://arxiv.org/abs/2107.11635v1 | https://arxiv.org/pdf/2107.11635v1.pdf | Clustering by Maximizing Mutual Information Across Views | We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method consists of two heads that share the same backbone network - a "representation learning" head and a "clustering" head. The "representation learning" head captures fine-grained patterns of objects... | ['Svetha Venkatesh', 'Truyen Tran', 'Kien Do'] | 2021-07-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Do_Clustering_by_Maximizing_Mutual_Information_Across_Views_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Do_Clustering_by_Maximizing_Mutual_Information_Across_Views_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-clustering'] | ['computer-vision'] | [-2.57876694e-01 -5.28299846e-02 -3.51382233e-02 -6.90228224e-01
-1.09022951e+00 -1.88224062e-01 3.42446506e-01 1.42398998e-01
-5.61759233e-01 1.61956564e-01 -7.23420316e-03 8.78135338e-02
1.38071701e-01 -3.95058125e-01 -9.43913162e-01 -1.02161300e+00
-2.18592778e-01 5.25295556e-01 2.69142121e-01 1.92882180... | [9.261744499206543, 3.137939453125] |
474399f8-2634-4acf-9a35-6579050dc4de | albmore-a-corpus-of-movie-reviews-for | 2306.08526 | null | https://arxiv.org/abs/2306.08526v1 | https://arxiv.org/pdf/2306.08526v1.pdf | AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian | Lack of available resources such as text corpora for low-resource languages seriously hinders research on natural language processing and computational linguistics. This paper presents AlbMoRe, a corpus of 800 sentiment annotated movie reviews in Albanian. Each text is labeled as positive or negative and can be used fo... | ['Erion Çano'] | 2023-06-14 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-5.07563762e-02 8.64642486e-02 -6.94227815e-01 -9.77628946e-01
-6.48498893e-01 -6.16871476e-01 5.52459180e-01 6.90075696e-01
-8.80022109e-01 6.74643874e-01 5.01752853e-01 -4.56354886e-01
6.35046482e-01 -3.20372820e-01 -1.48626819e-01 -1.65049270e-01
2.21581217e-02 3.54791194e-01 -3.08502704e-01 -8.21480453... | [11.206375122070312, 6.872526168823242] |
0c8a81bd-fd6e-4620-949f-0191183f77a5 | a-novel-blaschke-unwinding-adaptive-fourier | 1803.06441 | null | http://arxiv.org/abs/1803.06441v1 | http://arxiv.org/pdf/1803.06441v1.pdf | A Novel Blaschke Unwinding Adaptive Fourier Decomposition based Signal Compression Algorithm with Application on ECG Signals | This paper presents a novel signal compression algorithm based on the
Blaschke unwinding adaptive Fourier decomposition (AFD). The Blaschke unwinding
AFD is a newly developed signal decomposition theory. It utilizes the
Nevanlinna factorization and the maximal selection principle in each
decomposition step, and achieve... | ['Hau-Tieng Wu', 'Liming Zhang', 'Chunyu Tan'] | 2018-03-17 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.87436897e-01 -3.18511486e-01 -1.51495054e-01 2.51171850e-02
-5.07969379e-01 -2.05036610e-01 -3.40859443e-02 2.87745029e-01
-2.84416914e-01 8.15707624e-01 2.65023440e-01 -2.46518880e-01
-6.93752468e-01 -2.63903141e-01 -2.07646787e-02 -8.92435014e-01
-3.43441457e-01 6.62311018e-02 -2.43693292e-01 -4.26823609... | [14.20455265045166, 3.225172996520996] |
6f73aacf-dab5-40fa-a0fb-f5adca378f1f | distilling-knowledge-from-deep-networks-with | 1512.03542 | null | http://arxiv.org/abs/1512.03542v1 | http://arxiv.org/pdf/1512.03542v1.pdf | Distilling Knowledge from Deep Networks with Applications to Healthcare Domain | Exponential growth in Electronic Healthcare Records (EHR) has resulted in new
opportunities and urgent needs for discovery of meaningful data-driven
representations and patterns of diseases in Computational Phenotyping research.
Deep Learning models have shown superior performance for robust prediction in
computational... | ['Sanjay Purushotham', 'Zhengping Che', 'Yan Liu', 'Robinder Khemani'] | 2015-12-11 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [ 2.50635684e-01 2.48222932e-01 2.80568246e-02 -8.50016832e-01
-5.64240873e-01 6.47310093e-02 -1.76905259e-01 3.37411761e-01
1.70869097e-01 7.90564358e-01 3.90127718e-01 -4.12123233e-01
-6.05899513e-01 -5.47103822e-01 -6.02014244e-01 -7.19419003e-01
-3.43827069e-01 8.67212057e-01 -7.71796882e-01 1.62184075... | [7.930600166320801, 6.307981014251709] |
b895801f-15c1-4745-bffa-953c40c5b50a | turing-at-semeval-2017-task-8-sequential | 1704.07221 | null | http://arxiv.org/abs/1704.07221v1 | http://arxiv.org/pdf/1704.07221v1.pdf | Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM | This paper describes team Turing's submission to SemEval 2017 RumourEval:
Determining rumour veracity and support for rumours (SemEval 2017 Task 8,
Subtask A). Subtask A addresses the challenge of rumour stance classification,
which involves identifying the attitude of Twitter users towards the
truthfulness of the rumo... | ['Maria Liakata', 'Isabelle Augenstein', 'Elena Kochkina'] | 2017-04-24 | turing-at-semeval-2017-task-8-sequential-1 | https://aclanthology.org/S17-2083 | https://aclanthology.org/S17-2083.pdf | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-2.47471675e-01 3.41291487e-01 -1.22026302e-01 -3.50927591e-01
-3.01248610e-01 -1.44236535e-01 1.27315795e+00 4.59551424e-01
-1.05106227e-01 8.47607374e-01 7.94469535e-01 -5.80049455e-01
4.15707737e-01 -6.25832856e-01 -4.66328084e-01 -3.80655676e-01
-7.35717043e-02 6.36252940e-01 7.35821277e-02 -8.18760276... | [8.219131469726562, 10.11571979522705] |
