paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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f62e866b-1138-4d2c-8422-405c901a268a | is-syntax-structure-modeling-worth-leveraging | null | null | https://openreview.net/forum?id=L3TcOq4D2cD | https://openreview.net/pdf?id=L3TcOq4D2cD | Is syntax structure modeling worth? Leveraging pattern-driven modeling to enable affordable sentiment dependency learning | Is structure information modeling really worth in Aspect-based sentiment classification (ABSC)? Recent popular works tend to exploit syntactic information guiding sentiment dependency parsing, i.e., structure-based sentiment dependency learning. However, many works fall into the trap that confusing the concepts between... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['sentiment-dependency-learning'] | ['natural-language-processing'] | [-1.79063752e-01 -1.47253603e-01 -5.74404895e-01 -9.68392789e-01
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2.09580407e-01 3.80565941e-01 1.29749432e-01 -7.78102696... | [11.418614387512207, 6.714746952056885] |
30ec480b-967e-4dc2-bba3-15a476553d57 | from-knowledge-graph-embedding-to-ontology | 1805.10461 | null | http://arxiv.org/abs/1805.10461v3 | http://arxiv.org/pdf/1805.10461v3.pdf | From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules | Recent years have witnessed the successful application of low-dimensional
vector space representations of knowledge graphs to predict missing facts or
find erroneous ones. However, it is not yet well-understood to what extent
ontological knowledge, e.g. given as a set of (existential) rules, can be
embedded in a princi... | ['Víctor Gutiérrez-Basulto', 'Steven Schockaert'] | 2018-05-26 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [-8.18497315e-02 8.56225312e-01 -1.02621123e-01 -3.45967382e-01
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-2.20334321e-01 5.70219100e-01 3.11169297e-01 -7.61163652... | [8.845259666442871, 7.567572593688965] |
db2ca23d-06ce-412f-bb3b-6bed6b51ef42 | unsupervised-domain-adaptation-for-clinician | 2108.11801 | null | https://arxiv.org/abs/2108.11801v4 | https://arxiv.org/pdf/2108.11801v4.pdf | Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room | The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better understand the clinical activities and the spatial layout of the OR. ... | ['Nicolas Padoy', 'Afshin Gangi', 'Vinkle Srivastav'] | 2021-08-26 | null | null | null | null | ['semi-supervised-human-pose-estimation', '2d-human-pose-estimation', 'semi-supervised-person-instance-segmentation', 'semi-supervised-person-bounding-box-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.72356248e-01 5.44105113e-01 -3.45460773e-01 -5.76152146e-01
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3.31500590e-01 6.53548360e-01 -1.99145377e-02 2.71465600... | [14.590875625610352, -1.995203971862793] |
a9504174-7ab1-4d8a-a434-d5195d2b5812 | aom-detecting-aspect-oriented-information-for | 2306.01004 | null | https://arxiv.org/abs/2306.01004v1 | https://arxiv.org/pdf/2306.01004v1.pdf | AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis | Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments. Existing methods make great efforts to align the whole image to corresponding aspects. However, different regions of the image may relate to different aspects in the same sentence, and coarse... | ['Xiaojie Yuan', 'Ying Zhang', 'Shenglong Yu', 'Xumeng Liu', 'Wenya Guo', 'Ru Zhou'] | 2023-05-31 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 1.82989717e-01 -5.20903990e-02 -8.09174404e-03 -4.81317997e-01
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7.91281641e-01 1.67056412e-01 -5.16000111e-03 -3.83657873... | [10.7708740234375, 1.633555293083191] |
ea54e8b0-a724-49c2-95ff-7511b78af1fc | efficientphys-enabling-simple-fast-and-1 | 2110.04447 | null | https://arxiv.org/abs/2110.04447v3 | https://arxiv.org/pdf/2110.04447v3.pdf | EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement | Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "end-to-end" models; however these methods still require several preprocessing steps. These additional operations are often non-trivial to implement making replication a... | ['Daniel McDuff', 'Shwetak Patel', 'Ziheng Jiang', 'Brian L. Hill', 'Xin Liu'] | 2021-10-09 | null | null | null | null | ['photoplethysmography-ppg-heart-rate'] | ['medical'] | [ 1.65142000e-01 -3.43873829e-01 1.99242562e-01 -7.00965405e-01
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-3.24127935e-02 4.06574979e-02 1.56134039e-01 2.88356364... | [8.636618614196777, 2.752849578857422] |
c87206aa-0ddf-426b-8d99-52f28d1f0a2e | deep-incomplete-multi-view-clustering-with | 2303.15689 | null | https://arxiv.org/abs/2303.15689v2 | https://arxiv.org/pdf/2303.15689v2.pdf | Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment | The success of existing multi-view clustering relies on the assumption of sample integrity across multiple views. However, in real-world scenarios, samples of multi-view are partially available due to data corruption or sensor failure, which leads to incomplete multi-view clustering study (IMVC). Although several attem... | ['En Zhu', 'Xinwang Liu', 'Zhibin Dong', 'Siwei Wang', 'Jiaqi Jin'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_Deep_Incomplete_Multi-View_Clustering_With_Cross-View_Partial_Sample_and_Prototype_CVPR_2023_paper.pdf | cvpr-2023-1 | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [ 3.38248909e-02 -1.67751729e-01 -2.26003736e-01 -5.43314338e-01
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3.54857355e-01 5.11430979e-01 3.29271075e-03 1.79350749... | [8.413588523864746, 4.536715984344482] |
ae7495e8-0872-4716-b1b5-4ed474148c12 | one-shot-object-affordance-detection-in-the | 2108.03658 | null | https://arxiv.org/abs/2108.03658v1 | https://arxiv.org/pdf/2108.03658v1.pdf | One-Shot Object Affordance Detection in the Wild | Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we first study the challenging one-shot affordance detection problem in this paper, i.e., given... | ['DaCheng Tao', 'Yang Cao', 'Jing Zhang', 'Hongchen Luo', 'Wei Zhai'] | 2021-08-08 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 9.55578387e-02 -1.55317858e-01 -1.79748893e-01 -3.07042271e-01
-7.93122202e-02 -2.81000793e-01 3.65072846e-01 -2.13118196e-01
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-2.85183191e-01 -3.56087863e-01 -7.51196504e-01 -4.43795770e-01
-5.78447580e-02 2.57043332e-01 3.77194107e-01 -4.25125897... | [5.149901866912842, -0.0975063145160675] |
49645a75-75ea-403d-8a71-1e58a91fd052 | battle-royale-optimization-algorithm | null | null | https://link.springer.com/article/10.1007/s00521-020-05004-4 | https://link.springer.com/article/10.1007/s00521-020-05004-4 | Battle royale optimization algorithm | Recently, several metaheuristic optimization approaches have been developed for solving many complex problems in various areas. Most of these optimization algorithms are inspired by nature or the social behavior of some animals. However, there is no optimization algorithm which has been inspired by a game. In this pape... | ['Taymaz Rahkar-Farshi'] | 2020-06-02 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.84976935e-01 -2.40393341e-01 1.23315662e-01 1.48195028e-01
2.32575104e-01 -2.22522959e-01 3.80262673e-01 3.02309424e-01
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-8.82941604e-01 -8.74720156e-01 -2.89312243e-01 -1.01197410e+00
1.49310986e-02 5.94489753e-01 4.48691696e-02 -7.01317191... | [5.617990970611572, 3.480499505996704] |
f00aba91-e789-4a67-bec1-54cb670e7ef3 | automl-two-sample-test | 2206.08843 | null | https://arxiv.org/abs/2206.08843v3 | https://arxiv.org/pdf/2206.08843v3.pdf | AutoML Two-Sample Test | Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require specialized knowledge ab... | ['Bernhard Schölkopf', 'Krikamol Muandet', 'Simon Buchholz', 'Vincent Stimper', 'Jonas M. Kübler'] | 2022-06-17 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 7.07052127e-02 -1.68369114e-01 -2.70715445e-01 -4.76269990e-01
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-2.77525753e-01 7.48634636e-01 4.91957188e-01 2.36988932... | [7.58945894241333, 4.2512335777282715] |
0efea464-decb-47ca-8691-30a2ee9a2609 | semi-supervised-medical-image-segmentation-2 | 2112.04894 | null | https://arxiv.org/abs/2112.04894v2 | https://arxiv.org/pdf/2112.04894v2.pdf | Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer | Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has shown encouraging results in fully supervised medical image segmentation. However, it is still challenging for them to achieve good performance with limited annotations for training. In this work, we present a very simple yet efficie... | ['Shaoting Zhang', 'Guotai Wang', 'Tao Song', 'Minhao Hu', 'Xiangde Luo'] | 2021-12-09 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 1.24746390e-01 4.80986983e-01 -2.80125052e-01 -6.09905303e-01
-7.62835383e-01 -3.68913472e-01 1.69500619e-01 -7.63082877e-02
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2.04503179e-01 6.99179113e-01 4.10057008e-01 6.86754808... | [14.671899795532227, -2.409572124481201] |
eaa6c026-a74a-47ff-b0c7-01b53371b3f1 | viskop-visual-knowledge-oriented-programming | 2307.03130 | null | https://arxiv.org/abs/2307.03130v1 | https://arxiv.org/pdf/2307.03130v1.pdf | VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering | We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledg... | ['Juanzi Li', 'Lei Hou', 'Peng Zhang', 'Jianjun Xu', 'Hailong Jin', 'Jifan Yu', 'Amy Xin', 'Shulin Cao', 'Xin Lv', 'Yuanyong Chen', 'Zijun Yao'] | 2023-07-06 | null | null | null | null | ['program-induction', 'knowledge-base-question-answering', 'question-answering', 'slot-filling'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-9.91409123e-01 1.41355678e-01 -1.94604963e-01 -2.93121368e-01
-6.34786785e-01 -8.04618359e-01 -2.67650157e-01 4.90455367e-02
-2.01655328e-01 6.88155890e-01 -1.43522307e-01 -9.44512129e-01
-2.21028998e-01 -1.10371232e+00 -9.48485494e-01 -3.54341194e-02
7.12625310e-02 6.48019731e-01 4.72921431e-01 -4.25476283... | [9.81902027130127, 7.63580322265625] |
