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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6daa7768-c442-442f-bec6-0f7751428ac2 | exploration-by-random-network-distillation-1 | null | null | https://openreview.net/forum?id=Qg6O7xH7boe | https://openreview.net/pdf?id=Qg6O7xH7boe | Exploration by Random Network Distillation | We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method ... | ['Anonymous'] | 2022-01-17 | null | null | null | iclr-track-blog-2022-5 | ['montezumas-revenge'] | ['playing-games'] | [-2.19321668e-01 5.71273208e-01 -1.37304068e-01 4.76697832e-03
-5.98385513e-01 -5.28787911e-01 6.38256967e-01 -1.88456148e-01
-9.38650548e-01 1.18639350e+00 -3.22140932e-01 -3.18302423e-01
-1.67996243e-01 -7.56294310e-01 -8.19606543e-01 -6.68761551e-01
-6.44752502e-01 6.69023871e-01 6.66208640e-02 -7.54578471... | [3.892732620239258, 1.6744474172592163] |
0806f4cc-24fc-4d5f-ad14-a7cecb530728 | semi-supervised-few-shot-learning-for-medical | 2003.08462 | null | https://arxiv.org/abs/2003.08462v2 | https://arxiv.org/pdf/2003.08462v2.pdf | Semi-supervised few-shot learning for medical image segmentation | Recent years have witnessed the great progress of deep neural networks on semantic segmentation, particularly in medical imaging. Nevertheless, training high-performing models require large amounts of pixel-level ground truth masks, which can be prohibitive to obtain in the medical domain. Furthermore, training such mo... | ['Reza Azad', 'Abdur R Feyjie', 'Marco Pedersoli', 'Claude Kauffman', 'Jose Dolz', 'Ismail Ben Ayed'] | 2020-03-18 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.48781240e-01 4.09239560e-01 -2.92287469e-01 -4.74655867e-01
-1.03123593e+00 -1.69255525e-01 3.12728763e-01 1.71260640e-01
-5.30960917e-01 7.45611548e-01 -5.44861741e-02 1.20027483e-01
-5.76341860e-02 -9.07340288e-01 -6.46247566e-01 -8.85284603e-01
2.91689485e-01 5.16970813e-01 3.47385347e-01 7.53529519... | [14.62845230102539, -2.1764752864837646] |
3862ed33-74e8-4c3f-8dd0-41ee9268f535 | ltg-st-at-nadi-shared-task-1-arabic-dialect | null | null | https://aclanthology.org/2020.wanlp-1.34 | https://aclanthology.org/2020.wanlp-1.34.pdf | LTG-ST at NADI Shared Task 1: Arabic Dialect Identification using a Stacking Classifier | This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a sta... | ['Samia Touileb'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-3.12667787e-01 -1.80984028e-02 2.66764879e-01 -6.52767181e-01
-1.02232027e+00 -8.28980803e-01 8.62699211e-01 3.03572148e-01
-6.57550216e-01 7.89249957e-01 1.16622828e-01 -4.73526090e-01
-2.68632168e-04 -6.11538887e-01 -1.55233890e-01 -6.59118474e-01
-2.75190175e-01 8.51657510e-01 2.36553952e-01 -8.16986859... | [10.20268440246582, 10.715471267700195] |
fdc2f669-80a0-49c2-a388-56d328e26ef4 | semi-supervised-cell-recognition-under-point | 2306.08240 | null | https://arxiv.org/abs/2306.08240v1 | https://arxiv.org/pdf/2306.08240v1.pdf | Semi-supervised Cell Recognition under Point Supervision | Cell recognition is a fundamental task in digital histopathology image analysis. Point-based cell recognition (PCR) methods normally require a vast number of annotations, which is extremely costly, time-consuming and labor-intensive. Semi-supervised learning (SSL) can provide a shortcut to make full use of cell informa... | ['Lin Yang', 'Chenglu Zhu', 'Xiaoxuan Yu', 'Shichuan Zhang', 'Honglin Li', 'Yunlong Zhang', 'Sunyi Zheng', 'Yizhi Zhao', 'Zhongyi Shui'] | 2023-06-14 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.54247308e-01 -2.06690222e-01 -3.31317753e-01 -4.53415751e-01
-1.00354064e+00 -5.42982042e-01 3.73278052e-01 3.44187319e-01
-4.35706377e-01 1.00899386e+00 -3.73259634e-01 -4.50776279e-01
-1.44526303e-01 -6.87870622e-01 -4.44510370e-01 -1.28151703e+00
3.31040144e-01 6.29707038e-01 3.65388930e-01 1.24000318... | [14.989831924438477, -3.0314948558807373] |
3d618837-d444-4f9a-82cb-17e1134bd023 | learning-to-rank-in-generative-retrieval | 2306.15222 | null | https://arxiv.org/abs/2306.15222v1 | https://arxiv.org/pdf/2306.15222v1.pdf | Learning to Rank in Generative Retrieval | Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generation models and represents a new paradigm distinct from traditional learning-to-rank methods. However, despite its rapid development, ... | ['Wenjie Li', 'Furu Wei', 'Liang Wang', 'Nan Yang', 'Yongqi Li'] | 2023-06-27 | null | null | null | null | ['learning-to-rank', 'retrieval', 'learning-to-rank', 'text-generation', 'passage-ranking'] | ['graphs', 'methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [ 1.98062748e-01 -3.37434560e-01 -3.39761168e-01 -2.29703575e-01
-1.80738711e+00 -6.03420615e-01 1.10362792e+00 -6.03488386e-02
-1.48573473e-01 8.24720621e-01 4.39216912e-01 -1.46972686e-01
-2.81979144e-01 -9.69661713e-01 -6.40933096e-01 -6.73413396e-01
2.62631893e-01 8.59467208e-01 2.41596743e-01 -3.50184083... | [11.47722339630127, 7.6226887702941895] |
8e600ad8-0aa7-4334-b3fa-71c05c40b55c | exploiting-transliterated-words-for-finding | 2206.11860 | null | https://arxiv.org/abs/2206.11860v1 | https://arxiv.org/pdf/2206.11860v1.pdf | Exploiting Transliterated Words for Finding Similarity in Inter-Language News Articles using Machine Learning | Finding similarities between two inter-language news articles is a challenging problem of Natural Language Processing (NLP). It is difficult to find similar news articles in a different language other than the native language of user, there is a need for a Machine Learning based automatic system to find the similarity ... | ['Abdul Basit Mughal', 'Syed Mujtaba Haider', 'Dr. Arif ur Rahman', 'Sameea Naeem'] | 2022-05-29 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-4.12815660e-01 -5.23386240e-01 -3.12153697e-01 2.57482752e-02
-6.64291322e-01 -9.78259623e-01 9.29356337e-01 8.01802814e-01
-5.91749787e-01 8.67702842e-01 6.02699459e-01 -5.38888156e-01
3.74345124e-01 -8.92123401e-01 -5.44347644e-01 1.83931261e-01
4.98045206e-01 4.89429057e-01 4.28128570e-01 -7.78574765... | [10.576501846313477, 10.174420356750488] |
47aec201-9b45-402a-9ca7-813c5fd8d624 | zsd-yolo-zero-shot-yolo-detection-using | 2109.12066 | null | https://arxiv.org/abs/2109.12066v2 | https://arxiv.org/pdf/2109.12066v2.pdf | Zero-shot Object Detection Through Vision-Language Embedding Alignment | Recent approaches have shown that training deep neural networks directly on large-scale image-text pair collections enables zero-shot transfer on various recognition tasks. One central issue is how this can be generalized to object detection, which involves the non-semantic task of localization as well as semantic task... | ['Shuai Zheng', 'Johnathan Xie'] | 2021-09-24 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 1.90810949e-01 5.02581485e-02 -1.34274945e-01 -3.24990124e-01
-1.00494778e+00 -5.95605314e-01 7.04522431e-01 3.73372920e-02
-6.70349002e-01 4.03847396e-02 -8.30759183e-02 3.43025550e-02
4.42716807e-01 -5.83649158e-01 -1.05493915e+00 -3.89603347e-01
4.87825155e-01 5.68053961e-01 7.19531298e-01 -1.77739590... | [9.772953987121582, 1.6621167659759521] |
6dd9145b-09f4-447b-b1ae-42f7f68b96cc | ppgnet-deep-network-for-device-independent | 1903.08912 | null | http://arxiv.org/abs/1903.08912v1 | http://arxiv.org/pdf/1903.08912v1.pdf | PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram | Photoplethysmogram (PPG) is increasingly used to provide monitoring of the
cardiovascular system under ambulatory conditions. Wearable devices like
smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in
free living conditions. PPG based heart rate measurement is unfortunately
highly susceptible... | ['Vignesh Ravichandran', 'Mohanasankar Sivaprakasam', 'Jayaraj Joseph', 'Preejith S. P', 'Shyam A'] | 2019-03-21 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.41340137e-01 9.11360681e-02 -1.22871123e-01 -3.52876067e-01
-5.69725215e-01 -8.32189396e-02 -4.81161803e-01 1.15928918e-01
-2.98401088e-01 8.43747318e-01 2.88046926e-01 -4.24376965e-01
3.62034023e-01 -4.48475927e-01 -4.10320997e-01 -5.42821705e-01
-2.37096176e-01 5.16941771e-02 -7.02029288e-01 5.45427024... | [13.931180953979492, 3.002700090408325] |
f2fb9417-4645-40fe-b108-bf00bde56127 | source-free-open-set-domain-adaptation-for | 2307.04596 | null | https://arxiv.org/abs/2307.04596v1 | https://arxiv.org/pdf/2307.04596v1.pdf | Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer | There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the distributionally shifted unlabeled target domain, and simultaneou... | ['Jean-Philippe Thiran', 'Behzad Bozorgtabar', 'Devavrat Tomar', 'Guillaume Vray'] | 2023-07-10 | null | null | null | null | ['data-augmentation', 'domain-adaptation'] | ['methodology', 'methodology'] | [ 3.94193828e-01 4.50608492e-01 -3.35737139e-01 -1.45892128e-01
-1.27388942e+00 -6.53214037e-01 4.34687406e-01 4.19628918e-01
-6.33623898e-01 7.42602646e-01 1.22712925e-01 -3.68111908e-01
-9.84112546e-03 -5.76624393e-01 -5.43009400e-01 -1.24218488e+00
2.81261861e-01 6.13591135e-01 2.84684598e-01 1.17141291... | [15.046539306640625, -2.6981842517852783] |
5d6712ab-83e0-450e-9890-3ccff79c78f1 | mvloc-multimodal-variational-geometry-aware | 2003.07289 | null | https://arxiv.org/abs/2003.07289v5 | https://arxiv.org/pdf/2003.07289v5.pdf | VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization | Recent learning-based approaches have achieved impressive results in the field of single-shot camera localization. However, how best to fuse multiple modalities (e.g., image and depth) and to deal with degraded or missing input are less well studied. In particular, we note that previous approaches towards deep fusion d... | ['Muhamad Risqi U. Saputra', 'Kaichen Zhou', 'Bing Wang', 'Andrew Markham', 'Niki Trigoni', 'Changhao Chen'] | 2020-03-12 | null | null | null | null | ['camera-localization', 'camera-relocalization'] | ['computer-vision', 'computer-vision'] | [ 3.09499539e-02 -6.90212026e-02 -5.40733077e-02 -3.45927298e-01
-1.26405752e+00 -5.30576944e-01 6.91930890e-01 -3.11489761e-01
-3.89280915e-01 5.95575571e-01 5.09993851e-01 9.49157402e-02
-2.61382386e-02 -3.98819625e-01 -1.01000392e+00 -6.89699888e-01
6.16832614e-01 8.42709020e-02 -1.09901756e-01 1.60848483... | [10.348459243774414, 0.9594488739967346] |
3829396e-2e71-4efb-8c5f-8ddd15e31e0f | generalized-many-way-few-shot-video | 2007.04755 | null | https://arxiv.org/abs/2007.04755v2 | https://arxiv.org/pdf/2007.04755v2.pdf | Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation | Few-shot learning aims to recognize novel classes from a few examples. Although significant progress has been made in the image domain, few-shot video classification is relatively unexplored. We argue that previous methods underestimate the importance of video feature learning and propose to learn spatiotemporal featur... | ['Zeynep Akata', 'Lorenzo Torresani', 'Bruno Korbar', 'Yongqin Xian', 'Matthijs Douze', 'Bernt Schiele'] | 2020-07-09 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 2.63437212e-01 -3.45385790e-01 -3.95448893e-01 -4.89155829e-01
-1.19253135e+00 -5.77290893e-01 8.58634830e-01 -2.95575231e-01
-3.80306691e-01 7.49664903e-01 2.66528606e-01 2.16315016e-01
5.71714109e-03 -7.18895853e-01 -1.08061397e+00 -6.73754156e-01
-2.12080210e-01 5.91235422e-02 6.02800429e-01 -5.77868894... | [8.814264297485352, 0.9621472358703613] |
6738bb14-5e0a-4e44-9a5d-45a61a644033 | objects2action-classifying-and-localizing | 1510.06939 | null | http://arxiv.org/abs/1510.06939v1 | http://arxiv.org/pdf/1510.06939v1.pdf | Objects2action: Classifying and localizing actions without any video example | The goal of this paper is to recognize actions in video without the need for
examples. Different from traditional zero-shot approaches we do not demand the
design and specification of attribute classifiers and class-to-attribute
mappings to allow for transfer from seen classes to unseen classes. Our key
contribution is... | ['Thomas Mensink', 'Jan C. van Gemert', 'Mihir Jain', 'Cees G. M. Snoek'] | 2015-10-23 | objects2action-classifying-and-localizing-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Jain_Objects2action_Classifying_and_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Jain_Objects2action_Classifying_and_ICCV_2015_paper.pdf | iccv-2015-12 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.39992255e-01 -1.21841334e-01 -4.34138596e-01 -7.06677735e-01
-6.47685587e-01 -5.70990443e-01 8.74142945e-01 2.06412505e-02
-3.40734452e-01 3.43671471e-01 6.99689507e-01 2.42577821e-01
-3.36020738e-01 -5.30305147e-01 -6.43614769e-01 -5.83943427e-01
-3.45243067e-01 3.35747987e-01 3.83219510e-01 4.98291105... | [8.576416969299316, 0.9157078266143799] |
aae10f4c-abfd-4316-bb3d-22159d5e029c | machine-learned-adversarial-attacks-against | 2303.18136 | null | https://arxiv.org/abs/2303.18136v1 | https://arxiv.org/pdf/2303.18136v1.pdf | Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids | In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate t... | ['Giovanni Servedio', 'Fatemeh Nazary', 'Eugenio Di Sciascio', 'Tommaso Di Noia', 'Yashar Deldjoo', 'Carmelo Ardito'] | 2023-03-28 | null | null | null | null | ['fault-localization', 'defect-detection', 'load-forecasting', 'fault-detection'] | ['computer-code', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [-4.34419885e-02 -1.49515435e-01 4.26181018e-01 -9.30019692e-02
-4.21452194e-01 -4.87809539e-01 5.45989335e-01 1.93981960e-01
2.02166095e-01 1.09070671e+00 -2.14689046e-01 -3.69815409e-01
-3.84533912e-01 -1.01715982e+00 -2.62033641e-01 -1.35360694e+00
-6.73832238e-01 2.20351115e-01 -3.20826918e-01 -2.63832092... | [6.10638952255249, 2.58430552482605] |
6942b19e-a69a-436a-b8c4-5f3f76d269d2 | shadows-can-be-dangerous-stealthy-and | 2203.03818 | null | https://arxiv.org/abs/2203.03818v3 | https://arxiv.org/pdf/2203.03818v3.pdf | Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon | Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printi... | ['Xiangyang Ji', 'Junjun Jiang', 'Deming Zhai', 'Xianming Liu', 'Yiqi Zhong'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.pdf | cvpr-2022-1 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.13757509e-01 -1.31383777e-01 3.67328197e-01 1.76999301e-01
-2.62966156e-01 -1.05163884e+00 6.40249491e-01 -8.96188796e-01
-3.66391659e-01 9.10488725e-01 -3.69494975e-01 -3.45273852e-01
1.53340816e-01 -7.32365191e-01 -7.56895840e-01 -1.05452478e+00
6.04813322e-02 -4.39255200e-02 4.56792772e-01 -1.46626413... | [5.444616317749023, 7.904926776885986] |
8b6f15c3-8e7f-409d-a0e6-6565eca807ee | searching-for-pneumothorax-in-half-a-million | 2007.15429 | null | https://arxiv.org/abs/2007.15429v1 | https://arxiv.org/pdf/2007.15429v1.pdf | Searching for Pneumothorax in Half a Million Chest X-Ray Images | Pneumothorax, a collapsed or dropped lung, is a fatal condition typically detected on a chest X-ray by an experienced radiologist. Due to shortage of such experts, automated detection systems based on deep neural networks have been developed. Nevertheless, applying such systems in practice remains a challenge. These sy... | ['Antonio Sze-To', 'Hamid Tizhoosh'] | 2020-07-30 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 3.44664991e-01 -2.64497578e-01 -2.50939101e-01 -2.75969923e-01
-1.17117381e+00 -3.78297716e-01 4.12939668e-01 5.80882967e-01
-7.86371350e-01 5.52248418e-01 1.45833194e-01 -5.44910789e-01
-5.64573884e-01 -1.01632130e+00 -5.94782948e-01 -6.92825854e-01
1.25479996e-01 8.22300434e-01 3.67466599e-01 4.63889092... | [15.194732666015625, -1.959993839263916] |
cc2d9368-e198-485a-9181-e893b677e60a | picture-it-in-your-mind-generating-high-level | 1606.07287 | null | http://arxiv.org/abs/1606.07287v1 | http://arxiv.org/pdf/1606.07287v1.pdf | Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions | In this paper we tackle the problem of image search when the query is a short
textual description of the image the user is looking for. We choose to
implement the actual search process as a similarity search in a visual feature
space, by learning to translate a textual query into a visual representation.