b58be912-63c0-42ec-96d0-c0843a196fc0 | discovering-dynamic-causal-space-for-dag | 2306.02822 | null | https://arxiv.org/abs/2306.02822v1 | https://arxiv.org/pdf/2306.02822v1.pdf | Discovering Dynamic Causal Space for DAG Structure Learning | Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning. The recent invention of differentiable score-based DAG learners is a crucial enabler, which reframes the combinatorial optimization pr... | ['Tat-Seng Chua', 'Yueqi Duan', 'Xiang Wang', 'An Zhang', 'Wenchang Ma', 'Fangfu Liu'] | 2023-06-05 | null | null | null | null | ['causal-discovery', 'combinatorial-optimization'] | ['knowledge-base', 'methodology'] | [ 7.26402923e-02 2.32044056e-01 -4.54371572e-01 -2.68591523e-01
-2.94372648e-01 -7.67619967e-01 8.06868196e-01 4.54993755e-01
1.60105169e-01 8.68039370e-01 3.51587474e-01 -7.08220065e-01
-9.65962648e-01 -9.83598292e-01 -8.65568042e-01 -6.85838044e-01
-6.37309849e-01 2.49280557e-01 1.44941643e-01 6.77846074... | [7.776580333709717, 5.3696513175964355] |
c630b647-8a6e-49ca-a18a-03fb6eb39791 | icnn-input-conditioned-feature-representation | null | null | https://openreview.net/forum?id=SJecKyrKPH | https://openreview.net/pdf?id=SJecKyrKPH | ICNN: INPUT-CONDITIONED FEATURE REPRESENTATION LEARNING FOR TRANSFORMATION-INVARIANT NEURAL NETWORK | We propose a novel framework, ICNN, which combines the input-conditioned filter generation module and a decoder based network to incorporate contextual information present in images into Convolutional Neural Networks (CNNs). In contrast to traditional CNNs, we do not employ the same set of learned convolution filters f... | ['Abhay Kumar', 'Chirag Singh', 'Suraj Tripathi'] | 2019-09-25 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 4.37276751e-01 -6.40166178e-03 2.59126097e-01 -4.35275018e-01
-4.04597282e-01 -5.27836382e-01 6.45855010e-01 -3.96461248e-01
-9.17986810e-01 5.76088309e-01 3.11879236e-02 -1.56724870e-01
-2.00711221e-01 -8.42117071e-01 -1.17974079e+00 -7.00364113e-01
3.49958688e-01 -1.54862143e-02 2.16641963e-01 -1.64772183... | [9.119053840637207, 2.1820895671844482] |
cb68bc2a-b1a6-4752-a0f8-87e514019928 | main-multi-attention-instance-network-for | 1904.05847 | null | http://arxiv.org/abs/1904.05847v1 | http://arxiv.org/pdf/1904.05847v1.pdf | MAIN: Multi-Attention Instance Network for Video Segmentation | Instance-level video segmentation requires a solid integration of spatial and
temporal information. However, current methods rely mostly on domain-specific
information (online learning) to produce accurate instance-level segmentations.
We propose a novel approach that relies exclusively on the integration of
generic sp... | ['Bernard Ghanem', 'Maria A. Bravo', 'Thomas Brox', 'Pablo Arbelaez', 'Juan Leon Alcazar', 'Guillaume Jeanneret', 'Ali K. Thabet'] | 2019-04-11 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 4.34158325e-01 -1.78675145e-01 -3.85754138e-01 -4.55445647e-01
-1.09980810e+00 -6.27248168e-01 3.73465568e-01 1.14943244e-01
-6.67729437e-01 7.06232071e-01 -3.83441806e-01 -1.36195883e-01
-1.12684950e-01 -3.72988433e-01 -8.83102000e-01 -4.95677412e-01
-1.55286521e-01 4.79670763e-01 6.90280974e-01 1.13609672... | [9.116019248962402, -0.03024616837501526] |
65bd9b46-7022-4bff-80c3-2b6384b70b48 | instance-smoothed-contrastive-learning-for | 2305.07424 | null | https://arxiv.org/abs/2305.07424v2 | https://arxiv.org/pdf/2305.07424v2.pdf | Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding | Contrastive learning-based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings. However, in previous studies, each embedding used for contrastive learning only derived from one sentence instance, and we call these embeddings instance-level embed... | ['Yue Zhang', 'Zhenzhong Lan', 'Junlei Zhang', 'Hongliang He'] | 2023-05-12 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.06146231e-01 3.14100645e-02 1.01145342e-01 -4.29185569e-01
-6.73298717e-01 -3.19547802e-01 7.77069628e-01 7.37107754e-01
-8.33062530e-01 5.13858616e-01 3.89336854e-01 -5.49764521e-02
-1.32468045e-01 -6.41945422e-01 -5.58817506e-01 -5.59057713e-01
-7.77602717e-02 2.17529297e-01 4.54471767e-01 -4.08521354... | [10.885794639587402, 8.646835327148438] |
6a31ad63-aad0-4d00-bccd-813189198da4 | fight-fire-with-fire-reversing-skin | 2208.10373 | null | https://arxiv.org/abs/2208.10373v2 | https://arxiv.org/pdf/2208.10373v2.pdf | Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism | Reliable skin cancer diagnosis models play an essential role in early screening and medical intervention. Prevailing computer-aided skin cancer classification systems employ deep learning approaches. However, recent studies reveal their extreme vulnerability to adversarial attacks -- often imperceptible perturbations t... | ['Zhiqi Shen', 'Yuan Li', 'Yongwei Wang'] | 2022-08-22 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 7.53150523e-01 -1.46026582e-01 7.04735285e-03 -1.54547710e-02
-9.74027574e-01 -9.25089359e-01 4.85937983e-01 -9.06962603e-02
-3.64237726e-01 5.46149492e-01 4.44378033e-02 -2.73137212e-01
1.34024262e-01 -8.06671083e-01 -5.09349704e-01 -1.34259427e+00
1.34378031e-01 -4.77299541e-01 1.94663510e-01 -4.64986920... | [5.505206108093262, 7.94072961807251] |
98dd6362-cd27-45c2-b31d-4b9bc91d19ce | conditional-support-alignment-for-domain | 2305.18458 | null | https://arxiv.org/abs/2305.18458v1 | https://arxiv.org/pdf/2305.18458v1.pdf | Conditional Support Alignment for Domain Adaptation with Label Shift | Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-i... | ['Toan Tran', 'Tuan-Duy H. Nguyen', 'Anh Tong', 'Lam Tran', 'Anh T Nguyen'] | 2023-05-29 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 6.47300601e-01 1.30491123e-01 -2.70553201e-01 -5.95281303e-01
-1.02918053e+00 -4.67606455e-01 7.62553990e-01 1.18402079e-01
-3.91372979e-01 9.57918823e-01 3.05063035e-02 -4.41873185e-02
-2.16394350e-01 -6.86719239e-01 -8.23543191e-01 -1.08546209e+00