20ea5327-53b0-4c87-923e-d20899fbb495 | ccmi-classifier-based-conditional-mutual | 1906.01824 | null | https://arxiv.org/abs/1906.01824v1 | https://arxiv.org/pdf/1906.01824v1.pdf | CCMI : Classifier based Conditional Mutual Information Estimation | Conditional Mutual Information (CMI) is a measure of conditional dependence between random variables X and Y, given another random variable Z. It can be used to quantify conditional dependence among variables in many data-driven inference problems such as graphical models, causal learning, feature selection and time-se... | ['Sudipto Mukherjee', 'Himanshu Asnani', 'Sreeram Kannan'] | 2019-06-05 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 9.31375250e-02 -3.96780193e-01 -4.50037509e-01 -5.71467459e-01
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-1.75694153e-01 9.86520886e-01 -1.27496436e-01 -4.01157886e-01
-5.92266560e-01 -8.95563722e-01 -5.12714446e-01 -8.20119977e-01
-4.30256993e-01 3.87203962e-01 3.36254053e-02 4.29837018... | [7.4122633934021, 4.268125534057617] |
191f8a40-23b4-4cb8-94e1-7eb8e31ebb18 | dimsum-distributed-and-multilingual | null | null | https://aclanthology.org/2022.fnp-1.9 | https://aclanthology.org/2022.fnp-1.9.pdf | DiMSum: Distributed and Multilingual Summarization of Financial Narratives | This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop. The objective of the task was to generate not more than 1000 words summaries for the annual financial reports written in English, Spanish and Greek languages. The central idea of this paper is to demonstrate automatic ways o... | ['Msp Raja', 'Sangeeth Keeriyadath', 'Raghu Katikeri', 'Amit Vaid', 'Neelesh Shukla'] | null | null | null | null | fnp-lrec-2022-6 | ['document-ai'] | ['natural-language-processing'] | [ 2.20727831e-01 5.02484083e-01 -9.99037474e-02 -7.63047263e-02
-1.18411517e+00 -9.67678010e-01 1.03107655e+00 5.94128370e-01
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-1.68946996e-01 -6.32915318e-01 -1.99901104e-01 -1.27416745e-01
5.47237583e-02 1.53117478e-01 1.68264136e-01 -1.19497649... | [12.420710563659668, 9.523408889770508] |
905f62ad-414f-406f-ba62-9604d5ca266f | embracing-the-disharmony-in-heterogeneous | 2103.12857 | null | https://arxiv.org/abs/2103.12857v3 | https://arxiv.org/pdf/2103.12857v3.pdf | Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation | Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift cha... | ['Christos Davatzikos', 'Pratik Chaudhari', 'Rongguang Wang'] | 2021-03-23 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 4.54496205e-01 2.20073182e-02 -2.62759686e-01 -6.58800602e-01
-9.88490701e-01 -6.99448586e-01 3.37767810e-01 3.57638478e-01
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-1.71605840e-01 8.18487763e-01 3.83580476e-01 -9.54342932... | [14.526108741760254, -1.9041730165481567] |
5158fb26-7af0-48fc-a8f7-b64b92e23712 | pvgru-generating-diverse-and-relevant | 2212.09086 | null | https://arxiv.org/abs/2212.09086v4 | https://arxiv.org/pdf/2212.09086v4.pdf | PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism | We investigate response generation for multi-turn dialogue in generative-based chatbots. Existing generative models based on RNNs (Recurrent Neural Networks) usually employ the last hidden state to summarize the sequences, which makes models unable to capture the subtle variability observed in different dialogues and c... | ['Yifei Zhang', 'Hinrich Schütze', 'Daling Wang', 'Shi Feng', 'Yongkang Liu'] | 2022-12-18 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-2.80574355e-02 3.06567609e-01 7.09411874e-02 -5.83764851e-01
-8.99203956e-01 -4.35750306e-01 7.59640694e-01 -4.74875003e-01
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6.65454447e-01 8.41677666e-01 -6.16746768e-03 -7.55602777... | [12.562708854675293, 8.385823249816895] |
d7a2ebbf-4f57-403c-9790-53aafd96affe | deep-network-guided-proof-search | 1701.06972 | null | http://arxiv.org/abs/1701.06972v1 | http://arxiv.org/pdf/1701.06972v1.pdf | Deep Network Guided Proof Search | Deep learning techniques lie at the heart of several significant AI advances
in recent years including object recognition and detection, image captioning,
machine translation, speech recognition and synthesis, and playing the game of
Go. Automated first-order theorem provers can aid in the formalization and
verificatio... | ['Geoffrey Irving', 'Christian Szegedy', 'Cezary Kaliszyk', 'Sarah Loos'] | 2017-01-24 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 4.76342529e-01 3.71708304e-01 -2.90269911e-01 7.63854058e-03
-9.98184085e-01 -8.95912051e-01 6.27738297e-01 2.38991559e-01
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-8.26338306e-02 -1.02410579e+00 -1.35828388e+00 2.49348134e-02
-2.36553058e-01 4.97905731e-01 8.49161372e-02 -1.82663813... | [8.920608520507812, 7.069777965545654] |
d79dde35-7b36-49b9-a3bc-6915d30d2f26 | unicon-unsupervised-intent-discovery-via | null | null | https://openreview.net/forum?id=-jZkAHbpHlk | https://openreview.net/pdf?id=-jZkAHbpHlk | UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning | Discovering new intents is crucial for expanding domains in dialogue systems or natural language understanding (NLU) systems. A typical approach is to leverage unsupervised and semi-supervised learning to train a neural encoder to produce representations of utterances that are adequate for clustering then perform clust... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-augmentation', 'intent-discovery'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.66234398e-01 5.65382421e-01 1.15601113e-02 -5.65688610e-01
-5.33617735e-01 -4.31407481e-01 7.50496805e-01 2.46719301e-01
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1.37680015e-02 -7.59198904e-01 -5.62489688e-01 -2.77045786e-01
-1.30928271e-02 7.70122230e-01 -3.47989500e-02 -4.85504359... | [12.492609977722168, 7.40785551071167] |
551c8064-f463-477b-85c6-f794286c0017 | pneumonia-detection-on-chest-x-ray-using | 2101.04269 | null | https://arxiv.org/abs/2101.04269v2 | https://arxiv.org/pdf/2101.04269v2.pdf | Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning | Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can ex... | ['Yifan Peng', 'Ying Ding', 'Ahmed H Tewfik', 'Chongyan Chen', 'Yan Han'] | 2021-01-12 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 2.21030042e-01 4.88861501e-02 -2.51689941e-01 -5.49276531e-01
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-2.57650584e-01 -6.58301055e-01 -5.67758799e-01 -4.70453709e-01
1.28167793e-01 7.75131941e-01 4.57324572e-02 2.30223998... | [15.204936027526855, -2.000410318374634] |
d0a2f71b-6af2-4991-bf5c-c2af75125faa | dialogue-act-sequence-labeling-using | 1709.04250 | null | http://arxiv.org/abs/1709.04250v2 | http://arxiv.org/pdf/1709.04250v2.pdf | Dialogue Act Sequence Labeling using Hierarchical encoder with CRF | Dialogue Act recognition associate dialogue acts (i.e., semantic labels) to
utterances in a conversation. The problem of associating semantic labels to
utterances can be treated as a sequence labeling problem. In this work, we
build a hierarchical recurrent neural network using bidirectional LSTM as a
base unit and the... | ['Sachindra Joshi', 'Harshit Kumar', 'Riddhiman Dasgupta', 'Arvind Agarwal', 'Arun Kumar'] | 2017-09-13 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 2.93019474e-01 6.83093846e-01 -2.22156998e-02 -9.21680391e-01
-6.00842357e-01 -3.11323136e-01 6.96743309e-01 2.19892636e-01
-5.06163299e-01 9.19212580e-01 5.77200115e-01 -1.38947830e-01
4.94438529e-01 -7.73196161e-01 -1.81455195e-01 -4.48162079e-01
1.84446514e-01 7.71537364e-01 -4.03660424e-02 -4.69425023... | [12.745969772338867, 7.699461460113525] |
fd055e81-81ad-4f12-a4f8-91e65419330d | target-tracking-in-real-time-surveillance | 1506.06659 | null | http://arxiv.org/abs/1506.06659v1 | http://arxiv.org/pdf/1506.06659v1.pdf | Target Tracking In Real Time Surveillance Cameras and Videos | Security concerns has been kept on increasing, so it is important for
everyone to keep their property safe from thefts and destruction. So the need
for surveillance techniques are also increasing. The system has been developed
to detect the motion in a video. A system has been developed for real time
applications by us... | ['Nayyab Naseem', 'Mehreen Sirshar'] | 2015-06-22 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 7.04306185e-01 -4.64586258e-01 1.60978973e-01 5.16680852e-02
7.69078061e-02 -4.33172286e-01 3.87757599e-01 9.99973044e-02
-5.69509804e-01 6.63501024e-01 -8.18283632e-02 -2.52676904e-01
5.08451641e-01 -9.48666453e-01 -3.33403237e-02 -9.63752866e-01
2.83756226e-01 -5.36876380e-01 1.06918001e+00 -4.17528898... | [8.935999870300293, -0.9968603253364563] |
7379dacb-6cf9-4011-9986-f4b79cc151fd | scalable-multi-agent-model-based | 2205.15023 | null | https://arxiv.org/abs/2205.15023v1 | https://arxiv.org/pdf/2205.15023v1.pdf | Scalable Multi-Agent Model-Based Reinforcement Learning | Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed environments due to its capability to efficiently train decentralized policies. While in mixed envir... | ['Aleksei Shpilman', 'Vladimir Egorov'] | 2022-05-25 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.16523898e-01 1.46822199e-01 -1.60458907e-01 -8.33099335e-02
-5.72195947e-01 -6.56075776e-01 8.67933095e-01 3.34800392e-01
-7.21238315e-01 1.13473475e+00 -1.86421350e-01 -3.63810331e-01
-2.86208957e-01 -8.16558719e-01 -7.21066475e-01 -9.73900557e-01
-6.29189909e-01 8.02722216e-01 4.20022219e-01 -6.72137380... | [3.882767915725708, 1.9979344606399536] |
8dfb4e30-49d7-4488-a05d-dedaee49de01 | wavecrn-an-efficient-convolutional-recurrent | 2004.04098 | null | https://arxiv.org/abs/2004.04098v3 | https://arxiv.org/pdf/2004.04098v3.pdf | WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement | Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, the locality and temporal sequential properties of speech should be efficiently taken into account when modelling. However, in most current E... | ['Yu Tsao', 'Hsin-Min Wang', 'Tsun-An Hsieh', 'Xugang Lu'] | 2020-04-06 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.73439693e-01 -1.55043706e-01 3.54200602e-01 -2.79384673e-01
-5.09341955e-01 1.77227352e-02 4.09047812e-01 -2.55405575e-01
-5.14187574e-01 3.32757831e-01 3.90270084e-01 -4.57998276e-01
-3.89620692e-01 -5.49652994e-01 -6.30725324e-01 -8.59837055e-01
9.08169001e-02 -4.98053372e-01 1.47014931e-01 -4.11296964... | [14.90800666809082, 5.952182292938232] |
9dd26821-6319-43fb-987c-8508ffd55732 | joint-learning-for-pulmonary-nodule | 1802.03584 | null | http://arxiv.org/abs/1802.03584v1 | http://arxiv.org/pdf/1802.03584v1.pdf | Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction | Refer to the literature of lung nodule classification, many studies adopt
Convolutional Neural Networks (CNN) to directly predict the malignancy of lung
nodules with original thoracic Computed Tomography (CT) and nodule location.
