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values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
91dbe15b-b799-4ba5-9666-98507123f2ab | exploring-adversarial-learning-for-deep-semi | 2106.02258 | null | https://arxiv.org/abs/2106.02258v1 | https://arxiv.org/pdf/2106.02258v1.pdf | Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition | Current works formulate facial action unit (AU) recognition as a supervised learning problem, requiring fully AU-labeled facial images during training. It is challenging if not impossible to provide AU annotations for large numbers of facial images. Fortunately, AUs appear on all facial images, whether manually labeled... | ['Bowen Pan', 'Guozhu Peng', 'Yanan Chang', 'Shangfei Wang'] | 2021-06-04 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 4.40141171e-01 6.40832007e-01 -5.50433159e-01 -5.60875714e-01
-8.73503268e-01 -4.64792490e-01 6.90384954e-02 -6.03158832e-01
-1.01359271e-01 6.04300380e-01 -5.34800962e-02 3.08349341e-01
4.93156463e-01 -7.85731614e-01 -8.48541915e-01 -1.03178942e+00
2.28447974e-01 5.40121734e-01 -4.67275441e-01 1.65720090... | [13.65263843536377, 1.5549930334091187] |
59532dcf-9909-4520-9c21-ea2ea007a3dc | scireviewgen-a-large-scale-dataset-for | 2305.15186 | null | https://arxiv.org/abs/2305.15186v1 | https://arxiv.org/pdf/2305.15186v1.pdf | SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation | Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literat... | ['Ichiro Sakata', 'Junichiro Mori', 'Masaru Isonuma', 'Tetsu Kasanishi'] | 2023-05-24 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 5.56331612e-02 3.17867488e-01 -8.32222283e-01 1.30640000e-01
-1.52642024e+00 -7.02754259e-01 9.00321841e-01 4.01442260e-01
-1.28871009e-01 1.31223404e+00 7.39647627e-01 -5.50309479e-01
2.89835066e-01 -3.68663907e-01 -4.81478006e-01 -1.15211710e-01
6.79837406e-01 2.41730437e-01 -2.77971447e-01 -1.54530900... | [12.370509147644043, 9.561125755310059] |
bf5722e0-4d3d-477d-9ec0-4af30c6f54ec | radnet-incident-prediction-in-spatio-temporal | 2206.05602 | null | https://arxiv.org/abs/2206.05602v1 | https://arxiv.org/pdf/2206.05602v1.pdf | RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting | Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents using spatio-temporal forecasting. We consider the specific use case of road traffic systems where incidents ... | ['Chris Kettell', 'Matthew R. Wilkinson', 'Shreshth Tuli'] | 2022-06-11 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 4.79825586e-02 -1.00732274e-01 -2.99998254e-01 -5.90348303e-01
-5.78497589e-01 5.16803935e-02 8.08221400e-01 2.73774147e-01
-1.79094374e-01 7.76804507e-01 6.67723954e-01 -6.04970694e-01
-4.30584431e-01 -1.13174117e+00 -6.49106145e-01 -4.92014617e-01
-4.45892930e-01 5.51047087e-01 2.36102015e-01 -1.64012700... | [6.577518463134766, 2.457261085510254] |
e2224c51-4d23-4ff9-9a8c-3d7cfe7d87be | mavd-the-first-open-large-scale-mandarin | 2306.02263 | null | https://arxiv.org/abs/2306.02263v1 | https://arxiv.org/pdf/2306.02263v1.pdf | MAVD: The First Open Large-Scale Mandarin Audio-Visual Dataset with Depth Information | Audio-visual speech recognition (AVSR) gains increasing attention from researchers as an important part of human-computer interaction. However, the existing available Mandarin audio-visual datasets are limited and lack the depth information. To address this issue, this work establishes the MAVD, a new large-scale Manda... | ['Sen Li', 'Qi Li', 'Tianyi Xu', 'Li Liu', 'Yuchen Huo', 'Jianrong Wang'] | 2023-06-04 | null | null | null | null | ['visual-speech-recognition', 'audio-visual-speech-recognition'] | ['speech', 'speech'] | [ 1.58755165e-02 -2.98768818e-01 2.65088957e-02 -5.32244086e-01
-9.88013923e-01 -5.06650567e-01 6.22759581e-01 -1.69977799e-01
-4.30836737e-01 2.47500554e-01 4.23963785e-01 -1.15371421e-01
4.94248033e-01 -3.95493746e-01 -5.68754077e-01 -6.89373493e-01
3.04508269e-01 2.23096050e-02 1.32088020e-01 -1.30022010... | [14.316630363464355, 5.084782123565674] |
7731a82c-a63f-4344-b543-31471036dce7 | dynamic-inference-with-grounding-based-vision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.pdf | Dynamic Inference With Grounding Based Vision and Language Models | Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other ha... | ['Mohamed Omar', 'Xiaolong Wang', 'Jingru Yi', 'Keval Doshi', 'Wentao Zhu', 'Amanmeet Garg', 'Burak Uzkent'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-grounding', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 1.45649672e-01 2.99818218e-01 3.41208726e-02 -3.32464367e-01
-7.44181991e-01 -4.60927218e-01 6.63467467e-01 -8.42214301e-02
-7.69053519e-01 3.27237189e-01 -1.95826799e-01 -4.67979759e-01
3.42191875e-01 -7.76849926e-01 -1.17743790e+00 -5.89222491e-01
4.50932771e-01 5.17415762e-01 4.14991051e-01 -2.33283043... | [10.8071870803833, 1.514342188835144] |
26deb221-baad-4749-8635-f62278a0db86 | multi-view-self-supervised-deep-learning-for | 1609.09475 | null | http://arxiv.org/abs/1609.09475v3 | http://arxiv.org/pdf/1609.09475v3.pdf | Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge | Robot warehouse automation has attracted significant interest in recent
years, perhaps most visibly in the Amazon Picking Challenge (APC). A fully
autonomous warehouse pick-and-place system requires robust vision that reliably
recognizes and locates objects amid cluttered environments, self-occlusions,
sensor noise, an... | ['Ed Walker Jr.', 'Kuan-Ting Yu', 'Alberto Rodriguez', 'Jianxiong Xiao', 'Daniel Suo', 'Shuran Song', 'Andy Zeng'] | 2016-09-29 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-2.39794273e-02 -2.55757719e-01 1.64049640e-01 -7.58683026e-01
-7.83973932e-01 -1.08691216e+00 2.35541508e-01 1.86370477e-01
-3.33317339e-01 7.32125342e-02 -3.80193949e-01 -9.65044349e-02
1.17389046e-01 -6.36066020e-01 -1.05273771e+00 -3.91708344e-01
-3.20579745e-02 1.01982510e+00 2.39510462e-01 -1.62231475... | [6.094333171844482, -1.016058087348938] |
fe2b095b-4e9e-4870-9b29-f039810b333c | a-novel-embedding-architecture-and-score | null | null | https://ieeexplore.ieee.org/document/10068032 | https://ieeexplore.ieee.org/document/10068032 | A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics System | Abstract:
Significant progress in the field of ear-based biometrics has been made in recent studies. But methods to deal with degradation caused by hair occlusions during ear image acquisition have not been addressed yet. Use of occluded ear images from both sides can give better or comparable results to that of clean... | ['Vikram M. Gadre', 'Satish Mulleti', 'Divyang Sureshbhai Jadav', 'Saket Pateriya', 'A P Goutham', 'Archishman Biswas'] | 2023-03-21 | null | null | null | national-conference-on-communications-ncc | ['one-shot-learning'] | ['methodology'] | [ 2.60451108e-01 1.67085975e-01 6.81666791e-01 -4.48046654e-01
-7.87257910e-01 -1.77459657e-01 7.66324028e-02 6.69393223e-04
-3.17357063e-01 6.26304150e-01 3.16129386e-01 1.25771075e-01
-2.15823665e-01 -5.58296800e-01 -5.81563115e-01 -1.00238955e+00
-2.89261073e-01 1.67100206e-02 3.51753645e-02 -2.64337242... | [13.368940353393555, 0.9990918636322021] |
20fdf060-f696-4015-93af-c53fc2f224ca | weakly-supervised-temporal-action-7 | 2304.12616 | null | https://arxiv.org/abs/2304.12616v1 | https://arxiv.org/pdf/2304.12616v1.pdf | Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint | Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the training datasets. Due to the lack of boundary information during training, existing approaches formulate WTAL as a classificationproblem, i.e.,... | ['Xinbo Gao', 'Jie Li', 'Nannan Wang', 'Xinpeng Ding', 'De Cheng', 'Guozhang Li'] | 2023-04-25 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.15991449e-01 -5.42406328e-02 -6.15539908e-01 -2.35510603e-01
-3.31906527e-01 -4.82434690e-01 4.74424571e-01 -3.50750804e-01
-2.28781953e-01 5.69832981e-01 2.23430678e-01 -4.91143316e-02
-1.20633356e-01 -4.52241689e-01 -7.90336967e-01 -8.83596838e-01
1.92099102e-02 -5.62701486e-02 8.16094756e-01 2.39719406... | [8.562241554260254, 0.6454331278800964] |
46a0228b-38b4-4a3e-83a6-c13976e9ee1d | unsupervised-learning-with-contrastive-latent | 1811.06094 | null | http://arxiv.org/abs/1811.06094v1 | http://arxiv.org/pdf/1811.06094v1.pdf | Unsupervised learning with contrastive latent variable models | In unsupervised learning, dimensionality reduction is an important tool for
data exploration and visualization. Because these aims are typically
open-ended, it can be useful to frame the problem as looking for patterns that
are enriched in one dataset relative to another. These pairs of datasets occur
commonly, for ins... | ['Kristen Severson', 'Kenney Ng', 'Soumya Ghosh'] | 2018-11-14 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 5.19118726e-01 -1.34860501e-01 -1.55374691e-01 -6.86104238e-01
-5.31035244e-01 -5.11291921e-01 7.68411577e-01 3.94220829e-01
-2.61182636e-01 5.61377466e-01 4.58908409e-01 -1.57334022e-02
-1.05788338e+00 -6.36073530e-01 -2.27068469e-01 -1.03085625e+00
-4.22630489e-01 5.13247430e-01 2.05406725e-01 1.36578828... | [7.7718634605407715, 4.502693176269531] |
0c432d79-2d9b-4ff6-be4c-28433abceea3 | a-fast-attention-network-for-joint-intent | 2205.07646 | null | https://arxiv.org/abs/2205.07646v1 | https://arxiv.org/pdf/2205.07646v1.pdf | A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices | Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the... | ['Nan Gao', 'Feiyang Ye', 'Senjie Liang', 'Liang Huang'] | 2022-05-16 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-7.54188001e-03 4.00173873e-01 -4.28060532e-01 -5.54436147e-01
-6.77230358e-01 -2.96635926e-01 4.48349744e-01 3.38629261e-02
-6.30949974e-01 8.79251420e-01 3.32470775e-01 -6.88993573e-01
1.27485171e-01 -7.10593760e-01 -3.80123168e-01 1.65221374e-02
5.68922043e-01 6.57279491e-01 4.19950873e-01 -2.96704918... | [12.485857009887695, 7.430184364318848] |
cde5f6c4-33bc-4b18-b447-8831b22f0dc9 | computer-aided-arrhythmia-diagnosis-by | 1810.04123 | null | http://arxiv.org/abs/1810.04123v1 | http://arxiv.org/pdf/1810.04123v1.pdf | Computer-Aided Arrhythmia Diagnosis by Learning ECG Signal | Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to
monitor the condition of the human heart. Any abnormal pattern(s) in the ECG
signal is an indicative measure of malfunctioning of the heart, termed as
arrhythmia. Due to the lack of human expertise and high probability to
misdiagnose, computer-a... | [] | 2018-09-28 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.90056920e-01 -2.35175639e-01 8.57651606e-02 -1.46509215e-01
-4.41765457e-01 -5.17872214e-01 -5.28385043e-01 2.92592585e-01
-2.63080776e-01 7.68990993e-01 -5.15660107e-01 -4.50674415e-01
-1.34445980e-01 -5.09025335e-01 -1.12607680e-01 -6.19375944e-01
-3.96333873e-01 6.91331103e-02 -2.49999091e-01 2.05369174... | [14.225159645080566, 3.217386484146118] |
c3d608e5-7892-4dd5-b84f-ac2408ccdbb9 | can-dnns-learn-to-lipread-full-sentences | 1805.11685 | null | http://arxiv.org/abs/1805.11685v1 | http://arxiv.org/pdf/1805.11685v1.pdf | Can DNNs Learn to Lipread Full Sentences? | Finding visual features and suitable models for lipreading tasks that are
more complex than a well-constrained vocabulary has proven challenging. This
paper explores state-of-the-art Deep Neural Network architectures for
lipreading based on a Sequence to Sequence Recurrent Neural Network. We report
results for both han... | ['Naomi Harte', 'George Sterpu', 'Christian Saam'] | 2018-05-29 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.69682497e-01 -1.15794584e-01 -6.14719868e-01 -3.58565629e-01
-1.16282594e+00 -2.64875084e-01 6.89586937e-01 -5.42847455e-01
-5.81038833e-01 3.61467123e-01 6.24603629e-01 -7.34502494e-01
6.23226583e-01 3.06327492e-01 -6.48176193e-01 -4.25201476e-01
3.12329471e-01 2.32261494e-01 -7.77979800e-03 4.89905998... | [14.329901695251465, 5.022734642028809] |
996b762b-68a5-4daf-b0fa-82eddaba0121 | on-explaining-multimodal-hateful-meme | 2204.01734 | null | https://arxiv.org/abs/2204.01734v2 | https://arxiv.org/pdf/2204.01734v2.pdf | On Explaining Multimodal Hateful Meme Detection Models | Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task, and some of these solutions have yielded promising results. However, ... | ['Wen-Haw Chong', 'Roy Ka-Wei Lee', 'Ming Shan Hee'] | 2022-04-04 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.80652827e-01 -1.38035029e-01 -7.17747677e-03 -7.26166591e-02
-2.67451376e-01 -7.12116241e-01 9.02349055e-01 1.84797525e-01
-2.07914203e-01 5.17868936e-01 3.42916161e-01 -8.41682553e-02
3.83170903e-01 -3.42816651e-01 -4.79766786e-01 -6.79899275e-01