3.43425751e-01 4.99805093e-01 1.22330382e-01 3.96129265... | [10.367227554321289, 3.156066417694092] |
6e975359-76d3-43a7-bfd9-a7911c45782c | gae-isumm-unsupervised-graph-based | 2212.12937 | null | https://arxiv.org/abs/2212.12937v1 | https://arxiv.org/pdf/2212.12937v1.pdf | GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages | Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian la... | ['Radhika Mamidi', 'Subba Reddy Oota', 'Mounika Marreddy', 'Anudeep Ch', 'Lakshmi Sireesha Vakada'] | 2022-12-25 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 2.14290693e-01 2.90591598e-01 -2.06315249e-01 -2.00721145e-01
-9.37523484e-01 -4.49900031e-01 6.04285598e-01 5.11009753e-01
-1.86008528e-01 8.91462028e-01 1.09877670e+00 -3.12294215e-01
9.27771255e-02 -6.50862455e-01 -6.34967387e-01 -2.57248312e-01
4.49516512e-02 4.39140588e-01 4.28443588e-02 -3.57916206... | [12.529449462890625, 9.516813278198242] |
d7d56e45-7efe-41ae-883b-49a36ec0ace2 | joint-learning-for-aspect-and-polarity | 2201.06313 | null | https://arxiv.org/abs/2201.06313v3 | https://arxiv.org/pdf/2201.06313v3.pdf | A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews | Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most p... | ['Sepideh Saeedi Majd', 'Fatemeh Sadat Masoumi', 'Milad Vazan'] | 2022-01-17 | null | null | null | null | ['aspect-based-sentiment-analysis', 'aspect-category-polarity', 'persian-sentiment-anlysis', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.35482559e-02 -2.16560245e-01 3.28587629e-02 -4.80263174e-01
-4.18551534e-01 -5.67349911e-01 5.17667472e-01 6.08450472e-01
-5.40404618e-01 7.14350224e-01 1.23027347e-01 -1.62558645e-01
-3.61192636e-02 -9.44732487e-01 -2.49312133e-01 -5.74739456e-01
2.69200802e-01 3.44895154e-01 6.79396987e-02 -5.45150280... | [11.163753509521484, 6.740555286407471] |
b23b9ec3-bf3f-4990-b0d8-e1462f70dc2c | invaastcluster-on-applying-invariant-based | 2206.14175 | null | https://arxiv.org/abs/2206.14175v2 | https://arxiv.org/pdf/2206.14175v2.pdf | InvAASTCluster: On Applying Invariant-Based Program Clustering to Introductory Programming Assignments | Due to the vast number of students enrolled in Massive Open Online Courses (MOOCs), there has been an increasing number of automated program repair techniques focused on introductory programming assignments (IPAs). Such state-of-the-art techniques use program clustering to take advantage of previous correct student imp... | ['Vasco Manquinho', 'Mikoláš Janota', 'Pedro Orvalho'] | 2022-06-28 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-4.29372162e-01 -1.38208956e-01 2.49178782e-02 -3.16922605e-01
-3.95722598e-01 -9.04096723e-01 1.17998406e-01 1.03603852e+00
-2.81381235e-02 1.17877983e-01 -2.66992569e-01 -6.30681038e-01
-2.48613313e-01 -1.07778203e+00 -7.89641082e-01 -1.73416972e-01
1.69583693e-01 2.31937438e-01 7.25254178e-01 -2.23800465... | [7.861756324768066, 7.700655937194824] |
cf3f70c5-3f2c-4463-b27d-38a248dc246a | on-attention-modules-for-audio-visual | 1812.06071 | null | http://arxiv.org/abs/1812.06071v1 | http://arxiv.org/pdf/1812.06071v1.pdf | On Attention Modules for Audio-Visual Synchronization | With the development of media and networking technologies, multimedia
applications ranging from feature presentation in a cinema setting to video on
demand to interactive video conferencing are in great demand. Good
synchronization between audio and video modalities is a key factor towards
defining the quality of a mul... | ['Shervin Ardeshir', 'Naji Khosravan', 'Rohit Puri'] | 2018-12-14 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 1.68535545e-01 -4.37533528e-01 4.31424826e-02 1.94859337e-02
-6.66096866e-01 -6.96317732e-01 5.85825503e-01 6.59677505e-01
-3.01067829e-01 9.84874219e-02 1.17305666e-01 8.53453670e-03
-1.15341626e-01 -3.29493642e-01 -5.72440028e-01 -7.08842158e-01
-2.99645245e-01 -1.19180650e-01 4.21213597e-01 -1.24603868... | [14.59400463104248, 5.027501106262207] |
4704f449-830f-4a2a-97cd-26478ef39827 | event-causality-identification-via-derivative | null | null | https://aclanthology.org/2022.coling-1.200 | https://aclanthology.org/2022.coling-1.200.pdf | Event Causality Identification via Derivative Prompt Joint Learning | This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence. Regarding event causality identification as a supervised classification task, most existing methods suffer from the problem of insufficient annotated data. In this paper, we propose a n... | ['Guilin Qi', 'Tongtong Wu', 'Heng Zhou', 'Shirong Shen'] | null | null | null | null | coling-2022-10 | ['event-causality-identification'] | ['natural-language-processing'] | [ 2.14523330e-01 1.08221039e-01 -5.83728433e-01 -3.86537552e-01
-7.49889672e-01 -4.22194093e-01 8.62934530e-01 5.96593142e-01
-2.81037331e-01 1.17021823e+00 7.81893909e-01 -3.97639722e-01
-2.36527190e-01 -6.32439554e-01 -6.20951116e-01 -3.03325772e-01
-3.18194449e-01 5.55090234e-02 2.94151604e-01 2.98192829... | [9.073692321777344, 9.11007308959961] |
d7d2d099-0fd0-4e25-be2d-17411ce14044 | deep-neural-network-for-blind-visual-quality | 2206.04363 | null | https://arxiv.org/abs/2206.04363v1 | https://arxiv.org/pdf/2206.04363v1.pdf | Deep Neural Network for Blind Visual Quality Assessment of 4K Content | The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement of spatial resolution. However, existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents due to the expanded resolution and specific distortions. In this paper,... | ['Guangtao Zhai', 'Tao Wang', 'ZiCheng Zhang', 'Qiyuan Wang', 'Jun He', 'Quan Zhou', 'Wenhan Zhu', 'Xiongkuo Min', 'Wei Sun', 'Wei Lu'] | 2022-06-09 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-1.67882010e-01 -5.96043348e-01 1.77953586e-01 -2.93056101e-01