However, these studies cannot tell how the CNN works in terms of predicting the
malignancy... | ['Zhen Zhou', 'Yizhou Wang', 'Jianwei Wang', 'Botong Wu'] | 2018-02-10 | null | null | null | null | ['lung-nodule-segmentation', 'lung-nodule-classification'] | ['medical', 'medical'] | [ 2.04905067e-02 3.07174385e-01 -3.86187524e-01 -2.76899904e-01
-7.84821093e-01 -4.07235056e-01 3.30169648e-01 -1.20480597e-01
-1.89733952e-01 4.56364900e-01 1.12462148e-01 -6.19836569e-01
-2.88902372e-01 -8.32735538e-01 -6.12123489e-01 -1.02422643e+00
3.55816841e-01 8.93392205e-01 4.89788234e-01 1.49299160... | [15.343533515930176, -2.1540348529815674] |
8bd91d52-fe41-46a9-9929-7b0c2f9e88b8 | multilabel-automated-recognition-of-emotions | 1905.12629 | null | https://arxiv.org/abs/1905.12629v2 | https://arxiv.org/pdf/1905.12629v2.pdf | A New Multilabel System for Automatic Music Emotion Recognition | Achieving advancements in automatic recognition of emotions that music can induce require considering multiplicity and simultaneity of emotions. Comparison of different machine learning algorithms performing multilabel and multiclass classification is the core of our work. The study analyzes the implementation of the G... | ['Natalia Pichierri', 'Daniele Casali', 'Marco Matta', 'Giovanni Costantini', 'Fabio Paolizzo', 'Daniele Giardino'] | 2019-05-29 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 1.01162821e-01 -3.78829017e-02 -4.10109907e-01 -3.62001002e-01
-7.95246065e-01 -1.08249521e+00 3.44325006e-01 1.08144358e-01
-3.97930026e-01 7.23489165e-01 3.09549779e-01 3.21320951e-01
-5.31277597e-01 -2.27719218e-01 -1.20155640e-01 -6.98085725e-01
7.83033073e-02 7.42749155e-01 -6.41638279e-01 -5.39974719... | [15.880032539367676, 5.201945781707764] |
a2f5d081-dabe-43d9-967a-6e930a77f3e8 | receptive-field-regularization-techniques-for | 2105.12395 | null | https://arxiv.org/abs/2105.12395v1 | https://arxiv.org/pdf/2105.12395v1.pdf | Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks | In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their generalization. An insufficient RF limits the CNN's ability to fit the training data. In contrast, CNNs wi... | ['Gerhard Widmer', 'Hamid Eghbal-zadeh', 'Khaled Koutini'] | 2021-05-26 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [ 2.10499868e-01 -2.69075871e-01 2.07910001e-01 -5.08910954e-01
-3.06389153e-01 -6.18724406e-01 1.52491838e-01 -2.70593971e-01
-6.47325456e-01 3.19717944e-01 -2.23416984e-01 -5.70252426e-02
-1.53672487e-01 -5.39925933e-01 -7.68411756e-01 -5.24738014e-01
-2.47649178e-01 5.53597994e-02 4.77665335e-01 -3.30746442... | [15.384377479553223, 5.197680473327637] |
f68d1b3c-b7f6-45ba-a6bf-9b6efc80c5eb | learning-from-noisy-labels-with-noise | 2005.00596 | null | https://arxiv.org/abs/2005.00596v1 | https://arxiv.org/pdf/2005.00596v1.pdf | Learning from Noisy Labels with Noise Modeling Network | Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data. In this paper, we extend the state-of the-art of training classifiers to jointly deal with both... | ['Man-Hung Siu', 'Zhuolin Jiang', 'Herbert Gish', 'Jan Silovsky', 'William Hartmann', 'Sancar Adali'] | 2020-05-01 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.15957135e-01 -4.35980886e-01 1.12684362e-01 -6.29920006e-01
-1.26128852e+00 -5.37508905e-01 3.23072135e-01 2.35453218e-01
-6.81552827e-01 5.68972349e-01 -3.70934010e-01 6.17496297e-02
3.62836719e-02 -5.24510264e-01 -7.44724870e-01 -8.46610785e-01
6.15277648e-01 5.35273492e-01 -1.26608163e-02 1.13718964... | [9.406637191772461, 3.853140354156494] |
120855d9-ce91-44e6-ba21-ae38629b0b91 | generalized-multi-view-shared-subspace | 2005.06038 | null | https://arxiv.org/abs/2005.06038v1 | https://arxiv.org/pdf/2005.06038v1.pdf | Generalized Multi-view Shared Subspace Learning using View Bootstrapping | A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks. In this context, two open research questions remain: How can we model hundreds of views per event? Can we learn robust multi-view embeddings without any... | ['Shrikanth Narayanan', 'Krishna Somandepalli'] | 2020-05-12 | null | null | null | null | ['robust-face-recognition', '3d-object-classification'] | ['computer-vision', 'computer-vision'] | [ 4.80468944e-02 -4.70985100e-02 -4.23222780e-02 -5.33976793e-01
-9.08305645e-01 -7.10428059e-01 8.03089201e-01 -2.79462576e-01
-2.31926084e-01 2.89437473e-01 4.64080334e-01 1.50039196e-01
-1.51262939e-01 -5.82602859e-01 -9.57513928e-01 -7.86513567e-01
-5.03167771e-02 4.87369120e-01 -9.88302454e-02 5.77655956... | [8.43234920501709, 4.4713239669799805] |
f4fdfb90-2bfc-4405-946e-1678f98a054e | understanding-programs-by-exploiting-fuzzing | 2305.13592 | null | https://arxiv.org/abs/2305.13592v2 | https://arxiv.org/pdf/2305.13592v2.pdf | Understanding Programs by Exploiting (Fuzzing) Test Cases | Semantic understanding of programs has attracted great attention in the community. Inspired by recent successes of large language models (LLMs) in natural language understanding, tremendous progress has been made by treating programming language as another sort of natural language and training LLMs on corpora of progra... | ['Hao Chen', 'Yifeng He', 'Yiwen Guo', 'Yuyang Rong', 'Jianyu Zhao'] | 2023-05-23 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 1.59334600e-01 1.28902653e-02 -7.08963275e-01 -5.20246208e-01
-2.10064650e-01 -8.02473724e-01 5.22468865e-01 3.47205520e-01
8.87739733e-02 -1.90267675e-02 -1.00575767e-01 -7.51416445e-01
2.48498023e-01 -1.04890358e+00 -1.17377841e+00 -1.70664698e-01
5.48383892e-02 2.81131584e-02 2.93751091e-01 -2.95289487... | [7.599034786224365, 7.836158275604248] |
90df7b2d-1a08-46e6-ad59-2f3d6b9347c9 | gibbsddrm-a-partially-collapsed-gibbs-sampler | 2301.12686 | null | https://arxiv.org/abs/2301.12686v2 | https://arxiv.org/pdf/2301.12686v2.pdf | GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration | Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements. However, existing approaches require knowledge of the linear operator. In this paper, we propose GibbsDDRM, an extension of Denoising Diff... | ['Stefano Ermon', 'Yuki Mitsufuji', 'Toshimitsu Uesaka', 'Yuhta Takida', 'Chieh-Hsin Lai', 'Koichi Saito', 'Naoki Murata'] | 2023-01-30 | null | null | null | null | ['deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 4.11018133e-01 -8.59293267e-02 2.81406641e-01 -8.10845196e-02
-8.60686123e-01 -3.57049435e-01 7.77320802e-01 -6.42345250e-01
-2.44223565e-01 3.90237868e-01 5.47946274e-01 -1.03864729e-01
-2.38947704e-01 -3.07431370e-01 -5.54220200e-01 -1.11636806e+00
2.54537195e-01 5.87048769e-01 -9.86308325e-03 1.76577196... | [11.740579605102539, -2.429654359817505] |
6181ff3c-f15e-49a6-b51a-222c86286f82 | end-to-end-multimodal-emotion-recognition | 1704.08619 | null | http://arxiv.org/abs/1704.08619v1 | http://arxiv.org/pdf/1704.08619v1.pdf | End-to-End Multimodal Emotion Recognition using Deep Neural Networks | Automatic affect recognition is a challenging task due to the various
modalities emotions can be expressed with. Applications can be found in many
domains including multimedia retrieval and human computer interaction. In
recent years, deep neural networks have been used with great success in
determining emotional state... | ['Björn Schuller', 'Panagiotis Tzirakis', 'George Trigeorgis', 'Stefanos Zafeiriou', 'Mihalis A. Nicolaou'] | 2017-04-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.17666729e-01 -1.72212541e-01 2.96388239e-01 -5.44989645e-01
-3.04047167e-01 -1.97872713e-01 5.89335740e-01 1.85793012e-01
-6.45455420e-01 5.31090915e-01 2.78059363e-01 3.33274215e-01
6.09739982e-02 -4.96647447e-01 -3.55315328e-01 -4.63513166e-01
-1.44649729e-01 1.24370512e-02 -2.24244773e-01 -3.19739848... | [13.395405769348145, 5.2911601066589355] |
475ec35f-3a39-483b-92a7-6e26aeb29951 | collaborative-representation-for | 1403.1353 | null | http://arxiv.org/abs/1403.1353v1 | http://arxiv.org/pdf/1403.1353v1.pdf | Collaborative Representation for Classification, Sparse or Non-sparse? | Sparse representation based classification (SRC) has been proved to be a
simple, effective and robust solution to face recognition. As it gets popular,
doubts on the necessity of enforcing sparsity starts coming up, and primary
experimental results showed that simply changing the $l_1$-norm based
regularization to the ... | ['Michihiko Minoh', 'Yang Wu', 'Vansteenberge Jarich', 'Masayuki Mukunoki'] | 2014-03-06 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.72032231e-01 -2.35289142e-01 -2.51983464e-01 -5.47785282e-01
-6.01725817e-01 -1.09635159e-01 4.37605768e-01 4.68589220e-04
-1.82607621e-01 7.55058348e-01 2.34655797e-01 -1.94638029e-01
-4.32711065e-01 -6.35519385e-01 -3.84046942e-01 -8.85964513e-01
6.22963086e-02 2.48780221e-01 -1.03022501e-01 -2.74542809... | [12.425637245178223, 0.4367225468158722] |