Searching in t... | ['Alejandro Moreo Fernández', 'Andrea Esuli', 'Tiziano Fagni', 'Fabrizio Falchi', 'Fabio Carrara'] | 2016-06-23 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 4.24352974e-01 -3.17455642e-02 -1.30319400e-02 -4.95820582e-01
-7.60012031e-01 -5.12537122e-01 8.79876077e-01 1.54191107e-01
-8.17597568e-01 1.68215573e-01 2.15223301e-02 -1.88528910e-01
-1.06420054e-03 -5.28846681e-01 -8.99239421e-01 -5.09904146e-01
4.09273714e-01 5.92639327e-01 2.66660713e-02 1.38317823... | [10.796443939208984, 1.3976631164550781] |
d93e19ff-3451-4c72-8595-b105b9d24d5a | towards-robust-speech-to-text-adversarial | 2103.08095 | null | https://arxiv.org/abs/2103.08095v1 | https://arxiv.org/pdf/2103.08095v1.pdf | Towards Robust Speech-to-Text Adversarial Attack | This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on developing an extension for the conventional distortion condition of the adversarial optimization formulation using the Cram\`er integral probabil... | ['Alessandro Lameiras Koerich', 'Patrick Cardinal', 'Mohammad Esmaeilpour'] | 2021-03-15 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 3.41185242e-01 3.84125113e-01 7.18666434e-01 -1.40157074e-01
-1.55245709e+00 -9.84105766e-01 7.57108390e-01 -2.77590930e-01
-4.02733773e-01 6.67295992e-01 4.06385362e-01 -5.68886936e-01
3.23638245e-02 -4.76208240e-01 -8.29394400e-01 -9.71685529e-01
6.11352772e-02 1.72326043e-01 1.08031638e-01 -6.10587120... | [14.061844825744629, 5.8332200050354] |
77ca6202-7cc6-4628-bc72-e3e2c38431eb | tempsal-uncovering-temporal-information-for-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Aydemir_TempSAL_-_Uncovering_Temporal_Information_for_Deep_Saliency_Prediction_CVPR_2023_paper.pdf | TempSAL - Uncovering Temporal Information for Deep Saliency Prediction | Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. W... | ['Sabine Süsstrunk', 'Mathieu Salzmann', 'Tong Zhang', 'Ludo Hoffstetter', 'Bahar Aydemir'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['saliency-prediction', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.81653965e-01 -3.21621113e-02 -6.77183807e-01 -5.03506720e-01
-2.40422845e-01 -2.48408914e-01 4.63288248e-01 1.23862855e-01
-1.95135653e-01 4.19129729e-01 2.52089262e-01 -9.99869630e-02
-1.17436014e-01 -3.85589749e-02 -7.76301742e-01 -3.18839192e-01
-1.09730244e-01 -1.98614880e-01 1.04472256e+00 -2.40026310... | [9.961113929748535, 0.47956380248069763] |
20124511-bf6e-4663-9cf0-6f93bc0a4903 | memd-a-diversity-promoting-learning-framework | null | null | https://aclanthology.org/C18-1109 | https://aclanthology.org/C18-1109.pdf | MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation | Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years. However, conventional neural encoder-decoder models tend to generate commonplace responses like {``}I don{'}t know{''} regardless of what the input is. In this paper, we analyze th... | ['Zhi-Hong Deng', 'Haokun Liu', 'Meng Zou', 'Xihan Li'] | 2018-08-01 | memd-a-diversity-promoting-learning-framework-1 | https://aclanthology.org/C18-1109 | https://aclanthology.org/C18-1109.pdf | coling-2018-8 | ['short-text-conversation', 'conversational-response-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.13341832e-01 1.34543538e-01 3.58282682e-03 -5.20248175e-01
-7.46518493e-01 -2.74045080e-01 6.01489782e-01 -1.90852553e-01
-3.22149962e-01 9.30180967e-01 6.90474153e-01 -2.85073370e-01
2.86477655e-01 -7.42514670e-01 -3.63589078e-01 -4.55524296e-01
7.39081144e-01 4.58475262e-01 -1.77993774e-01 -6.30599499... | [12.528215408325195, 8.370595932006836] |
db08a914-951e-4648-a88f-e2cedf5996e9 | aggregating-layers-for-deepfake-detection | 2210.05478 | null | https://arxiv.org/abs/2210.05478v1 | https://arxiv.org/pdf/2210.05478v1.pdf | Aggregating Layers for Deepfake Detection | The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network. This is no... | ['Shai Avidan', 'Amir Jevnisek'] | 2022-10-11 | null | null | null | null | ['synthetic-image-detection'] | ['computer-vision'] | [ 3.00879717e-01 2.98861712e-01 7.12189674e-02 -1.75840840e-01
-4.13828075e-01 -7.66475201e-01 6.75017357e-01 -4.70051497e-01
-1.95179477e-01 6.09216630e-01 -1.51804969e-01 -1.73432961e-01
3.63490015e-01 -8.11789811e-01 -9.22602773e-01 -7.71322846e-01
4.97885421e-02 1.92484334e-01 2.59295911e-01 -2.64264554... | [12.575138092041016, 1.0558768510818481] |
4642c52c-401e-4c8b-8dc6-8db01c5fc067 | differentially-private-synthetic-data-using | 2306.13211 | null | https://arxiv.org/abs/2306.13211v1 | https://arxiv.org/pdf/2306.13211v1.pdf | Differentially Private Synthetic Data Using KD-Trees | Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based approaches have emerged in recent years for answering statistical queries in a differentially private manner. However, for synthetic data gen... | ['Manuela Veloso', 'Tucker Balch', 'Vamsi K. Potluru', 'Navid Nouri', 'Eleonora Kreačić'] | 2023-06-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.21924147e-01 1.95970207e-01 7.32278004e-02 -5.48276722e-01
-1.35999429e+00 -8.47785115e-01 3.84520501e-01 2.92868558e-02
-2.30132103e-01 9.77243781e-01 2.13641912e-01 -4.57710654e-01
-9.68965441e-02 -1.02624619e+00 -6.50160134e-01 -9.06276524e-01
2.16918424e-01 4.52208817e-01 -2.14624740e-02 7.85245225... | [6.056003093719482, 6.813288688659668] |
d3b3fb9a-bcbd-4f0a-bdfa-b53bfd0ccf49 | domain-agnostic-learning-with-anatomy | 1908.10489 | null | https://arxiv.org/abs/1908.10489v1 | https://arxiv.org/pdf/1908.10489v1.pdf | Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation | Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from the source domain to a specific known target domain. Domain Agnostic Learning (DAL) proposes a new task of transferring knowledge from the s... | ['Juntang Zhuang', 'Junlin Yang', 'James S. Duncan', 'MingDe Lin', 'Julius Chapiro', 'Nicha C. Dvornek', 'Fan Zhang'] | 2019-08-27 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.46516338e-01 4.46925849e-01 -6.74316809e-02 -1.71177343e-01
-9.11273479e-01 -8.52053523e-01 7.12466896e-01 -2.18260348e-01
-1.61545604e-01 9.60340083e-01 4.46659118e-01 -1.21141404e-01
-3.12179774e-01 -8.80356669e-01 -6.65971100e-01 -9.67540383e-01
6.13885149e-02 4.34344679e-01 -1.59989014e-01 -3.13474506... | [14.56751823425293, -2.003491163253784] |
70aacf5d-1ccc-43f4-b3b7-caf0fa023825 | algorithms-for-audio-inpainting-based-on | 2206.13768 | null | https://arxiv.org/abs/2206.13768v2 | https://arxiv.org/pdf/2206.13768v2.pdf | Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization | Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing sample... | ['Cédric Févotte', 'Thomas Oberlin', 'Paul Magron', 'Ondřej Mokrý'] | 2022-06-28 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 5.02539396e-01 -2.18820125e-01 2.10585184e-02 5.44735976e-02
-1.12727845e+00 -5.49607933e-01 1.66407228e-01 -4.14303660e-01
-1.04456447e-01 8.19419920e-01 4.88744438e-01 -1.15373150e-01
-3.23518276e-01 -3.93484086e-01 -6.27317727e-01 -8.33815932e-01
1.08423017e-01 3.03728729e-02 -3.69185150e-01 -1.64144989... | [15.452241897583008, 5.616896152496338] |
1450f0b6-bd32-49db-b1c1-aafa85f53946 | epos-estimating-6d-pose-of-objects-with | 2004.00605 | null | https://arxiv.org/abs/2004.00605v1 | https://arxiv.org/pdf/2004.00605v1.pdf | EPOS: Estimating 6D Pose of Objects with Symmetries | We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. The method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symme... | ['Jiri Matas', 'Daniel Barath', 'Tomas Hodan'] | 2020-04-01 | epos-estimating-6d-pose-of-objects-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hodan_EPOS_Estimating_6D_Pose_of_Objects_With_Symmetries_CVPR_2020_paper.pdf | cvpr-2020-6 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 1.94432005e-01 2.07926780e-02 -2.70719767e-01 -3.05787355e-01
-1.02427745e+00 -5.59739530e-01 4.00947660e-01 -3.38027894e-01
-1.47292376e-01 2.20492199e-01 -9.91454069e-03 1.38253063e-01
-1.16786957e-01 -5.24314344e-01 -1.12899244e+00 -7.26853967e-01
1.57696471e-01 1.13183296e+00 4.60594654e-01 1.52300254... | [7.556819915771484, -2.6707570552825928] |
39639a8d-4600-4cf6-92c5-cac5349c9d4d | first-person-hand-action-benchmark-with-rgb-d | 1704.02463 | null | http://arxiv.org/abs/1704.02463v2 | http://arxiv.org/pdf/1704.02463v2.pdf | First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations | In this work we study the use of 3D hand poses to recognize first-person
dynamic hand actions interacting with 3D objects. Towards this goal, we
collected RGB-D video sequences comprised of more than 100K frames of 45 daily
hand action categories, involving 26 different objects in several hand
configurations. To obtain... | ['Tae-Kyun Kim', 'Guillermo Garcia-Hernando', 'Seungryul Baek', 'Shanxin Yuan'] | 2017-04-08 | first-person-hand-action-benchmark-with-rgb-d-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Garcia-Hernando_First-Person_Hand_Action_CVPR_2018_paper.pdf | cvpr-2018-6 | ['egocentric-activity-recognition'] | ['computer-vision'] | [-4.48606946e-02 -3.10845137e-01 -1.66767150e-01 1.85013320e-02
-5.31236470e-01 -7.54072964e-01 6.19063139e-01 -7.13946760e-01
-4.67803866e-01 2.65002996e-01 5.64130008e-01 2.03111976e-01
-9.81173813e-02 1.69066116e-02 -6.60013437e-01 -7.23348141e-01
-7.93557838e-02 1.03000391e+00 3.08497667e-01 -8.53423700... | [6.46824312210083, -0.8818086981773376] |
39a5084a-00d2-4b53-8c36-2531add22146 | boosting-semantic-segmentation-with-semantic | 2304.09427 | null | https://arxiv.org/abs/2304.09427v1 | https://arxiv.org/pdf/2304.09427v1.pdf | Boosting Semantic Segmentation with Semantic Boundaries | In this paper, we present the Semantic Boundary Conditioned Backbone (SBCB) framework, a simple yet effective training framework that is model-agnostic and boosts segmentation performance, especially around the boundaries. Motivated by the recent development in improving semantic segmentation by incorporating boundarie... | ['Yoshimitsu Aoki', 'Haruya Ishikawa'] | 2023-04-19 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.42890370e-01 5.11618257e-01 -2.20184609e-01 -4.73460138e-01
-8.34860206e-01 -4.73164320e-01 4.97727066e-01 -1.35021329e-01
-5.95400631e-01 2.72357464e-01 1.47066325e-01 -2.90206105e-01
1.96153060e-01 -7.41144657e-01 -8.92388821e-01 -4.93855417e-01
2.95522988e-01 5.64833403e-01 9.02825713e-01 -1.49584517... | [9.578916549682617, 0.49823087453842163] |
3d3eeffa-ba3f-4008-a485-08dc4ca7186d | a-fusion-based-gender-recognition-method | 1711.06451 | null | http://arxiv.org/abs/1711.06451v1 | http://arxiv.org/pdf/1711.06451v1.pdf | A Fusion-based Gender Recognition Method Using Facial Images | This paper proposes a fusion-based gender recognition method which uses
facial images as input. Firstly, this paper utilizes pre-processing and a
landmark detection method in order to find the important landmarks of faces.