-1.07367921e+00 -7.63209388e-02 1.84708774e-01 9.86896008e-02
-2.51263976e-01 4.87504989e-01 3.72488379e-01 8.68192539e-02
-6.64517164e-01 -9.12220001e-01 -6.07629120e-01 -9.22864139e-01
-1.95745558e-01 -4.44041878e-01 2.00058937e-01 -4.29000370... | [11.758156776428223, -1.9264256954193115] |
123e3cca-0b78-4bee-a3cd-ae3cad995ea3 | eye-movements-biometrics-a-bibliometric | 2006.01310 | null | https://arxiv.org/abs/2006.01310v1 | https://arxiv.org/pdf/2006.01310v1.pdf | Eye Movements Biometrics: A Bibliometric Analysis from 2004 to 2019 | Person identification based on eye movements is getting more and more attention, as it is anti-spoofing resistant and can be useful for continuous authentication. Therefore, it is noteworthy for researchers to know who and what is relevant in the field, including authors, journals, conferences, and institutions. This p... | ['Karin Satie Komati', 'Jefferson Oliveira Andrade', 'Antonio Ricardo Alexandre Brasil'] | 2020-06-01 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-3.55009586e-01 -4.98787612e-01 -6.46744132e-01 4.49682176e-01
2.32544124e-01 -4.31446731e-01 3.81360114e-01 4.83738750e-01
-8.23280334e-01 7.71477759e-01 1.69391721e-01 -4.34956104e-01
-2.58018792e-01 -5.24143219e-01 -1.35911509e-01 -3.19383681e-01
2.97780573e-01 -2.10032225e-01 2.18323022e-01 4.08715680... | [13.34234619140625, 0.8238323926925659] |
31e16417-02ff-4e31-b95a-af8960ae63be | masked-contrastive-pre-training-for-efficient | 2212.00986 | null | https://arxiv.org/abs/2212.00986v2 | https://arxiv.org/pdf/2212.00986v2.pdf | Masked Contrastive Pre-Training for Efficient Video-Text Retrieval | We present a simple yet effective end-to-end Video-language Pre-training (VidLP) framework, Masked Contrastive Video-language Pretraining (MAC), for video-text retrieval tasks. Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-tr... | ['Si Liu', 'Jinqiao Wang', 'Yousong Zhu', 'Xiaobo Li', 'Wenyu Sun', 'Shuwen Xiao', 'Yue Liao', 'Biaolong Chen', 'Fangxun Shu'] | 2022-12-02 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 3.22082102e-01 -4.39520925e-01 -6.29363179e-01 -3.08811069e-01
-1.16102695e+00 -3.65015715e-01 6.73261046e-01 -3.85880053e-01
-6.63475633e-01 1.50065646e-01 4.53349978e-01 -1.95459783e-01
1.81488827e-01 -1.42467812e-01 -9.05005276e-01 -4.81592119e-01
9.66494456e-02 2.80546516e-01 -2.46365368e-02 1.05540030... | [10.300466537475586, 0.9696208834648132] |
15b30588-84db-4257-b0b9-b4d1d4c4e451 | spoof-face-detection-via-semi-supervised | 2005.10999 | null | https://arxiv.org/abs/2005.10999v1 | https://arxiv.org/pdf/2005.10999v1.pdf | Spoof Face Detection Via Semi-Supervised Adversarial Training | Face spoofing causes severe security threats in face recognition systems. Previous anti-spoofing works focused on supervised techniques, typically with either binary or auxiliary supervision. Most of them suffer from limited robustness and generalization, especially in the cross-dataset setting. In this paper, we propo... | ['Xuequan Lu', 'Wang Yuan', 'Chengwei Chen', 'Lizhuang Ma'] | 2020-05-22 | null | null | null | null | ['face-presentation-attack-detection', 'gan-image-forensics'] | ['computer-vision', 'computer-vision'] | [ 5.52106321e-01 -1.56313162e-02 -2.25260571e-01 -2.16920510e-01
-9.59891677e-02 -6.52043760e-01 6.73193872e-01 -4.67598081e-01
-3.94038297e-02 4.11760479e-01 -1.25910088e-01 -2.89598852e-01
2.27881387e-01 -8.26734424e-01 -7.68715024e-01 -1.02770948e+00
-2.15077907e-01 1.82205960e-01 3.04274447e-02 -1.10217698... | [13.039645195007324, 1.1734243631362915] |
1f3fbe2d-fb73-4466-9374-6b3bd03cc421 | neural-face-editing-with-intrinsic-image | 1704.04131 | null | http://arxiv.org/abs/1704.04131v1 | http://arxiv.org/pdf/1704.04131v1.pdf | Neural Face Editing with Intrinsic Image Disentangling | Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled r... | ['Eli Shechtman', 'Sunil Hadap', 'Ersin Yumer', 'Kalyan Sunkavalli', 'Zhixin Shu', 'Dimitris Samaras'] | 2017-04-13 | neural-face-editing-with-intrinsic-image-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Shu_Neural_Face_Editing_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Shu_Neural_Face_Editing_CVPR_2017_paper.pdf | cvpr-2017-7 | ['facial-editing'] | ['computer-vision'] | [ 5.84918082e-01 4.12596226e-01 4.12285239e-01 -5.94012678e-01
-5.19583642e-01 -6.94788694e-01 6.00100398e-01 -6.33021593e-01
-6.97583109e-02 6.69776857e-01 -4.18100208e-02 2.63712313e-02
5.53464442e-02 -7.04764009e-01 -8.99309456e-01 -8.43704998e-01
7.98254982e-02 4.02174085e-01 -3.89401793e-01 -2.74516940... | [12.617537498474121, -0.3037738800048828] |
ca8d7db6-de26-4798-a330-1f1d76e0acda | improving-video-text-retrieval-by-multi | 2109.04290 | null | https://arxiv.org/abs/2109.04290v3 | https://arxiv.org/pdf/2109.04290v3.pdf | Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss | Employing large-scale pre-trained model CLIP to conduct video-text retrieval task (VTR) has become a new trend, which exceeds previous VTR methods. Though, due to the heterogeneity of structures and contents between video and text, previous CLIP-based models are prone to overfitting in the training phase, resulting in ... | ['Dong Shen', 'Fan Yang', 'Xiangyu Wu', 'Hezheng Lin', 'Xing Cheng'] | 2021-09-09 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.25461131e-01 -6.22507572e-01 -1.79200828e-01 -1.05276167e-01
-1.12496352e+00 -3.32293868e-01 7.97524333e-01 -8.40762928e-02
-4.71054614e-01 2.30366990e-01 2.36601070e-01 6.85337465e-03
-1.49852872e-01 -2.26461962e-01 -5.65832436e-01 -6.50600970e-01
2.93095708e-01 4.61194605e-01 2.44533852e-01 -2.70245701... | [10.324411392211914, 0.9434652328491211] |