6249b5b9-2ad9-4d7b-8698-dff6da419df1 | second-language-acquisition-of-neural | 2306.02920 | null | https://arxiv.org/abs/2306.02920v1 | https://arxiv.org/pdf/2306.02920v1.pdf | Second Language Acquisition of Neural Language Models | With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while previous work has typically explored their first language (L1) acquisition. Specifically, we trained bilingual LMs with a scenario similar t... | ['Taro Watanabe', 'Hiroki Ouchi', 'Tatsuki Kuribayashi', 'Miyu Oba'] | 2023-06-05 | null | null | null | null | ['language-acquisition', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-2.41238073e-01 1.95726901e-01 -5.95090330e-01 -3.20572883e-01
-4.80272055e-01 -8.41151655e-01 8.63495708e-01 3.24611604e-01
-6.28485143e-01 3.82598728e-01 3.55593860e-01 -1.05209064e+00
2.62566824e-02 -7.28479564e-01 -8.98501337e-01 -2.90969580e-01
1.71970725e-02 3.81603539e-01 -3.41875665e-02 -5.63494563... | [10.822381973266602, 9.930713653564453] |
c7bdbca5-2b3f-4a0b-a4f0-3d6f84cade0b | subgroup-fairness-in-graph-based-spam | 2204.11164 | null | https://arxiv.org/abs/2204.11164v2 | https://arxiv.org/pdf/2204.11164v2.pdf | Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection | User-generated reviews of products are vital assets of online commerce, such as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN is the state-of-the-art method that detects suspicious reviewers by exploiting the topologies of the graph connecting reviewers, reviews, and target products. Howev... | ['Sihong Xie', 'Xi Zhang', 'Yuefei Lyu', 'Jiaxin Liu'] | 2022-04-24 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-9.40315127e-02 3.68198842e-01 -5.65431058e-01 -8.50241363e-01
-3.23684335e-01 -8.86917233e-01 6.83299482e-01 1.96212515e-01
1.09094836e-01 2.59213001e-01 -1.72240455e-02 -5.43461204e-01
-5.21179587e-02 -7.55962491e-01 -3.49034458e-01 -1.79784387e-01
1.27217725e-01 4.02429581e-01 3.55734080e-01 -2.14504287... | [7.905447006225586, 10.048912048339844] |
76991b19-f2ee-4e09-bd1e-7f9de154264e | variable-viewpoint-representations-for-3d | 2002.03131 | null | https://arxiv.org/abs/2002.03131v1 | https://arxiv.org/pdf/2002.03131v1.pdf | Variable-Viewpoint Representations for 3D Object Recognition | For the problem of 3D object recognition, researchers using deep learning methods have developed several very different input representations, including "multi-view" snapshots taken from discrete viewpoints around an object, as well as "spherical" representations consisting of a dense map of essentially ray-traced samp... | ['Tengyu Ma', 'Maithilee Kunda', 'Joel Michelson', 'Deepayan Sanyal', 'Xiaohan Wang', 'James Ainooson'] | 2020-02-08 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 1.28310889e-01 -4.13643979e-02 1.46602169e-01 -5.08567393e-01
-5.26492178e-01 -9.02112961e-01 1.03444695e+00 5.65692745e-02
-1.09963395e-01 2.69133002e-01 4.51596320e-01 -1.51830211e-01
-3.01591039e-01 -9.80368316e-01 -6.38574898e-01 -7.07796454e-01
9.44717303e-02 6.90557420e-01 1.43928200e-01 -7.62943625... | [8.472466468811035, -3.0203824043273926] |
4d35aea4-98d8-4fb9-8730-fbdcb8ace2cb | finding-mirror-symmetry-via-registration | 1611.05971 | null | http://arxiv.org/abs/1611.05971v2 | http://arxiv.org/pdf/1611.05971v2.pdf | Finding Mirror Symmetry via Registration | Symmetry is prevalent in nature and a common theme in man-made designs. Both
the human visual system and computer vision algorithms can use symmetry to
facilitate object recognition and other tasks. Detecting mirror symmetry in
images and data is, therefore, useful for a number of applications. Here, we
demonstrate tha... | ['Marcelo Cicconet', 'David G. C. Hildebrand', 'Hunter Elliott'] | 2016-11-18 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.39269245e-01 -2.14951321e-01 4.44200873e-01 -2.34942883e-01
-4.70318854e-01 -6.28881872e-01 7.43237793e-01 -1.74537674e-01
-4.85754371e-01 1.38628095e-01 8.34957510e-02 -1.68266818e-01
-5.33611238e-01 -4.78753924e-01 -6.19850218e-01 -5.69338620e-01
-2.09790424e-01 5.17029047e-01 3.11216861e-01 -2.66310394... | [8.383743286132812, -2.347079038619995] |
d4ff37b8-acd5-4d03-a36a-b4156b6a8dca | learning-with-privileged-information-for | 1703.09911 | null | http://arxiv.org/abs/1703.09911v1 | http://arxiv.org/pdf/1703.09911v1.pdf | Learning with Privileged Information for Multi-Label Classification | In this paper, we propose a novel approach for learning multi-label
classifiers with the help of privileged information. Specifically, we use
similarity constraints to capture the relationship between available
information and privileged information, and use ranking constraints to capture
the dependencies among multipl... | ['Xiaoxiao Shi', 'Tanfang Chen', 'Shiyu Chen', 'Shangfei Wang'] | 2017-03-29 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 6.53996706e-01 2.95514357e-03 -7.67403126e-01 -8.32134426e-01
-6.09401882e-01 -3.30399662e-01 3.55499148e-01 1.17131531e-01
-5.69654405e-01 6.38899744e-01 4.29848284e-02 2.36319005e-01
-4.55501646e-01 -4.38348919e-01 -2.15858176e-01 -8.77865374e-01
1.50609806e-01 2.16362774e-02 2.45755777e-01 1.25290811... | [9.368966102600098, 4.009395122528076] |
55f0492e-97bb-4fdc-90f4-8502b30778cf | look-before-you-leap-bridging-model-free-and | 1803.07729 | null | http://arxiv.org/abs/1803.07729v2 | http://arxiv.org/pdf/1803.07729v2.pdf | Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation | Existing research studies on vision and language grounding for robot
navigation focus on improving model-free deep reinforcement learning (DRL)
models in synthetic environments. However, model-free DRL models do not
consider the dynamics in the real-world environments, and they often fail to
generalize to new scenes. I... | ['William Yang Wang', 'Wenhan Xiong', 'Hongmin Wang', 'Xin Wang'] | 2018-03-21 | look-before-you-leap-bridging-model-free-and-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Wang_Look_Before_You_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Wang_Look_Before_You_ECCV_2018_paper.pdf | eccv-2018-9 | ['vision-language-navigation'] | ['computer-vision'] | [-1.64846033e-01 -3.32873518e-04 -1.83497250e-01 -4.65670675e-01
-5.31616926e-01 -3.84253442e-01 6.95621729e-01 -4.06096816e-01
-7.24494100e-01 7.85387933e-01 6.06525242e-02 -6.65607095e-01
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-5.92528097e-02 4.29590821e-01 3.74448299e-01 -8.09698641... | [4.498002529144287, 0.6220483183860779] |
c1d52fe7-1a22-49a2-8a91-ec8d92a72dd9 | shift-memory-network-for-temporal-scene | 2202.08399 | null | https://arxiv.org/abs/2202.08399v1 | https://arxiv.org/pdf/2202.08399v1.pdf | Shift-Memory Network for Temporal Scene Segmentation | Semantic segmentation has achieved great accuracy in understanding spatial layout. For real-time tasks based on dynamic scenes, we extend semantic segmentation in temporal domain to enhance the spatial accuracy with motion. We utilize a shift-mode network over streaming input to ensure zero-latency output. For the data... | ['Jiang Yu Zheng', 'Guo Cheng'] | 2022-02-17 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 8.37717950e-01 -6.15686141e-02 -5.01396596e-01 -5.56045890e-01
-2.61927575e-01 -6.31277323e-01 -7.00319558e-02 -3.64777029e-01
-5.09860933e-01 3.74323010e-01 -5.83714060e-02 -8.00592899e-01
-2.97570438e-03 -9.55794632e-01 -8.22993219e-01 -3.69859278e-01
-1.08951643e-01 3.01205926e-02 9.71475899e-01 3.82341266... | [9.328585624694824, -0.03744969144463539] |
792d61df-7211-451f-88fa-3671109912bb | unsupervised-hyper-alignment-for-multilingual | null | null | https://openreview.net/forum?id=HJe62s09tX | https://openreview.net/pdf?id=HJe62s09tX | Unsupervised Hyper-alignment for Multilingual Word Embeddings | We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages ... | ['Armand Joulin', 'Marco Cuturi', 'Edouard Grave', 'Jean Alaux'] | null | null | null | null | iclr-2019-5 | ['multilingual-word-embeddings'] | ['methodology'] | [ 3.72669280e-01 6.79876655e-02 -5.22075593e-01 -3.59894127e-01
-1.22044885e+00 -9.59940493e-01 7.43924320e-01 -3.83546464e-02
-6.74564838e-01 9.36650455e-01 4.55128610e-01 -5.96925855e-01
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4.16496933e-01 7.40708292e-01 -1.49376601e-01 -5.38291574... | [11.21617317199707, 10.194050788879395] |
441d7bbe-136e-4897-9ae2-4d51ae07f9df | pose-oriented-transformer-with-uncertainty | 2302.07408 | null | https://arxiv.org/abs/2302.07408v1 | https://arxiv.org/pdf/2302.07408v1.pdf | Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation | There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies. However, existing transformer-based methods treat body joints as equally important inputs and ignore the prior knowledge of human skeleton topolo... | ['Hongkai Xiong', 'Junni Zou', 'Chenlin Li', 'Min Guo', 'Yu Sun', 'Botao Wang', 'Hongwei Zheng', 'Wenrui Dai', 'Bowen Shi', 'Han Li'] | 2023-02-15 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-5.25410175e-01 4.72864211e-01 7.39881629e-03 -2.40162820e-01
-6.41003847e-01 1.96393073e-01 3.13098073e-01 -1.73862875e-01
-3.31844956e-01 5.79104185e-01 5.96044779e-01 2.99160838e-01
-2.53485858e-01 -6.93701386e-01 -1.02037370e+00 -4.91069913e-01
-2.46587664e-01 1.07713187e+00 4.87917185e-01 -5.03726184... | [7.0936479568481445, -0.668847918510437] |