3.28534395e-01 4.26139757e-02 -4.31396961e-02 -2.65057087... | [8.481924057006836, 10.689946174621582] |
3324b33d-8010-4b87-806a-df9e276523bd | semantically-aligned-task-decomposition-in | 2305.10865 | null | https://arxiv.org/abs/2305.10865v1 | https://arxiv.org/pdf/2305.10865v1.pdf | Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning | The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing subgoals in intrinsically motivated reinf... | ['Hongyuan Zha', 'Bo Jin', 'Xiangfeng Wang', 'Baoxiang Wang', 'Dan Qiao', 'Wenhao Li'] | 2023-05-18 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 2.66638756e-01 5.27368724e-01 -4.06116545e-01 -1.23678386e-01
-1.16792464e+00 -2.03206345e-01 6.05264068e-01 2.14800790e-01
-4.57988709e-01 1.01930761e+00 6.19437635e-01 -7.95426685e-03
-4.34950888e-01 -5.64695120e-01 -3.45106572e-01 -6.80091381e-01
-2.25394115e-01 7.39521861e-01 -2.85891056e-01 -7.29093134... | [4.053691864013672, 1.756701946258545] |
7ff4bda1-9860-49c9-9892-b117df6bb0f5 | an-emotional-comfort-framework-for-improving | null | null | https://aclanthology.org/2021.naacl-industry.17 | https://aclanthology.org/2021.naacl-industry.17.pdf | An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots | E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention. We presented AliMe Assist, a Chinese intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), Ali... | ['Huan Chen', 'Haiqing Chen', 'Chao Wang', 'Shuangyong Song'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['answer-selection'] | ['natural-language-processing'] | [-6.83680832e-01 2.15864498e-02 -3.57520103e-01 -7.68797576e-01
-4.14509624e-01 -4.13656861e-01 5.30816838e-02 1.54980376e-01
-3.81754607e-01 1.88719869e-01 -4.61639240e-02 -2.05440432e-01
-4.16009612e-02 -8.67947459e-01 2.02947646e-01 -5.13574839e-01
3.49641949e-01 4.32703793e-01 -2.07293645e-01 -7.07211375... | [12.934101104736328, 6.179238319396973] |
1b344a7e-79bf-4c46-b0ee-3e5b34abb017 | mixed-reality-depth-contour-occlusion-using | 2203.02300 | null | https://arxiv.org/abs/2203.02300v1 | https://arxiv.org/pdf/2203.02300v1.pdf | Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation | Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is b... | ['Dingguo Yu', 'Youbing Zhao', 'Haoxiang Zhang', 'Fan Zhang', 'Naye Ji'] | 2022-03-04 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 3.05865437e-01 -1.85582846e-01 3.32792848e-01 -1.07400060e-01
-1.81793824e-01 -3.25985968e-01 4.67756033e-01 -1.10910945e-01
-4.71901834e-01 7.32065797e-01 6.30571023e-02 -2.20089659e-01
6.99818358e-02 -8.51408660e-01 -4.20366317e-01 -5.81791818e-01
1.80483535e-01 2.31873289e-01 5.53477645e-01 -3.66582833... | [9.330720901489258, -2.481557607650757] |
5f7cfb45-6237-4307-bccb-b7b462d4386d | distributed-multi-agent-deep-q-learning-for | 2304.01210 | null | https://arxiv.org/abs/2304.01210v1 | https://arxiv.org/pdf/2304.01210v1.pdf | Distributed Multi-Agent Deep Q-Learning for Fast Roaming in IEEE 802.11ax Wi-Fi Systems | The innovation of Wi-Fi 6, IEEE 802.11ax, was be approved as the next sixth-generation (6G) technology of wireless local area networks (WLANs) by improving the fundamental performance of latency, throughput, and so on. The main technical feature of orthogonal frequency division multiple access (OFDMA) supports multi-us... | ['Kai-Ten Feng', 'Li-Hsiang Shen', 'Ting-Hui Wang'] | 2023-03-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 5.67856506e-02 1.41913474e-01 -6.45705879e-01 -1.38885811e-01
-4.77047771e-01 -1.53837949e-01 -2.75034338e-01 -2.16433525e-01
-4.41053092e-01 1.14421916e+00 -1.55170202e-01 -5.72721899e-01
-1.00403237e+00 -1.19560754e+00 -3.73693079e-01 -1.09524119e+00
-8.78895879e-01 1.58650964e-01 -2.31340504e-03 -4.01554883... | [6.018355369567871, 1.5609800815582275] |
17db3e2b-6220-4d5e-b0fa-7df90b364e4a | knowrob-2-0-a-2nd-generation-knowledge | null | null | https://ieeexplore.ieee.org/abstract/document/8460964 | https://ieeexplore.ieee.org/abstract/document/8460964 | KnowRob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-enabled Robotic Agents | Abstract— In this paper we present K NOW R OB 2, a second
generation knowledge representation and reasoning framework
for robotic agents. K NOW R OB 2 is an extension and partial
redesign of K NOW R OB , currently one of the most advanced
knowledge processing systems for robots that has enabled them
to successfull... | ['Georg Bartels', 'Asil Kaan Bozcuo ̆glu', 'Mihai Pomarlan', 'Andrei Haidu', 'Daniel Beßler', 'Michael Beetz'] | 2018-05-21 | null | null | null | 2018-ieee-international-conference-on | ['motion-planning'] | ['robots'] | [-1.56903833e-01 5.57995677e-01 -5.04609227e-01 -7.22371489e-02
1.88322663e-01 -1.02463138e+00 3.93475384e-01 3.97664428e-01
-3.22847694e-01 1.18937230e+00 -3.38048965e-01 -5.19064546e-01
-8.79613400e-01 -1.16898811e+00 -9.19326782e-01 -9.07564014e-02
-3.16772938e-01 9.13917184e-01 5.04513979e-01 -7.74723530... | [4.51237678527832, 0.9926543235778809] |
d63cc79e-4c20-44ac-b8dd-ecd356f5c032 | frustum-pointpillars-a-multi-stage-approach | null | null | https://ieeexplore.ieee.org/document/9607424 | https://hal.archives-ouvertes.fr/hal-03354114/document | Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR | Accurate 3D object detection is a key part of the perception module for autonomous vehicles. A better understanding of the objects in 3D facilitates better decision-making and path planning. RGB Cameras and LiDAR are the most commonly used sensors in autonomous vehicles for environment perception. Many approaches have ... | ['Christian Laugier', 'Özgür Erkent', 'David Sierra-Gonzalez', 'Anshul Paigwar'] | 2021-10-11 | null | null | null | 2021-ieee-cvf-international-conference-on | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-1.78721204e-01 -6.73467040e-01 3.94005105e-02 -4.26619917e-01
-6.53746545e-01 -6.58949435e-01 4.09193575e-01 2.04376757e-01
-7.88664699e-01 1.38007373e-01 -5.08540750e-01 -5.12887836e-01
2.90105641e-01 -1.03523302e+00 -9.40950215e-01 -4.88434881e-01
-1.02495879e-01 6.26825869e-01 8.37772369e-01 -2.48212054... | [7.734566688537598, -2.5401973724365234] |
6e33be63-e3ca-4fac-b643-a87fb3e1814a | vit5-pretrained-text-to-text-transformer-for | 2205.06457 | null | https://arxiv.org/abs/2205.06457v2 | https://arxiv.org/pdf/2205.06457v2.pdf | ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation | We present ViT5, a pretrained Transformer-based encoder-decoder model for the Vietnamese language. With T5-style self-supervised pretraining, ViT5 is trained on a large corpus of high-quality and diverse Vietnamese texts. We benchmark ViT5 on two downstream text generation tasks, Abstractive Text Summarization and Name... | ['Trieu H. Trinh', 'Hieu Nguyen', 'Hieu Tran', 'Long Phan'] | 2022-05-13 | null | https://aclanthology.org/2022.naacl-srw.18 | https://aclanthology.org/2022.naacl-srw.18.pdf | naacl-acl-2022-7 | ['named-entity-recognition-in-vietnamese'] | ['natural-language-processing'] | [ 4.54191923e-01 4.35272485e-01 -3.31100941e-01 -2.47615322e-01
-1.44276178e+00 -4.80335832e-01 7.38170564e-01 1.88767701e-01
-6.98368013e-01 1.08446586e+00 1.23842430e+00 -3.26734781e-01
6.32554531e-01 -5.59103966e-01 -6.81613266e-01 -2.15878367e-01
1.71044484e-01 8.08001757e-01 4.70827594e-02 -6.32119596... | [12.283703804016113, 9.499119758605957] |
76f87031-ec31-41b6-8790-47f4836166e0 | semi-supervised-local-cluster-extraction-by | 2211.11114 | null | https://arxiv.org/abs/2211.11114v1 | https://arxiv.org/pdf/2211.11114v1.pdf | Semi-supervised Local Cluster Extraction by Compressive Sensing | Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be th... | ['Sheng Li', 'Ming-Jun Lai', 'Zhaiming Shen'] | 2022-11-20 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.51415658e-01 2.75752127e-01 -2.75004476e-01 7.53917843e-02
-6.01721525e-01 -4.49982792e-01 1.70201689e-01 -1.27771452e-01
1.20420679e-01 4.81000453e-01 1.80455267e-01 -5.13451658e-02
-3.69075686e-01 -7.35461473e-01 -7.12071121e-01 -8.98294330e-01
1.04252966e-02 8.45967829e-02 1.85500264e-01 3.78434658... | [7.7162933349609375, 4.748518943786621] |
afa53faa-2bf7-4e9a-9d17-552086228ff6 | action-units-that-constitute-trainable-micro | 2112.01730 | null | https://arxiv.org/abs/2112.01730v7 | https://arxiv.org/pdf/2112.01730v7.pdf | How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset? | This paper does not contain technical novelty but introduces our key discoveries in a data generation protocol, a database and insights. We aim to address the lack of large-scale datasets in micro-expression (MiE) recognition due to the prohibitive cost of data collection, which renders large-scale training less feasib... | ['Liang Zheng', 'Tom Gedeon', 'Zhongdao Wang', 'Yuchi Liu'] | 2021-12-03 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 3.95966291e-01 8.58682021e-02 -9.17622168e-03 -7.11272359e-01
-6.57264471e-01 -4.69537020e-01 5.87701797e-01 -9.76751566e-01
8.27751495e-03 7.92538583e-01 -5.15740514e-02 2.61767179e-01
4.17747349e-02 -6.60489321e-01 -8.55034947e-01 -6.49000823e-01
-2.60952920e-01 2.43407711e-01 -4.31660384e-01 -3.14476639... | [13.391661643981934, 1.3999828100204468] |
448b1faf-ac08-40ca-839e-97850224aa69 | a-hybrid-cnn-rnn-approach-for-survival | 2303.10789 | null | https://arxiv.org/abs/2303.10789v1 | https://arxiv.org/pdf/2303.10789v1.pdf | A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study | In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investig... | ['Joseph Jacob', 'Daniel C. Alexander', 'Mark Emberton', 'David Barber', 'Ahmed Shahin', 'An Zhao', 'Shahab Aslani', 'Yaozhi Lu'] | 2023-03-19 | null | null | null | null | ['mortality-prediction', 'survival-analysis'] | ['medical', 'miscellaneous'] | [ 3.49125341e-02 1.03163876e-01 -5.27332187e-01 -2.54877150e-01
-8.34840357e-01 -2.28469774e-01 4.37513828e-01 3.85276794e-01
-8.05301845e-01 5.52107990e-01 4.92792547e-01 -7.16735482e-01
-5.36492109e-01 -8.17408979e-01 -1.60874519e-02 -7.29163349e-01
-4.81793106e-01 4.54487592e-01 7.45129436e-02 1.94140077... | [15.2874116897583, -2.2323901653289795] |
fb89ed00-cadd-42b6-b623-c4a7b04c2a7c | gender-lost-in-translation-how-bridging-the | 2305.16935 | null | https://arxiv.org/abs/2305.16935v1 | https://arxiv.org/pdf/2305.16935v1.pdf | Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation | Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation b... | ['Jan Niehues', 'Lena Cabrera'] | 2023-05-26 | null | null | null | null | ['nmt'] | ['computer-code'] | [-3.60204548e-01 5.40473819e-01 -6.47363842e-01 -6.73111558e-01
-7.57873714e-01 -5.34319162e-01 1.08600473e+00 5.30482754e-02
-7.33178556e-01 7.72146702e-01 6.30051613e-01 -6.38378680e-01
2.08115995e-01 -8.00127983e-01 -7.52662361e-01 -5.83626270e-01
4.28734004e-01 1.00404334e+00 -8.43437970e-01 -7.13982224... | [9.61294174194336, 10.253093719482422] |
b33e325d-9271-4336-84ec-144ddbf27dac | snore-gans-improving-automatic-snore-sound | 1903.12422 | null | http://arxiv.org/abs/1903.12422v1 | http://arxiv.org/pdf/1903.12422v1.pdf | Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data | One of the frontier issues that severely hamper the development of automatic
snore sound classification (ASSC) associates to the lack of sufficient
supervised training data. To cope with this problem, we propose a novel data
augmentation approach based on semi-supervised conditional Generative
Adversarial Networks (scG... | ['Christoph Janott', 'Zixing Zhang', 'Yanan Guo', 'Kun Qian', 'Jing Han', 'Bjoern Schuller'] | 2019-03-29 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.97967571e-01 2.14364886e-01 3.50898385e-01 -1.59919962e-01
-1.06407714e+00 -4.76859391e-01 7.45484233e-01 -3.14536363e-01
-3.82678330e-01 8.33922803e-01 1.17800526e-01 -2.46716112e-01
4.73060943e-02 -6.13722920e-01 -3.99545819e-01 -9.51438963e-01
3.83590281e-01 7.48103619e-01 1.12828054e-01 -4.22031254... | [15.537519454956055, 5.868506908416748] |
f5ab3495-ec62-4a6f-b9ee-1f8be151cb92 | logltn-differentiable-fuzzy-logic-in-the | 2306.14546 | null | https://arxiv.org/abs/2306.14546v1 | https://arxiv.org/pdf/2306.14546v1.pdf | logLTN: Differentiable Fuzzy Logic in the Logarithm Space | The AI community is increasingly focused on merging logic with deep learning to create Neuro-Symbolic (NeSy) paradigms and assist neural approaches with symbolic knowledge. A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and... | ['Michael Spranger', 'Luciano Serafini', 'Samy Badreddine'] | 2023-06-26 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-1.79532602e-01 6.59223571e-02 -1.70513406e-01 -5.69972396e-01