229aab39-6405-4b69-b18b-96d3ccf97d17 | detecting-and-tracking-small-and-dense-moving | 2111.12960 | null | https://arxiv.org/abs/2111.12960v1 | https://arxiv.org/pdf/2111.12960v1.pdf | Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark | Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications. However, achieving moving object detection and tracking in satellite videos remains challenging due to the insufficient appearance information of objects and lack of high-quality d... | ['Yulan Guo', 'Wei An', 'Zaiping Lin', 'Yingqian Wang', 'Feng Zhang', 'Hao liu', 'Qingyong Hu', 'Qian Yin'] | 2021-11-25 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.35702584e-02 -9.38183188e-01 -2.25328520e-01 -1.53877407e-01
-9.29822028e-01 -7.00603485e-01 3.87734026e-01 -4.17484283e-01
-4.60890651e-01 5.62232375e-01 8.62262100e-02 -8.15451145e-04
-3.73793952e-02 -7.11323977e-01 -7.53691673e-01 -1.04943168e+00
-6.21370077e-01 -9.07658637e-02 5.91529548e-01 7.74673522... | [8.912240982055664, -0.7323412895202637] |
f307281c-f376-4aab-966a-7ec815df8c12 | large-scale-mixed-bandwidth-deep-neural | 1907.04887 | null | https://arxiv.org/abs/1907.04887v1 | https://arxiv.org/pdf/1907.04887v1.pdf | Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition | In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models. Therefore mixed-bandwidth (MB) acoustic modeling has important practical values for ASR system deployment. In this paper, we extensively investigate large-scale M... | ['Wei zhang', 'Khoi-Nguyen C. Mac', 'Xiaodong Cui', 'Michael Picheny'] | 2019-07-10 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [-2.62297913e-02 -4.75398481e-01 1.33843452e-01 -4.93023664e-01
-1.10718811e+00 -6.02168776e-03 2.67396271e-01 -1.63590789e-01
-6.36168838e-01 3.36452663e-01 7.79913142e-02 -9.33310688e-01
3.31313372e-01 -4.72430944e-01 -5.46145380e-01 -6.54457688e-01
-1.38262674e-01 4.28378940e-01 2.69048095e-01 -1.21383041... | [14.513089179992676, 6.438774108886719] |
61f98861-7854-462f-9a50-30c580efcda6 | a-robust-kernel-machine-regression-towards | 2201.05060 | null | https://arxiv.org/abs/2201.05060v1 | https://arxiv.org/pdf/2201.05060v1.pdf | A robust kernel machine regression towards biomarker selection in multi-omics datasets of osteoporosis for drug discovery | Many statistical machine approaches could ultimately highlight novel features of the etiology of complex diseases by analyzing multi-omics data. However, they are sensitive to some deviations in distribution when the observed samples are potentially contaminated with adversarial corrupted outliers (e.g., a fictional da... | ['Hong-Wen Deng', 'Hui Shen', 'Md ashad Alam'] | 2022-01-13 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 2.73739785e-01 -1.53202206e-01 -2.75329143e-01 -1.89552516e-01
-7.16351151e-01 -2.31772915e-01 4.01208460e-01 5.40822208e-01
-1.92222372e-02 1.01696670e+00 1.06118575e-01 -3.96030694e-01
-8.15496564e-01 -6.67572260e-01 -1.06895292e+00 -8.02658737e-01
-4.08133477e-01 3.77313465e-01 -3.05530697e-01 1.94180831... | [6.429522514343262, 5.523967742919922] |
3965592a-cfa4-400e-aaac-4f459ca66e82 | improving-adversarial-robustness-via-mutual | 2207.12203 | null | https://arxiv.org/abs/2207.12203v1 | https://arxiv.org/pdf/2207.12203v1.pdf | Improving Adversarial Robustness via Mutual Information Estimation | Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective ... | ['Tongliang Liu', 'Yibing Zhan', 'Xiaoyu Wang', 'Bo Han', 'Xinbo Gao', 'Nannan Wang', 'Dawei Zhou'] | 2022-07-25 | null | null | null | null | ['adversarial-defense', 'mutual-information-estimation'] | ['adversarial', 'methodology'] | [ 3.62629890e-01 4.42063510e-01 2.87380248e-01 -4.30817157e-01
-3.32305968e-01 -9.84540582e-01 6.72855139e-01 -2.52058804e-01
-4.02887553e-01 4.91334766e-01 8.36892724e-02 -2.82720868e-02
8.84554163e-02 -1.11751354e+00 -1.08663511e+00 -5.65896392e-01
1.26169622e-01 2.04886228e-01 7.63576999e-02 -2.11501583... | [5.689352512359619, 7.883220195770264] |
c22fccaa-7bf0-4978-aaed-0c598a33ae3e | del-dock-molecular-docking-enabled-modeling | 2212.00136 | null | https://arxiv.org/abs/2212.00136v2 | https://arxiv.org/pdf/2212.00136v2.pdf | DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries | DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection... | ['Theofanis Karaletsos', 'Mohammad M. Sultan', 'Benson Chen', 'Kirill Shmilovich'] | 2022-11-30 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.60328841e-01 -5.24033248e-01 -1.94027096e-01 -2.90997207e-01
-1.41871560e+00 -1.02581155e+00 4.07723159e-01 4.18220818e-01
-5.27612984e-01 1.51219308e+00 3.13217849e-01 -2.37571687e-01
-1.65946752e-01 -7.16072142e-01 -1.12239969e+00 -9.97820675e-01
8.28641355e-02 1.11302078e+00 3.95333245e-02 -1.25719711... | [4.855458736419678, 5.597195148468018] |
94dcb244-f054-4e5e-ac73-39062feba9d6 | rethinking-multi-modal-alignment-in-video | 2204.11544 | null | https://arxiv.org/abs/2204.11544v2 | https://arxiv.org/pdf/2204.11544v2.pdf | Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives | Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA).The major stumbling block to achieve this purpose is the semantic gap between language and video since they are at different levels of abstraction. Existing efforts mainly focus on designing sophisti... | ['Jun Xiao', 'Zhimeng Zhang', 'Yi Yang', 'Zhao Wang', 'Kaifeng Gao', 'Long Chen', 'Shaoning Xiao'] | 2022-04-25 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-1.50792256e-01 -3.27415287e-01 -3.18028986e-01 -3.42145860e-01
-6.85298026e-01 -4.65117395e-01 8.02245080e-01 -1.44783869e-01
-1.54288188e-01 2.34846741e-01 6.29912376e-01 -1.75261468e-01
-7.34794140e-02 -6.34154856e-01 -7.29476571e-01 -4.45081085e-01