Thereafter, four different frameworks are proposed which are inspired by
state-of-the-art gender ... | ['Hoda Mohammadzade', 'Saeed Bagheri Shouraki', 'Benyamin Ghojogh', 'Ensieh Iranmehr'] | 2017-11-17 | null | null | null | null | ['age-and-gender-classification', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [-2.91739181e-02 -1.62101492e-01 -2.17507482e-01 -4.50849175e-01
-8.35526511e-02 -1.89234197e-01 4.40310121e-01 -1.49011806e-01
-4.65193659e-01 6.37199640e-01 -1.88317850e-01 -4.98532504e-02
-2.17799649e-01 -9.87083197e-01 2.47775018e-02 -1.03740597e+00
2.48351730e-02 1.69909626e-01 1.73802793e-01 -8.24192390... | [13.189077377319336, 0.8155798316001892] |
0cdd5d97-19fb-452f-a0e8-d54b29d7cb5d | efficient-universal-shuffle-attack-for-visual | 2203.06898 | null | https://arxiv.org/abs/2203.06898v1 | https://arxiv.org/pdf/2203.06898v1.pdf | Efficient universal shuffle attack for visual object tracking | Recently, adversarial attacks have been applied in visual object tracking to deceive deep trackers by injecting imperceptible perturbations into video frames. However, previous work only generates the video-specific perturbations, which restricts its application scenarios. In addition, existing attacks are difficult to... | ['Zhongxue Gan', 'Wenqiang Zhang', 'Jiafeng Wang', 'Jiwei Zhu', 'Wei Li', 'Zhaoyu Chen', 'Siao Liu'] | 2022-03-14 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-2.58539945e-01 -3.87435108e-01 -1.55434668e-01 1.57637849e-01
-4.02111322e-01 -1.00742006e+00 5.59244037e-01 -7.74005890e-01
-3.49023789e-01 7.60164559e-01 -1.68752268e-01 -3.24482054e-01
3.79518867e-01 -1.93555117e-01 -8.76201570e-01 -7.85651207e-01
-8.88511539e-02 -5.81081733e-02 4.54173237e-01 1.97741881... | [5.337996482849121, 7.980066299438477] |
d97365a4-0b2a-40e8-90ac-f51766d24782 | random-forest-model-identifies-serve-strength | 1910.03203 | null | https://arxiv.org/abs/1910.03203v1 | https://arxiv.org/pdf/1910.03203v1.pdf | Random forest model identifies serve strength as a key predictor of tennis match outcome | Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of par... | ['Zijian Gao', 'Amanda Kowalczyk'] | 2019-10-08 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-2.33330116e-01 -1.15655214e-01 -7.31480837e-01 -2.20946223e-01
-8.91330898e-01 -4.20947850e-01 1.84379891e-01 6.93810999e-01
-8.42369556e-01 7.14819252e-01 3.79151255e-01 -1.65064752e-01
-4.67601061e-01 -1.37862766e+00 -5.10213375e-01 2.33361110e-01
-1.39804482e-01 9.03698742e-01 6.50312006e-01 -6.81024492... | [6.589406967163086, 0.3809841573238373] |
f1196285-ae47-4cf8-97d0-250a9d52df60 | unav-an-infrastructure-independent-vision | 2209.11336 | null | https://arxiv.org/abs/2209.11336v1 | https://arxiv.org/pdf/2209.11336v1.pdf | UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision | Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible... | ['John-Ross Rizzo', 'Chen Feng', 'Pattanasak Mongkolwat', 'Wachara Riewpaiboon', 'Rajesh Vedanthan', 'Todd E Hudson', 'Mahya Beheshti', 'Anbang Yang'] | 2022-09-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 3.94392163e-02 -1.21504508e-01 1.61546677e-01 -3.39875728e-01
-1.03937376e+00 -8.34810913e-01 3.39595795e-01 2.10101888e-01
-8.22899878e-01 3.94941956e-01 3.32667083e-02 -5.69205582e-01
-2.00634047e-01 -8.26972723e-01 -6.37362719e-01 -1.83978602e-01
3.88942622e-02 4.41234171e-01 3.47963065e-01 -1.21904708... | [7.448888301849365, -2.021043300628662] |
05f5d74b-fe48-4322-9e99-803c109b2bc2 | relative-instance-credibility-inference-for | null | null | https://openreview.net/forum?id=tvKdi-Nodsx | https://openreview.net/pdf?id=tvKdi-Nodsx | Relative Instance Credibility Inference for Learning with Noisy Labels | The existence of noisy labels usually leads to the degradation of generalization and robustness of neural networks in supervised learning. In this paper, we propose to use a simple theoretically guaranteed sample selection framework as a plug-in module to handle noisy labels. Specifically, we re-purpose a sparse linear... | ['Yanwei Fu', 'Xinwei Sun', 'Yikai Wang'] | 2021-09-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.33791372e-01 9.51297358e-02 -6.82958886e-02 -6.13555491e-01
-1.11456239e+00 -3.09991211e-01 2.24656954e-01 -1.87996209e-01
-3.67666632e-01 8.31678867e-01 -1.02160156e-01 -2.74046790e-02
-3.75074685e-01 -5.29798865e-01 -8.97139966e-01 -1.11233318e+00
9.44187492e-02 1.18633233e-01 -7.10301027e-02 4.99631435... | [9.357398986816406, 3.8994176387786865] |
5a969baa-0ad8-4ac6-8da2-a096a8ca63d4 | landmark-detection-and-3d-face-reconstruction | 2004.09190 | null | https://arxiv.org/abs/2004.09190v2 | https://arxiv.org/pdf/2004.09190v2.pdf | Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model | Caricature is an artistic abstraction of the human face by distorting or exaggerating certain facial features, while still retains a likeness with the given face. Due to the large diversity of geometric and texture variations, automatic landmark detection and 3D face reconstruction for caricature is a challenging probl... | ['Yudong Guo', 'Hongrui Cai', 'Juyong Zhang', 'Zhuang Peng'] | 2020-04-20 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-1.53759152e-01 9.73888040e-02 -7.08578601e-02 -3.11998844e-01
-3.03295374e-01 -5.27361333e-01 3.71742368e-01 -8.26414287e-01
4.31114554e-01 2.10198015e-01 4.26509082e-02 5.35372235e-02
2.31267095e-01 -4.63049680e-01 -7.39023983e-01 -5.51516533e-01
1.34304032e-01 5.72560012e-01 -1.85565159e-01 -9.25813541... | [12.81057357788086, -0.16989654302597046] |
e57691b0-f28f-49ca-90d3-ae5f660c894e | vitpose-vision-transformer-foundation-model | 2212.04246 | null | https://arxiv.org/abs/2212.04246v1 | https://arxiv.org/pdf/2212.04246v1.pdf | ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation | In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed V... | ['DaCheng Tao', 'Qiming Zhang', 'Jing Zhang', 'Yufei Xu'] | 2022-12-07 | null | null | null | null | ['keypoint-detection', 'animal-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.95352748e-01 5.10995388e-02 -1.66022867e-01 -1.17267303e-01
-6.87462568e-01 -5.12225330e-01 1.80606768e-01 -2.58103639e-01
-5.04254162e-01 2.12443128e-01 2.21202925e-01 2.34758317e-01
-9.39970016e-02 -5.48130810e-01 -1.12656987e+00 -4.48590219e-01
-1.40834466e-01 7.86773443e-01 4.04718518e-01 -3.67253214... | [7.145142555236816, -0.7835395932197571] |
485ca345-e82f-4a41-a810-c3a0944d7528 | federated-cycling-fedcy-semi-supervised | 2203.07345 | null | https://arxiv.org/abs/2203.07345v2 | https://arxiv.org/pdf/2203.07345v2.pdf | Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases | Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering th... | ['Nicolas Padoy', 'Alexandros Karargyris', 'AI4SafeChole Consortium', 'Pietro Mascagni', 'Deepak Alapatt', 'Hasan Kassem'] | 2022-03-14 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 1.46802574e-01 8.23871419e-03 -7.90153921e-01 -5.01611948e-01
-9.57140982e-01 -7.55983949e-01 1.51226878e-01 5.10015905e-01
-5.24823070e-01 3.97464186e-01 2.87516475e-01 -4.58923101e-01
-4.38072771e-01 -4.51590687e-01 -5.51413715e-01 -8.68732333e-01
-4.80473250e-01 3.40003192e-01 -1.33422002e-01 2.11089209... | [14.14041805267334, -3.0088231563568115] |
5f6443f6-8f1f-406c-a91a-5ee8fab1ab71 | logically-sound-arguments-for-the | 2111.02649 | null | https://arxiv.org/abs/2111.02649v2 | https://arxiv.org/pdf/2111.02649v2.pdf | Logically Sound Arguments for the Effectiveness of ML Safety Measures | We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening ... | ['Simon Burton', 'Tobias Schuster', 'Chih-Hong Cheng'] | 2021-11-04 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.38376200e-01 9.35899019e-01 -1.09219179e-01 -2.52409101e-01
-4.16414440e-01 -8.49301279e-01 7.77674556e-01 4.78108019e-01
-5.65211296e-01 8.35546136e-01 3.54520939e-02 -1.20371771e+00
-5.77884495e-01 -7.65791118e-01 -9.14820194e-01 -5.12954056e-01
2.82558217e-03 2.70857126e-01 6.35965407e-01 -2.28039315... | [8.574846267700195, 6.571816444396973] |
f59bdb25-18c9-4e2f-a7c2-655605fe5563 | detection-segmentation-convolutional-neural | 2306.17485 | null | https://arxiv.org/abs/2306.17485v1 | https://arxiv.org/pdf/2306.17485v1.pdf | Detection-segmentation convolutional neural network for autonomous vehicle perception | Object detection and segmentation are two core modules of an autonomous vehicle perception system. They should have high efficiency and low latency while reducing computational complexity. Currently, the most commonly used algorithms are based on deep neural networks, which guarantee high efficiency but require high-pe... | ['Tomasz Kryjak', 'Mateusz Wasala', 'Robert Synoczek', 'Maciej Baczmanski'] | 2023-06-30 | null | null | null | null | ['object-detection', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-8.43078643e-02 -1.13825826e-02 1.45630285e-01 -2.58958012e-01
-1.55464951e-02 -3.28048170e-01 3.83999884e-01 4.20227796e-02
-9.02693212e-01 3.80808145e-01 -8.53306711e-01 -5.65699399e-01
1.50268331e-01 -1.09167719e+00 -6.92661166e-01 -6.97401106e-01
1.12331130e-01 4.67354208e-01 1.06420338e+00 -3.66959393... | [8.083333015441895, -1.1577796936035156] |
230f4f6d-034b-497a-a151-2902833324eb | unsupervised-object-discovery-and-co | 1707.06397 | null | http://arxiv.org/abs/1707.06397v1 | http://arxiv.org/pdf/1707.06397v1.pdf | Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming | Reusable model design becomes desirable with the rapid expansion of computer
vision and machine learning applications. In this paper, we focus on the
reusability of pre-trained deep convolutional models. Specifically, different
from treating pre-trained models as feature extractors, we reveal more
treasures beneath con... | ['Zhi-Hua Zhou', 'Chen-Lin Zhang', 'Xiu-Shen Wei', 'Chunhua Shen', 'Jianxin Wu'] | 2017-07-20 | null | null | null | null | ['single-object-discovery'] | ['computer-vision'] | [-1.12727717e-01 -3.64436775e-01 -1.79764792e-01 -3.18436533e-01
-7.89395034e-01 -7.04871953e-01 6.14631295e-01 1.24914259e-01
-2.49025896e-01 3.01182240e-01 -7.52039924e-02 1.51224047e-01
-2.52398014e-01 -7.09043801e-01 -7.71991551e-01 -9.59909260e-01
9.39876586e-02 -3.48286182e-02 3.50902110e-01 1.67578489... | [9.658490180969238, 1.9665896892547607] |
36c08087-510d-4434-9f62-74aaabf6b71e | cost-effective-selection-of-pretraining-data | 2010.01150 | null | https://arxiv.org/abs/2010.01150v1 | https://arxiv.org/pdf/2010.01150v1.pdf | Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media | Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject matter, e.g., biology or computer science. Given the range of applications using so... | ['Cecile Paris', 'Ben Hachey', 'Sarvnaz Karimi', 'Xiang Dai'] | 2020-10-02 | null | https://aclanthology.org/2020.findings-emnlp.151 | https://aclanthology.org/2020.findings-emnlp.151.pdf | findings-of-the-association-for-computational | ['clinical-concept-extraction'] | ['medical'] | [-1.16303399e-01 -5.12430556e-02 -5.95013797e-01 -7.21079409e-01
-6.55651927e-01 -5.82147956e-01 7.27058768e-01 1.91035360e-01
-8.57406199e-01 9.43740845e-01 5.35122573e-01 -5.59475005e-01
-1.56753846e-02 -7.66308188e-01 -5.94595730e-01 -1.37512535e-01
3.85214351e-02 5.16843915e-01 9.66301411e-02 -2.99428821... | [10.639775276184082, 8.433591842651367] |
7ca5f5d2-da51-46f3-9757-d17d1f2d3329 | feature-selection-simultaneously-preserving | 2307.03902 | null | https://arxiv.org/abs/2307.03902v1 | https://arxiv.org/pdf/2307.03902v1.pdf | Feature selection simultaneously preserving both class and cluster structures | When a data set has significant differences in its class and cluster structure, selecting features aiming only at the discrimination of classes would lead to poor clustering performance, and similarly, feature selection aiming only at preserving cluster structures would lead to poor classification performance. To the b... | ['Nikhil R. Pal', 'Suchismita Das'] | 2023-07-08 | null | null | null | null | ['clustering', 'classification-1'] | ['methodology', 'methodology'] | [ 4.79813367e-01 -4.03596818e-01 -1.66475937e-01 -3.68823349e-01
-2.42235348e-01 -3.28133792e-01 1.83359921e-01 7.06217110e-01
-1.68582037e-01 6.85180664e-01 -2.83141136e-01 -2.40131631e-01
-7.80093431e-01 -1.04540884e+00 1.76991343e-01 -9.55577254e-01