-2.39130226e-03 -4.58101422e-01 3.62759620e-01 1.87081963e-01
-5.63314259e-01 7.71809995e-01 -3.24932903e-01 -3.55388999e-01
-7.80593574e-01 -1.13053977e+00 -7.38268614e-01 -2.44728655e-01
-4.32545185e-01 6.91236258e-01 3.55220437e-01 -6.97151303... | [8.641151428222656, 6.466348171234131] |
6089bc1d-ecd6-44cf-8e25-364356f0db9c | an-ensemble-approach-for-automated-theorem | 2305.08676 | null | https://arxiv.org/abs/2305.08676v1 | https://arxiv.org/pdf/2305.08676v1.pdf | An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations | Using reinforcement learning for automated theorem proving has recently received much attention. Current approaches use representations of logical statements that often rely on the names used in these statements and, as a result, the models are generally not transferable from one domain to another. The size of these re... | ['Radu Marinescu', 'Ndivhuwo Makondo', 'Guilherme Lima', 'Akihiro Kishimoto', 'Shajith Ikbal', 'Maxwell Crouse', 'Ibrahim Abdelaziz', 'Achille Fokoue'] | 2023-05-15 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 8.0087423e-02 2.1622506e-01 -5.0389349e-01 -2.6766181e-01
-7.2069335e-01 -6.2342548e-01 4.8406529e-01 3.8555011e-01
1.3883583e-01 8.3771634e-01 -1.8327719e-01 -9.8390657e-01
-4.4463229e-01 -1.1704704e+00 -1.0570214e+00 -6.3104667e-02
-2.4765311e-01 8.3836097e-01 5.2166361e-01 -4.0572190e-01
1.9206588e-01... | [8.941193580627441, 7.181590557098389] |
362c925e-c58b-4885-998c-578bb7726b3b | speaker-diarization-and-identification-from | 2207.00660 | null | https://arxiv.org/abs/2207.00660v1 | https://arxiv.org/pdf/2207.00660v1.pdf | Speaker Diarization and Identification from Single-Channel Classroom Audio Recording Using Virtual Microphones | Speaker identification in noisy audio recordings, specifically those from collaborative learning environments, can be extremely challenging. There is a need to identify individual students talking in small groups from other students talking at the same time. To solve the problem, we assume the use of a single microphon... | ['Antonio Gomez'] | 2022-07-01 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-1.23691469e-01 -3.28180730e-01 6.82788491e-01 -3.29368263e-01
-1.19404078e+00 -9.46891963e-01 1.14577010e-01 1.59346849e-01
8.82907212e-02 2.02140287e-01 1.75428659e-01 -3.89002711e-01
-3.36453766e-01 -3.31352293e-01 -5.80039024e-01 -9.10481751e-01
-8.84923562e-02 7.04031661e-02 1.07646808e-01 6.56448007... | [14.832048416137695, 5.863192081451416] |
1e59363f-56be-4d17-9def-ad02dd608740 | the-sound-of-silence-in-eeg-cognitive-voice | 2010.05497 | null | https://arxiv.org/abs/2010.05497v1 | https://arxiv.org/pdf/2010.05497v1.pdf | The "Sound of Silence" in EEG -- Cognitive voice activity detection | Speech cognition bears potential application as a brain computer interface that can improve the quality of life for the otherwise communication impaired people. While speech and resting state EEG are popularly studied, here we attempt to explore a "non-speech"(NS) state of brain activity corresponding to the silence re... | ['Hema A Murthy', 'Rini A Sharon'] | 2020-10-12 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 4.04750288e-01 -5.90151437e-02 3.75513524e-01 -3.14581573e-01
-2.83511460e-01 -3.70946527e-01 7.01452434e-01 -9.69453454e-02
-4.42487508e-01 6.88069165e-01 3.91021132e-01 -4.86892134e-01
-2.90565014e-01 -3.11363906e-01 -2.12403283e-01 -8.45053136e-01
-2.77568758e-01 -1.11229114e-01 -3.28181945e-02 -1.78844213... | [13.240442276000977, 3.3468427658081055] |
f12d9468-34e5-4aa6-b36d-0db0b24c3c5b | automatic-true-false-question-generation-for | null | null | https://aclanthology.org/2022.bea-1.10 | https://aclanthology.org/2022.bea-1.10.pdf | Automatic True/False Question Generation for Educational Purpose | In field of teaching, true/false questioning is an important educational method for assessing students’ general understanding of learning materials. Manually creating such questions requires extensive human effort and expert knowledge. Question Generation (QG) technique offers the possibility to automatically generate ... | ['Ai Ti Aw', 'Liangming Pan', 'Pengfei Li', 'Bowei Zou'] | null | null | null | null | naacl-bea-2022-7 | ['question-generation'] | ['natural-language-processing'] | [ 2.15667859e-01 5.53835511e-01 6.43299162e-01 -3.72517228e-01
-9.91214752e-01 -9.63348269e-01 7.82380819e-01 3.64204198e-01
1.00059763e-01 1.24310291e+00 2.48659048e-02 -8.72942686e-01
-4.56132561e-01 -1.28704858e+00 -7.65780687e-01 5.32255694e-02
4.33658063e-01 5.34787536e-01 6.78542614e-01 -7.00603426... | [11.339670181274414, 7.99311637878418] |
5ba84b9e-7b45-4053-bb51-406100f4fddc | sudowoodo-contrastive-self-supervised | 2207.04122 | null | https://arxiv.org/abs/2207.04122v2 | https://arxiv.org/pdf/2207.04122v2.pdf | Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation | Machine learning (ML) is playing an increasingly important role in data management tasks, particularly in Data Integration and Preparation (DI&P). The success of ML-based approaches, however, heavily relies on the availability of large-scale, high-quality labeled datasets for different tasks. Moreover, the wide variety... | [] | 2022-07-08 | sudowoodo-contrastive-self-supervised-1 | https://ui.adsabs.harvard.edu/abs/2022arXiv220704122W/abstract | https://arxiv.org/pdf/2207.04122.pdf | arxiv-2022-10 | ['data-integration'] | ['knowledge-base'] | [-4.12939377e-02 -1.05955169e-01 -4.86114204e-01 -5.11647582e-01
-9.39758122e-01 -4.64863569e-01 5.12478769e-01 8.81878912e-01
-4.82753843e-01 5.79743385e-01 -3.11152432e-02 -2.15803340e-01
-4.07801509e-01 -8.30313802e-01 -8.69744778e-01 -3.66358608e-01
-7.15549961e-02 7.38820672e-01 9.57163200e-02 -1.52281210... | [9.494815826416016, 8.479342460632324] |
43cf82d1-938e-461c-8aeb-78f4e860c337 | insights-into-the-robustness-of-control-point | 1803.03025 | null | http://arxiv.org/abs/1803.03025v2 | http://arxiv.org/pdf/1803.03025v2.pdf | Insights into the robustness of control point configurations for homography and planar pose estimation | In this paper, we investigate the influence of the spatial configuration of a
number of $n \geq 4$ control points on the accuracy and robustness of space
resection methods, e.g. used by a fiducial marker for pose estimation. We find
robust configurations of control points by minimizing the first order perturbed
solutio... | ['Volker Willert', 'Raul Acuna'] | 2018-03-08 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.34168893e-01 1.48078844e-01 -3.83946374e-02 1.74952537e-01
-5.99771142e-01 -6.73813045e-01 5.07293880e-01 2.14273408e-01
-5.83398938e-01 6.70804679e-01 -1.71440318e-01 6.22486556e-03
-5.88512480e-01 -3.59649748e-01 -8.38345945e-01 -8.12199533e-01
7.20349550e-02 5.61455727e-01 3.68558615e-02 -1.63347170... | [7.9368414878845215, -2.2082462310791016] |
2cd0e7fb-3731-418c-b75a-29bcb5fc7432 | idt5-indonesian-version-of-multilingual-t5 | 2302.00856 | null | https://arxiv.org/abs/2302.00856v1 | https://arxiv.org/pdf/2302.00856v1.pdf | idT5: Indonesian Version of Multilingual T5 Transformer | Indonesian language is spoken by almost 200 million people and is the 10th most spoken language in the world, but it is under-represented in NLP (Natural Language Processing) research. A sparsity of language resources has hampered previous work on Indonesian. The Transformer is a new architecture rapidly becoming domin... | ['Surya Sumpeno', 'Adhi Dharma Wibawa', 'Mukhlish Fuadi'] | 2023-02-02 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-9.01545808e-02 2.20166564e-01 2.39151910e-01 -3.59349608e-01
-1.05621195e+00 -6.37889862e-01 5.51652133e-01 9.53854546e-02
-6.16004288e-01 8.03106666e-01 4.12335753e-01 -8.32983911e-01
2.98754990e-01 -9.58638847e-01 -5.70645511e-01 -3.53327662e-01
3.11972022e-01 6.58448756e-01 -1.53255425e-02 -7.13584483... | [10.968360900878906, 9.756656646728516] |
0ba17b48-c0e3-48e0-b34b-ae8563e99de9 | uukg-unified-urban-knowledge-graph-dataset | 2306.11443 | null | https://arxiv.org/abs/2306.11443v1 | https://arxiv.org/pdf/2306.11443v1.pdf | UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction | Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the development and operation of the smart city. As an emerging building block, multi-sourced urban data are usually integrated as urban knowledge graphs (UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction models. However... | ['Hui Xiong', 'Zhenyu Zeng', 'Hao Wang', 'Hao liu', 'Yansong Ning'] | 2023-06-20 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-6.45465255e-01 2.91489244e-01 -6.65891230e-01 -1.90655321e-01
-6.65120184e-01 -1.84146985e-01 7.16235399e-01 3.17192435e-01
1.41274944e-01 8.25693965e-01 8.67610335e-01 -5.46258271e-01
-3.15144867e-01 -1.46196008e+00 -7.16457188e-01 -5.87372780e-01
-1.56257153e-01 4.52555150e-01 5.11540353e-01 -3.56350124... | [6.494648456573486, 2.055243492126465] |
ed20babf-8b58-4fdd-8374-a76f444b23fe | fishing-for-clickbaits-in-social-images-and | 1710.06390 | null | http://arxiv.org/abs/1710.06390v1 | http://arxiv.org/pdf/1710.06390v1.pdf | Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models | This paper presents the results and conclusions of our participation in the
Clickbait Challenge 2017 on automatic clickbait detection in social media. We
first describe linguistically-infused neural network models and identify
informative representations to predict the level of clickbaiting present in
Twitter posts. Ou... | ['Ellyn Ayton', 'Maria Glenski', 'Dustin Arendt', 'Svitlana Volkova'] | 2017-10-17 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-7.47837797e-02 -2.38039687e-01 -3.30928206e-01 -4.82966393e-01
-1.00629389e+00 -6.15509987e-01 9.58075345e-01 3.16006154e-01
-6.21445239e-01 5.04320204e-01 2.03485176e-01 -4.69735116e-01
-1.12358462e-02 -6.54651344e-01 -7.43318737e-01 -1.81057081e-01
-7.53373355e-02 2.88397819e-01 1.49996668e-01 3.94947417... | [7.793302059173584, 9.814762115478516] |
8450804e-436b-4791-ba31-b11db81fd22b | putting-humans-in-the-image-captioning-loop | 2306.03476 | null | https://arxiv.org/abs/2306.03476v1 | https://arxiv.org/pdf/2306.03476v1.pdf | Putting Humans in the Image Captioning Loop | Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC mode... | ['Daniel Sonntag', 'Mareike Hartmann', 'Aliki Anagnostopoulou'] | 2023-06-06 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 5.82126021e-01 4.29606736e-01 2.81990208e-02 -4.91145164e-01
-6.19201660e-01 -5.73825419e-01 6.37469172e-01 1.03446968e-01
-4.54976231e-01 7.50414431e-01 3.84623528e-01 -1.53769240e-01
5.27291417e-01 -3.92756134e-01 -9.55328822e-01 -2.35129431e-01
2.01895684e-01 8.15249205e-01 3.66371185e-01 -4.59120534... | [10.97092056274414, 0.9697921276092529] |
83b9fedf-138e-4a87-8daa-52abeb92fab6 | soda-story-oriented-dense-video-captioning | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6406_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510511.pdf | SODA: Story Oriented Dense Video Captioning Evaluation Framework | Dense Video Captioning (DVC) is a challenging task that localizes all events in a short video and describes them with natural language sentences. The main goal of DVC is video story description, that is, to generate a concise video story that supports human video comprehension without watching it. In recent years, DVC ... | ['Tsutomu Hirao', 'Masaaki Nagata', 'Hidetaka Kamigaito', 'Soichiro Fujita', 'Manabu Okumura'] | null | null | null | null | eccv-2020-8 | ['dense-video-captioning'] | ['computer-vision'] | [ 2.21957371e-01 -9.74597111e-02 -9.42025483e-02 -1.48768082e-01
-9.93582785e-01 -6.01893306e-01 8.34752738e-01 3.82897295e-02
-2.63336033e-01 7.94094861e-01 5.90171337e-01 1.49940878e-01
2.70929605e-01 -2.82396317e-01 -8.79905164e-01 -4.25320119e-01
4.63947691e-02 4.12836999e-01 6.10150576e-01 -8.75959992... | [10.527751922607422, 0.6423630714416504] |
444cff91-c415-42c7-9aa7-700d789f1211 | time-expressions-in-mental-health-records-for | null | null | https://aclanthology.org/W18-5621 | https://aclanthology.org/W18-5621.pdf | Time Expressions in Mental Health Records for Symptom Onset Extraction | For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in c... | ["Andr{\\'e} Bittar", 'Rina Dutta', 'Natalia Viani', 'Lucia Yin', 'Rashmi Patel', 'Robert Stewart', 'Sumithra Velupillai', 'Joyce Kam', 'Ayunni Alawi'] | 2018-10-01 | null | null | null | ws-2018-10 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 4.06758673e-02 3.04826409e-01 -4.07152176e-01 -5.61452210e-01
-5.21148741e-01 -5.76453209e-01 3.58512282e-01 1.08800209e+00
-6.27901971e-01 9.45647478e-01 6.46308541e-01 -3.23193163e-01