1.43764317e-01 -5.06583899e-02 4.72166300e-01 -2.46372283... | [10.223023414611816, 0.9652472734451294] |
ac6dece1-dd46-4607-8219-f7212f0a8731 | real-time-visual-tracking-by-deep-reinforced | 1702.06291 | null | http://arxiv.org/abs/1702.06291v2 | http://arxiv.org/pdf/1702.06291v2.pdf | Real-time visual tracking by deep reinforced decision making | One of the major challenges of model-free visual tracking problem has been
the difficulty originating from the unpredictable and drastic changes in the
appearance of objects we target to track. Existing methods tackle this problem
by updating the appearance model on-line in order to adapt to the changes in
the appearan... | ['Janghoon Choi', 'Kyoung Mu Lee', 'Junseok Kwon'] | 2017-02-21 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [ 6.01823330e-02 -4.27839816e-01 -9.35395658e-02 -6.95485203e-03
-2.51615167e-01 -6.27923608e-01 4.39575106e-01 -1.66815564e-01
-6.11812532e-01 7.24204063e-01 -5.38654327e-01 9.01421010e-02
1.61778986e-01 -4.58930045e-01 -7.91860282e-01 -8.32583189e-01
1.66277677e-01 3.79059792e-01 7.80103505e-01 4.46335636... | [6.396175384521484, -2.0624725818634033] |
927888ec-ab4b-46e1-a8db-3b559dccf544 | transcg-a-large-scale-real-world-dataset-for | 2202.08471 | null | https://arxiv.org/abs/2202.08471v2 | https://arxiv.org/pdf/2202.08471v2.pdf | TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline | Transparent objects are common in our daily life and frequently handled in the automated production line. Robust vision-based robotic grasping and manipulation for these objects would be beneficial for automation. However, the majority of current grasping algorithms would fail in this case since they heavily rely on th... | ['Cewu Lu', 'Sheng Xu', 'Hao-Shu Fang', 'Hongjie Fang'] | 2022-02-17 | null | null | null | null | ['transparent-objects', 'depth-completion', 'transparent-object-depth-estimation', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'robots'] | [ 3.20025295e-01 -1.94403023e-01 2.21367687e-01 -4.44121748e-01
-2.62102932e-01 -5.12848377e-01 1.25519782e-01 -2.74787247e-01
-1.50826871e-01 3.52469265e-01 -1.33585751e-01 1.10451810e-01
-8.80730748e-02 -9.33748782e-01 -7.84344375e-01 -6.94847345e-01
2.28116978e-02 5.47675669e-01 6.45598471e-01 -2.40447938... | [5.997312545776367, -1.0513148307800293] |
75bd931c-a8d9-414c-a505-d874e3453f46 | vision-language-adaptive-mutual-decoder-for | 2209.00859 | null | https://arxiv.org/abs/2209.00859v1 | https://arxiv.org/pdf/2209.00859v1.pdf | Vision-Language Adaptive Mutual Decoder for OOV-STR | Recent works have shown huge success of deep learning models for common in vocabulary (IV) scene text recognition. However, in real-world scenarios, out-of-vocabulary (OOV) words are of great importance and SOTA recognition models usually perform poorly on OOV settings. Inspired by the intuition that the learned langua... | ['Bing Yin', 'Jiajia Wu', 'Fengli yu', 'Xuyang Zhu', 'Qiandong Yan', 'Chenyu Liu', 'Jinshui Hu'] | 2022-09-02 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.30054042e-01 -6.99974373e-02 -3.24126601e-01 -2.47507811e-01
-6.79659247e-01 -3.77200931e-01 9.64236856e-01 -1.61709622e-01
-6.22011960e-01 9.93481055e-02 4.14881319e-01 -5.93887687e-01
6.01988018e-01 -4.46856320e-01 -7.96793044e-01 -3.71885687e-01
7.01983511e-01 5.11554122e-01 2.40848124e-01 -1.36895880... | [11.529302597045898, 1.952925443649292] |
3145e8a2-b28a-40d0-bb49-250a19248a23 | emerging-properties-in-self-supervised-vision | 2104.14294 | null | https://arxiv.org/abs/2104.14294v2 | https://arxiv.org/pdf/2104.14294v2.pdf | Emerging Properties in Self-Supervised Vision Transformers | In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised methods to this architecture works particularly well, we make the following observations: first, self-sup... | ['Armand Joulin', 'Piotr Bojanowski', 'Julien Mairal', 'Hervé Jégou', 'Ishan Misra', 'Hugo Touvron', 'Mathilde Caron'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Caron_Emerging_Properties_in_Self-Supervised_Vision_Transformers_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification', 'single-object-discovery'] | ['computer-vision', 'computer-vision'] | [ 3.39823246e-01 7.18485534e-01 -1.68890238e-01 -5.39889038e-01
-3.05313051e-01 -6.66997254e-01 8.23397756e-01 -1.47078574e-01
-6.58029497e-01 5.78698695e-01 1.47772536e-01 -1.48672476e-01
-5.87256625e-02 -6.89374685e-01 -1.04657793e+00 -6.60258174e-01
9.04241577e-02 2.18065947e-01 4.50547367e-01 -2.43464649... | [9.636892318725586, 2.060929298400879] |
2eed535f-8cae-4c81-a76d-709d83451147 | high-precision-automated-reconstruction-of | null | null | https://doi.org/10.1038/s41592-018-0049-4 | https://www.biorxiv.org/content/biorxiv/early/2017/10/09/200675.full-text.pdf | High-Precision Automated Reconstruction of Neurons with Flood-filling Networks | Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We presen... | ['Jeremy Maitin-Shepard', 'Jörgen Kornfeld', 'Michał Januszewski', 'Winfried Denk', 'Art Pope', 'Viren Jain', 'Peter H. Li', 'Larry Lindsey', 'Tim Blakely', 'Mike Tyka'] | 2017-10-09 | null | null | null | nature-methods-2017-10 | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 4.00381386e-01 3.05838734e-01 7.80183434e-01 -1.98600605e-01
-4.31700379e-01 -7.53590882e-01 3.66087496e-01 2.18729988e-01
-9.74359155e-01 1.05879450e+00 -7.12675571e-01 -6.05602324e-01
3.23353037e-02 -6.02463007e-01 -7.32469916e-01 -6.82920754e-01
2.78334487e-02 9.18060958e-01 5.41305006e-01 2.49308690... | [14.2518892288208, -3.134556531906128] |