-1.26256645e-01 9.27881449e-02 -1.69278663e-02 1.61223352... | [9.732722282409668, -1.759468674659729] |
6e89bca5-85c5-4754-a2a6-6dd33b573ad2 | reconstruction-distortion-of-learned-image | 2306.01125 | null | https://arxiv.org/abs/2306.01125v1 | https://arxiv.org/pdf/2306.01125v1.pdf | Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations | Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance. Despite its popularity, the robustness of LIC with respect to the quality of image reconstruction remains under-explored. In this paper, we introduce an imperceptible attack approach designe... | ['Zhenzhong Chen', 'Shan Liu', 'Xiaozhong Xu', 'Xiang Pan', 'Ding Ding', 'Zhuohang Li', 'Yang Sui'] | 2023-06-01 | null | null | null | null | ['image-reconstruction', 'image-compression'] | ['computer-vision', 'computer-vision'] | [ 7.71007955e-01 -6.58785552e-02 1.04883812e-01 1.64439492e-02
-8.47348750e-01 -7.67840087e-01 3.18343759e-01 -2.91176558e-01
-2.23993883e-01 5.74684441e-01 1.66843086e-01 -3.42111409e-01
-2.14432720e-02 -5.83321214e-01 -8.04197550e-01 -7.81373084e-01
-2.61733800e-01 -6.53682947e-01 -1.15945540e-01 6.43174946... | [5.293633460998535, 7.9171342849731445] |
ee9584c9-119f-486f-9a3a-766455ff3267 | pelphix-surgical-phase-recognition-from-x-ray | 2304.09285 | null | https://arxiv.org/abs/2304.09285v1 | https://arxiv.org/pdf/2304.09285v1.pdf | Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation | Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided perc... | ['Mathias Unberath', 'Greg Osgood', 'Russel H. Taylor', 'Mehran Armand', 'Jan Mangulabnan', 'Han Zhang', 'Benjamin D. Killeen'] | 2023-04-18 | null | null | null | null | ['surgical-phase-recognition', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 4.09530133e-01 4.75871205e-01 -4.74056721e-01 4.03682478e-02
-8.21060359e-01 -5.21042287e-01 3.88591439e-01 2.69074231e-01
-3.18728834e-01 2.58558571e-01 2.69529432e-01 -1.03284919e+00
-4.43500936e-01 -6.55725956e-01 -8.05783093e-01 -2.40058094e-01
-4.32418406e-01 6.31595671e-01 2.29269132e-01 -1.17723331... | [14.015645980834961, -3.317051649093628] |
f6e18fae-3422-4f1e-9288-35aa7abc372c | diffusion-based-mel-spectrogram-enhancement | 2305.10891 | null | https://arxiv.org/abs/2305.10891v2 | https://arxiv.org/pdf/2305.10891v2.pdf | Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data | Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training. This paper investigates the ... | ['Tan Lee', 'Wei Liu', 'Yusheng Tian'] | 2023-05-18 | null | null | null | null | ['speech-enhancement', 'speech-synthesis'] | ['speech', 'speech'] | [ 3.88712078e-01 1.64291020e-02 3.83690417e-01 -1.16762973e-01
-1.09462118e+00 -2.90984869e-01 3.52666736e-01 -1.43586427e-01
-2.77132720e-01 5.79562306e-01 6.22182667e-01 -2.06133157e-01
9.99806225e-02 -3.40227932e-01 -4.57470119e-01 -8.65500808e-01
1.72998205e-01 -1.59437612e-01 2.08442450e-01 -2.11307585... | [15.040033340454102, 5.974122047424316] |
6d901c68-e19b-4786-aa43-2f51603b7b3d | depth-supervised-nerf-fewer-views-and-faster | 2107.02791 | null | https://arxiv.org/abs/2107.02791v2 | https://arxiv.org/pdf/2107.02791v2.pdf | Depth-supervised NeRF: Fewer Views and Faster Training for Free | A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We for... | ['Deva Ramanan', 'Jun-Yan Zhu', 'Andrew Liu', 'Kangle Deng'] | 2021-07-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Deng_Depth-Supervised_NeRF_Fewer_Views_and_Faster_Training_for_Free_CVPR_2022_paper.pdf | cvpr-2022-1 | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 3.92793000e-01 4.41753209e-01 -1.69407219e-01 -6.82002902e-01
-8.43934894e-01 -6.10776961e-01 5.76693475e-01 -1.23908058e-01
-1.91365138e-01 5.43120384e-01 2.37917796e-01 -5.32058597e-01
2.34043151e-01 -9.99588251e-01 -1.36843145e+00 -3.98564488e-01
5.15963398e-02 2.95312643e-01 2.40867957e-01 -8.89461562... | [8.861213684082031, -2.8767099380493164] |
4f7f688d-2041-4571-b38b-6b6f0daf9b14 | a-novel-perspective-to-look-at-attention-bi | 2203.07216 | null | https://arxiv.org/abs/2203.07216v2 | https://arxiv.org/pdf/2203.07216v2.pdf | A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification | Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep learning models and the flexibility of the attention mechanism increase the models' complexity, thus leading to challenges in model explainabi... | ['Ruihai Dong', 'Derek Greene', 'Dairui Liu'] | 2022-03-14 | null | https://aclanthology.org/2022.findings-acl.178 | https://aclanthology.org/2022.findings-acl.178.pdf | findings-acl-2022-5 | ['news-classification'] | ['natural-language-processing'] | [-1.24114677e-01 4.51515615e-01 -3.92883420e-01 -4.76232797e-01
-4.74654526e-01 -6.63940236e-02 6.20720625e-01 -6.91729039e-02
-7.95565769e-02 6.20183349e-01 3.99972379e-01 -6.61591828e-01
-2.04656661e-01 -4.72641349e-01 -6.62013471e-01 -3.35044354e-01
4.92735773e-01 5.46344459e-01 -1.77155495e-01 -1.87974125... | [9.188793182373047, 6.042069911956787] |
046ac6cb-6c49-419a-8a63-dcc975a8a788 | how-useful-is-active-learning-for-image-based | 2006.04255 | null | https://arxiv.org/abs/2006.04255v3 | https://arxiv.org/pdf/2006.04255v3.pdf | How useful is Active Learning for Image-based Plant Phenotyping? | Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant scienc... | ['Fateme Fotouhi Ardakani', 'Baskar Ganapathysubramanian', 'Asheesh K. Singh', 'Arti Singh', 'Talukder Z. Jubery', 'Koushik Nagasubramanian', 'Soumik Sarkar', 'Seyed Vahid Mirnezami'] | 2020-06-07 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 5.94999909e-01 2.32380286e-01 -7.15510845e-01 -2.89368629e-01
-6.71904206e-01 -6.99950933e-01 2.17202291e-01 7.15310872e-01
-2.90830702e-01 7.30332851e-01 -6.32043362e-01 -4.53007758e-01
-3.79073113e-01 -1.00153744e+00 -5.28860092e-01 -1.09211814e+00
-7.44779855e-02 9.08821106e-01 4.15616423e-01 2.71208286... | [9.118500709533691, -1.5137619972229004] |
dcfeaf3d-c45c-425f-a402-5d95d796e737 | spatio-temporal-cnn-baseline-method-for-the | 2112.12074 | null | https://arxiv.org/abs/2112.12074v1 | https://arxiv.org/pdf/2112.12074v1.pdf | Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark | This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored acco... | ['Pierre-Etienne Martin'] | 2021-12-16 | null | null | null | null | ['stroke-classification'] | ['methodology'] | [ 8.66350755e-02 1.27961963e-01 -2.95163840e-01 -1.13111034e-01
-6.02908373e-01 -3.98330986e-01 8.67809415e-01 -2.00433075e-01
-1.07328737e+00 6.16099298e-01 4.78748083e-01 8.53958819e-03
1.20354086e-01 -4.55078185e-01 -4.97965723e-01 -4.58535701e-01
9.21546966e-02 2.94308573e-01 1.05634248e+00 -3.73276085... | [7.960418701171875, 0.20580005645751953] |
c112e91f-2747-481a-a346-d374926d3b04 | can-chatgpt-s-responses-boost-traditional | 2307.04648 | null | https://arxiv.org/abs/2307.04648v1 | https://arxiv.org/pdf/2307.04648v1.pdf | Can ChatGPT's Responses Boost Traditional Natural Language Processing? | The employment of foundation models is steadily expanding, especially with the launch of ChatGPT and the release of other foundation models. These models have shown the potential of emerging capabilities to solve problems, without being particularly trained to solve. A previous work demonstrated these emerging capabili... | ['Björn W. Schuller', 'Erik Cambria', 'Mostafa M. Amin'] | 2023-07-06 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 1.46119431e-01 5.80986798e-01 2.19081238e-01 -6.32478297e-01
-6.97163880e-01 -3.01337272e-01 6.54307902e-01 5.60455978e-01
-5.63262880e-01 7.04317987e-01 5.60221195e-01 8.56486633e-02
-4.39123362e-01 -4.67597574e-01 1.96567670e-01 -5.01737714e-01
-5.44060804e-02 7.09424615e-01 -3.48612010e-01 -5.93270779... | [12.726964950561523, 6.444259166717529] |
5459552b-6d68-426c-b3d6-8713a4e47752 | masked-multi-step-multivariate-time-series | 2209.14413 | null | https://arxiv.org/abs/2209.14413v1 | https://arxiv.org/pdf/2209.14413v1.pdf | Masked Multi-Step Multivariate Time Series Forecasting with Future Information | In this paper, we introduce Masked Multi-Step Multivariate Forecasting (MMMF), a novel and general self-supervised learning framework for time series forecasting with known future information. In many real-world forecasting scenarios, some future information is known, e.g., the weather information when making a short-t... | ['Nurali Virani', 'Honggang Wang', 'Yiwei Fu'] | 2022-09-28 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.93901077e-01 -4.46032703e-01 -3.74541551e-01 -7.11528718e-01
-1.88074216e-01 -5.73678017e-01 8.93127561e-01 -5.51834442e-02
3.99779007e-02 9.64840174e-01 5.23050390e-02 -9.40667748e-01
-2.74065763e-01 -9.96862531e-01 -5.37348807e-01 -8.67223382e-01
-3.95893335e-01 1.91896498e-01 -2.20092878e-01 -4.47611034... | [6.647786617279053, 2.996732711791992] |
5ef171ca-5b0e-4b7a-9c30-ffde946a4cd5 | adversarial-genetic-programming-for-cyber | 2004.04647 | null | https://arxiv.org/abs/2004.04647v1 | https://arxiv.org/pdf/2004.04647v1.pdf | Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters | Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Progr... | ['Dennis Garcia', "Una-May O'Reilly", 'Jamal Toutouh', 'Erik Hemberg', 'Daniel Prado Sanchez', 'Anthony Erb Luogo', 'Marcos Pertierra', 'Jonathan Kelly'] | 2020-04-07 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.74950594e-01 5.18529594e-01 1.33832067e-01 6.65368974e-01
1.21057630e-01 -1.26269042e+00 1.02804267e+00 9.08343028e-03
1.88898221e-01 4.92915362e-01 -1.88724861e-01 -9.74980414e-01
-4.22647417e-01 -1.16658759e+00 -5.09427726e-01 -7.84941137e-01
-5.46197951e-01 1.58902213e-01 -1.54558375e-01 -8.36932123... | [5.696785926818848, 7.607706069946289] |
ab701653-73eb-4050-b8b7-2c3b51766fe6 | dreamcoder-growing-generalizable | 2006.08381 | null | https://arxiv.org/abs/2006.08381v1 | https://arxiv.org/pdf/2006.08381v1.pdf | DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning | Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by c... | ['Joshua B. Tenenbaum', 'Armando Solar-Lezama', 'Mathias Sable-Meyer', 'Maxwell Nye', 'Lucas Morales', 'Kevin Ellis', 'Luc Cary', 'Catherine Wong', 'Luke Hewitt'] | 2020-06-15 | null | null | null | null | ['program-induction', 'drawing-pictures'] | ['computer-code', 'computer-vision'] | [-1.36398718e-01 2.35921875e-01 1.08410753e-01 -5.42513788e-01
-1.47666872e-01 -8.82591784e-01 6.32655025e-01 9.98423100e-02
-6.48815781e-02 7.18076289e-01 3.73289399e-02 -8.14560175e-01
-2.61642933e-01 -1.16828740e+00 -9.09295559e-01 -3.88118476e-01
-4.96202439e-01 6.06868386e-01 7.46066635e-03 -7.16976643... | [9.130596160888672, 7.045917987823486] |
8089d054-213d-4704-844f-a8f6b5be3724 | hybrid-sd-text-h-text-sd-a-new-hybrid | 2211.01722 | null | https://arxiv.org/abs/2211.01722v2 | https://arxiv.org/pdf/2211.01722v2.pdf | Hybrid-SD (H_SD): A new hybrid evaluation metric for automatic speech recognition tasks | Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H_SD), a new hybrid evaluation... | ['T. V. Prabhakar', 'Supreeth Rao', 'Harsha Yelchuri', 'Zitha Sasindran'] | 2022-11-03 | null | null | null | null | ['intent-recognition', 'spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.00555964e-01 3.54138874e-02 2.31982291e-01 -6.87335014e-01
-1.07836139e+00 -3.49650472e-01 7.20896661e-01 -7.08889365e-02
-9.16313052e-01 4.73388642e-01 6.40742302e-01 -7.96279669e-01
2.35123768e-01 -3.95593435e-01 2.27866378e-02 -2.40463793e-01
1.85574710e-01 2.93039888e-01 6.92116097e-02 -5.45263648... | [14.327512741088867, 6.891331672668457] |
c9c7d7e7-e6da-4fde-b15d-e0a8c7b50c3f | a-multifactor-analysis-model-for-stock-market | null | null | https://www.ijcst.org/Volume14/Issue1.html | https://www.ijcst.org/Volume14/Issue1/p1_14_1.pdf | A Multifactor Analysis Model for Stock Market Prediction | Stock Market predictions have historically been a problem tackled by different singular approaches even though markets are influenced by many different factors. This paper presents a novel multi-factor analysis model for stock price prediction that combines Technical analysis, Fundamental analysis, Machine learning, an... | ['Akash Deep'] | 2023-03-05 | null | null | null | international-journal-of-computer-science-and | ['stock-market-prediction', 'stock-price-prediction'] | ['time-series', 'time-series'] | [-6.20264888e-01 -6.37627900e-01 -6.43573940e-01 -9.89721864e-02