-3.58174622e-01 -5.00514150e-01 3.56094658e-01 -4.15687025e-01
-5.14266968e-01 9.62632060e-01 -1.55176312e-01 1.77553833... | [8.56897258758545, 8.97439956665039] |
3273ba70-1e47-4f51-8c92-b64732c41a0c | decentralized-equalization-for-massive-mimo | 2305.12805 | null | https://arxiv.org/abs/2305.12805v1 | https://arxiv.org/pdf/2305.12805v1.pdf | Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples | Recently, the decentralized baseband processing (DBP) paradigm and relevant detection methods have been proposed to enable extremely large-scale massive multiple-input multiple-output technology. Under the DBP architecture, base station antennas are divided into several independent clusters, each connected to a local c... | ['Qingjiang Shi', 'Tsung-Hui Chang', 'Enbin Song', 'Bo wang', 'Mian Li', 'Xiaotong Zhao'] | 2023-05-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.73807129e-01 -3.07562083e-01 1.88253298e-01 8.74612108e-02
-6.34298921e-01 -5.50483823e-01 -2.22377460e-02 -5.68942353e-02
3.89862806e-02 7.67932951e-01 1.96886942e-01 -3.79757166e-01
-2.60224789e-01 -6.98274910e-01 -3.88467610e-01 -1.20453298e+00
-1.05775870e-01 -5.69316223e-02 -3.31215501e-01 -2.27997035... | [6.210707664489746, 1.3965989351272583] |
61f7e02c-6c99-4baf-b023-45487a96af16 | domain-smoothing-network-for-zero-shot-sketch | 2106.11841 | null | https://arxiv.org/abs/2106.11841v1 | https://arxiv.org/pdf/2106.11841v1.pdf | Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval | Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achiev... | ['Cheng Deng', 'Aming Wu', 'Jiexi Yan', 'Hao Wang', 'Zhipeng Wang'] | 2021-06-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 9.24848020e-02 -5.56947649e-01 -6.43953919e-01 -2.80696034e-01
-1.16707146e+00 -3.69546324e-01 8.87226999e-01 -2.00808987e-01
-1.72694817e-01 5.47261178e-01 2.37054780e-01 2.60351330e-01
-5.51707566e-01 -7.75124371e-01 -3.81592751e-01 -4.96101588e-01
1.57040581e-01 3.50566715e-01 1.72568724e-01 -4.21542466... | [11.5636625289917, 0.722950279712677] |
7fd18580-8dca-43ec-b924-2897fa334b13 | ultra-fast-image-categorization-in-vivo-and | 2205.03635 | null | https://arxiv.org/abs/2205.03635v4 | https://arxiv.org/pdf/2205.03635v4.pdf | Ultrafast Image Categorization in Biology and Neural Models | Humans are able to categorize images very efficiently, in particular to detect the presence of an animal very quickly. Recently, deep learning algorithms based on convolutional neural networks (CNNs) have achieved higher than human accuracy for a wide range of visual categorization tasks. However, the tasks on which th... | ['Laurent U Perrinet', 'Jean-Nicolas Jérémie'] | 2022-05-07 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.94433326e-01 -1.97070196e-01 5.01744092e-01 -3.02607864e-01
2.82620609e-01 -7.74202406e-01 6.16669834e-01 3.69782776e-01
-8.36533427e-01 4.17266935e-01 -4.48982298e-01 -2.75834978e-01
9.31172147e-02 -7.51886070e-01 -9.92525339e-01 -7.49908745e-01
7.82597288e-02 -1.96077731e-02 3.60545844e-01 -2.26039916... | [9.854888916015625, 2.326892614364624] |
08982b06-fc06-4116-b68f-06bd55129132 | rmes-real-time-micro-expression-spotting | 2305.05523 | null | https://arxiv.org/abs/2305.05523v1 | https://arxiv.org/pdf/2305.05523v1.pdf | RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid | Micro-expressions (MEs) are involuntary and subtle facial expressions that are thought to reveal feelings people are trying to hide. ME spotting detects the temporal intervals containing MEs in videos. Detecting such quick and subtle motions from long videos is difficult. Recent works leverage detailed facial motion re... | ['Bertram Shi', 'Frederic Jumelle', 'Liang Wu', 'Didan Deng', 'Yini Fang'] | 2023-05-09 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 1.17608495e-02 -3.04329544e-01 -3.25809419e-01 -3.18425179e-01
-4.61495608e-01 -4.26143765e-01 5.07971942e-01 -4.19734180e-01
-5.15510738e-01 1.09372534e-01 3.26636314e-01 1.84979409e-01
3.22865874e-01 -5.45844674e-01 -4.12978083e-01 -7.10478783e-01
-4.24626797e-01 -2.24071309e-01 6.65631369e-02 5.56571372... | [13.58696460723877, 1.761581540107727] |
ca59ae54-44a3-4d23-b5d1-8ec884cad3da | adaptive-student-s-t-distribution-with-method | 2304.03069 | null | https://arxiv.org/abs/2304.03069v2 | https://arxiv.org/pdf/2304.03069v2.pdf | Adaptive Student's t-distribution with method of moments moving estimator for nonstationary time series | The real life time series are usually nonstationary, bringing a difficult question of model adaptation. Classical approaches like ARMA-ARCH assume arbitrary type of dependence. To avoid such bias, we will focus on recently proposed agnostic philosophy of moving estimator: in time $t$ finding parameters optimizing e.g. ... | ['Jarek Duda'] | 2023-04-06 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-3.99459213e-01 -2.41275191e-01 -3.55439857e-02 -2.27160335e-01
-9.35600936e-01 -6.19686067e-01 2.43258566e-01 -1.61683813e-01
-5.88081956e-01 1.16891491e+00 -6.07583821e-01 -9.08299685e-01
-7.72116542e-01 -9.94005322e-01 -6.57430530e-01 -9.24019754e-01
-7.99030960e-01 4.63863045e-01 -1.36974752e-01 -1.57555953... | [6.245291709899902, 4.163983345031738] |
b2bdac5c-5fd9-4964-9202-be853ea2f13c | purifier-defending-data-inference-attacks-via | 2212.00612 | null | https://arxiv.org/abs/2212.00612v1 | https://arxiv.org/pdf/2212.00612v1.pdf | Purifier: Defending Data Inference Attacks via Transforming Confidence Scores | Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the... | ['Kui Ren', 'Fan Zhang', 'Ee-Chien Chang', 'Ziming Zhao', 'Jie Wan', 'Da Yang', 'Lijin Wang', 'Ziqi Yang'] | 2022-12-01 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.03654343e-01 3.58126044e-01 6.00658134e-02 -5.04000664e-01
-6.46073937e-01 -1.04615045e+00 3.75369191e-01 3.23696807e-02
-3.57396543e-01 9.68173862e-01 -4.37055826e-01 -3.39417636e-01
3.38544697e-02 -1.12236631e+00 -1.02158642e+00 -9.35310185e-01
-2.52430469e-01 4.60635394e-01 2.80662179e-01 1.41818166... | [5.8664751052856445, 7.290331840515137] |
d0ff97e1-2fc7-42e5-b12e-3e80c66ea4d4 | on-the-role-of-event-boundaries-in-egocentric | 1809.00402 | null | http://arxiv.org/abs/1809.00402v2 | http://arxiv.org/pdf/1809.00402v2.pdf | On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams | Event boundaries play a crucial role as a pre-processing step for detection,
localization, and recognition tasks of human activities in videos. Typically,
although their intrinsic subjectiveness, temporal bounds are provided manually
as input for training action recognition algorithms. However, their role for
activity ... | ['Estefania Talavera', 'Petia Radeva', 'Alejandro Cartas', 'Mariella Dimiccoli'] | 2018-09-02 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 5.04704177e-01 -1.56287849e-01 -1.98842272e-01 -3.87437373e-01
-2.42268533e-01 -5.59786856e-01 7.22761631e-01 1.16410799e-01
-7.85522580e-01 6.48217499e-01 5.39673209e-01 2.58541733e-01
-7.37229511e-02 -3.42476189e-01 -6.05108202e-01 -6.26485467e-01
-4.65268433e-01 -9.49916765e-02 1.19219624e-01 2.01544330... | [8.188790321350098, 0.5390657782554626] |
05599e05-2b8f-4aa6-8db0-380f73cf0260 | learning-non-linguistic-skills-without | 2305.08246 | null | https://arxiv.org/abs/2305.08246v1 | https://arxiv.org/pdf/2305.08246v1.pdf | Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency | The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and subsequently, arithmetic reasoning). However, non-linguistic skill injection typically comes at a cost for LLMs: it leads to catastrophic for... | ['Naren Ramakrishnan', 'Nikhil Muralidhar', 'Mandar Sharma'] | 2023-05-14 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 3.62347692e-01 5.69819868e-01 2.50444096e-02 -1.49580464e-02
-6.15684927e-01 -5.77443182e-01 6.09145641e-01 6.80425227e-01
-5.87730348e-01 7.94300973e-01 1.83670714e-01 -5.55696845e-01
-5.76514721e-01 -1.19595611e+00 -1.12579048e+00 -3.95268708e-01
-9.05601755e-02 5.85447073e-01 2.87038326e-01 -4.04917538... | [9.594071388244629, 7.317295551300049] |
8692e2a3-2b98-4ca6-9608-c7951949e698 | restyle-a-residual-based-stylegan-encoder-via | 2104.02699 | null | https://arxiv.org/abs/2104.02699v2 | https://arxiv.org/pdf/2104.02699v2.pdf | ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement | Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent code of the trained GAN is of utmost importance as it allows for the manipulation of real images, leveraging the rich se... | ['Daniel Cohen-Or', 'Or Patashnik', 'Yuval Alaluf'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Alaluf_ReStyle_A_Residual-Based_StyleGAN_Encoder_via_Iterative_Refinement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Alaluf_ReStyle_A_Residual-Based_StyleGAN_Encoder_via_Iterative_Refinement_ICCV_2021_paper.pdf | iccv-2021-1 | ['real-to-cartoon-translation'] | ['computer-vision'] | [ 7.92749763e-01 5.29164493e-01 -1.70219958e-01 -1.05651215e-01
-8.49878967e-01 -6.47255898e-01 7.21942425e-01 -5.16300142e-01
-1.66244134e-01 7.90616870e-01 1.61269188e-01 -3.90304267e-01
2.92930007e-01 -7.82396495e-01 -1.22197437e+00 -6.17890894e-01
2.22747341e-01 5.23307145e-01 -3.68757546e-02 -1.63420901... | [11.611285209655762, -0.4108065068721771] |
4f7fcb0b-99a1-4a21-95b1-1438edc6efbe | when-age-invariant-face-recognition-meets | 2103.01520 | null | https://arxiv.org/abs/2103.01520v2 | https://arxiv.org/pdf/2103.01520v2.pdf | When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework | To minimize the effects of age variation in face recognition, previous work either extracts identity-related discriminative features by minimizing the correlation between identity- and age-related features, called age-invariant face recognition (AIFR), or removes age variation by transforming the faces of different age... | ['Hongming Shan', 'Junping Zhang', 'Zhizhong Huang'] | 2021-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Huang_When_Age-Invariant_Face_Recognition_Meets_Face_Age_Synthesis_A_Multi-Task_CVPR_2021_paper.pdf | cvpr-2021-1 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.14179114e-01 -1.36268631e-01 -2.89567690e-02 -8.78749132e-01
-6.68393791e-01 -2.46898368e-01 5.56218803e-01 -4.39155728e-01
-1.69894829e-01 5.54627895e-01 2.29780644e-01 2.41232261e-01
-5.76379970e-02 -5.12422442e-01 -6.00578308e-01 -9.19494450e-01
1.55142829e-01 1.11781821e-01 -4.70301986e-01 6.55148551... | [13.296815872192383, 0.6241703629493713] |
9f1975ea-901d-4af8-b913-3147040dcf6c | real-time-controllable-denoising-for-image | 2303.16425 | null | https://arxiv.org/abs/2303.16425v1 | https://arxiv.org/pdf/2303.16425v1.pdf | Real-time Controllable Denoising for Image and Video | Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this can be easily achieved by adjusting the filtering strength. However, for NN (Neural Network)-based models, adjusting the final denoising stre... | ['Jinwei Gu', 'Kaimo Lin', 'Ping Luo', 'Xiaogang Wang', 'Wenqi Shao', 'Yitong Jiang', 'Zhaoyang Zhang'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Real-Time_Controllable_Denoising_for_Image_and_Video_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-denoising'] | ['computer-vision'] | [ 3.96963000e-01 4.80450206e-02 5.01521587e-01 -3.95063758e-01
-4.28888977e-01 -5.33066571e-01 4.20454383e-01 -1.36743084e-01
-4.65064436e-01 3.76204640e-01 -7.86745083e-03 -3.51551056e-01
-1.99399679e-03 -9.81012046e-01 -8.08995128e-01 -7.78183818e-01
3.45977396e-01 -1.83103696e-01 2.27862999e-01 -4.00492311... | [11.51828670501709, -2.130319833755493] |
c70bb256-5112-4978-9bc6-6aa317c39940 | deep-bilateral-learning-for-real-time-image | 1707.02880 | null | http://arxiv.org/abs/1707.02880v2 | http://arxiv.org/pdf/1707.02880v2.pdf | Deep Bilateral Learning for Real-Time Image Enhancement | Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affi... | ['Frédo Durand', 'Samuel W. Hasinoff', 'Jonathan T. Barron', 'Michaël Gharbi', 'Jiawen Chen'] | 2017-07-10 | null | null | null | null | ['image-retouching'] | ['computer-vision'] | [ 5.27673662e-01 -2.85515726e-01 6.53056204e-02 -4.45990890e-01
-8.35385561e-01 -6.03148222e-01 4.06072497e-01 -2.49881893e-01
-6.44647717e-01 2.07104936e-01 -3.50556709e-03 -5.94581068e-01
2.71244764e-01 -9.77701366e-01 -1.16849506e+00 -2.56763190e-01
9.57740322e-02 2.66401261e-01 4.76444811e-01 -2.08796263... | [9.983405113220215, -2.318472146987915] |
20eb4700-3924-4ddf-b720-031d91a42a90 | multitask-detection-of-speaker-changes | 2210.14755 | null | https://arxiv.org/abs/2210.14755v2 | https://arxiv.org/pdf/2210.14755v2.pdf | Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0 | Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classificat... | ['Zbyněk Zajíc', 'Marie Kunešová'] | 2022-10-26 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 1.54160962e-01 -1.35767430e-01 -9.05847270e-03 -3.98309350e-01