4ad47bcf-d1e6-4834-9f70-c73479c8cc37 | realy-rethinking-the-evaluation-of-3d-face | 2203.09729 | null | https://arxiv.org/abs/2203.09729v2 | https://arxiv.org/pdf/2203.09729v2.pdf | REALY: Rethinking the Evaluation of 3D Face Reconstruction | The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and impro... | ['Linchao Bao', 'Chun Yuan', 'Xuefei Zhe', 'Zhengzhuo Xu', 'Di Kang', 'Jing Ren', 'Haoxian Zhang', 'Zenghao Chai'] | 2022-03-18 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.75401315e-01 -1.52641714e-01 1.35989159e-01 -5.77170670e-01
-7.72543430e-01 -5.61377287e-01 5.65685511e-01 -4.30767119e-01
2.78581113e-01 2.30848372e-01 1.10159710e-01 2.74898827e-01
7.44059086e-02 -7.09496737e-01 -6.99170768e-01 -5.03968477e-01
2.62360722e-01 1.09770930e+00 -8.09034929e-02 -1.19844206... | [13.196359634399414, 0.031733930110931396] |
c75ab7e2-dd7f-4cd9-a742-71283d358134 | properties-of-winning-tickets-on-skin-lesion | 2008.12141 | null | https://arxiv.org/abs/2008.12141v1 | https://arxiv.org/pdf/2008.12141v1.pdf | Properties Of Winning Tickets On Skin Lesion Classification | Skin cancer affects a large population every year -- automated skin cancer detection algorithms can thus greatly help clinicians. Prior efforts involving deep learning models have high detection accuracy. However, most of the models have a large number of parameters, with some works even using an ensemble of models to ... | ['Sherin Muckatira'] | 2020-08-25 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.42668140e-01 5.75447381e-01 -5.88301063e-01 -1.93739280e-01
-3.43968034e-01 9.77029130e-02 3.08325738e-01 2.44964287e-01
-5.35384476e-01 1.02362382e+00 -3.32683437e-02 -4.29760039e-01
-4.62507546e-01 -1.14675403e+00 -1.84899271e-01 -7.62453496e-01
-1.01951703e-01 2.66276956e-01 3.23449820e-01 -1.08215578... | [15.621345520019531, -2.937554359436035] |
57b3e88f-748f-4f02-a6ab-decb2fffa784 | ost-efficient-one-stream-network-for-3d | 2210.08518 | null | https://arxiv.org/abs/2210.08518v1 | https://arxiv.org/pdf/2210.08518v1.pdf | OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds | Although recent Siamese network-based trackers have achieved impressive perceptual accuracy for single object tracking in LiDAR point clouds, they advance with some heavy correlation operations on relation modeling and overlook the inherent merit of arbitrariness compared to multiple object tracking. In this work, we p... | ['Xiuping Liu', 'Jian Liu', 'Shengjing Tian', 'Yinan Han', 'Xiantong Zhao'] | 2022-10-16 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [ 7.82812908e-02 -3.56862903e-01 -1.76444381e-01 -3.40027273e-01
-2.92198956e-01 -6.05136514e-01 7.29200125e-01 -1.86343268e-02
-4.74006295e-01 3.84573877e-01 -4.43384737e-01 -8.65551308e-02
-4.26318854e-01 -8.10856283e-01 -6.80568635e-01 -7.47354150e-01
-8.21904317e-02 5.54456115e-01 8.47940743e-01 -9.42050517... | [6.467754364013672, -2.2567920684814453] |
a40e1a84-d7fa-41bd-ae3f-c8efbd7f64ec | scene-consistency-representation-learning-for | 2205.05487 | null | https://arxiv.org/abs/2205.05487v1 | https://arxiv.org/pdf/2205.05487v1.pdf | Scene Consistency Representation Learning for Video Scene Segmentation | A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a sce... | ['Linlin Shen', 'Weicheng Xie', 'Haozhe Liu', 'Bo Ren', 'Ruizhi Qiao', 'Yanan Luo', 'Keyu Chen', 'Haoqian Wu'] | 2022-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wu_Scene_Consistency_Representation_Learning_for_Video_Scene_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-segmentation'] | ['computer-vision'] | [ 2.08377764e-01 -2.82445520e-01 -5.71475863e-01 -6.57695472e-01
-6.48044884e-01 -6.13871276e-01 5.00810921e-01 -1.15585119e-01
-1.88530818e-01 3.67279232e-01 3.42330635e-01 -1.73752457e-02
7.02798069e-02 -6.13618851e-01 -1.06165922e+00 -5.65270305e-01
-1.28049999e-01 1.07494041e-01 6.22008264e-01 1.26305833... | [9.384827613830566, 0.5332509875297546] |
f09551c0-543b-481f-8e51-f851f0a72161 | kodf-a-large-scale-korean-deepfake-detection | 2103.10094 | null | https://arxiv.org/abs/2103.10094v2 | https://arxiv.org/pdf/2103.10094v2.pdf | KoDF: A Large-scale Korean DeepFake Detection Dataset | A variety of effective face-swap and face-reenactment methods have been publicized in recent years, democratizing the face synthesis technology to a great extent. Videos generated as such have come to be called deepfakes with a negative connotation, for various social problems they have caused. Facing the emerging thre... | ['Gyeongsu Chae', 'Sungwoo Park', 'Gyuhyeon Nam', 'Jaeseong You', 'Patrick Kwon'] | 2021-03-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kwon_KoDF_A_Large-Scale_Korean_DeepFake_Detection_Dataset_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kwon_KoDF_A_Large-Scale_Korean_DeepFake_Detection_Dataset_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-reenactment'] | ['computer-vision'] | [-2.10434079e-01 8.97127092e-02 -8.57637674e-02 -3.37569475e-01
-6.17496789e-01 -7.51849949e-01 7.25552976e-01 -8.08676660e-01
-9.70248207e-02 6.85271502e-01 7.64199376e-01 3.62622857e-01
1.60707861e-01 -5.10437727e-01 -6.05346203e-01 -4.75263059e-01
-9.03014019e-02 1.94506180e-02 -3.83862853e-01 -2.68815488... | [12.6832914352417, 1.0215203762054443] |
fd611bae-8b71-4413-bd76-0f72b7f6a294 | split-embed-and-merge-an-accurate-table | 2107.05214 | null | https://arxiv.org/abs/2107.05214v3 | https://arxiv.org/pdf/2107.05214v3.pdf | Split, embed and merge: An accurate table structure recognizer | Table structure recognition is an essential part for making machines understand tables. Its main task is to recognize the internal structure of a table. However, due to the complexity and diversity in their structure and style, it is very difficult to parse the tabular data into the structured format which machines can... | ['Jun Du', 'Jianshu Zhang', 'Zhenrong Zhang'] | 2021-07-12 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 1.40902445e-01 9.75209773e-02 -3.22395653e-01 -3.71608287e-01
-9.25351143e-01 -8.61035764e-01 2.34761953e-01 7.08272457e-01
-2.36680433e-01 5.84815025e-01 2.13523984e-01 -4.38507348e-01
1.62473798e-01 -9.99456286e-01 -1.15924072e+00 -5.01810014e-01
2.79682964e-01 7.69480944e-01 1.34293750e-01 1.00968853... | [11.700858116149902, 3.0511741638183594] |
e978db9f-3e3b-4924-bfd1-3b2d0665b0ae | text-classification-for-azerbaijani-language | 1912.13362 | null | https://arxiv.org/abs/1912.13362v1 | https://arxiv.org/pdf/1912.13362v1.pdf | Text Classification for Azerbaijani Language Using Machine Learning and Embedding | Text classification systems will help to solve the text clustering problem in the Azerbaijani language. There are some text-classification applications for foreign languages, but we tried to build a newly developed system to solve this problem for the Azerbaijani language. Firstly, we tried to find out potential practi... | ['Umid Suleymanov', 'Behnam Kiani Kalejahi', 'Rashid Badirkhanli', 'Elkhan Amrahov'] | 2019-12-26 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-4.99274969e-01 -2.52472788e-01 -2.65302807e-01 -5.22191703e-01
-1.06729768e-01 -3.88622850e-01 7.75559127e-01 7.48823285e-01
-3.20693821e-01 4.68438655e-01 1.98417470e-01 -6.13281846e-01
-3.39278579e-02 -1.04374027e+00 7.30154589e-02 -5.92293382e-01
3.96063775e-01 6.33802354e-01 2.41208509e-01 -7.29120851... | [10.910591125488281, 7.036771774291992] |
25dc2574-6d31-43a8-b2d3-039c195fa484 | deep-reinforcement-learning-for-asset-1 | 2301.05300 | null | https://arxiv.org/abs/2301.05300v1 | https://arxiv.org/pdf/2301.05300v1.pdf | Deep Reinforcement Learning for Asset Allocation: Reward Clipping | Recently, there are many trials to apply reinforcement learning in asset allocation for earning more stable profits. In this paper, we compare performance between several reinforcement learning algorithms - actor-only, actor-critic and PPO models. Furthermore, we analyze each models' character and then introduce the ad... | ['Bo-Kwan Jeon', 'HyungJun Moon', 'KangHun Lee', 'Moon-Ju Kang', 'Jiwon Kim'] | 2023-01-02 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.84853756e-01 1.79751337e-01 -5.57514191e-01 6.09798096e-02
-1.54680446e-01 -4.00065154e-01 4.73991781e-01 -8.42569321e-02
-4.41063464e-01 1.50865245e+00 1.06540598e-01 -4.60920334e-01
-5.28814316e-01 -1.08570421e+00 -1.76171958e-01 -6.30511940e-01
-4.80024785e-01 7.72599161e-01 9.33080167e-02 -6.75009310... | [4.4214396476745605, 3.840658664703369] |
9e279e84-ecd1-4b44-8f26-9633fc38a277 | bridging-the-gap-between-indexing-and | 2206.10128 | null | https://arxiv.org/abs/2206.10128v3 | https://arxiv.org/pdf/2206.10128v3.pdf | Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation | The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an im... | ['Daxin Jiang', 'Guido Zuccon', 'Ming Gong', 'Jian Pei', 'Linjun Shou', 'Houxing Ren', 'Shengyao Zhuang'] | 2022-06-21 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 2.48892739e-01 -2.07591116e-01 -6.02896988e-01 1.91684105e-02