a9ef8075-6241-46e4-93b3-ccf86015558f | crosslingual-generalization-through-multitask | 2211.01786 | null | https://arxiv.org/abs/2211.01786v2 | https://arxiv.org/pdf/2211.01786v2.pdf | Crosslingual Generalization through Multitask Finetuning | Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on English data and models. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ an... | ['Colin Raffel', 'Edward Raff', 'Albert Webson', 'Zaid Alyafeai', 'Samuel Albanie', 'Khalid Almubarak', 'Alham Fikri Aji', 'Dragomir Radev', 'Xiangru Tang', 'Hailey Schoelkopf', 'Zheng-Xin Yong', 'Sheng Shen', 'M Saiful Bari', 'Teven Le Scao', 'Stella Biderman', 'Adam Roberts', 'Lintang Sutawika', 'Thomas Wang', 'Nikla... | 2022-11-03 | null | null | null | null | ['coreference-resolution', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.07267909e-02 -1.12919874e-01 -2.31484026e-01 -4.52255398e-01
-1.17352688e+00 -8.40698957e-01 7.59850383e-01 -1.94276534e-02
-1.01061225e+00 8.84591341e-01 3.47088099e-01 -8.04074764e-01
4.38752882e-02 -4.59177405e-01 -8.81057739e-01 -7.01276287e-02
2.19330013e-01 8.14227164e-01 1.80725202e-01 -6.72396779... | [11.067535400390625, 9.590680122375488] |
e63f114d-f01d-4690-90bb-697bad77a2f8 | taco-temporal-latent-action-driven | 2306.13229 | null | https://arxiv.org/abs/2306.13229v1 | https://arxiv.org/pdf/2306.13229v1.pdf | TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning | Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle. Prior works have attempted to address this challenge by creating self-supervised auxiliary tasks, aiming to enrich the agent's learned representations with control-relevant inform... | ['Furong Huang', 'Hal Daumé III', 'Huazhe Xu', 'Jieyu Zhao', 'Shuang Ma', 'Yanchao Sun', 'Xiyao Wang', 'Ruijie Zheng'] | 2023-06-22 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'continuous-control'] | ['computer-vision', 'methodology', 'playing-games'] | [ 2.74011999e-01 2.36627180e-02 -5.97806931e-01 -9.54457093e-03
-7.98806787e-01 -5.92126846e-01 1.10741723e+00 5.32485470e-02
-5.11850893e-01 9.00837958e-01 3.62692118e-01 -3.01578879e-01
3.02167907e-02 -3.44072878e-01 -7.56964862e-01 -8.30771506e-01
-4.45591748e-01 3.99001926e-01 1.33667346e-02 -1.43855065... | [4.245301246643066, 1.507858157157898] |
0e6892e1-436c-4f4b-8eb3-5a84c7814405 | faster-stochastic-first-order-method-for | 2211.12880 | null | https://arxiv.org/abs/2211.12880v1 | https://arxiv.org/pdf/2211.12880v1.pdf | Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography | In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits. It is therefore desirable to develop a stochastic first-order method, just like stochastic gradient descent for modern machine learning, to compute the maximum-likelihood estimate. To this en... | ['Yen-Huan Li', 'Hao-Chung Cheng', 'Chung-En Tsai'] | 2022-11-23 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 2.18614265e-02 2.91113257e-02 1.20206647e-01 -3.56395394e-01
-1.20099092e+00 -3.76920730e-01 2.22848818e-01 8.45718384e-02
-9.78985071e-01 9.18759823e-01 -4.93258506e-01 -7.99597442e-01
5.33418022e-02 -7.56784379e-01 -5.07137418e-01 -8.42989802e-01
-3.60141903e-01 6.93175018e-01 7.15750530e-02 -5.67648187... | [5.7243170738220215, 4.8523478507995605] |
a7518356-ac26-41a3-8b53-2b1376008e2f | very-fast-streaming-submodular-function | 2010.10059 | null | https://arxiv.org/abs/2010.10059v5 | https://arxiv.org/pdf/2010.10059v5.pdf | Very Fast Streaming Submodular Function Maximization | Data summarization has become a valuable tool in understanding even terabytes of data. Due to their compelling theoretical properties, submodular functions have been in the focus of summarization algorithms. These algorithms offer worst-case approximations guarantees to the expense of higher computation and memory requ... | ['Lukas Pfahler', 'Katharina Morik', 'Philipp-Jan Honysz', 'Sebastian Buschjäger'] | 2020-10-20 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.10120622e-02 5.89914024e-02 -4.31382209e-01 -3.96466166e-01
-1.02653170e+00 -5.71377635e-01 8.59324262e-02 5.82188189e-01
-3.87923360e-01 9.85360980e-01 1.76448092e-01 -1.92544591e-02
-4.92289960e-01 -6.66426480e-01 -8.85065138e-01 -8.62238824e-01
-9.78596658e-02 9.65322077e-01 2.33877137e-01 -2.87936274... | [6.599205493927002, 4.9251484870910645] |
a2aaa5cf-ef4b-4aba-86c5-5660978e09fa | deltaedit-exploring-text-free-training-for | 2303.06285 | null | https://arxiv.org/abs/2303.06285v1 | https://arxiv.org/pdf/2303.06285v1.pdf | DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation | Text-driven image manipulation remains challenging in training or inference flexibility. Conditional generative models depend heavily on expensive annotated training data. Meanwhile, recent frameworks, which leverage pre-trained vision-language models, are limited by either per text-prompt optimization or inference-tim... | ['Tieniu Tan', 'Jing Dong', 'Dongliang He', 'Fu Li', 'Tianwei Lin', 'Yueming Lyu'] | 2023-03-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lyu_DeltaEdit_Exploring_Text-Free_Training_for_Text-Driven_Image_Manipulation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lyu_DeltaEdit_Exploring_Text-Free_Training_for_Text-Driven_Image_Manipulation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation'] | ['computer-vision'] | [ 2.71268874e-01 -2.23668709e-01 -1.05745882e-01 -6.18992865e-01
-8.00445676e-01 -6.59275413e-01 7.78095841e-01 -5.66546977e-01
-1.67041928e-01 3.77262682e-01 6.22970089e-02 -9.07527879e-02
2.37304419e-01 -5.48921347e-01 -9.25398469e-01 -6.76881492e-01
5.71908951e-01 4.00693089e-01 1.30808214e-03 -1.74947426... | [11.280091285705566, -0.2179359644651413] |