-3.50142986e-01 -4.64191765e-01 7.27128565e-01 -3.61370556e-02
-2.40069583e-01 7.78949976e-01 6.20256484e-01 -7.33483016e-01
-5.11418544e-02 -1.21402800e+00 -2.94809937e-01 -3.87780845e-01
-6.92359880e-02 -3.72729599e-02 -1.50377184e-01 -5.70716798... | [4.468801021575928, 4.2745490074157715] |
19c915f7-097d-4321-a3c9-fca2fe88d1ae | testing-the-robustness-of-learned-index | 2207.11575 | null | https://arxiv.org/abs/2207.11575v1 | https://arxiv.org/pdf/2207.11575v1.pdf | Testing the Robustness of Learned Index Structures | While early empirical evidence has supported the case for learned index structures as having favourable average-case performance, little is known about their worst-case performance. By contrast, classical structures are known to achieve optimal worst-case behaviour. This work evaluates the robustness of learned index s... | ['Benjamin I. P. Rubinstein', 'Renata Borovica-Gajic', 'Matthias Bachfischer'] | 2022-07-23 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.48218781e-01 -1.34229183e-01 -1.04089342e-01 2.90867239e-01
-6.20804906e-01 -9.08726454e-01 6.13102376e-01 3.91496986e-01
-5.53494453e-01 6.64281309e-01 -1.62507787e-01 -5.89906156e-01
7.60595500e-03 -8.44221413e-01 -9.10622001e-01 -9.95900393e-01
-4.35706377e-01 7.26028502e-01 6.51320875e-01 -6.16336912... | [5.817105770111084, 7.5702691078186035] |
c5ce7343-1207-44e2-9888-86531b1237dd | small-footprint-slimmable-networks-for | 2304.12183 | null | https://arxiv.org/abs/2304.12183v1 | https://arxiv.org/pdf/2304.12183v1.pdf | Small-footprint slimmable networks for keyword spotting | In this work, we present Slimmable Neural Networks applied to the problem of small-footprint keyword spotting. We show that slimmable neural networks allow us to create super-nets from Convolutioanl Neural Networks and Transformers, from which sub-networks of different sizes can be extracted. We demonstrate the usefuln... | ['Yuzong Liu', 'Dongsu Du', 'Mohammad Omar Khursheed', 'Zuhaib Akhtar'] | 2023-04-21 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [-6.11753240e-02 1.06036127e-01 -1.58536404e-01 -1.91239446e-01
-6.20611429e-01 -7.96658218e-01 2.95166850e-01 -3.46551538e-01
-5.29442430e-01 5.16492069e-01 5.76315224e-02 -1.09480119e+00
-1.17295153e-01 -4.42384362e-01 -7.82597601e-01 5.56471124e-02
7.16228932e-02 6.25624716e-01 3.53857547e-01 -3.59824359... | [14.114484786987305, 6.378697395324707] |
8034f100-b686-4355-baf5-c20be5d1f75a | a-bayesian-algorithm-for-retrosynthesis | 2003.03190 | null | https://arxiv.org/abs/2003.03190v1 | https://arxiv.org/pdf/2003.03190v1.pdf | A Bayesian algorithm for retrosynthesis | The identification of synthetic routes that end with a desired product has been an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited fraction of the entire reaction space. At present, emerging machine-learning technologies are overturning the process of retrosynthetic p... | ['Ryo Yoshida', 'Stephen Wu', 'Mitsuru Ohno', 'Zhongliang Guo'] | 2020-03-06 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.23820007e-01 -9.93295237e-02 -1.62808180e-01 -1.30072251e-01
-9.07282412e-01 -1.13388610e+00 9.14682925e-01 6.45167053e-01
-5.80995262e-01 1.11139536e+00 -2.85172641e-01 -7.88135827e-01
-6.26796857e-02 -9.67408776e-01 -8.24306607e-01 -9.78421926e-01
1.28364936e-01 8.19599450e-01 2.10057572e-01 7.97027871... | [4.479728698730469, 6.118112564086914] |
eb830c35-daf7-4e42-8b4f-edd4ca920738 | tfcnet-temporal-fully-connected-networks-for | 2203.05928 | null | https://arxiv.org/abs/2203.05928v1 | https://arxiv.org/pdf/2203.05928v1.pdf | TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning | Temporal Reasoning is one important functionality for vision intelligence. In computer vision research community, temporal reasoning is usually studied in the form of video classification, for which many state-of-the-art Neural Network structures and dataset benchmarks are proposed in recent years, especially 3D CNNs a... | ['Shiwen Zhang'] | 2022-03-11 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.72153085e-01 -2.88326740e-01 -6.63087308e-01 -1.37211144e-01
1.37780607e-01 -4.64997202e-01 8.87866199e-01 -7.11306036e-01
-5.61888754e-01 4.28606182e-01 1.99288115e-01 -4.43193823e-01
-2.61237979e-01 -5.95310688e-01 -9.09452736e-01 -7.89435208e-01
-7.48626888e-02 3.27543765e-02 7.75498688e-01 -3.22020769... | [8.697357177734375, 0.5894866585731506] |
3c258235-14e0-422c-a0b9-a846f399b61e | machine-learning-assisted-bayesian-inference | 2304.13660 | null | https://arxiv.org/abs/2304.13660v1 | https://arxiv.org/pdf/2304.13660v1.pdf | Machine Learning-assisted Bayesian Inference for Jamming Detection in 5G NR | The increased flexibility and density of spectrum access in 5G NR have made jamming detection a critical research area. To detect coexisting jamming and subtle interference that can affect legitimate communications performance, we introduce machine learning (ML)-assisted Bayesian Inference for jamming detection methodo... | ['Lingjia Liu', 'Shehadi Dayekh', 'Ishan Aryendu', 'Ying Wang', 'Shashank Jere'] | 2023-04-26 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.44300899e-02 -2.07307279e-01 -1.11102894e-01 2.51210600e-01
-4.57665890e-01 -5.48383653e-01 3.03771526e-01 5.90795614e-02
1.78737953e-01 8.09663117e-01 -3.77729386e-02 -1.24824929e+00
-6.52744830e-01 -5.14026523e-01 1.39783263e-01 -9.42413449e-01
-1.37595868e+00 2.25850597e-01 -9.42367874e-03 -1.83976039... | [6.169260501861572, 1.4606088399887085] |
af46b785-2a1d-4ac8-8973-5887cd682c42 | unsupervised-part-representation-by-flow | 2011.13920 | null | https://arxiv.org/abs/2011.13920v2 | https://arxiv.org/pdf/2011.13920v2.pdf | Unsupervised part representation by Flow Capsules | Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose a way to learn primary capsule encoders that detect atomic parts from a single image. During traini... | ['David J. Fleet', 'Geoffrey E. Hinton', 'Soroosh Yazdani', 'Andrea Tagliasacchi', 'Sara Sabour'] | 2020-11-27 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [ 4.88180459e-01 5.37597835e-01 -5.06740987e-01 -3.11399400e-01
-6.27113461e-01 -8.50547612e-01 4.22067463e-01 4.80692126e-02
-2.22780317e-01 6.29488289e-01 4.03990895e-01 7.19163269e-02
1.22640163e-01 -5.27793407e-01 -1.06214416e+00 -3.64024431e-01
-9.55226719e-02 3.82316649e-01 5.06651521e-01 1.06129557... | [9.484716415405273, -0.016131611540913582] |
6566ac32-b19d-4e5c-81e0-4b6681ff9585 | hate-speech-and-offensive-language-detection-2 | 2302.08777 | null | https://arxiv.org/abs/2302.08777v1 | https://arxiv.org/pdf/2302.08777v1.pdf | Hate Speech and Offensive Language Detection using an Emotion-aware Shared Encoder | The rise of emergence of social media platforms has fundamentally altered how people communicate, and among the results of these developments is an increase in online use of abusive content. Therefore, automatically detecting this content is essential for banning inappropriate information, and reducing toxicity and vio... | ['Noel Crespi', 'Reza Farahbakhsh', 'Praboda Rajapaksha', 'Khouloud Mnassri'] | 2023-02-17 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-8.67372826e-02 -1.60723001e-01 -2.78864861e-01 -1.78558052e-01
-9.79690135e-01 -4.98891205e-01 4.21886861e-01 2.31055394e-01
-3.78674150e-01 6.87330246e-01 4.25464600e-01 1.27239168e-01
3.02001059e-01 -4.06099677e-01 -3.43304276e-01 -5.04989088e-01
8.66174027e-02 -4.10189480e-02 -1.60551324e-01 -4.65718478... | [8.802321434020996, 10.599199295043945] |
74f70295-c8e1-4ae1-81b2-a1f40002b758 | efficient-multi-view-performance-capture-of | 1602.02023 | null | http://arxiv.org/abs/1602.02023v1 | http://arxiv.org/pdf/1602.02023v1.pdf | Efficient Multi-view Performance Capture of Fine-Scale Surface Detail | We present a new effective way for performance capture of deforming meshes
with fine-scale time-varying surface detail from multi-view video. Our method
builds up on coarse 4D surface reconstructions, as obtained with commonly used
template-based methods. As they only capture models of coarse-to-medium scale
detail, fi... | ['Edilson de Aguiar', 'Thomas Helten', 'Nadia Robertini', 'Christian Theobalt'] | 2016-02-05 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.26868272e-01 -4.03495207e-02 4.08058614e-01 -1.87711507e-01
-7.88390875e-01 -5.26680350e-01 2.65613765e-01 -2.04514325e-01
4.48847450e-02 7.03270495e-01 -1.26450747e-01 5.24973869e-01
3.43047548e-03 -8.37339938e-01 -8.30653310e-01 -5.96210361e-01
1.66730687e-01 8.89931798e-01 6.68701053e-01 -2.51716405... | [9.116972923278809, -2.981657028198242] |
91a3e70c-d781-473c-9786-4186e8a46afc | the-joint-role-of-geometry-and-illumination | 2101.02496 | null | https://arxiv.org/abs/2101.02496v2 | https://arxiv.org/pdf/2101.02496v2.pdf | The joint role of geometry and illumination on material recognition | Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accura... | ['Belen Masia', 'Diego Gutierrez', 'Ana Serrano', 'Manuel Lagunas'] | 2021-01-07 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 2.61437088e-01 -8.07913482e-01 7.07428232e-02 -3.87623310e-01
-3.87819827e-01 -6.27024174e-01 7.22768545e-01 5.07210195e-01
-5.27007163e-01 1.02116831e-01 1.05819479e-01 -9.18210298e-02
-2.28442699e-01 -6.89093113e-01 -8.78631175e-01 -6.38287604e-01
1.75494537e-01 2.33383492e-01 2.62459427e-01 -1.08024320... | [10.034969329833984, 2.150377035140991] |
f5bf0e12-2680-4a8d-b2e5-9ad8b8d58b5a | streaming-small-footprint-keyword-spotting | 1710.09617 | null | http://arxiv.org/abs/1710.09617v1 | http://arxiv.org/pdf/1710.09617v1.pdf | Streaming Small-Footprint Keyword Spotting using Sequence-to-Sequence Models | We develop streaming keyword spotting systems using a recurrent neural
network transducer (RNN-T) model: an all-neural, end-to-end trained,
sequence-to-sequence model which jointly learns acoustic and language model
components. Our models are trained to predict either phonemes or graphemes as
subword units, thus allowi... | ['Kanishka Rao', 'Anton Bakhtin', 'Wei Li', 'Yanzhang He', 'Rohit Prabhavalkar', 'Ian McGraw'] | 2017-10-26 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 7.00326443e-01 4.29218225e-02 -3.45592409e-01 -2.96743333e-01
-1.24870288e+00 -6.88867092e-01 5.21860957e-01 -1.38745129e-01
-7.03899860e-01 1.83023170e-01 2.94552803e-01 -9.67921913e-01
5.07116616e-01 -3.35711777e-01 -9.43074584e-01 -6.39022291e-01
3.08082346e-03 3.14517409e-01 3.07937682e-01 -3.15116704... | [14.36801528930664, 6.698555946350098] |
9cf3a274-781c-448d-b5ca-9977521eabde | inferring-sensitive-attributes-from-model | 2208.09967 | null | https://arxiv.org/abs/2208.09967v2 | https://arxiv.org/pdf/2208.09967v2.pdf | Inferring Sensitive Attributes from Model Explanations | Model explanations provide transparency into a trained machine learning model's blackbox behavior to a model builder. They indicate the influence of different input attributes to its corresponding model prediction. The dependency of explanations on input raises privacy concerns for sensitive user data. However, current... | ['Antoine Boutet', 'Vasisht Duddu'] | 2022-08-21 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 5.80466568e-01 5.61023891e-01 -5.66414714e-01 -7.17890859e-01
-5.28651476e-01 -1.15561628e+00 5.92408538e-01 3.56468529e-01
-1.54753581e-01 5.23395956e-01 -2.93128937e-02 -8.79859447e-01
-9.45961028e-02 -8.78003180e-01 -9.62234259e-01 -5.37339211e-01
-6.39300272e-02 2.92330235e-01 -8.87561664e-02 3.87744248... | [5.922633171081543, 7.181882858276367] |
4a134913-b4a2-4e65-8e72-48086682723c | towards-code-generation-from-bdd-test-case | 2305.11619 | null | https://arxiv.org/abs/2305.11619v1 | https://arxiv.org/pdf/2305.11619v1.pdf | Towards Code Generation from BDD Test Case Specifications: A Vision | Automatic code generation has recently attracted large attention and is becoming more significant to the software development process. Solutions based on Machine Learning and Artificial Intelligence are being used to increase human and software efficiency in potent and innovative ways. In this paper, we aim to leverage... | ['Mira Mezini', 'Krishna Narasimhan', 'Mariam Naveed', 'Hani Aldebes', 'David Reichenbach', 'Leon Chemnitz'] | 2023-05-19 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.27117097e-01 1.53435752e-01 -1.45436645e-01 -3.83257687e-01