-1.38661659e+00 -9.94599044e-01 1.13270354e+00 3.15658063e-01
-4.54285830e-01 3.85050207e-01 4.35049385e-01 -1.03344150e-01
-7.99242556e-01 -6.20341361e-01 -4.84949708e-01 -1.19729526e-01
4.72475290e-02 1.10779691e+00 5.40371716e-01 -4.38760579... | [11.540877342224121, 7.64747953414917] |
d358f74d-22e9-4f35-a633-d1a288ed31fa | assessing-rate-limits-using-behavioral-and | 2305.03297 | null | https://arxiv.org/abs/2305.03297v1 | https://arxiv.org/pdf/2305.03297v1.pdf | Assessing Rate limits Using Behavioral and Neural Responses of Interaural-Time-Difference Cues in Fine-Structure and Envelope | The objective was to determine the effect of pulse rate on the sensitivity to use interaural-time-difference (ITD) cues and to explore the mechanisms behind rate-dependent degradation in ITD perception in bilateral cochlear implant (CI) listeners using CI simulations and electroencephalogram (EEG) measures. To eliminat... | ['Deborah Vickers', 'Birger Kollmeier', 'Stephan Ewert', 'Hongmei Hu'] | 2023-05-05 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.33870964e-02 -5.52940607e-01 5.20806015e-01 1.52540803e-01
-8.89964044e-01 -6.89295590e-01 2.09230796e-01 4.53535408e-01
-7.73702025e-01 5.95276535e-01 5.15091896e-01 -2.27623180e-01
-2.36384585e-01 -1.59795508e-01 -5.37373722e-01 -6.14168644e-01
-5.32497764e-01 -3.54684770e-01 5.58778226e-01 -4.85283509... | [13.35891056060791, 3.57037353515625] |
93cc23d7-4023-4306-a7e0-aff0dcaff1ef | human-in-the-loop-how-to-effectively-create | 2212.09422 | null | https://arxiv.org/abs/2212.09422v1 | https://arxiv.org/pdf/2212.09422v1.pdf | Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class | Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple t... | ['Benjamin Säfken', 'Christoph Weisser', 'Anton Thielmann'] | 2022-12-19 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 1.52473629e-01 1.19616084e-01 -7.44490743e-01 -4.96406138e-01
-1.10325289e+00 -6.33872002e-02 9.71677482e-01 6.12473607e-01
-2.26820290e-01 7.19033480e-01 3.53542417e-01 1.65648147e-01
8.95389691e-02 -7.98613787e-01 -1.29540533e-01 -5.15735865e-01
-1.75590850e-02 7.28738487e-01 3.03627759e-01 -1.44565515... | [10.418275833129883, 7.014618396759033] |
c32880fb-f6c9-41d1-bc2c-4f2bb8a7591c | bedlam-a-synthetic-dataset-of-bodies-1 | 2306.16940 | null | https://arxiv.org/abs/2306.16940v1 | https://arxiv.org/pdf/2306.16940v1.pdf | BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion | We show, for the first time, that neural networks trained only on synthetic data achieve state-of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real images. Previous synthetic datasets have been small, unrealistic, or lacked realistic clothing. Achieving sufficient realism is non-triv... | ['Jinlong Yang', 'Joachim Tesch', 'Priyanka Patel', 'Michael J. Black'] | 2023-06-29 | bedlam-a-synthetic-dataset-of-bodies | http://openaccess.thecvf.com//content/CVPR2023/html/Black_BEDLAM_A_Synthetic_Dataset_of_Bodies_Exhibiting_Detailed_Lifelike_Animated_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Black_BEDLAM_A_Synthetic_Dataset_of_Bodies_Exhibiting_Detailed_Lifelike_Animated_CVPR_2023_paper.pdf | cvpr-2023-1 | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.02116078e-01 -1.43182173e-01 2.84057826e-01 -3.09659839e-01
-6.19377911e-01 -6.13389373e-01 4.65544015e-01 -7.07507670e-01
-1.34717867e-01 6.89527810e-01 3.02974492e-01 9.26035792e-02
4.38806713e-01 -5.68753064e-01 -1.12783742e+00 -4.32172805e-01
1.80355348e-02 5.59824705e-01 -4.66119200e-02 -3.93826246... | [7.206432342529297, -1.2211016416549683] |
2ac9fcf3-fa9c-46b8-8ed1-0807a44a8536 | deep-learning-for-video-game-genre | 2011.12143 | null | https://arxiv.org/abs/2011.12143v1 | https://arxiv.org/pdf/2011.12143v1.pdf | Deep learning for video game genre classification | Video game genre classification based on its cover and textual description would be utterly beneficial to many modern identification, collocation, and retrieval systems. At the same time, it is also an extremely challenging task due to the following reasons: First, there exists a wide variety of video game genres, many... | ['Lukun Zheng', 'Yuhang Jiang'] | 2020-11-21 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [-5.50029762e-02 -8.89244795e-01 -2.64946043e-01 7.19670132e-02
-7.60823965e-01 -5.49681127e-01 5.49709141e-01 -2.42718175e-01
-2.78267086e-01 4.84018832e-01 1.27905771e-01 3.35772224e-02
-1.08386710e-01 -8.23823929e-01 -3.68467808e-01 -6.53295577e-01
1.53683394e-01 3.94062787e-01 3.26898128e-01 -6.52552724... | [11.77140998840332, 2.1921300888061523] |
6f35d7b0-fbbd-43a5-b5ca-7fb8aeb902b5 | a-report-on-the-vardial-evaluation-campaign | null | null | https://aclanthology.org/2020.vardial-1.1 | https://aclanthology.org/2020.vardial-1.1.pdf | A Report on the VarDial Evaluation Campaign 2020 | This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2020. The campaign included three shared tasks each focusing on a different challenge of ... | ['Marcos Zampieri', 'Yves Scherrer', 'Christoph Purschke', 'Niko Partanen', 'Nikola Ljubešić', 'Krister Lindén', 'Tommi Jauhiainen', 'Heidi Jauhiainen', 'Radu Tudor Ionescu', 'Dirk Hovy', 'Mihaela Gaman'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-3.70766848e-01 -2.48679817e-01 -8.98671970e-02 -5.16142607e-01
-1.31558323e+00 -1.15833795e+00 1.08172417e+00 4.19320285e-01
-5.90477645e-01 5.18654406e-01 6.18330538e-01 -2.35639498e-01
2.42339447e-01 -5.04807770e-01 -2.46927693e-01 -1.69282705e-01
-1.83278188e-01 1.03751266e+00 -1.41185150e-01 -3.33179206... | [10.19018840789795, 10.733905792236328] |
47126b6b-538e-4548-8ca3-675d6cce9f12 | end-to-end-learning-of-keypoint-detection-and | 2104.01085 | null | https://arxiv.org/abs/2104.01085v1 | https://arxiv.org/pdf/2104.01085v1.pdf | End-to-end learning of keypoint detection and matching for relative pose estimation | We propose a new method for estimating the relative pose between two images, where we jointly learn keypoint detection, description extraction, matching and robust pose estimation. While our architecture follows the traditional pipeline for pose estimation from geometric computer vision, all steps are learnt in an end-... | ['Marco Paladini', 'Nikola Sivacki', 'Luca Del Pero', 'Antoine Fond'] | 2021-04-02 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.84467621e-03 -2.36359611e-01 4.30733487e-02 -4.13125277e-01
-1.25916576e+00 -1.05539095e+00 5.77045441e-01 2.96307147e-01
-6.14600420e-01 1.15973633e-02 -2.38373861e-01 -1.04454964e-01
-2.00702213e-02 -1.45252243e-01 -1.16624308e+00 -1.26094759e-01
-1.58639923e-02 7.27723002e-01 5.02243876e-01 1.83840185... | [7.615121841430664, -2.301884412765503] |
23262fa0-9e2b-4163-880d-87e81f3e38b0 | dense-captioning-events-in-videos-sysu | 2006.11693 | null | https://arxiv.org/abs/2006.11693v2 | https://arxiv.org/pdf/2006.11693v2.pdf | Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020 | This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relati... | ['Teng Wang', 'Mingjing Yu', 'Huicheng Zheng'] | 2020-06-21 | null | null | null | null | ['dense-captioning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 1.07761994e-01 -1.23847112e-01 -3.56514424e-01 -4.86153036e-01
-1.16451085e+00 -4.19802725e-01 8.66625667e-01 3.63293886e-02
-5.07565856e-01 7.04060793e-01 9.42573607e-01 3.40266943e-01
2.29082808e-01 -2.93941081e-01 -8.46661627e-01 -1.94904074e-01
-4.22596663e-01 4.18029308e-01 6.92701399e-01 5.52851856... | [10.449316024780273, 0.6853233575820923] |
438add8f-b9a5-4b37-a9bb-ef3269c41fed | softgym-benchmarking-deep-reinforcement | 2011.07215 | null | https://arxiv.org/abs/2011.07215v2 | https://arxiv.org/pdf/2011.07215v2.pdf | SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation | Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcemen... | ['David Held', 'Jake Olkin', 'YuFei Wang', 'Xingyu Lin'] | 2020-11-14 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-1.19939387e-01 -9.62196440e-02 -2.93544918e-01 -1.13002785e-01
-2.25786984e-01 -8.10050130e-01 5.14648914e-01 -4.52172965e-01
-3.74518782e-01 9.32822764e-01 3.35874036e-02 8.50533098e-02
-4.42997009e-01 -5.81938922e-01 -9.36888039e-01 -9.88021433e-01
-6.62305593e-01 6.81090415e-01 4.77671742e-01 -7.32492030... | [4.739785671234131, 0.6384881138801575] |
f53c3588-e39a-4a72-ab07-2dda73b0d7f6 | doric-domain-robust-fine-tuning-for-open | 2303.09827 | null | https://arxiv.org/abs/2303.09827v1 | https://arxiv.org/pdf/2303.09827v1.pdf | DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing | We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11). DSTC11-Track2 aims to provide a benchmark for zero-shot, cross-domain, intent-set induction. In the absence of in-domain training dataset, robust utterance representation that can be used across domains is necessary to induce users' ... | ['Gary Geunbae Lee', 'Yunsu Kim', 'Seungyeon Seo', 'Jihyun Lee'] | 2023-03-17 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-1.50614213e-02 4.29202408e-01 -1.70412734e-01 -7.53015995e-01
-9.15978014e-01 -7.60026872e-01 1.04260409e+00 -1.45884648e-01