-1.12944639e+00 -4.72497702e-01 6.82980359e-01 2.51630753e-01
-4.75516409e-01 5.43369055e-01 4.37464952e-01 -3.03117841e-01
2.73661494e-01 -2.40302518e-01 -3.06712717e-01 -5.59894085e-01
-1.31000370e-01 3.17458808e-01 4.55707580e-01 -2.13552728... | [14.567239761352539, 6.241490364074707] |
41d3442e-7657-45a7-af56-c29afb22d6a3 | on-the-sample-complexity-of-estimation-in | 2307.04191 | null | https://arxiv.org/abs/2307.04191v1 | https://arxiv.org/pdf/2307.04191v1.pdf | On the sample complexity of estimation in logistic regression | The logistic regression model is one of the most popular data generation model in noisy binary classification problems. In this work, we study the sample complexity of estimating the parameters of the logistic regression model up to a given $\ell_2$ error, in terms of the dimension and the inverse temperature, with sta... | ['Arya Mazumdar', 'Daniel Hsu'] | 2023-07-09 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 1.73938453e-01 -1.88229516e-01 -3.50472361e-01 -5.14699697e-01
-8.90405715e-01 -2.69132912e-01 4.51376796e-01 4.46415663e-01
-5.43132842e-01 9.55715477e-01 -3.16526711e-01 -4.06922221e-01
-5.24327338e-01 -5.51089227e-01 -5.94485879e-01 -1.24845970e+00
-9.37030464e-02 3.57498497e-01 -4.03491706e-02 9.32425261... | [7.9209184646606445, 4.533568382263184] |
263dc5bd-8c65-4696-bbf4-b577e23a3fcc | a-scalable-reinforcement-learning-based | 2307.01599 | null | https://arxiv.org/abs/2307.01599v1 | https://arxiv.org/pdf/2307.01599v1.pdf | A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management | On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intrig... | ['Fumihide Tanaka', 'Zhenhan Huang'] | 2023-07-04 | null | null | null | null | ['reinforcement-learning-1', 'management'] | ['methodology', 'miscellaneous'] | [-5.69381297e-01 -1.18770629e-01 -5.43120086e-01 6.59147501e-02
-4.42289710e-01 -1.22673833e+00 8.49814594e-01 1.75798252e-01
-2.01295480e-01 8.05471659e-01 1.69079855e-01 -9.34983552e-01
-3.83356720e-01 -8.63441050e-01 -6.08064771e-01 -6.63049698e-01
-4.78245199e-01 5.43721914e-01 -1.91203728e-02 -4.36018944... | [4.667259693145752, 4.129644870758057] |
5f46cd0a-16bd-4034-9f45-8e494cdc0de2 | perspective-fields-for-single-image-camera | 2212.03239 | null | https://arxiv.org/abs/2212.03239v2 | https://arxiv.org/pdf/2212.03239v2.pdf | Perspective Fields for Single Image Camera Calibration | Geometric camera calibration is often required for applications that understand the perspective of the image. We propose perspective fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an up vector ... | ['David F. Fouhey', 'Matthew Sticha', 'Kevin Matzen', 'Oliver Wang', 'Yannick Hold-Geoffroy', 'Jianming Zhang', 'Linyi Jin'] | 2022-12-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Perspective_Fields_for_Single_Image_Camera_Calibration_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 5.86646855e-01 -5.33478111e-02 -8.92592147e-02 -9.02188659e-01
-8.76756012e-02 -1.03973949e+00 8.73972714e-01 -9.18300897e-02
-5.25259972e-02 2.11546361e-01 1.72482207e-01 -2.39664048e-01
1.69050112e-01 -8.40074003e-01 -1.21003973e+00 -2.89805621e-01
3.22803497e-01 3.30955684e-01 1.29406050e-01 -2.35189259... | [8.907977104187012, -2.7787654399871826] |
cbb81a7b-836b-4c51-9066-a262c458e2db | outlier-galaxy-images-in-the-dark-energy | 2305.01720 | null | https://arxiv.org/abs/2305.01720v1 | https://arxiv.org/pdf/2305.01720v1.pdf | Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning | The Dark Energy Survey is able to collect image data of an extremely large number of extragalactic objects, and it can be reasonably assumed that many unusual objects of high scientific interest are hidden inside these data. Due to the extreme size of DES data, identifying these objects among many millions of other cel... | ['Lior Shamir'] | 2023-05-02 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-3.00276548e-01 -3.09408337e-01 5.65317512e-01 -1.83443204e-01
-4.26138610e-01 -4.80015606e-01 5.77677131e-01 2.69812196e-01
-4.53968108e-01 5.97084999e-01 -3.74738276e-01 -2.99715191e-01
1.08636923e-01 -9.03521061e-01 -5.64669669e-01 -8.85101318e-01
-2.42251649e-01 7.70810544e-01 8.37719738e-01 1.18032560... | [7.605134963989258, 2.640451669692993] |
51908f0b-b7f5-4e3b-adde-178ca7fb2664 | attention-based-multi-modal-fusion-network | 2003.13910 | null | https://arxiv.org/abs/2003.13910v2 | https://arxiv.org/pdf/2003.13910v2.pdf | Attention-based Multi-modal Fusion Network for Semantic Scene Completion | This paper presents an end-to-end 3D convolutional network named attention-based multi-modal fusion network (AMFNet) for the semantic scene completion (SSC) task of inferring the occupancy and semantic labels of a volumetric 3D scene from single-view RGB-D images. Compared with previous methods which use only the seman... | ['Yipeng Li', 'Yue Gao', 'Changqing Zou', 'Xibin Zhao', 'Siqi Li'] | 2020-03-31 | null | null | null | null | ['3d-semantic-scene-completion', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 0.27993727 0.429499 0.3401492 -0.5925499 -0.87773997 -0.29842946
0.46930563 -0.1870732 -0.5168587 0.31501403 0.08388811 -0.03601874
-0.01749409 -0.77968633 -0.8484009 -0.5068115 0.21681349 0.46259356
0.3608091 -0.07802644 0.13352542 0.5666377 -1.79825 0.05100548
0.67291 1.6522852 0.... | [8.52104377746582, -2.8607521057128906] |
d2ba01f6-06e8-461a-896a-6b1cd3ee1947 | data-poisoning-attacks-against-multimodal | 2209.15266 | null | https://arxiv.org/abs/2209.15266v2 | https://arxiv.org/pdf/2209.15266v2.pdf | Data Poisoning Attacks Against Multimodal Encoders | Recently, the newly emerged multimodal models, which leverage both visual and linguistic modalities to train powerful encoders, have gained increasing attention. However, learning from a large-scale unlabeled dataset also exposes the model to the risk of potential poisoning attacks, whereby the adversary aims to pertur... | ['Yang Zhang', 'Pascal Berrang', 'Mathias Humbert', 'Michael Backes', 'Zheng Li', 'Xinlei He', 'Ziqing Yang'] | 2022-09-30 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.33511114e-03 -9.27287787e-02 -2.77607560e-01 3.91041525e-02
-9.52530026e-01 -1.30119562e+00 6.92070186e-01 1.49979100e-01
-5.10819793e-01 3.62640500e-01 3.76064107e-02 -5.66655219e-01
4.53131586e-01 -5.30503452e-01 -9.06967759e-01 -6.69964492e-01
-5.66140190e-03 -3.09144333e-02 3.13708693e-01 -1.47439599... | [5.8301777839660645, 7.821191787719727] |
366fa215-6ce8-458d-a292-9158f0b1184c | multilingual-bottleneck-features-for | 2011.03118 | null | https://arxiv.org/abs/2011.03118v1 | https://arxiv.org/pdf/2011.03118v1.pdf | Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages | In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages. The unavailability of annotated corpora in the languages of interest has always been a primary challenge when developing sp... | ['Thomas Niesler', 'Ewald van der Westhuizen', 'Febe De Wet', 'Astik Biswas', 'Trideba Padhi'] | 2020-10-31 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 6.34573847e-02 -1.09817259e-01 1.46237090e-01 -5.20029485e-01
-1.23766816e+00 -5.39900482e-01 4.46649134e-01 -9.28337798e-02
-6.54967785e-01 5.49653411e-01 3.70419264e-01 -1.03535795e+00
7.76949376e-02 -3.94229144e-02 -4.29806054e-01 -5.64777613e-01
-5.65669648e-02 3.97892982e-01 1.91597864e-01 -4.66307104... | [14.306580543518066, 6.958385944366455] |
1be8918e-0d4c-4531-980e-db8888b55c43 | understanding-neural-code-intelligence | 2106.03353 | null | https://arxiv.org/abs/2106.03353v2 | https://arxiv.org/pdf/2106.03353v2.pdf | Understanding Neural Code Intelligence Through Program Simplification | A wide range of code intelligence (CI) tools, powered by deep neural networks, have been developed recently to improve programming productivity and perform program analysis. To reliably use such tools, developers often need to reason about the behavior of the underlying models and the factors that affect them. This is ... | ['Mohammad Amin Alipour', 'Vincent J. Hellendoorn', 'Md Rafiqul Islam Rabin'] | 2021-06-07 | null | null | null | null | ['variable-misuse', 'method-name-prediction'] | ['computer-code', 'natural-language-processing'] | [-9.94901657e-02 -6.34745369e-03 -4.38608587e-01 -3.70254070e-01
2.96669770e-02 -5.65635800e-01 1.88839599e-01 1.25214398e-01
1.27388224e-01 2.02613801e-01 -8.75958428e-02 -8.11253667e-01
-6.00364320e-02 -7.11440384e-01 -8.60562444e-01 -1.23733461e-01
-1.78097069e-01 5.01589430e-03 2.24507973e-01 -4.35815990... | [7.634208679199219, 7.693853378295898] |
af0d9eb0-81a0-49d7-91cd-014c006cc4c9 | sparse-video-representation-using-steered | 2209.05993 | null | https://arxiv.org/abs/2209.05993v1 | https://arxiv.org/pdf/2209.05993v1.pdf | Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation | Steered-Mixtures-of Experts (SMoE) present a unified framework for sparse representation and compression of image data with arbitrary dimensionality. Recent work has shown great improvements in the performance of such models for image and light-field representation. However, for the case of videos the straight-forward ... | ['Thomas Sikora', 'Erik Bochinski', 'Rolf Jongebloed'] | 2022-09-13 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.58927196e-01 -1.65006742e-01 4.94937040e-02 -5.31733967e-02
-5.39211988e-01 -2.22062051e-01 5.97858787e-01 -3.61013800e-01
-3.77012372e-01 4.49363530e-01 3.83185297e-01 -4.95885946e-02
-1.48401961e-01 -4.92282182e-01 -5.22464633e-01 -9.43905175e-01
-1.00403979e-01 -1.37651011e-01 5.24729371e-01 4.53741141... | [11.351507186889648, -2.331185817718506] |
8f3f187f-b6e6-46c8-bb88-3561e8b06a2f | american-cultural-regions-mapped-through-the | 2208.07649 | null | https://arxiv.org/abs/2208.07649v2 | https://arxiv.org/pdf/2208.07649v2.pdf | American cultural regions mapped through the lexical analysis of social media | Cultural areas represent a useful concept that cross-fertilizes diverse fields in social sciences. Knowledge of how humans organize and relate their ideas and behavior within a society helps to understand their actions and attitudes towards different issues. However, the selection of common traits that shape a cultural... | ['Jack Grieve', 'David Sanchez', 'Jose J. Ramasco', 'Bruno Gonçalves', 'Thomas Louf'] | 2022-08-16 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-2.11635321e-01 -1.48055464e-01 -3.58969390e-01 -2.38566741e-01
-2.83228397e-01 -8.21800947e-01 1.10002053e+00 5.42390883e-01
-3.59109968e-01 3.85935545e-01 1.11369455e+00 -4.19515789e-01
-1.26151770e-01 -8.75350952e-01 -1.26639381e-01 -8.04666519e-01
8.95451233e-02 1.12419531e-01 2.33995374e-02 -4.60713238... | [8.89621639251709, 9.892915725708008] |
f305f90f-c9f3-4263-afe9-d2cdc95ae2f3 | distilling-cross-temporal-contexts-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.pdf | Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition | Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the front... | ['ShengYong Chen', 'Tiantian Yuan', 'Kaihua Zhang', 'Bo Liu', 'Qing Guo', 'Wanli Xue', 'Leming Guo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['sign-language-recognition'] | ['computer-vision'] | [ 3.58070955e-02 -4.00287271e-01 -3.28624278e-01 -4.90017682e-01
-7.60532320e-01 -4.41353947e-01 6.63293898e-01 -4.24391627e-01
-6.51709318e-01 3.18154931e-01 4.46888179e-01 -2.93759495e-01
-2.39631198e-02 -4.17136341e-01 -5.53180993e-01 -8.00161123e-01
6.26591686e-03 -1.49665177e-01 6.49111032e-01 -1.44249529... | [9.233084678649902, -6.527484893798828] |
99917ee2-8d10-41f3-b130-0e9923299a4c | 3d-surfel-map-aided-visual-relocalization | 2104.03856 | null | https://arxiv.org/abs/2104.03856v1 | https://arxiv.org/pdf/2104.03856v1.pdf | 3D Surfel Map-Aided Visual Relocalization with Learned Descriptors | In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optim... | ['Ming Liu', 'Timothy Sandy', 'Marco Hutter', 'Huaiyang Huang', 'Haoyang Ye'] | 2021-04-08 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-4.12392646e-01 -5.07241249e-01 -4.19303536e-01 -1.36667088e-01
-3.65539879e-01 -9.70386267e-01 3.81115168e-01 -2.60278396e-02
-3.27786952e-01 1.30567282e-01 1.23823404e-01 1.80580959e-01
-2.72306800e-02 -4.75421786e-01 -7.70269096e-01 -2.11077109e-01
2.22818792e-01 4.11681950e-01 6.56101882e-01 -9.09249410... | [7.4667067527771, -2.2127039432525635] |