-6.70156598e-01 -5.40005803e-01 4.14631337e-01 2.80724000e-02
2.46784359e-01 4.28997010e-01 -1.65936336e-01 -5.24687827e-01
-2.70194467e-02 -9.14484620e-01 -3.06884080e-01 -3.61160859e-02
3.60348254e-01 3.57620150e-01 1.56313196e-01 -2.49360770... | [7.991806507110596, 7.432701587677002] |
af4d2b69-f9b6-4028-a5c7-9e749c5558fc | towards-comparability-in-non-intrusive-load | 2001.07708 | null | https://arxiv.org/abs/2001.07708v1 | https://arxiv.org/pdf/2001.07708v1.pdf | Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation | Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techn... | ['Stephen Makonin', 'Wilfried Elmenreich', 'Christoph Klemenjak'] | 2020-01-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.80131093e-01 -2.17018366e-01 -2.03341350e-01 -5.53886533e-01
-5.81074476e-01 -6.82707310e-01 7.72869289e-01 5.56263924e-01
-1.10943295e-01 7.13824987e-01 1.62316188e-01 -2.16541946e-01
-6.35540545e-01 -8.96912336e-01 -1.94343388e-01 -8.46864522e-01
-1.83326274e-01 3.60919118e-01 -3.14420938e-01 1.45661414... | [6.052030563354492, 2.617649555206299] |
05a4b96c-9aeb-4a25-affe-f3d1210cabb4 | on-the-performance-of-data-compression-in | 2207.00223 | null | https://arxiv.org/abs/2207.00223v1 | https://arxiv.org/pdf/2207.00223v1.pdf | On the Performance of Data Compression in Clustered Fog Radio Access Networks | The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in user equipments (UEs) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirem... | ['Jie Zhang', 'Qianbin Chen', 'Yanan Zheng', 'Jiliang Zhang', 'Yan Jiang', 'Haonan Hu'] | 2022-07-01 | null | null | null | null | ['data-compression'] | ['time-series'] | [-1.50432780e-01 -2.97708809e-02 1.22312173e-01 3.56127024e-01
1.33605167e-01 -1.58266738e-01 8.71082861e-03 -5.73178753e-02
-3.95379901e-01 6.27018571e-01 -3.32445979e-01 -4.17691052e-01
-7.10954070e-01 -8.08777511e-01 -2.86407590e-01 -1.13279200e+00
-3.46711248e-01 2.87717819e-01 2.76962072e-01 3.95967253... | [5.93824577331543, 1.623590350151062] |
6164391f-587b-4b97-b890-e79767c21e79 | modelling-sequential-music-track-skips-using | 1903.08408 | null | http://arxiv.org/abs/1903.08408v1 | http://arxiv.org/pdf/1903.08408v1.pdf | Modelling Sequential Music Track Skips using a Multi-RNN Approach | Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequ... | ['Christina Lioma', 'Christian Hansen', 'Stephen Alstrup', 'Casper Hansen', 'Jakob Grue Simonsen'] | 2019-03-20 | null | null | null | null | ['sequential-skip-prediction'] | ['time-series'] | [ 2.28533283e-01 4.26076651e-02 -3.94552737e-01 -3.24235559e-01
-1.25807774e+00 -5.23393393e-01 1.80274680e-01 -2.59511411e-01
-3.30420852e-01 3.00547153e-01 8.71539176e-01 -1.36144638e-01
-1.74604103e-01 -3.57174486e-01 -7.25798368e-01 -4.84616935e-01
-7.62141570e-02 4.44740653e-01 4.68851998e-02 -1.96788058... | [15.679920196533203, 5.235088348388672] |
744751e3-3ffe-4f31-a135-ea4fc712774d | low-complexity-acoustic-echo-cancellation | 2207.11388 | null | https://arxiv.org/abs/2207.11388v2 | https://arxiv.org/pdf/2207.11388v2.pdf | Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering | The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance. The performance of Kalman filter is closely related to the estimation accuracy of the state noise covariance and the observation noise covariance. The estimation er... | ['Muyong Cao', 'Xuefei Fang', 'Wei Wu', 'Fei Jiang', 'Dong Yang'] | 2022-07-23 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-1.96875691e-01 -4.40347493e-01 4.24705088e-01 -3.74910980e-02
-5.42775214e-01 -2.13313624e-01 3.19892943e-01 -2.64712542e-01
-4.04732078e-01 4.85757470e-01 1.85980663e-01 -3.21798891e-01
-2.54471838e-01 -1.99122891e-01 -2.83511549e-01 -9.74799335e-01
-2.03991070e-01 -4.09816414e-01 4.27708685e-01 2.15936191... | [15.09217643737793, 5.8270087242126465] |
c174fb00-fe5b-4e5f-bae1-8d179a1a495c | efficient-sparse-spherical-k-means-for | 2108.00895 | null | https://arxiv.org/abs/2108.00895v1 | https://arxiv.org/pdf/2108.00895v1.pdf | Efficient Sparse Spherical k-Means for Document Clustering | Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the... | ['Thomas Ertl', 'Steffen Koch', 'Johannes Knittel'] | 2021-07-30 | null | null | null | null | ['text-clustering', 'short-text-clustering', 'unsupervised-spatial-clustering'] | ['natural-language-processing', 'natural-language-processing', 'time-series'] | [-3.67079765e-01 -5.52097023e-01 -1.42844304e-01 -3.25397074e-01
-6.86292171e-01 -9.57294822e-01 4.40501750e-01 7.28745639e-01
-5.74817002e-01 1.06138222e-01 3.72697085e-01 -2.65484035e-01
-6.34599268e-01 -8.64929438e-01 -2.10744143e-01 -8.99316549e-01
-2.15812996e-01 4.89638984e-01 4.16693747e-01 -1.20687515... | [7.390870094299316, 4.826218605041504] |
bad163b9-1cdd-4fd6-b6db-3ba8566c0a56 | visual-semantic-matching-by-exploring-high | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Visual-Semantic_Matching_by_Exploring_High-Order_Attention_and_Distraction_CVPR_2020_paper.pdf | Visual-Semantic Matching by Exploring High-Order Attention and Distraction | Cross-modality semantic matching is a vital task in computer vision and has attracted increasing attention in recent years. Existing methods mainly explore object-based alignment between image objects and text words. In this work, we address this task from two previously-ignored aspects: high-order semantic information... | [' Yadong Mu', ' Duo Zhang', 'Yongzhi Li'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['graph-similarity'] | ['graphs'] | [ 2.75176674e-01 -6.71839714e-02 -6.53802454e-02 -1.99956834e-01
-8.74623179e-01 -4.63850379e-01 7.05329597e-01 3.52119416e-01
-3.09352070e-01 5.45225479e-02 3.63406718e-01 3.08502428e-02
-3.08418036e-01 -5.30442119e-01 -6.13263547e-01 -4.00057822e-01
3.29551548e-01 3.52626383e-01 1.65326744e-01 -1.19238891... | [10.771134376525879, 1.2817810773849487] |
d3929ec1-24a5-4dad-90f3-866d550acb72 | keynet-keypoint-detection-by-handcrafted-and | 1904.00889 | null | https://arxiv.org/abs/1904.00889v3 | https://arxiv.org/pdf/1904.00889v3.pdf | Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters | We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the netw... | ['Axel Barroso-Laguna', 'Krystian Mikolajczyk', 'Edgar Riba', 'Daniel Ponsa'] | 2019-04-01 | key-net-keypoint-detection-by-handcrafted-and | http://openaccess.thecvf.com/content_ICCV_2019/html/Barroso-Laguna_Key.Net_Keypoint_Detection_by_Handcrafted_and_Learned_CNN_Filters_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Barroso-Laguna_Key.Net_Keypoint_Detection_by_Handcrafted_and_Learned_CNN_Filters_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-matching'] | ['computer-vision'] | [-3.35937023e-01 -4.23908979e-01 -3.47412467e-01 -3.59482914e-01
-1.06157780e+00 -7.60106683e-01 6.77824974e-01 1.88367307e-01
-7.12762177e-01 2.24915609e-01 1.92049831e-01 3.81748050e-01
-2.27491483e-01 -6.87410891e-01 -1.12116730e+00 -6.98696226e-02
-4.62291241e-01 -1.69724956e-01 8.53005648e-01 -3.61024439... | [8.008512496948242, -2.0080058574676514] |
6d7043f0-738c-4f38-8b14-ef27af7380b5 | counterfactual-explanations-of-neural-network | 2304.04063 | null | https://arxiv.org/abs/2304.04063v2 | https://arxiv.org/pdf/2304.04063v2.pdf | Counterfactual Explanations of Neural Network-Generated Response Curves | Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (re... | ['John Sheppard', 'Giorgio Morales'] | 2023-04-08 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [ 7.67961860e-01 5.33441566e-02 -6.43073693e-02 -3.88131499e-01
2.02492159e-02 -7.56369174e-01 4.83921349e-01 5.66704631e-01
-2.51172870e-01 7.06600785e-01 -1.45920282e-02 -4.55198884e-01
-8.92498791e-01 -1.22512996e+00 -9.48841512e-01 -8.57644737e-01
1.33511256e-02 -7.12756068e-02 2.40936335e-02 -5.16663671... | [8.379546165466309, 5.292119979858398] |
538a9f7c-a496-4e21-b71f-c473c0b7a326 | dwnet-deep-wide-network-for-3d-action | 1908.11036 | null | https://arxiv.org/abs/1908.11036v1 | https://arxiv.org/pdf/1908.11036v1.pdf | DWnet: Deep-Wide Network for 3D Action Recognition | We propose in this paper a deep-wide network (DWnet) which combines the deep structure with the broad learning system (BLS) to recognize actions. Compared with the deep structure, the novel model saves lots of testing time and almost achieves real-time testing. Furthermore, the DWnet can capture better features than br... | ['Jianqin Yin', 'Fuxing Yang', 'Yonghao Dang'] | 2019-08-29 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [-2.04000458e-01 -1.27814829e-01 -4.56817120e-01 -1.17637686e-01
-2.97313035e-01 2.24678993e-01 8.85637403e-02 -4.65060055e-01
-3.76453191e-01 7.82754540e-01 4.51666385e-01 3.83083999e-01
-5.78221858e-01 -1.06918156e+00 -4.19261307e-01 -6.77909911e-01
-1.61925271e-01 2.19370097e-01 8.70383263e-01 -1.03099570... | [8.092700004577637, 0.39396560192108154] |
137afd30-8ccb-42ee-bcb9-c15e72b44744 | smart-non-intrusive-appliance-identification | 2102.04808 | null | https://arxiv.org/abs/2102.04808v1 | https://arxiv.org/pdf/2102.04808v1.pdf | Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier | Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power hi... | ['Abbes Amira', 'Faycal Bensaali', 'Abdullah Alsalemi', 'Yassine Himeur'] | 2021-02-09 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.64911941e-01 -1.55992568e-01 -3.26064348e-01 -2.86756217e-01
-8.22581112e-01 -2.65523046e-01 6.36395037e-01 1.51318833e-01
4.18416187e-02 5.24953008e-01 5.94524331e-02 2.83738852e-01
-5.20791292e-01 -8.47234786e-01 1.64448634e-01 -1.21127784e+00
-7.90387541e-02 1.07285798e-01 -3.40344727e-01 2.03130677... | [6.022439956665039, 2.5790350437164307] |
88793fdf-9df3-41e9-bac7-a3462418921b | weakly-supervised-learning-of-instance | 1904.05044 | null | https://arxiv.org/abs/1904.05044v3 | https://arxiv.org/pdf/1904.05044v3.pdf | Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations | This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. For generating the pseudo labels, we first identify confident seed areas ... | ['Jiwoon Ahn', 'Sunghyun Cho', 'Suha Kwak'] | 2019-04-10 | weakly-supervised-learning-of-instance-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ahn_Weakly_Supervised_Learning_of_Instance_Segmentation_With_Inter-Pixel_Relations_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ahn_Weakly_Supervised_Learning_of_Instance_Segmentation_With_Inter-Pixel_Relations_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-level-supervised-instance-segmentation'] | ['computer-vision'] | [ 6.01409376e-01 8.24395120e-01 -4.34921652e-01 -6.06537342e-01
-8.07105660e-01 -5.93432426e-01 4.41341639e-01 1.14492126e-01
-4.41513509e-01 7.55330086e-01 -5.33428550e-01 -2.31085941e-02
2.03346699e-01 -8.31562936e-01 -1.10433400e+00 -5.22296369e-01
2.65744682e-02 7.59323299e-01 7.10067749e-01 2.51023531... | [9.556869506835938, 0.5578796863555908] |
1a69c4af-d99e-4199-8201-a7d869dab206 | wave-u-net-a-multi-scale-neural-network-for | 1806.03185 | null | http://arxiv.org/abs/1806.03185v1 | http://arxiv.org/pdf/1806.03185v1.pdf | Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation | Models for audio source separation usually operate on the magnitude spectrum,
which ignores phase information and makes separation performance dependant on
hyper-parameters for the spectral front-end. Therefore, we investigate
end-to-end source separation in the time-domain, which allows modelling phase
information and... | ['Simon Dixon', 'Sebastian Ewert', 'Daniel Stoller'] | 2018-06-08 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 3.83245498e-01 -4.04320657e-01 -7.22447701e-04 -1.52200937e-01
-1.09299064e+00 -7.71410644e-01 2.05247015e-01 1.57350495e-01
-1.44593164e-01 3.66873562e-01 5.18772304e-01 -9.81444791e-02
-6.27803981e-01 -2.76835531e-01 -2.08668336e-01 -6.68397188e-01
-3.59189212e-01 -2.19652325e-01 4.07855213e-01 -8.35241154... | [15.399945259094238, 5.574965476989746] |
62de4b9d-8972-4f7b-8ae4-a23b694bcc92 | weakly-supervised-temporal-action-6 | 2303.12332 | null | https://arxiv.org/abs/2303.12332v1 | https://arxiv.org/pdf/2303.12332v1.pdf | Weakly-Supervised Temporal Action Localization by Inferring Snippet-Feature Affinity | Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos, only taking video-level labels as the supervised information. Pseudo label generation is a promising strategy to solve the challenging problem, but most existing methods are limited to employ... | ['Huadong Ma', 'Chuanming Wang', 'Mengshi Qi', 'Wulian Yun'] | 2023-03-22 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'pseudo-label'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.07320344e-01 -1.92889675e-01 -7.93130159e-01 -4.74817961e-01