-1.55777052e-01 7.76088357e-01 8.13740671e-01 -1.38086393e-01
-8.04480538e-02 -1.57325149e-01 -8.88848454e-02 -1.63180344e-02
2.43997604e-01 1.04980159e+00 2.77701795e-01 -7.03593671... | [12.657414436340332, 7.71690034866333] |
0019d61c-4a5c-498c-9d9e-751b05492998 | from-learning-to-relearning-a-framework-for | 2101.02647 | null | https://arxiv.org/abs/2101.02647v2 | https://arxiv.org/pdf/2101.02647v2.pdf | From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation | The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and ... | ['Abhinav Valada', 'Laura Londoño', 'Juana Valeria Hurtado'] | 2021-01-07 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 2.31938124e-01 7.87870467e-01 -2.64194980e-02 -3.82761508e-01
2.34766960e-01 -3.12261015e-01 4.76028085e-01 1.36812210e-01
-6.66836977e-01 9.77938056e-01 2.96864323e-02 -2.48976544e-01
-5.11619091e-01 -6.59675479e-01 -5.94694436e-01 -5.62747240e-01
2.79567838e-02 2.06042528e-01 -1.82228923e-01 -4.53628898... | [4.895188808441162, 1.0220483541488647] |
905e3e69-f145-4c36-ae93-edaae7359a71 | integrative-feature-and-cost-aggregation-with | 2209.08742 | null | https://arxiv.org/abs/2209.08742v2 | https://arxiv.org/pdf/2209.08742v2.pdf | Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence | We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggregation. However, they generally aggregate one or the other but not both, though joint aggregation would boost each other by providing infor... | ['Stephen Lin', 'Seungryong Kim', 'Seokju Cho', 'Sunghwan Hong'] | 2022-09-19 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [ 2.23992437e-01 -1.18314378e-01 -8.77719074e-02 -4.86822993e-01
-9.77600098e-01 -1.98902726e-01 6.65509880e-01 3.96643102e-01
-3.34708840e-01 3.36285293e-01 4.83083099e-01 5.91900684e-02
-9.58133712e-02 -1.07524586e+00 -7.39404142e-01 -7.31856287e-01
2.01914102e-01 3.20695132e-01 3.79000753e-01 -3.05919945... | [10.1117525100708, 0.2077493667602539] |
85fa53af-07c1-4fd7-881c-1b801a112add | cloning-outfits-from-real-world-images-to-3d | 2204.02611 | null | https://arxiv.org/abs/2204.02611v2 | https://arxiv.org/pdf/2204.02611v2.pdf | Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification | Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoon-like and in random dress collocation, which limits their performance. To address this, in this work, an automatic approach is proposed to ... | ['Shengcai Liao', 'Xuezhi Liang', 'Yanan Wang'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-person-re-identification', 'generalizable-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 1.26695752e-01 -1.90196812e-01 2.10749283e-01 -3.15310061e-01
-1.04382738e-01 -7.01911926e-01 4.74752665e-01 -4.07657772e-01
-9.24487188e-02 6.49445415e-01 -3.47424634e-02 4.74073142e-01
4.66830462e-01 -1.04980099e+00 -8.07284534e-01 -6.05243504e-01
3.38465512e-01 7.58406043e-01 -6.06813729e-02 -2.79421002... | [12.120467185974121, -0.7940322160720825] |
558ca1a0-51f5-401b-9fca-0bb0456bdd4f | user-simulation-for-evaluating-information | 2306.08550 | null | https://arxiv.org/abs/2306.08550v1 | https://arxiv.org/pdf/2306.08550v1.pdf | User Simulation for Evaluating Information Access Systems | Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge ... | ['ChengXiang Zhai', 'Krisztian Balog'] | 2023-06-14 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-7.11452365e-02 5.07913120e-02 -4.88435656e-01 -2.63740569e-01
-2.58797407e-01 -7.92037249e-01 6.10069871e-01 -5.41099422e-02
-6.19596004e-01 5.94862103e-01 2.41052993e-02 -8.78993273e-01
-5.45661688e-01 -4.66556996e-01 1.26012787e-01 -9.60430652e-02
-2.13928744e-02 6.26810849e-01 -7.86182284e-02 -8.09966326... | [12.271880149841309, 7.6825432777404785] |
6f38bd60-ae7c-4f54-ade0-21847451c5e9 | stable-and-compact-face-recognition-via | 2111.02847 | null | https://arxiv.org/abs/2111.02847v1 | https://arxiv.org/pdf/2111.02847v1.pdf | Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification | Sparse representation-based classification (SRC) has attracted much attention by casting the recognition problem as simple linear regression problem. SRC methods, however, still is limited to enough labeled samples per category, insufficient use of unlabeled samples, and instability of representation. For tackling thes... | ['Haolin Chen', 'Yiming Xu', 'Licheng Jiao', 'Huan Wu', 'Zheng Wang', 'XiaoHui Yang'] | 2021-11-04 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 1.88426912e-01 -1.55573264e-01 -4.94098634e-01 -6.21935368e-01
-8.70955944e-01 7.31580928e-02 2.91703820e-01 -8.01464915e-01
2.40607131e-02 8.59785557e-01 3.92003566e-01 2.51892328e-01
-3.16077858e-01 -3.53374749e-01 -2.96161026e-01 -9.14008319e-01
3.76928598e-01 3.86973083e-01 -6.13273919e-01 -1.85706411... | [12.473637580871582, 0.387382835149765] |
0dcf471a-f6f5-42b5-a4d9-b457de8290dc | low-resource-speech-to-text-translation | 1803.09164 | null | http://arxiv.org/abs/1803.09164v2 | http://arxiv.org/pdf/1803.09164v2.pdf | Low-Resource Speech-to-Text Translation | Speech-to-text translation has many potential applications for low-resource
languages, but the typical approach of cascading speech recognition with
machine translation is often impossible, since the transcripts needed to train
a speech recognizer are usually not available for low-resource languages.
Recent work has fo... | ['Adam Lopez', 'Karen Livescu', 'Sameer Bansal', 'Sharon Goldwater', 'Herman Kamper'] | 2018-03-24 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.86595857e-01 8.91130865e-02 -3.08901846e-01 -3.02334189e-01
-1.48011363e+00 -5.68738759e-01 4.47916925e-01 -5.60656637e-02
-5.66074073e-01 8.70600462e-01 2.35740736e-01 -1.02266896e+00
5.83812296e-01 -4.72451389e-01 -5.68907917e-01 -4.55076724e-01
2.75567144e-01 5.26955009e-01 8.26421902e-02 -3.20402682... | [14.449347496032715, 7.139906406402588] |
6e6201bc-e80c-4a85-b9c3-19c4bbef7aa9 | airex-neural-network-based-approach-for-air | 2108.07120 | null | https://arxiv.org/abs/2108.07120v1 | https://arxiv.org/pdf/2108.07120v1.pdf | AIREX: Neural Network-based Approach for Air Quality Inference in Unmonitored Cities | Urban air pollution is a major environmental problem affecting human health and quality of life. Monitoring stations have been established to continuously obtain air quality information, but they do not cover all areas. Thus, there are numerous methods for spatially fine-grained air quality inference. Since existing me... | ['Makoto Onizuka', 'Shohei Yamasaki', 'Kei Harada', 'Yuya Sasaki'] | 2021-08-16 | null | null | null | null | ['air-quality-inference'] | ['miscellaneous'] | [ 9.29788314e-03 -4.22639847e-01 -1.92826644e-01 -9.36284885e-02
-9.56461370e-01 -4.16593701e-01 4.77785736e-01 1.51812449e-01
-2.47331351e-01 9.17969048e-01 2.28135273e-01 -7.35449493e-01
-5.15283763e-01 -1.57226014e+00 -7.33909309e-01 -7.12677181e-01
5.00406623e-01 3.90429586e-01 -8.81162286e-03 2.42973015... | [6.254217624664307, 2.50225567817688] |
267043ed-0d6f-4483-b7dc-5d6cdde2175a | the-open-corpus-of-the-veps-and-karelian | 2206.03870 | null | https://arxiv.org/abs/2206.03870v1 | https://arxiv.org/pdf/2206.03870v1.pdf | The Open corpus of the Veps and Karelian languages: overview and applications | A growing priority in the study of Baltic-Finnic languages of the Republic of Karelia has been the methods and tools of corpus linguistics. Since 2016, linguists, mathematicians, and programmers at the Karelian Research Centre have been working with the Open Corpus of the Veps and Karelian Languages (VepKar), which is ... | ['Aleksandra Rodionova', 'Nataliya Pellinen', 'Irina Novak', 'Andrew Krizhanovsky', 'Natalia Krizhanovskaya', 'Nina Zaitseva', 'Tatyana Boyko'] | 2022-06-08 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-4.84660059e-01 1.76768750e-02 -4.28775474e-02 -1.18513443e-01
-4.91247743e-01 -8.42245996e-01 6.28530741e-01 1.65764004e-01
-6.98508620e-01 5.58151424e-01 5.24457455e-01 -7.55964339e-01
-2.40836173e-01 -6.21629655e-01 1.63277656e-01 -2.95243144e-01
1.06117278e-02 6.32308662e-01 9.73474458e-02 -5.82781017... | [10.368390083312988, 10.21158504486084] |
19f60b90-9d5d-43e0-b96a-6ca8556ed1fb | gif-generative-interpretable-faces | 2009.00149 | null | https://arxiv.org/abs/2009.00149v2 | https://arxiv.org/pdf/2009.00149v2.pdf | GIF: Generative Interpretable Faces | Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unrealistic images, on the other hand, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lac... | ['Michael Black', 'Anurag Ranjan', 'Pravir Singh Gupta', 'Timo Bolkart', 'Roy Uziel', 'Partha Ghosh'] | 2020-08-31 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [ 2.55306035e-01 3.00960183e-01 4.17527109e-01 -4.01705474e-01
-3.92139882e-01 -8.96050274e-01 9.55772936e-01 -6.24135315e-01
2.51984209e-01 4.65810120e-01 2.28981435e-01 -1.17900521e-01
3.69845390e-01 -7.95987785e-01 -7.13073850e-01 -7.58776724e-01
7.13664107e-03 5.50304532e-01 -4.11440194e-01 -2.26897389... | [12.441621780395508, -0.330223023891449] |