6517a1fb-93b0-4489-ac20-abf9838dc8c1 | cssam-code-search-via-attention-matching-of | 2208.03922 | null | https://arxiv.org/abs/2208.03922v1 | https://arxiv.org/pdf/2208.03922v1.pdf | CSSAM:Code Search via Attention Matching of Code Semantics and Structures | Despite the continuous efforts in improving both the effectiveness and efficiency of code search, two issues remained unsolved. First, programming languages have inherent strong structural linkages, and feature mining of code as text form would omit the structural information contained inside it. Second, there is a pot... | ['Yaoxiang Yu', 'Bo Cai', 'Yi Hu'] | 2022-08-08 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-9.40103903e-02 -4.20490056e-01 -4.32042897e-01 -1.62359178e-01
-6.22636557e-01 -6.11362457e-01 7.39727169e-02 5.64603984e-01
-5.40591367e-02 -2.72280008e-01 4.91147578e-01 -4.01143491e-01
-3.13316286e-01 -5.62155902e-01 -3.36785018e-01 -2.54246980e-01
1.46518624e-03 -3.50644290e-01 2.47481957e-01 -1.64508045... | [7.488550662994385, 8.065035820007324] |
75e7b3a9-9263-41a5-99ad-101a8ef2b5d9 | a-robust-framework-for-deep-learning | 2201.12705 | null | https://arxiv.org/abs/2201.12705v1 | https://arxiv.org/pdf/2201.12705v1.pdf | A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation | Facial emotion recognition is a vast and complex problem space within the domain of computer vision and thus requires a universally accepted baseline method with which to evaluate proposed models. While test datasets have served this purpose in the academic sphere real world application and testing of such models lacks... | ['Mitchell Hanson', 'Dylan Black', 'Thomas Reither', 'Tyler Bauer', 'Rushit Dave', 'Nyle Siddiqui'] | 2022-01-30 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 6.33344427e-02 -7.42992386e-03 3.21858317e-01 -7.12252855e-01
-2.52171308e-01 -3.83729249e-01 6.56801939e-01 -7.91027471e-02
-6.84236884e-01 3.94719332e-01 -3.91381353e-01 1.57253236e-01
-5.56861013e-02 -6.33604288e-01 -3.83584410e-01 -6.33987069e-01
-1.66715026e-01 3.17294240e-01 -3.37089419e-01 -3.61369342... | [13.545973777770996, 1.94833242893219] |
d1f5bd26-1b07-48bb-9c44-46a6d02b7641 | learning-high-fidelity-depths-of-dressed | 2103.03319 | null | https://arxiv.org/abs/2103.03319v3 | https://arxiv.org/pdf/2103.03319v3.pdf | Self-supervised 3D Representation Learning of Dressed Humans from Social Media Videos | A key challenge of learning a visual representation for the 3D high fidelity geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real-world imagery. We address this challe... | ['Hyun Soo Park', 'Yasamin Jafarian'] | 2021-03-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jafarian_Learning_High_Fidelity_Depths_of_Dressed_Humans_by_Watching_Social_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 2.66118377e-01 1.49390921e-01 3.05341333e-02 -3.92338246e-01
-5.60048401e-01 -3.42746198e-01 3.86882603e-01 -1.21465452e-01
-1.41250670e-01 4.03880507e-01 2.87114978e-01 6.18253767e-01
2.96674401e-01 -7.84442127e-01 -1.04531705e+00 -4.56928521e-01
4.58630174e-02 7.20166743e-01 7.97205865e-02 -2.57112414... | [7.214889049530029, -1.262578010559082] |
760f2f1d-7c20-4b93-a6be-ef06e918c336 | tactical-rewind-self-correction-via | 1903.02547 | null | http://arxiv.org/abs/1903.02547v2 | http://arxiv.org/pdf/1903.02547v2.pdf | Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation | We present the Frontier Aware Search with backTracking (FAST) Navigator, a
general framework for action decoding, that achieves state-of-the-art results
on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson
et. al. (2018). Given a natural language instruction and photo-realistic image
views of ... | ['Jianfeng Gao', 'Yejin Choi', 'Liyiming Ke', 'Jingjing Liu', 'Zhe Gan', 'Ari Holtzman', 'Yonatan Bisk', 'Xiujun Li', 'Siddhartha Srinivasa'] | 2019-03-06 | tactical-rewind-self-correction-via-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ke_Tactical_Rewind_Self-Correction_via_Backtracking_in_Vision-And-Language_Navigation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ke_Tactical_Rewind_Self-Correction_via_Backtracking_in_Vision-And-Language_Navigation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vision-language-navigation'] | ['computer-vision'] | [ 3.06641340e-01 7.04709217e-02 -2.19329566e-01 -2.42806673e-01
-1.15135539e+00 -8.47627878e-01 7.57003188e-01 1.21607587e-01
-7.96596646e-01 5.69658816e-01 6.17551565e-01 -7.42492497e-01
-1.51953176e-01 -4.71928358e-01 -1.08663034e+00 -6.15051448e-01
-2.18621433e-01 4.23472643e-01 4.24252361e-01 -2.89588302... | [4.506296157836914, 0.560080885887146] |
61c14d18-82cc-4d7d-aedb-e167addc383b | spatially-varying-exposure-with-2-by-2 | 2306.17367 | null | https://arxiv.org/abs/2306.17367v1 | https://arxiv.org/pdf/2306.17367v1.pdf | Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality | The advancement of new digital image sensors has enabled the design of exposure multiplexing schemes where a single image capture can have multiple exposures and conversion gains in an interlaced format, similar to that of a Bayer color filter array. In this paper, we ask the question of how to design such multiplexing... | ['Stanley H. Chan', 'Yiheng Chi', 'Xiangyu Qu'] | 2023-06-30 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 1.01235223e+00 -2.08402351e-01 3.18391204e-01 -8.97827223e-02
-5.46465337e-01 -5.87206304e-01 2.48418897e-01 -2.60225058e-01
-5.84550917e-01 5.38333058e-01 2.71610301e-02 -4.00314987e-01
-3.63545418e-01 -6.96603000e-01 -6.92117751e-01 -9.59489226e-01
1.34970784e-01 -1.93187341e-01 3.44005764e-01 3.88139077... | [10.888848304748535, -2.4356725215911865] |
4c44738a-5a66-4792-84c8-131d88d3fceb | wind-farm-layout-optimisation-using-set-based | 2203.17065 | null | https://arxiv.org/abs/2203.17065v2 | https://arxiv.org/pdf/2203.17065v2.pdf | Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation | Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their produc... | ['Endi Ymeraj', 'Tinkle Chugh'] | 2022-03-31 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-6.17323071e-02 -3.14292878e-01 3.06040615e-01 9.33737531e-02
-3.82849306e-01 -6.35972440e-01 5.43134570e-01 8.56860802e-02
-3.58702362e-01 1.09652436e+00 -9.07234028e-02 -5.29824376e-01
-9.60862458e-01 -1.05875409e+00 -2.53284603e-01 -1.23171234e+00
-1.51473150e-01 6.35949910e-01 3.09109390e-02 -1.44783944... | [6.1404290199279785, 3.4539601802825928] |
e50d795b-dc2d-4a29-aab3-e1e77699625e | confidence-preserving-machine-for-facial | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zeng_Confidence_Preserving_Machine_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zeng_Confidence_Preserving_Machine_ICCV_2015_paper.pdf | Confidence Preserving Machine for Facial Action Unit Detection | Varied sources of error contribute to the challenge of facial action unit detection. Previous approaches address specific and known sources. However, many sources are unknown. To address the ubiquity of error, we propose a Confident Preserving Machine (CPM) that follows an easy-to-hard classification strategy. During t... | ['Jeffrey F. Cohn', 'Fernando de la Torre', 'Wen-Sheng Chu', 'Jiabei Zeng', 'Zhang Xiong'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.87985635e-01 3.82228613e-01 -6.45605087e-01 -6.80666149e-01
-1.13078749e+00 -3.12931985e-01 7.23871350e-01 -1.17697775e-01
-3.49964589e-01 9.82054949e-01 -1.32179543e-01 2.64987379e-01
2.20038727e-01 -2.65413344e-01 -5.78310668e-01 -9.45588291e-01
-2.58765459e-01 4.41515028e-01 2.87835747e-01 1.18373416... | [13.581794738769531, 1.5928034782409668] |
79f9e8c9-fca6-40ff-9642-5ab2dd1bc2cb | fully-test-time-adaptation-by-entropy | 2006.10726 | null | https://arxiv.org/abs/2006.10726v3 | https://arxiv.org/pdf/2006.10726v3.pdf | Tent: Fully Test-time Adaptation by Entropy Minimization | A model must adapt itself to generalize to new and different data during testing. In this setting of fully test-time adaptation the model has only the test data and its own parameters. We propose to adapt by test entropy minimization (tent): we optimize the model for confidence as measured by the entropy of its predict... | ['Shaoteng Liu', 'Evan Shelhamer', 'Bruno Olshausen', 'Trevor Darrell', 'Dequan Wang'] | 2020-06-18 | tent-fully-test-time-adaptation-by-entropy | https://openreview.net/forum?id=uXl3bZLkr3c | https://openreview.net/pdf?id=uXl3bZLkr3c | iclr-2021-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.08832318e-01 -5.93194403e-02 1.17920049e-01 -7.53279388e-01
-1.05344975e+00 -8.82568777e-01 4.32245940e-01 -2.43749052e-01
-1.03412688e+00 9.02517557e-01 -5.67924023e-01 -4.46913838e-01
1.06731161e-01 -4.47983712e-01 -9.02940094e-01 -4.91231263e-01
-3.33228558e-02 7.89510548e-01 1.38655186e-01 4.44850624... | [9.77973461151123, 2.990612506866455] |
0d3fbc20-cfe7-49d2-958c-4c68beba804d | affirmative-algorithms-the-legal-grounds-for | 2012.14285 | null | https://arxiv.org/abs/2012.14285v1 | https://arxiv.org/pdf/2012.14285v1.pdf | Affirmative Algorithms: The Legal Grounds for Fairness as Awareness | While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We make three contributions. First, we discuss how such approaches will likely be deemed "algorithmic affirmative action," posing serious legal r... | ['Alice Xiang', 'Daniel E. Ho'] | 2020-12-18 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [ 3.74356210e-01 2.84750581e-01 -9.58559513e-01 -8.00241411e-01
-5.56330562e-01 -6.82202399e-01 5.18375039e-01 3.76330107e-01
-8.51068974e-01 6.82793081e-01 9.29956198e-01 -1.27500987e+00
-5.54650605e-01 -7.88424790e-01 -8.95063728e-02 -3.47589821e-01
6.23898029e-01 1.29756210e-02 -7.07108974e-01 -5.08765541... | [8.86178970336914, 5.620482921600342] |
aad94f79-8652-40c8-90a5-769c5565c422 | differencing-based-self-supervised | 2208.05838 | null | https://arxiv.org/abs/2208.05838v1 | https://arxiv.org/pdf/2208.05838v1.pdf | Differencing based Self-supervised pretraining for Scene Change Detection | Scene change detection (SCD), a crucial perception task, identifies changes by comparing scenes captured at different times. SCD is challenging due to noisy changes in illumination, seasonal variations, and perspective differences across a pair of views. Deep neural network based solutions require a large quantity of a... | ['Bahram Zonooz', 'Elahe Arani', 'Vijaya Raghavan T. Ramkumar'] | 2022-08-11 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 4.41770226e-01 -7.00451553e-01 2.19621316e-01 -6.92587674e-01
-6.24014199e-01 -7.74532139e-01 5.86254656e-01 -1.37206465e-01
-5.71586072e-01 6.76691532e-01 1.75626308e-01 2.22101286e-01
1.97122380e-01 -5.36971688e-01 -1.06001186e+00 -6.12920880e-01
9.28448215e-02 -7.32152956e-03 3.96132886e-01 -2.14972153... | [9.527865409851074, -1.3389488458633423] |
f29c8a44-d52c-40a8-bc95-f8af95920d97 | a-robust-feature-downsampling-module-for | null | null | https://ieeexplore.ieee.org/document/10142024 | https://ieeexplore.ieee.org/document/10142024 | A Robust Feature Downsampling Module for Remote Sensing Visual Tasks | Remote-sensing (RS) images present unique challenges for computer vision (CV) due to lower resolution, smaller objects, and fewer features. Mainstream backbone networks show promising results for traditional visual tasks. However, they use convolution to reduce feature map dimensionality, which can result in informatio... | ['and Bin Luo', 'Chris H. Q. Ding', 'Jin Tang', 'Si-Bao Chen', 'Wei Lu'] | 2023-06-01 | null | null | null | ieee-transactions-on-geoscience-and-remote-19 | ['object-detection-in-aerial-images', 'object-detection', 'instance-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.07766074e-01 -4.39088106e-01 -2.57456694e-02 -3.47940356e-01
-3.88506979e-01 -5.42666197e-01 4.09409612e-01 -2.76528031e-01
-4.44970608e-01 2.54074901e-01 -7.72962198e-02 -3.23377639e-01
4.26135771e-02 -1.08233035e+00 -7.09390163e-01 -6.02643490e-01
3.97936180e-02 -4.03030962e-01 4.30751711e-01 -3.20986301... | [9.031492233276367, -0.9071186184883118] |
40e02ec5-a20d-480c-95a6-ad808b97cfc3 | mcae-masked-contrastive-autoencoder-for-face | 2302.08674 | null | https://arxiv.org/abs/2302.08674v2 | https://arxiv.org/pdf/2302.08674v2.pdf | EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing | The face anti-spoofing (FAS) method performs well under intra-domain setups. However, its cross-domain performance is unsatisfactory. As a result, the domain generalization (DG) method has gained more attention in FAS. Existing methods treat FAS as a simple binary classification task and propose a heuristic training ob... | ['Tianyi Zheng'] | 2023-02-17 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 4.20132458e-01 -4.73739766e-02 -7.32615471e-01 -4.27855641e-01