-7.20566154e-01 -5.17585278e-01 6.10917568e-01 -1.95492253e-01
-3.66005659e-01 7.43919194e-01 7.25210071e-01 3.23304951e-01
-1.75433829e-01 -3.34476501e-01 -6.39041543e-01 -9.02285457e-01
-1.63031653e-01 7.33500421e-02 6.69076204e-01 2.53803641... | [8.425143241882324, 0.574627161026001] |
2f051e3f-2148-4f54-a72e-bbcf8043effc | carrnn-a-continuous-autoregressive-recurrent | 2104.03739 | null | https://arxiv.org/abs/2104.03739v1 | https://arxiv.org/pdf/2104.03739v1.pdf | CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data | Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hinder... | ['Mads Nielsen', 'Sébastien Ourselin', 'Lauge Sørensen', 'Mostafa Mehdipour Ghazi'] | 2021-04-08 | null | null | null | null | ['icu-mortality', 'time-series-regression'] | ['medical', 'time-series'] | [ 1.69307724e-01 -3.00530583e-01 -1.86152253e-02 -3.67266655e-01
-6.76057816e-01 2.08395615e-01 4.16190743e-01 2.31164470e-01
-5.50939500e-01 7.52322853e-01 3.78818959e-01 -6.05089366e-01
-5.71950376e-01 -4.94474560e-01 -6.51261806e-01 -7.48498738e-01
-6.67037129e-01 5.60174584e-01 5.27847733e-04 -1.97745189... | [7.069216728210449, 3.207071304321289] |
aed55ed8-4eeb-4a9e-b8f4-62a16f457601 | object-contour-detection-with-a-fully | 1603.04530 | null | http://arxiv.org/abs/1603.04530v1 | http://arxiv.org/pdf/1603.04530v1.pdf | Object Contour Detection with a Fully Convolutional Encoder-Decoder Network | We develop a deep learning algorithm for contour detection with a fully
convolutional encoder-decoder network. Different from previous low-level edge
detection, our algorithm focuses on detecting higher-level object contours. Our
network is trained end-to-end on PASCAL VOC with refined ground truth from
inaccurate poly... | ['Ming-Hsuan Yang', 'Brian Price', 'Scott Cohen', 'Honglak Lee', 'Jimei Yang'] | 2016-03-15 | object-contour-detection-with-a-fully-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Yang_Object_Contour_Detection_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Yang_Object_Contour_Detection_CVPR_2016_paper.pdf | cvpr-2016-6 | ['contour-detection'] | ['computer-vision'] | [ 1.04583964e-01 1.59994915e-01 -9.22092572e-02 -2.15259999e-01
-1.34432650e+00 -7.85776019e-01 9.45340842e-02 1.50124252e-01
-5.26576877e-01 2.49059722e-01 -1.24805108e-01 -1.67554572e-01
7.70818591e-01 -9.71547246e-01 -1.15915334e+00 -1.02398194e-01
-1.95077315e-01 4.38436300e-01 1.08602476e+00 -1.50206268... | [9.458334922790527, 0.22598394751548767] |
2a286283-b62f-422a-90b3-8e5ef3c52873 | interpretable-unified-language-checking | 2304.03728 | null | https://arxiv.org/abs/2304.03728v1 | https://arxiv.org/pdf/2304.03728v1.pdf | Interpretable Unified Language Checking | Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-task language checkers based on their latent representations of natural and social knowledge. We present an interpretable, unified, language... | ['James Glass', 'Helen Meng', 'Danny Fox', 'Xixin Wu', 'Thomas Hartvigsen', 'Luc Gaitskell', 'Wei Fang', 'Yung-Sung Chuang', 'Hongyin Luo', 'Tianhua Zhang'] | 2023-04-07 | null | null | null | null | ['misinformation', 'hate-speech-detection'] | ['miscellaneous', 'natural-language-processing'] | [-6.74602017e-02 4.84004319e-01 -4.67631221e-01 -3.55107874e-01
-7.58923888e-01 -5.48433900e-01 9.79313552e-01 5.35171330e-01
-1.13747686e-01 4.37660813e-01 7.76222229e-01 -5.04227042e-01
2.33696967e-01 -2.11714208e-01 -4.02200788e-01 -1.00414492e-01
2.50830978e-01 3.30498844e-01 -4.57555987e-02 -2.58862615... | [8.715636253356934, 10.484015464782715] |
73163e6a-0871-40f1-86c7-03a88dd2c9ba | a-semi-supervised-algorithm-for-improving-the | 2209.04360 | null | https://arxiv.org/abs/2209.04360v1 | https://arxiv.org/pdf/2209.04360v1.pdf | A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification | Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with such contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data, as it was done to generate the ... | ['David Atienza', 'Tomas Teijeiro', 'Lara Orlandic'] | 2022-09-09 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.43346378e-01 8.00303444e-02 -1.33009717e-01 -1.69176236e-01
-1.06680024e+00 -8.28716874e-01 -1.52248472e-01 4.62800145e-01
-1.55758366e-01 4.65446681e-01 2.68125534e-01 -3.63553688e-02
-1.33046703e-02 -3.70887756e-01 -4.59026605e-01 -7.77535498e-01
2.88994879e-01 4.61305648e-01 2.67511994e-01 7.61296451... | [14.482741355895996, 3.833463430404663] |
8ce94333-a3f8-48f5-a4db-6c5ff0d20a9c | boundary-regularized-building-footprint-1 | null | null | https://www.researchgate.net/publication/343400679_BOUNDARY_REGULARIZED_BUILDING_FOOTPRINT_EXTRACTION_FROM_SATELLITE_IMAGES_USING_DEEP_NEURAL_NETWORKS | https://arxiv.org/ftp/arxiv/papers/2006/2006.13176.pdf | BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS | In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to
support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce
and update spatial maps of built environment due ... | ['Gunho Sohn', 'Muhammad Kamran', 'Kang Zhao'] | 2020-06-23 | null | null | null | arxiv-2020-6 | ['object-localization'] | ['computer-vision'] | [ 4.08560544e-01 -2.73087204e-01 9.59968194e-02 -4.00039226e-01
-5.48210144e-01 -3.24722618e-01 5.72850525e-01 1.46125242e-01
-3.91462535e-01 4.05836791e-01 -1.33987293e-01 -1.06221803e-01
-1.46691397e-01 -1.41768634e+00 -6.89125896e-01 -5.69282472e-01
-2.15935662e-01 4.60291773e-01 5.59132993e-01 -1.54478729... | [9.54326343536377, -1.3007292747497559] |
d23c4e84-b1f7-490a-bd6f-050149f16d5e | adaptively-scheduled-multitask-learning-the | null | null | https://aclanthology.org/D19-5618 | https://aclanthology.org/D19-5618.pdf | Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation | Neural Machine Translation (NMT), a data-hungry technology, suffers from the lack of bilingual data in low-resource scenarios. Multitask learning (MTL) can alleviate this issue by injecting inductive biases into NMT, using auxiliary syntactic and semantic tasks. However, an effective \textit{training schedule} is requi... | ['Gholamreza Haffari', 'Poorya Zaremoodi'] | 2019-11-01 | null | null | null | ws-2019-11 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.60599250e-01 2.21704226e-03 -4.44425046e-01 -5.16504824e-01
-1.33311224e+00 -8.88576448e-01 6.72211409e-01 -4.46540006e-02
-6.53109789e-01 9.79007602e-01 2.94553846e-01 -8.86085987e-01
1.50469886e-02 -3.34880799e-01 -1.08720410e+00 -5.28159201e-01
4.71149117e-01 9.50446904e-01 -1.24238774e-01 -5.89462161... | [11.534756660461426, 10.19305419921875] |
0756f5c0-c9ce-4ca8-85eb-eb817cb809e8 | direction-of-arrival-estimation-and-phase | 2202.07781 | null | https://arxiv.org/abs/2202.07781v1 | https://arxiv.org/pdf/2202.07781v1.pdf | Direction of Arrival Estimation and Phase-Correction for Non-Coherent Sub-Arrays: A Convex Optimization Approach | Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization and radar. In this paper, we consider a challenging multi-source DOA estimation task, where the receiving antenna array is composed of non-coherent sub-arrays, i.e., sub-arrays that observ... | ['Oded Bialer', 'Tom Tirer'] | 2022-02-15 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.22097838e-02 -1.30614176e-01 2.10061625e-01 1.55637071e-01
-9.37622547e-01 -7.64166117e-01 1.41659409e-01 -2.77435452e-01
-1.56582475e-01 7.96331763e-01 3.14062715e-01 -1.24343254e-01
-4.27671224e-01 -4.13570046e-01 -6.80373430e-01 -1.29206073e+00
-2.73518711e-01 1.32334724e-01 -4.64734912e-01 -1.47445232... | [6.457461357116699, 1.342749834060669] |
ca834361-04e3-4cd8-a699-56764e3856c3 | temporal-context-matters-enhancing-single | 2203.01933 | null | https://arxiv.org/abs/2203.01933v2 | https://arxiv.org/pdf/2203.01933v2.pdf | Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations | Clinical outcome or severity prediction from medical images has largely focused on learning representations from single-timepoint or snapshot scans. It has been shown that disease progression can be better characterized by temporal imaging. We therefore hypothesized that outcome predictions can be improved by utilizing... | ['Prateek Prasanna', 'Chao Chen', 'Joseph Bae', 'Xuan Xu', 'Aishik Konwer'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Konwer_Temporal_Context_Matters_Enhancing_Single_Image_Prediction_With_Disease_Progression_CVPR_2022_paper.pdf | cvpr-2022-1 | ['severity-prediction'] | ['computer-vision'] | [ 4.59327012e-01 -9.16069653e-03 -5.95720410e-01 -8.05826068e-01
-1.28032458e+00 6.63490500e-03 5.62392652e-01 2.55322337e-01
-2.38432422e-01 5.41832328e-01 6.56483233e-01 -1.06897101e-01
-6.52495027e-01 -4.69913363e-01 -4.32652414e-01 -5.90221524e-01
-5.63700676e-01 7.38931060e-01 2.10623562e-01 1.20575912... | [15.057899475097656, -2.0158298015594482] |
53f5459e-b928-47e2-939a-81f25aa512c4 | forecasting-human-object-interaction-joint | 1911.10967 | null | https://arxiv.org/abs/1911.10967v2 | https://arxiv.org/pdf/1911.10967v2.pdf | Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video | We address the challenging task of anticipating human-object interaction in first person videos. Most existing methods ignore how the camera wearer interacts with the objects, or simply consider body motion as a separate modality. In contrast, we observe that the international hand movement reveals critical information... | ['James Rehg', 'Yin Li', 'Miao Liu', 'Siyu Tang'] | 2019-11-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2641_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460681.pdf | eccv-2020-8 | ['action-anticipation'] | ['computer-vision'] | [ 1.37820885e-01 -5.30604720e-02 -5.79196572e-01 -3.34484190e-01
-2.10430980e-01 -2.11327508e-01 6.40426517e-01 -6.90587938e-01
-1.83972552e-01 4.15512353e-01 1.04007387e+00 1.61767602e-01
5.58267757e-02 -1.59049019e-01 -6.36110127e-01 -5.45560896e-01
-1.45102635e-01 -9.09805074e-02 -1.06272005e-01 1.49453729... | [8.186346054077148, 0.5273988842964172] |
6b07f941-39ca-49f9-98b4-012e3c4c72c5 | t5lephone-bridging-speech-and-text-self | 2211.00586 | null | https://arxiv.org/abs/2211.00586v1 | https://arxiv.org/pdf/2211.00586v1.pdf | T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5 | In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous works use pretrained language models with subword-based tokenization. However, the granularity of input units affects the alignment of speech m... | ['Yu Tsao', 'Hung-Yi Lee', 'Ho-Lam Chung', 'Chan-Jan Hsu'] | 2022-11-01 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.06829095e-01 4.10507262e-01 -1.66418895e-01 -8.57450545e-01
-9.50261474e-01 -5.71321607e-01 4.97680455e-01 -2.21302971e-01
-6.40158832e-01 4.88760322e-01 5.37800074e-01 -7.77004063e-01
5.89448512e-01 -6.81105137e-01 -8.95358086e-01 -1.54802293e-01
3.29146653e-01 5.31234801e-01 -1.99004542e-02 -2.33589426... | [14.043503761291504, 7.003237247467041] |
b26f7bcf-1573-4439-9246-eb3a02625a71 | a-dataset-for-sentence-retrieval-for-open | 2205.11685 | null | https://arxiv.org/abs/2205.11685v1 | https://arxiv.org/pdf/2205.11685v1.pdf | A Dataset for Sentence Retrieval for Open-Ended Dialogues | We address the task of sentence retrieval for open-ended dialogues. The goal is to retrieve sentences from a document corpus that contain information useful for generating the next turn in a given dialogue. Prior work on dialogue-based retrieval focused on specific types of dialogues: either conversational QA or conver... | ['Oren Kurland', 'Idan Szpektor', 'Hagai Taitelbaum', 'Itay Harel'] | 2022-05-24 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.01104605e-01 7.19227374e-01 -2.70567741e-03 -5.59745610e-01
-1.67150509e+00 -8.92704546e-01 1.07403171e+00 2.06716374e-01
-4.57334220e-01 1.04653656e+00 8.76462042e-01 -3.35942000e-01
2.70426590e-02 -5.26881218e-01 -2.44067535e-01 -2.38117754e-01
1.49283819e-02 1.17653549e+00 1.77278206e-01 -9.76129174... | [12.46794605255127, 7.939432621002197] |