ccb00935-242a-4420-9f9c-c463fb9ccdb0 | exploring-the-power-of-generative-deep | 2303.09012 | null | https://arxiv.org/abs/2303.09012v1 | https://arxiv.org/pdf/2303.09012v1.pdf | Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review | Deep learning has become a prominent computational modeling tool in the areas of computer vision and image processing in recent years. This research comprehensively analyzes the different deep-learning methods used for image-to-image translation and reconstruction in the natural and medical imaging domains. We examine ... | ['Yuda Bi'] | 2023-03-16 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.34337842e-01 2.70395458e-01 -1.03909457e-02 -2.99990028e-01
-6.55094028e-01 -1.04331262e-01 4.64107037e-01 -5.67532897e-01
-1.63733035e-01 5.20246148e-01 2.56837428e-01 -2.42856532e-01
-8.19063038e-02 -9.36032951e-01 -5.74244916e-01 -1.07564020e+00
1.97066426e-01 4.86204654e-01 -3.96105111e-01 -1.14203587... | [14.053439140319824, -2.0098636150360107] |
e272515f-c17a-443e-b5a4-c2cc4eb8725a | influence-of-color-spaces-for-deep-learning | 2204.02850 | null | https://arxiv.org/abs/2204.02850v1 | https://arxiv.org/pdf/2204.02850v1.pdf | Influence of Color Spaces for Deep Learning Image Colorization | Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtain... | ['Patricia Vitoria', 'Lara Raad', 'Rémi Giraud', 'Michaël Clément', 'Hernan Carrillo', 'Aurélie Bugeau', 'Coloma Ballester'] | 2022-04-06 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-2.95930147e-01 -4.42693561e-01 2.46803477e-01 -3.84827197e-01
-8.07846338e-02 -7.26601660e-01 5.05873680e-01 2.53066868e-02
-8.70748162e-01 7.29452133e-01 -2.26240277e-01 -4.24981296e-01
2.06083685e-01 -8.40720236e-01 -4.45174813e-01 -8.63082111e-01
2.82067537e-01 2.82121271e-01 1.23992100e-01 -3.37456971... | [10.443824768066406, -2.3916285037994385] |
9e67dcd2-0421-4c31-817c-2216fcecea4a | leveraging-wikidata-s-edit-history-in | 2210.15495 | null | https://arxiv.org/abs/2210.15495v1 | https://arxiv.org/pdf/2210.15495v1.pdf | Leveraging Wikidata's edit history in knowledge graph refinement tasks | Knowledge graphs have been adopted in many diverse fields for a variety of purposes. Most of those applications rely on valid and complete data to deliver their results, pressing the need to improve the quality of knowledge graphs. A number of solutions have been proposed to that end, ranging from rule-based approaches... | ['Daniel Gayo-Avello', 'Alejandro Gonzalez-Hevia'] | 2022-10-27 | null | null | null | null | ['type-prediction', 'knowledge-graph-embedding'] | ['computer-code', 'graphs'] | [ 4.81941178e-02 5.54707468e-01 -4.51522022e-01 -1.02529399e-01
8.54824334e-02 -5.21651685e-01 8.74966145e-01 8.20081174e-01
-3.25630933e-01 7.70435691e-01 3.10420364e-01 7.01229647e-02
-7.97607183e-01 -1.33689833e+00 -5.25098860e-01 -4.54171449e-01
-7.54444525e-02 6.19367898e-01 6.52520478e-01 -3.20836097... | [8.945575714111328, 7.892004489898682] |
4703c1ad-f154-4664-ab11-8e1338c3f5ac | improved-dynamic-memory-network-for-dialogue | 1811.05021 | null | http://arxiv.org/abs/1811.05021v1 | http://arxiv.org/pdf/1811.05021v1.pdf | Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training | Dialogue Act (DA) classification is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DA classification problem ranging from multi-classification to
structured prediction, which... | ['Yao Wan', 'Philip S. Yu', 'Jian Wu', 'Zhou Zhao', 'Wenqiang Yan', 'Jianwei Gao'] | 2018-11-12 | null | null | null | null | ['dialogue-act-classification', 'dialogue-interpretation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.95049781e-01 4.01988834e-01 3.86278890e-02 -7.52323806e-01
-8.11450362e-01 -4.79189605e-01 8.63806069e-01 -2.15344667e-01
-3.33178729e-01 9.60733235e-01 5.56497812e-01 -1.86450601e-01
4.90016669e-01 -5.29954195e-01 -2.84471869e-01 -5.67741811e-01
3.86568159e-01 7.79470861e-01 3.52506220e-01 -6.30059600... | [12.737732887268066, 7.7289252281188965] |
c6fb18b4-328e-4e74-9407-e03f644bfd8c | automatic-milp-solver-configuration-by | 2307.00670 | null | https://arxiv.org/abs/2307.00670v1 | https://arxiv.org/pdf/2307.00670v1.pdf | Automatic MILP Solver Configuration By Learning Problem Similarities | A large number of real-world optimization problems can be formulated as Mixed Integer Linear Programs (MILP). MILP solvers expose numerous configuration parameters to control their internal algorithms. Solutions, and their associated costs or runtimes, are significantly affected by the choice of the configuration param... | ['Sherief Reda', 'Abdelrahman Hosny'] | 2023-07-02 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-6.68146238e-02 -2.69928783e-01 -3.22511792e-01 -4.04936492e-01
-9.70020533e-01 -9.23840821e-01 -4.71564569e-02 4.17813301e-01
-1.75000519e-01 9.81056929e-01 -1.84391901e-01 -5.10649383e-02
-7.74814248e-01 -1.02234638e+00 -7.40579367e-01 -8.23882580e-01
-1.12119600e-01 1.02579308e+00 -4.27387774e-01 -6.99748099... | [5.190420150756836, 2.969623327255249] |
b71b02c0-1920-4842-af14-7d83eeaf6c41 | cross-modality-sub-image-retrieval-using | 2201.03597 | null | https://arxiv.org/abs/2201.03597v2 | https://arxiv.org/pdf/2201.03597v2.pdf | Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations | In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality im... | ['Nataša Sladoje', 'Joakim Lindblad', 'Elisabeth Wetzer', 'Eva Breznik'] | 2022-01-10 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.78432959e-01 -4.35385883e-01 -1.02138050e-01 -1.45389259e-01
-1.65699875e+00 -7.83041775e-01 1.00831258e+00 4.50206399e-01
-5.49622834e-01 4.09690350e-01 3.57910067e-01 -2.99779065e-02
-6.32857442e-01 -4.86417413e-01 -2.86709040e-01 -1.15815997e+00
9.66910943e-02 4.87052768e-01 8.36954415e-02 -5.15506528... | [14.363710403442383, -1.5736665725708008] |
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