-7.03881562e-01 -6.90865517e-01 6.48679912e-01 -1.77778482e-01
6.68899566e-02 9.50916231e-01 -1.06070966e-01 -2.25094974e-01
-1.68170542e-01 -6.82537317e-01 -6.49276137e-01 -8.24775100e-01
-2.56707221e-01 2.16540471e-01 1.44791707e-01 -2.10631117... | [13.060860633850098, 1.185052514076233] |
5bb65abd-59a2-474b-b71a-68dcb4c6f40f | facial-expression-recognition-based-on-multi | 2203.13235 | null | https://arxiv.org/abs/2203.13235v1 | https://arxiv.org/pdf/2203.13235v1.pdf | Facial Expression Recognition based on Multi-head Cross Attention Network | Facial expression in-the-wild is essential for various interactive computing domains. In this paper, we proposed an extended version of DAN model to address the VA estimation and facial expression challenges introduced in ABAW 2022. Our method produced preliminary results of 0.44 of mean CCC value for the VA estimation... | ['Jin-Woo Jeong', 'Yuchul Jung', 'Daun Kim', 'Yeong-Gi Hong', 'Jae-Yeop Jeong'] | 2022-03-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-1.64024442e-01 8.33195597e-02 -2.42883161e-01 -1.07536089e+00
-7.84200907e-01 -2.46832758e-01 3.85643184e-01 -9.79048371e-01
-2.43552834e-01 9.49219882e-01 1.00330170e-02 1.96268588e-01
6.35751724e-01 -1.84895009e-01 -1.54296666e-01 -5.64867139e-01
-2.93785006e-01 -1.55082196e-01 -5.42453825e-01 -4.52261209... | [13.574628829956055, 1.86173677444458] |
a8ebc66b-16bb-45e6-8427-5bf7f0907290 | co-occurrence-feature-learning-from-skeleton | 1804.06055 | null | http://arxiv.org/abs/1804.06055v1 | http://arxiv.org/pdf/1804.06055v1.pdf | Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation | Skeleton-based human action recognition has recently drawn increasing
attentions with the availability of large-scale skeleton datasets. The most
crucial factors for this task lie in two aspects: the intra-frame
representation for joint co-occurrences and the inter-frame representation for
skeletons' temporal evolution... | ['ShiLiang Pu', 'Di Xie', 'Chao Li', 'Qiaoyong Zhong'] | 2018-04-17 | null | null | null | null | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 9.36920717e-02 -4.38557357e-01 -1.39001772e-01 -4.01214093e-01
-7.23185301e-01 -4.18272838e-02 7.12969780e-01 1.84528783e-01
-6.11116588e-01 5.53811014e-01 5.55416763e-01 5.23944736e-01
-1.34129688e-01 -7.04537988e-01 -5.50928473e-01 -6.42610788e-01
-2.74644643e-01 1.48316503e-01 7.34817982e-01 -3.00554693... | [7.838171005249023, 0.3969959020614624] |
b4d7eb3c-db8d-4874-9e4c-fc66ec299d4f | mestereo-du2cnn-a-novel-dual-channel-cnn-for | 2206.10375 | null | https://arxiv.org/abs/2206.10375v1 | https://arxiv.org/pdf/2206.10375v1.pdf | MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications | Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D technologies to the next level. Depth estimation of multi-exposure stereo image sequences is an essential task in the development of cost-effective 3D HDR video content. In ... | ['Rithvik Anil', 'Uma T V', 'Mansi Sharma', 'Rohit Choudhary'] | 2022-06-21 | null | null | null | null | ['stereo-depth-estimation', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 3.48188788e-01 -5.11811197e-01 4.30521578e-01 -5.33270001e-01
-5.30094504e-01 -2.08346084e-01 4.73687887e-01 -2.92848349e-01
-6.07629418e-01 6.21777952e-01 2.34523579e-01 4.86993678e-02
1.23725019e-01 -9.23424363e-01 -6.09262884e-01 -6.56612575e-01
2.13304237e-01 -3.07302084e-02 5.92506528e-01 -4.79809463... | [8.99902057647705, -2.415095329284668] |
3ea6adb7-3b28-449e-93ac-e56896000cbd | clip-count-towards-text-guided-zero-shot | 2305.07304 | null | https://arxiv.org/abs/2305.07304v1 | https://arxiv.org/pdf/2305.07304v1.pdf | CLIP-Count: Towards Text-Guided Zero-Shot Object Counting | Recent advances in visual-language models have shown remarkable zero-shot text-image matching ability that is transferable to down-stream tasks such as object detection and segmentation. However, adapting these models for object counting, which involves estimating the number of objects in an image, remains a formidable... | ['Changwen Chen', 'Lingbo Liu', 'Ruixiang Jiang'] | 2023-05-12 | null | null | null | null | ['object-counting', 'crowd-counting'] | ['computer-vision', 'computer-vision'] | [-1.74892750e-02 -2.86892742e-01 -9.26935077e-02 -5.95399857e-01
-8.37094367e-01 -4.72145468e-01 7.42958367e-01 2.82543302e-01
-6.90066695e-01 1.71584487e-01 1.15710430e-01 -5.04643023e-02
3.20439249e-01 -8.64339173e-01 -1.03546035e+00 -3.79734904e-01
1.80631831e-01 8.90213609e-01 4.91096377e-01 6.59513101... | [9.099711418151855, 0.5496980547904968] |
6d35144b-7277-4291-9d8f-2c016d80fdc8 | textit-what-textit-when-and-textit-how-to | 2306.03361 | null | https://arxiv.org/abs/2306.03361v3 | https://arxiv.org/pdf/2306.03361v3.pdf | WHAT, WHEN, and HOW to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue | This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted... | ['Eric Davis', 'Taeyoon Kim', 'Seojin Lee', 'Ki Hyun Kim', 'Sunwoo Lee', 'Deuksin Kwon'] | 2023-06-06 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.16352881e-02 9.18478608e-01 1.08207442e-01 -5.88556588e-01
-5.21418989e-01 -5.52397311e-01 8.39106798e-01 -1.47913948e-01
-7.35891983e-02 9.84808147e-01 1.07383215e+00 1.03904158e-01
-2.39226446e-02 -6.49123311e-01 3.18540305e-01 -1.91192031e-01
4.84624863e-01 9.74238992e-01 -2.09634021e-01 -1.02799416... | [12.737165451049805, 8.1053466796875] |
f7700559-4a6b-44f1-b863-03ab07be17e5 | adaptive-bi-recommendation-and-self-improving | 2303.14317 | null | https://arxiv.org/abs/2303.14317v1 | https://arxiv.org/pdf/2303.14317v1.pdf | Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection | As Internet of Things devices become prevalent, using intrusion detection to protect IoT from malicious intrusions is of vital importance. However, the data scarcity of IoT hinders the effectiveness of traditional intrusion detection methods. To tackle this issue, in this paper, we propose the Adaptive Bi-Recommendatio... | ['Kenneth B. Kent', 'Chengzhong Xu', 'Hao Dai', 'Yang Wang', 'Jiashu Wu'] | 2023-03-25 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 3.97934228e-01 7.03216344e-02 -4.76073742e-01 -3.52094918e-01
-1.84083804e-02 -4.67314005e-01 3.75132978e-01 -7.99730942e-02
-4.60203111e-01 7.40200937e-01 -1.31999150e-01 -1.30931750e-01
-7.37057507e-01 -1.25140500e+00 -3.70572358e-01 -7.52379537e-01
3.25966090e-01 6.45361900e-01 3.91771108e-01 -2.33203039... | [5.285915374755859, 7.133382320404053] |
dcbcbf4d-5be2-49e5-ab82-2277c0893001 | understanding-and-constructing-latent | 2303.05952 | null | https://arxiv.org/abs/2303.05952v1 | https://arxiv.org/pdf/2303.05952v1.pdf | Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning | Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly match each other in the latent space. Yet it remains an open question how the modality alignment affects the downstream task performance. In... | ['Trishul Chilimbi', 'Belinda Zeng', 'Yi Xu', 'Son Dinh Tran', 'Qing Ping', 'Liqun Chen', 'Han Zhao', 'Changyou Chen', 'Qian Jiang'] | 2023-03-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'few-shot-image-classification', 'open-question', 'visual-reasoning', 'visual-entailment'] | ['computer-vision', 'computer-vision', 'natural-language-processing', 'reasoning', 'reasoning'] | [ 5.45885623e-01 -3.72077674e-02 -5.66984892e-01 -3.56089950e-01
-1.13211477e+00 -4.16728079e-01 8.29174995e-01 1.64196149e-01
-2.31157750e-01 2.94133365e-01 5.68829000e-01 -2.01217920e-01
-3.03572625e-01 -4.03404027e-01 -8.43898356e-01 -8.25789392e-01
3.86436433e-01 9.32267830e-02 -4.50619236e-02 -7.06737265... | [10.684000968933105, 1.4462110996246338] |
39c31d64-6641-4934-a4cf-702ea9ba371a | hierarchical-graph-neural-networks-for-1 | 2306.15858 | null | https://arxiv.org/abs/2306.15858v1 | https://arxiv.org/pdf/2306.15858v1.pdf | Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects | Robotic manipulation, in particular in-hand object manipulation, often requires an accurate estimate of the object's 6D pose. To improve the accuracy of the estimated pose, state-of-the-art approaches in 6D object pose estimation use observational data from one or more modalities, e.g., RGB images, depth, and tactile r... | ['Nawid Jamali', 'Soshi Iba', 'Snehal Dikhale', 'Alireza Rezazadeh'] | 2023-06-28 | null | null | null | null | ['pose-estimation', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.04058200e-01 -1.94747373e-01 -1.65034756e-01 -3.01226061e-02
-4.07594770e-01 -6.43743634e-01 2.83389956e-01 1.70776322e-01
-1.94873422e-01 4.65195209e-01 -1.23549737e-01 8.41255710e-02
-4.03882146e-01 -6.39573634e-01 -9.97150421e-01 -3.55882734e-01
-1.23045132e-01 6.51572645e-01 3.47222000e-01 -9.09752175... | [5.9403839111328125, -0.8989889621734619] |
5c06c566-8072-49cc-826e-cfa488ec634e | exploring-techniques-for-the-analysis-of | 2205.13963 | null | https://arxiv.org/abs/2205.13963v1 | https://arxiv.org/pdf/2205.13963v1.pdf | Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications | This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchmarks and realistic proxy applications with the regular compute-communicate structure on two differen... | ['Stefano Markidis', 'Gerhard Wellein', 'Georg Hager', 'Ayesha Afzal'] | 2022-05-27 | null | null | null | null | ['classification'] | ['methodology'] | [-4.47772861e-01 -8.22508812e-01 -2.13995293e-01 -8.25099945e-02
-6.24023438e-01 -5.91343462e-01 8.21624875e-01 6.85609937e-01
-1.72312275e-01 5.27265370e-01 1.34719342e-01 -4.68326896e-01
-4.13051277e-01 -6.95242465e-01 -2.12142214e-01 -1.12406957e+00
-8.50119233e-01 8.62138748e-01 4.28324461e-01 -6.41037896... | [5.998636245727539, 4.594879150390625] |
a5f3e929-2ac1-46b6-9ff3-a9de5215da01 | a-cryptanalysis-of-two-cancelable-biometric | 1910.01389 | null | https://arxiv.org/abs/1910.01389v3 | https://arxiv.org/pdf/1910.01389v3.pdf | A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing | Cancelable biometric schemes generate secure biometric templates by combining user specific tokens and biometric data. The main objective is to create irreversible, unlinkable, and revocable templates, with high accuracy in matching. In this paper, we cryptanalyze two recent cancelable biometric schemes based on a part... | ['Koray Karabina', 'Loubna Ghammam', 'Patrick Lacharme', 'Kevin Atighehchi'] | 2019-10-03 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 6.49117649e-01 4.48273383e-02 -2.22379491e-02 -8.32747668e-02
-5.38584769e-01 -1.42916644e+00 7.94841588e-01 -1.33209080e-01
-2.52911508e-01 8.38501871e-01 -1.23679750e-02 -4.85828757e-01
-2.87035435e-01 -9.48447704e-01 -6.23981833e-01 -8.16964924e-01
-9.82576236e-02 2.93809891e-01 1.36862367e-01 -1.64253771... | [13.02570915222168, 1.1091883182525635] |
b5ad567e-bb06-4c4b-a290-00dfd2466582 | an-unsupervised-segmentation-of-vocal-breath | 2304.03758 | null | https://arxiv.org/abs/2304.03758v1 | https://arxiv.org/pdf/2304.03758v1.pdf | An unsupervised segmentation of vocal breath sounds | Breathing is an essential part of human survival, which carries information about a person's physiological and psychological state. Generally, breath boundaries are marked by experts before using for any task. An unsupervised algorithm for breath boundary detection has been proposed for breath sounds recorded at the mo... | ['Prasanta Kumar Ghosh', 'Uma Maheswari K.', 'Dipanjan Gope', 'Shivani Yadav'] | 2023-04-07 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.41527448e-02 7.02896789e-02 5.88365644e-02 -9.78182033e-02
-3.62339735e-01 -2.23258182e-01 -2.21015334e-01 1.41826928e-01
-3.30619544e-01 7.32709944e-01 -1.14669643e-01 -5.53381555e-02
-1.44893199e-01 -5.05774140e-01 6.71890154e-02 -8.60121012e-01
-2.92085499e-01 5.86255156e-02 4.32178676e-01 1.46979734... | [14.232559204101562, 3.4679455757141113] |
3a643f89-58de-471d-941d-accf9f38c82e | a-hierarchical-bayesian-model-for-deep-few | 2306.09702 | null | https://arxiv.org/abs/2306.09702v1 | https://arxiv.org/pdf/2306.09702v1.pdf | A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning | We propose a novel hierarchical Bayesian model for learning with a large (possibly infinite) number of tasks/episodes, which suits well the few-shot meta learning problem. We consider episode-wise random variables to model episode-specific target generative processes, where these local random variables are governed by ... | ['Timothy Hospedales', 'Minyoung Kim'] | 2023-06-16 | null | null | null | null | ['bayesian-inference', 'meta-learning'] | ['methodology', 'methodology'] | [-7.01895803e-02 2.16008708e-01 -1.29080757e-01 -3.05591166e-01