8e62fe10-5cdf-4cf5-ac11-95e691055952 | neuroadaptive-electroencephalography-a-proof | 2106.06029 | null | https://arxiv.org/abs/2106.06029v1 | https://arxiv.org/pdf/2106.06029v1.pdf | Neuroadaptive electroencephalography: a proof-of-principle study in infants | A core goal of functional neuroimaging is to study how the environment is processed in the brain. The mainstream paradigm involves concurrently measuring a broad spectrum of brain responses to a small set of environmental features preselected with reference to previous studies or a theoretical framework. As a complemen... | ['Emily J. H. Jones', 'Robert Leech', 'Anna Gui', 'Luke Mason', 'Elena Throm', 'Rianne Haartsen', 'Pedro F. da Costa'] | 2021-06-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 8.18005860e-01 -2.60310024e-01 1.59904420e-01 -5.27947664e-01
-5.33252478e-01 -5.55995762e-01 5.41515946e-01 1.63029373e-01
-8.28476191e-01 2.84686625e-01 2.21199900e-01 -2.37859607e-01
-5.41981578e-01 -4.63067800e-01 -8.07684839e-01 -6.82302535e-01
-1.08297177e-01 3.58696043e-01 -1.00075208e-01 3.01618814... | [12.904004096984863, 3.399095058441162] |
89fa43d1-3491-4cc6-bc67-933dadd9e751 | spin-nerf-multiview-segmentation-and | 2211.12254 | null | https://arxiv.org/abs/2211.12254v2 | https://arxiv.org/pdf/2211.12254v2.pdf | SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields | Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the repl... | ['Alex Levinshtein', 'Igor Gilitschenski', 'Marcus A. Brubaker', 'Jonathan Kelly', 'Konstantinos G. Derpanis', 'Tristan Aumentado-Armstrong', 'Ashkan Mirzaei'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-instance-segmentation-1', '3d-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.76229668e-01 1.59098804e-02 -1.82322562e-02 -2.93332219e-01
-9.61349189e-01 -8.12102616e-01 6.39054179e-01 -2.17925668e-01
-1.14227660e-01 5.59233427e-01 3.79552573e-01 4.51084860e-02
1.42652333e-01 -5.44448733e-01 -1.04329360e+00 -3.70631158e-01
4.72370386e-01 3.97866189e-01 6.65448830e-02 -6.57058135... | [9.237131118774414, -3.0719258785247803] |
95e8a31a-9207-443d-b424-496f949069f6 | relation-modeling-in-spatio-temporal-action | 2106.08061 | null | https://arxiv.org/abs/2106.08061v2 | https://arxiv.org/pdf/2106.08061v2.pdf | Relation Modeling in Spatio-Temporal Action Localization | This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for spatio-temporal action detection and adopts a training strategy to integrate multiple relation modeling in end-to-end training over the two... | ['Yue Gao', 'Mingqian Tang', 'Shiwei Zhang', 'Xiang Wang', 'Zhiwu Qing', 'Ziyuan Huang', 'Jianwen Jiang', 'Yutong Feng'] | 2021-06-15 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 9.38558206e-02 -3.64620119e-01 -6.34822071e-01 -2.61597455e-01
-8.59523177e-01 -5.26169777e-01 5.26250362e-01 -2.12568849e-01
-6.97536349e-01 9.04639423e-01 3.59266460e-01 -1.82933807e-01
-3.01390439e-01 -3.28813583e-01 -6.61235392e-01 -6.59270048e-01
-6.74481571e-01 2.80044585e-01 7.97524393e-01 1.80713125... | [8.410088539123535, 0.43271324038505554] |
7d8d38e2-db60-4462-b50b-be9a6783587b | neural-label-search-for-zero-shot-multi | 2204.13512 | null | https://arxiv.org/abs/2204.13512v2 | https://arxiv.org/pdf/2204.13512v2.pdf | Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization | In zero-shot multilingual extractive text summarization, a model is typically trained on English summarization dataset and then applied on summarization datasets of other languages. Given English gold summaries and documents, sentence-level labels for extractive summarization are usually generated using heuristics. How... | ['Furu Wei', 'Zheng Lin', 'Shi Wang', 'Yanan Cao', 'Xingxing Zhang', 'Ruipeng Jia'] | 2022-04-28 | null | https://aclanthology.org/2022.acl-long.42 | https://aclanthology.org/2022.acl-long.42.pdf | acl-2022-5 | ['extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.17711890e-01 3.83699447e-01 -4.97234702e-01 -2.87391841e-01
-1.48611784e+00 -8.39089036e-01 7.11244643e-01 6.67852759e-01
-4.25993800e-01 1.08894384e+00 1.09619665e+00 -3.97091582e-02
3.14676672e-01 -5.22265196e-01 -5.96068323e-01 -3.07967573e-01
3.57417285e-01 7.60136247e-01 4.98737991e-02 -3.48115623... | [12.357656478881836, 9.479325294494629] |
7481d0de-a3bb-4362-820e-4bd48ee38c37 | improving-named-entity-recognition-with | 2010.15466 | null | https://arxiv.org/abs/2010.15466v1 | https://arxiv.org/pdf/2010.15466v1.pdf | Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information | Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing s... | ['Xiang Wan', 'Xiang Ao', 'Yan Song', 'Yuanhe Tian', 'Yuyang Nie'] | 2020-10-29 | null | https://aclanthology.org/2020.findings-emnlp.378 | https://aclanthology.org/2020.findings-emnlp.378.pdf | findings-of-the-association-for-computational | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.14031501e-01 -1.37986228e-01 -1.51902318e-01 -5.26798785e-01
-1.21443532e-01 -5.23908615e-01 5.16847908e-01 2.76495606e-01
-9.59425986e-01 9.20377076e-01 7.44974911e-01 -2.09932536e-01
-2.25138336e-01 -9.15248811e-01 -4.04780507e-01 -2.79533327e-01
-7.79035091e-02 1.28643196e-02 2.19077945e-01 -2.57786483... | [9.764104843139648, 9.598548889160156] |
c3aae061-5404-41da-ad19-6153a9788b5a | defocus-blur-detection-via-multi-stream | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Defocus_Blur_Detection_CVPR_2018_paper.pdf | Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network | Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation of homogeneous regions and detection of low-contrast focal regions, as well as suppression of backg... | ['Wenda Zhao', 'Fan Zhao', 'Huchuan Lu', 'Dong Wang'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['defocus-blur-detection', 'defocus-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.79794565e-01 -7.63757885e-01 3.62077743e-01 -5.98756552e-01
-3.50686461e-01 -3.74416202e-01 3.74600440e-01 -2.65564412e-01
-2.57989228e-01 6.80239856e-01 4.67921406e-01 -5.90951405e-02
-1.24045506e-01 -4.11654323e-01 -5.53841174e-01 -8.22385788e-01
6.91665802e-03 -2.83200741e-01 7.51422524e-01 -2.03508548... | [11.342671394348145, -2.7208964824676514] |
5d1099aa-a167-4441-a609-1453825bb5dc | dual-embodied-symbolic-concept | 2203.00600 | null | https://arxiv.org/abs/2203.00600v1 | https://arxiv.org/pdf/2203.00600v1.pdf | Dual Embodied-Symbolic Concept Representations for Deep Learning | Motivated by recent findings from cognitive neural science, we advocate the use of a dual-level model for concept representations: the embodied level consists of concept-oriented feature representations, and the symbolic level consists of concept graphs. Embodied concept representations are modality specific and exist ... | ['Daniel T. Chang'] | 2022-03-01 | null | null | null | null | ['scene-graph-generation', 'knowledge-graph-embeddings', 'few-shot-class-incremental-learning', 'knowledge-graph-embeddings'] | ['computer-vision', 'graphs', 'methodology', 'methodology'] | [ 3.19486171e-01 4.35817540e-01 -2.51996666e-01 -3.68983835e-01
-3.00600111e-01 -5.76387882e-01 1.17010593e+00 6.90737724e-01
-4.11359221e-02 1.58772215e-01 4.18355912e-01 -2.59086072e-01
-5.71715176e-01 -1.07909656e+00 -6.55034482e-01 -4.55934793e-01
-1.64424032e-01 3.22242171e-01 -2.27229834e-01 -6.56507432... | [10.523575782775879, 2.192143440246582] |
a9da8fe7-46f6-4c0f-bd6b-935f00dde8e9 | learning-to-forecast-and-refine-residual | 1807.09951 | null | http://arxiv.org/abs/1807.09951v1 | http://arxiv.org/pdf/1807.09951v1.pdf | Learning to Forecast and Refine Residual Motion for Image-to-Video Generation | We consider the problem of image-to-video translation, where an input image
is translated into an output video containing motions of a single object.
Recent methods for such problems typically train transformation networks to
generate future frames conditioned on the structure sequence. Parallel work has
shown that sho... | ['Dimitris Metaxas', 'Xi Peng', 'Yu Tian', 'Mubbasir Kapadia', 'Long Zhao'] | 2018-07-26 | learning-to-forecast-and-refine-residual-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Long_Zhao_Learning_to_Forecast_ECCV_2018_paper.pdf | eccv-2018-9 | ['human-pose-forecasting', 'image-to-video'] | ['computer-vision', 'computer-vision'] | [ 6.73270047e-01 2.28940219e-01 -1.42105877e-01 -4.74241287e-01
-8.24117422e-01 -5.24686992e-01 7.74993122e-01 -1.04245472e+00
-1.67379513e-01 7.37887204e-01 4.92640167e-01 1.43229887e-01
6.25376940e-01 -2.92570651e-01 -1.13762951e+00 -6.09094024e-01
2.76988775e-01 1.65982440e-01 3.26531120e-02 -6.31575286... | [10.88844108581543, -0.6773315072059631] |
f772fdb7-943d-4737-9e1a-6257315cf6b2 | denmune-density-peak-based-clustering-using | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0031320320303927 | https://www.sciencedirect.com/science/article/abs/pii/S0031320320303927 | DenMune: Density peak based clustering using mutual nearest neighbors | Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm “DenMune” is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neigh... | ['Amin Shoukry', 'Adel El-Zoghabi', 'Mohamed Abbas'] | 2021-01-01 | null | null | null | pattern-recognition-2021-1 | ['unsupervised-mnist'] | ['methodology'] | [-4.55973953e-01 -4.28355336e-01 -9.69137475e-02 -9.79722068e-02
-4.34236676e-01 -7.82149851e-01 6.01141691e-01 4.91851538e-01
-3.16001415e-01 4.30126399e-01 1.95019208e-02 -4.18830179e-02
-6.77759230e-01 -9.47760999e-01 -1.43717295e-02 -1.05599427e+00
-1.50275618e-01 1.13075650e+00 5.11319399e-01 1.73740327... | [7.594224452972412, 4.551315784454346] |
7160c74e-27c5-48c1-a5a9-d177796e23e9 | the-use-of-ai-for-thermal-emotion-recognition | 2009.10589 | null | https://arxiv.org/abs/2009.10589v1 | https://arxiv.org/pdf/2009.10589v1.pdf | The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data | With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psycho... | ['Sanjay Purushotham', 'Edward Raff', 'Catherine Ordun'] | 2020-09-22 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 6.00876153e-01 -2.12576360e-01 -2.92701591e-02 -6.15819633e-01
-5.77963531e-01 -4.21864182e-01 8.11689794e-02 -4.95741159e-01
-6.36046171e-01 2.66466349e-01 8.68318081e-02 -2.07451224e-01
9.85312536e-02 -9.32487100e-02 -1.49995029e-01 -1.02689421e+00
1.60773873e-01 -3.08022350e-01 -7.89676070e-01 4.83136624... | [13.501052856445312, 2.1079370975494385] |
b2e3aba6-fa80-4b20-9fc4-371a7a8a6317 | efficient-constituency-parsing-by-pointing-1 | 2006.13557 | null | https://arxiv.org/abs/2006.13557v1 | https://arxiv.org/pdf/2006.13557v1.pdf | Efficient Constituency Parsing by Pointing | We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-do... | ['Xiao-Li Li', 'Xuan-Phi Nguyen', 'Thanh-Tung Nguyen', 'Shafiq Joty'] | 2020-06-24 | efficient-constituency-parsing-by-pointing | https://aclanthology.org/2020.acl-main.301 | https://aclanthology.org/2020.acl-main.301.pdf | acl-2020-6 | ['constituency-parsing'] | ['natural-language-processing'] | [-6.81200251e-03 6.90306723e-01 -3.92213941e-01 -6.76737249e-01
-1.61260009e+00 -8.62003028e-01 1.91376314e-01 1.76588312e-01
-3.76219869e-01 7.34627604e-01 2.88393199e-01 -7.70230234e-01
4.58515614e-01 -7.91420043e-01 -9.40389752e-01 -2.59869874e-01
5.85580096e-02 5.71011186e-01 4.16657388e-01 -3.82161081... | [10.354251861572266, 9.6187105178833] |
8bfe260a-6ac4-47b0-927a-b4895d1d532f | scene-text-recognition-with-single-point | 2209.01914 | null | https://arxiv.org/abs/2209.01914v1 | https://arxiv.org/pdf/2209.01914v1.pdf | Scene Text Recognition with Single-Point Decoding Network | In recent years, attention-based scene text recognition methods have been very popular and attracted the interest of many researchers. Attention-based methods can adaptively focus attention on a small area or even single point during decoding, in which the attention matrix is nearly one-hot distribution. Furthermore, t... | ['XuCheng Yin', 'Chun Yang', 'Shi-Xue Zhang', 'Haibo Qin', 'Lei Chen'] | 2022-09-05 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.02394828e-01 -5.36879182e-01 -9.48240682e-02 -2.97811180e-01
-5.32527626e-01 -7.56451562e-02 1.98723733e-01 1.44266590e-01
-5.07223964e-01 2.57129353e-02 2.14677542e-01 -1.08699284e-01
1.29697308e-01 -7.31877744e-01 -7.27587283e-01 -7.77358294e-01
8.02785933e-01 2.42179483e-01 5.80723941e-01 1.03478052... | [11.914837837219238, 2.1841330528259277] |
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