-1.00167680e+00 -1.79232702e-01 9.41410005e-01 -5.67026772e-02
-5.95941246e-01 1.18575704e+00 6.97417408e-02 9.66947302e-02
-6.00339830e-01 -1.04058933e+00 -8.72961760e-01 -1.14596713e+00
-2.23277528e-02 9.76120532e-01 1.97112098e-01 3.32678139... | [6.993046283721924, 3.8269379138946533] |
5c76de77-231c-4c2e-b755-c5555ef65342 | toward-unifying-text-segmentation-and-long | 2210.16422 | null | https://arxiv.org/abs/2210.16422v1 | https://arxiv.org/pdf/2210.16422v1.pdf | Toward Unifying Text Segmentation and Long Document Summarization | Text segmentation is important for signaling a document's structure. Without segmenting a long document into topically coherent sections, it is difficult for readers to comprehend the text, let alone find important information. The problem is only exacerbated by a lack of segmentation in transcripts of audio/video reco... | ['Dong Yu', 'Fei Liu', 'Xiaoyang Wang', 'Kaiqiang Song', 'Sangwoo Cho'] | 2022-10-28 | null | null | null | null | ['extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.35608339e-01 2.69985884e-01 -2.79064059e-01 -4.80631948e-01
-1.59472489e+00 -9.29931879e-01 5.38515925e-01 6.04233265e-01
-4.32034135e-01 6.41438425e-01 7.84763396e-01 -3.01224887e-01
1.29119962e-01 -1.66354224e-01 -6.40382707e-01 -4.09359664e-01
1.26144290e-01 2.51950234e-01 2.10630894e-03 -6.14524782... | [12.499053001403809, 9.417186737060547] |
a6eb1f60-4a66-4222-acc7-1ac98ccb2bdd | hydramix-net-a-deep-multi-task-semi | 2008.04753 | null | https://arxiv.org/abs/2008.04753v1 | https://arxiv.org/pdf/2008.04753v1.pdf | HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification | Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task classification and localization approach HydraMix-Net in the field of medical imagining where labelling is tim... | ['Nasir M. Rajpoot', 'Talha Qaiser', 'Shan E Ahmed Raza', 'R. M. Saad Bashir'] | 2020-08-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 6.24819160e-01 8.06667626e-01 -1.10598795e-01 -5.60883999e-01
-1.47766495e+00 -1.63820624e-01 4.61684048e-01 3.49242598e-01
-6.92285061e-01 1.30953455e+00 1.64671019e-01 1.26140058e-01
-1.58299968e-01 -4.11385030e-01 -4.64337498e-01 -1.05236793e+00
2.51389146e-01 9.27507341e-01 -1.92859359e-02 2.12511435... | [14.736019134521484, -2.2614588737487793] |
90519a52-dbe1-4ab0-96fa-c6da815359d2 | genetic-algorithm-optimized-neural-networks | 2010.04340 | null | https://arxiv.org/abs/2010.04340v2 | https://arxiv.org/pdf/2010.04340v2.pdf | Genetic-algorithm-optimized neural networks for gravitational wave classification | Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks rema... | ['Gaurav Khanna', 'Collin D. Capano', 'Scott E. Field', 'Dwyer S. Deighan'] | 2020-10-09 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [ 3.37330878e-01 -3.97573337e-02 3.46573710e-01 -9.60532650e-02
-4.55859721e-01 -6.87803805e-01 2.50040174e-01 -1.80255428e-01
-7.26894379e-01 6.16665244e-01 -1.49963170e-01 -4.34602261e-01
-5.67362547e-01 -8.65970373e-01 -5.17600656e-01 -9.00803506e-01
-3.76244724e-01 3.15734535e-01 4.58149493e-01 -4.23615128... | [8.311131477355957, 3.2378203868865967] |
f4974866-432c-4a17-acfb-aac5ce8324a7 | pvrnet-point-view-relation-neural-network-for | 1812.00333 | null | http://arxiv.org/abs/1812.00333v1 | http://arxiv.org/pdf/1812.00333v1.pdf | PVRNet: Point-View Relation Neural Network for 3D Shape Recognition | Three-dimensional (3D) shape recognition has drawn much research attention in
the field of computer vision. The advances of deep learning encourage various
deep models for 3D feature representation. For point cloud and multi-view data,
two popular 3D data modalities, different models are proposed with remarkable
perfor... | ['Yue Gao', 'Changqing Zou', 'Yifan Feng', 'Xibin Zhao', 'Rongrong Ji', 'Haoxuan You'] | 2018-12-02 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-6.36853456e-01 -7.65569806e-01 1.06509589e-01 -4.74014461e-01
-5.14865279e-01 -3.63168746e-01 8.36510956e-01 -8.40810835e-02
1.31015703e-01 -1.81058556e-01 1.02245890e-01 2.37424478e-01
-3.65583181e-01 -8.41723800e-01 -4.34820175e-01 -7.19320536e-01
3.14800680e-01 4.50123668e-01 3.18467140e-01 -3.18757713... | [8.144280433654785, -3.8474597930908203] |
269cd5e2-fb20-4d3f-8a79-2f5af98106d4 | spatially-dependent-u-nets-highly-accurate | 2103.11713 | null | https://arxiv.org/abs/2103.11713v1 | https://arxiv.org/pdf/2103.11713v1.pdf | Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation | In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation. The quality of this image segmentation step critically affects the subsequent clinical assessment of the patient status. To enable high accuracy, automatic image segmentation, we int... | ['Joachim M. Buhmann', 'Đorđe Miladinović', 'João A. Santinha', 'João B. S. Carvalho'] | 2021-03-22 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.71391642e-01 1.51479125e-01 -1.36816368e-01 -4.63829726e-01
-6.49192333e-01 -6.29775763e-01 3.30816031e-01 6.65663660e-01
-8.32864761e-01 6.60400867e-01 9.41183269e-02 -4.79114175e-01
-2.97249913e-01 -6.17426217e-01 -6.85971200e-01 -7.56928325e-01
-4.23691690e-01 5.12900651e-01 7.55042285e-02 8.37937668... | [14.526983261108398, -2.654114007949829] |
b6bdb3f0-49f8-4074-a6d2-a7aad473a763 | video-guided-curriculum-learning-for-spoken | 2209.00277 | null | https://arxiv.org/abs/2209.00277v1 | https://arxiv.org/pdf/2209.00277v1.pdf | Video-Guided Curriculum Learning for Spoken Video Grounding | In this paper, we introduce a new task, spoken video grounding (SVG), which aims to localize the desired video fragments from spoken language descriptions. Compared with using text, employing audio requires the model to directly exploit the useful phonemes and syllables related to the video from raw speech. Moreover, w... | ['Yi Ren', 'Haoyuan Li', 'Yang Zhao', 'Shangwei Ye', 'Zhou Zhao', 'Yan Xia'] | 2022-09-01 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.70006418e-01 1.33299053e-01 -2.51820028e-01 -1.85484990e-01
-1.02642167e+00 -3.47096324e-01 1.49512902e-01 -3.77908856e-01
-3.25049698e-01 4.30803388e-01 4.19640183e-01 -1.29785597e-01
2.42143065e-01 -6.13798141e-01 -1.22800303e+00 -7.57068992e-01
1.56057686e-01 -7.54987076e-02 1.64788321e-01 8.61117467... | [10.131696701049805, 0.7167659997940063] |
e1e5e4a5-f671-4579-af95-af65e32d1a29 | temporal-cascade-and-structural-modelling-of | 2102.02586 | null | https://arxiv.org/abs/2102.02586v1 | https://arxiv.org/pdf/2102.02586v1.pdf | Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction | Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist clinical decision making. Recurrent neural network (RNN) and point process models are usually employe... | ['Wray Buntine', 'Suong Le', 'Yuan-Fang Li', 'Weiqing Wang', 'Bhagya Hettige'] | 2021-02-04 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 2.17625186e-01 3.48487347e-02 -1.82251796e-01 -2.70014554e-01
-2.24821091e-01 7.50926882e-02 4.41033751e-01 7.14367867e-01
-5.46396710e-02 6.10216737e-01 7.99762964e-01 -7.86092639e-01
-5.29610515e-01 -9.85816121e-01 -5.49780667e-01 -5.54939032e-01
-4.26015764e-01 9.97761786e-01 -1.94833919e-01 -1.44514292... | [7.775449275970459, 6.037852764129639] |
1edd242d-cc72-4255-8908-239c7a6e7127 | a-contact-safe-reinforcement-learning | 2207.13438 | null | https://arxiv.org/abs/2207.13438v1 | https://arxiv.org/pdf/2207.13438v1.pdf | A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation | Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since unexpected dangerous collisions might happen when the RL policy is imperfect during training or in unseen scenarios. In this paper, we propos... | ['Jianyu Chen', 'Shucheng Kang', 'Xiang Zhu'] | 2022-07-27 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 1.86675772e-01 6.53807402e-01 -1.73867971e-01 4.06278610e-01
-1.85007766e-01 -5.84510386e-01 3.53294402e-01 -1.00652814e-01
-6.86946690e-01 1.09521997e+00 -5.69977224e-01 -2.81008810e-01
-5.46703696e-01 -4.42500800e-01 -9.84658897e-01 -8.93095016e-01
-4.75753158e-01 6.11217320e-01 4.12901223e-01 -6.02265596... | [4.741327285766602, 1.5924125909805298] |
9b4ca236-71f1-4fa1-8ecb-dcdec0a8ac1b | evolutionary-multi-objective-optimization-of | 1803.10316 | null | http://arxiv.org/abs/1803.10316v1 | http://arxiv.org/pdf/1803.10316v1.pdf | Evolutionary Multi-objective Optimization of Real-Time Strategy Micro | We investigate an evolutionary multi-objective approach to good micro for
real-time strategy games. Good micro helps a player win skirmishes and is one
of the keys to developing better real-time strategy game play. In prior work,
the same multi-objective approach of maximizing damage done while minimizing
damage receiv... | ['Joseph Ghantous', 'Siming Liu', 'Rahul Dubey', 'Sushil Louis'] | 2018-03-27 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 9.75671560e-02 -2.09719408e-02 1.47982374e-01 3.22700441e-01
-2.60609668e-02 -5.85334241e-01 3.01165462e-01 2.64609635e-01
-8.03200006e-01 7.38138378e-01 3.71365137e-02 -6.42533526e-02
-9.04993296e-01 -1.38592768e+00 -3.28851670e-01 -6.06414139e-01
-2.72065818e-01 5.33035755e-01 3.06120366e-01 -8.88087213... | [3.484910488128662, 1.5851147174835205] |
99054aae-5304-4b85-8ea2-ab5cd2e1b191 | revisiting-open-world-object-detection | 2201.00471 | null | https://arxiv.org/abs/2201.00471v2 | https://arxiv.org/pdf/2201.00471v2.pdf | Revisiting Open World Object Detection | Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. We find that although the only previous OWOD work constructively puts forward to the OWOD definition, the exper... | ['Yixuan Qiao', 'Duorui Wang', 'Yuqing Ma', 'Yifan Shen', 'Xianglong Liu', 'Xiaowei Zhao'] | 2022-01-03 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [-7.86072984e-02 2.21126020e-01 -2.81557381e-01 -2.32000023e-01
-6.53163671e-01 -6.20710731e-01 4.73365992e-01 -6.63590357e-02
-3.78245026e-01 8.37292910e-01 -2.47791156e-01 -1.03365131e-01
-2.81780690e-01 -7.89533436e-01 -6.45841360e-01 -6.99114859e-01
-4.85480353e-02 7.95728981e-01 8.24751914e-01 2.42635936... | [9.259479522705078, 1.4269248247146606] |
cd649b39-7110-4a0c-8ce9-f5d4c1008cf5 | wasserstein-dictionaries-of-persistence | 2304.14852 | null | https://arxiv.org/abs/2304.14852v1 | https://arxiv.org/pdf/2304.14852v1.pdf | Wasserstein Dictionaries of Persistence Diagrams | This paper presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters [99], [101] of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization... | ['Julien Tierny', 'Julie Delon', 'Keanu Sisouk'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.22766823e-01 -4.36686724e-02 1.94253162e-01 -2.25764830e-02
-7.46983767e-01 -8.09791028e-01 7.31100798e-01 3.50139111e-01
-2.42762998e-01 3.47560644e-01 3.44267815e-01 -3.63033682e-01
-3.16093832e-01 -7.62113035e-01 -6.46510363e-01 -9.58631098e-01
-4.93884623e-01 5.49888253e-01 -5.19516766e-02 -3.36710662... | [7.423518180847168, 4.299807071685791] |
1ea0cc69-1552-4400-8a52-8c3a53ef5354 | medical-concept-normalization-in-user | 2006.04014 | null | https://arxiv.org/abs/2006.04014v1 | https://arxiv.org/pdf/2006.04014v1.pdf | Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings | Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a vocabulary. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as eithe... | ['Katikapalli Subramanyam Kalyan', 'S. Sangeetha'] | 2020-06-07 | null | null | null | null | ['medical-concept-normalization'] | ['medical'] | [ 6.04630470e-01 2.81982154e-01 -4.24473614e-01 -4.72791106e-01
-6.74967825e-01 -2.66090572e-01 5.04884720e-01 1.33933127e+00
-9.68135417e-01 4.33715880e-01 6.70881629e-01 -7.00856224e-02
-2.86877543e-01 -1.14845896e+00 -1.84041128e-01 -6.77159071e-01
1.74897775e-01 6.34765625e-01 7.77907372e-02 -4.19876307... | [8.617269515991211, 8.53349781036377] |
1ad35b6b-0260-444f-b125-e1f08a9593bf | grouped-adaptive-loss-weighting-for-person | 2209.11492 | null | https://arxiv.org/abs/2209.11492v1 | https://arxiv.org/pdf/2209.11492v1.pdf | Grouped Adaptive Loss Weighting for Person Search | Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search feat... | ['Jian Yang', 'Shanshan Zhang', 'Yunan Liu', 'Di Chen', 'Yanling Tian'] | 2022-09-23 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.40949015e-02 -4.14241284e-01 1.30291045e-01 -4.65273201e-01
-4.78679806e-01 -2.69611329e-01 3.61209065e-01 9.45766270e-02
-7.69014060e-01 6.92728639e-01 1.25905529e-01 2.58347720e-01
-2.87850767e-01 -4.19529438e-01 -3.35413128e-01 -8.96186173e-01
3.89533669e-01 6.52043402e-01 4.63246733e-01 2.00227067... | [14.746322631835938, 0.8293699026107788] |
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