f9807d11-d10c-4b7c-a273-58cd26f995c9 | shifts-2-0-extending-the-dataset-of-real | 2206.15407 | null | https://arxiv.org/abs/2206.15407v2 | https://arxiv.org/pdf/2206.15407v2.pdf | Shifts 2.0: Extending The Dataset of Real Distributional Shifts | Distributional shift, or the mismatch between training and deployment data, is a significant obstacle to the usage of machine learning in high-stakes industrial applications, such as autonomous driving and medicine. This creates a need to be able to assess how robustly ML models generalize as well as the quality of the... | ['Elena Volf', 'Efi Tsompopoulou', 'Vasileios Tsarsitalidis', 'Eli Sivena', 'Francesco La Rosa', 'Vatsal Raina', 'Antonis Nikitakis', 'Nataliia Molchanova', 'Po-Jui Lu', 'Konstantinos Kyriakopoulos', 'Nikolay Kartashev', 'Mara Graziani', 'Cristina Granziera', 'Mark J. F. Gales', 'Meritxell Bach Cuadra', 'Muhamed Barako... | 2022-06-30 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 2.36655623e-01 -4.67628315e-02 -3.05135041e-01 -4.28744107e-01
-8.48586261e-01 -7.55583525e-01 9.02822912e-01 1.09781899e-01
-5.08268416e-01 9.41964149e-01 -1.58883199e-01 -5.03995776e-01
-4.49423283e-01 -4.61400658e-01 -8.87798965e-01 -8.24770629e-01
-1.59660831e-01 4.84968394e-01 -1.02825963e-03 2.07019880... | [8.2706937789917, 3.849998712539673] |
a30f526c-13a7-487c-9e16-13afc64a9b59 | tjudem-a-combination-classifier-for-aspect | null | null | https://aclanthology.org/S15-2131 | https://aclanthology.org/S15-2131.pdf | TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification | null | ['Jian-Yun Nie', 'Zhifei Zhang', 'Hongling Wang'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['aspect-category-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.285002708435059, 3.6735422611236572] |
36f2e781-ecbe-4fec-bebc-b074598170eb | actionspotter-deep-reinforcement-learning | 2004.06971 | null | https://arxiv.org/abs/2004.06971v2 | https://arxiv.org/pdf/2004.06971v2.pdf | ActionSpotter: Deep Reinforcement Learning Framework for Temporal Action Spotting in Videos | Summarizing video content is an important task in many applications. This task can be defined as the computation of the ordered list of actions present in a video. Such a list could be extracted using action detection algorithms. However, it is not necessary to determine the temporal boundaries of actions to know their... | ['Adrien Chan-Hon-Tong', 'Guillaume Vaudaux-Ruth', 'Catherine Achard'] | 2020-04-15 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 4.65502709e-01 -3.15020591e-01 -4.69934553e-01 -7.00931028e-02
-5.54972589e-01 -5.23774028e-01 4.69522774e-01 8.58265758e-02
-7.00887561e-01 4.67916399e-01 2.26412550e-01 4.03400278e-03
-1.08692190e-02 -5.99877834e-01 -7.06559956e-01 -6.10308826e-01
-2.27206081e-01 2.10058540e-01 7.11151540e-01 3.61773074... | [8.400504112243652, 0.4452205300331116] |
48f32043-f8b5-4172-b327-e344222685ee | conceptdistil-model-agnostic-distillation-of | 2205.03601 | null | https://arxiv.org/abs/2205.03601v1 | https://arxiv.org/pdf/2205.03601v1.pdf | ConceptDistil: Model-Agnostic Distillation of Concept Explanations | Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, images), and by either trying to extract concepts from an already trained network or training self-exp... | ['Pedro Bizarro', 'Pedro Saleiro', 'Vladimir Balayan', 'Ricardo Moreira', 'João Bento Sousa'] | 2022-05-07 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.11931282e-01 1.08221960e+00 -1.63267732e-01 -4.21606302e-01
-2.69069165e-01 -3.22059780e-01 7.70373106e-01 4.04598027e-01
1.96833119e-01 5.58841825e-01 -6.27458021e-02 -7.36678541e-01
-2.97944605e-01 -7.62388527e-01 -7.19730973e-01 -1.04800232e-01
7.41434619e-02 9.62692499e-01 5.15995286e-02 -1.21174172... | [8.88485050201416, 5.779441833496094] |
f30d5ff9-e9d2-495f-99cf-0ec95a8e7f63 | heuristic-ternary-error-correcting-output | 1303.2132 | null | http://arxiv.org/abs/1303.2132v2 | http://arxiv.org/pdf/1303.2132v2.pdf | Heuristic Ternary Error-Correcting Output Codes Via Weight Optimization and Layered Clustering-Based Approach | One important classifier ensemble for multiclass classification problems is
Error-Correcting Output Codes (ECOCs). It bridges multiclass problems and
binary-class classifiers by decomposing multiclass problems to a serial
binary-class problems. In this paper, we present a heuristic ternary code,
named Weight Optimizati... | ['Xiao-Lei Zhang'] | 2013-03-08 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 6.68657303e-01 4.77043800e-02 -2.82112390e-01 -1.14444651e-01
-7.87717402e-01 -3.84935886e-01 2.91006472e-02 3.17369044e-01
-3.98939550e-01 7.09833562e-01 -3.31287324e-01 -6.64809883e-01
-5.83280504e-01 -5.40771127e-01 -3.89473706e-01 -9.29075241e-01
-9.68342274e-02 4.32198524e-01 3.15206826e-01 2.16817856... | [8.873046875, 4.119327068328857] |
cd8b2570-acab-44bd-8e09-07150ce1705d | temporal-modeling-matters-a-novel-temporal | 2211.08233 | null | https://arxiv.org/abs/2211.08233v2 | https://arxiv.org/pdf/2211.08233v2.pdf | Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition | Speech emotion recognition (SER) plays a vital role in improving the interactions between humans and machines by inferring human emotion and affective states from speech signals. Whereas recent works primarily focus on mining spatiotemporal information from hand-crafted features, we explore how to model the temporal pa... | ['Xin-Cheng Wen', 'Hongming Shan', 'KunHong Liu', 'Yong Xu', 'Yujie Wei', 'Jiaxin Ye'] | 2022-11-14 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-7.51981512e-02 -2.96007603e-01 -1.48453489e-01 -6.63394988e-01
-7.30566442e-01 -2.82799900e-01 4.65174884e-01 -1.14830382e-01
-2.84128577e-01 6.16908848e-01 4.50119406e-01 1.57794073e-01
8.11522827e-03 -3.07353735e-01 -2.65244573e-01 -6.33655787e-01
-3.60236764e-01 -1.28012523e-01 -2.10497659e-02 -4.82239455... | [13.449735641479492, 5.693144798278809] |
bd4866e5-8e4c-4e38-a0db-60fb43a49306 | delayed-feedback-in-kernel-bandits | 2302.00392 | null | https://arxiv.org/abs/2302.00392v1 | https://arxiv.org/pdf/2302.00392v1.pdf | Delayed Feedback in Kernel Bandits | Black box optimisation of an unknown function from expensive and noisy evaluations is a ubiquitous problem in machine learning, academic research and industrial production. An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at opt... | ['Ciara Pike-Burke', 'Alberto Bernacchia', 'Danyal Ahmed', 'Sattar Vakili'] | 2023-02-01 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.15005866e-01 6.50967434e-02 -3.31715077e-01 -2.11639062e-01
-1.01683068e+00 -7.29445577e-01 -2.97098104e-02 2.90602982e-01
-9.04148519e-01 1.12753081e+00 -3.77952993e-01 -8.69003296e-01
-9.17106509e-01 -5.79165995e-01 -1.05020010e+00 -1.08223140e+00
-5.42213023e-01 2.70978361e-01 -1.98086482e-02 7.84463957... | [4.654546737670898, 3.393979549407959] |
b6d9e75f-e6c1-4469-bf01-a3f4e916a954 | deep-identity-aware-transfer-of-facial | 1610.05586 | null | http://arxiv.org/abs/1610.05586v2 | http://arxiv.org/pdf/1610.05586v2.pdf | Deep Identity-aware Transfer of Facial Attributes | This paper presents a Deep convolutional network model for Identity-Aware
Transfer (DIAT) of facial attributes. Given the source input image and the
reference attribute, DIAT aims to generate a facial image that owns the
reference attribute as well as keeps the same or similar identity to the input
image. In general, o... | ['WangMeng Zuo', 'David Zhang', 'Mu Li'] | 2016-10-18 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 5.04422665e-01 4.43082780e-01 1.82846829e-01 -5.81779718e-01
-4.57470655e-01 -2.24709451e-01 3.13933104e-01 -4.23697352e-01
-2.46095479e-01 6.68170273e-01 -4.41034511e-02 3.26850027e-01
1.19867690e-01 -9.27704453e-01 -8.23688030e-01 -1.03719890e+00
4.51679915e-01 4.89107035e-02 -2.58654833e-01 -2.24973261... | [12.783480644226074, 0.05958179011940956] |
99220286-819f-47bf-91ae-306000991893 | automatic-spatial-context-sensitive | 1701.04256 | null | http://arxiv.org/abs/1701.04256v1 | http://arxiv.org/pdf/1701.04256v1.pdf | Automatic Spatial Context-Sensitive Cloud/Cloud-Shadow Detection in Multi-Source Multi-Spectral Earth Observation Images: AutoCloud+ | The proposed Earth observation (EO) based value adding system (EO VAS),
hereafter identified as AutoCloud+, consists of an innovative EO image
understanding system (EO IUS) design and implementation capable of automatic
spatial context sensitive cloud/cloud shadow detection in multi source multi
spectral (MS) EO imager... | ['Andrea Baraldi'] | 2017-01-16 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 5.48157275e-01 -7.46999800e-01 1.98547244e-01 -9.25400387e-03
-2.55014002e-01 -8.92830789e-01 5.24942100e-01 2.96222448e-01
-2.88496912e-01 5.72912335e-01 -4.04660851e-01 -6.49538219e-01
-5.76615334e-01 -1.07326257e+00 -1.15718648e-01 -6.29669130e-01
-2.40163669e-01 1.24733873e-01 1.93273276e-01 -8.91042233... | [9.728398323059082, -1.7752231359481812] |
3fa89a66-02bb-4c95-a0e2-15f7bb4412f0 | artificial-counselor-system-for-stock-1 | 1903.00955 | null | https://arxiv.org/abs/1903.00955v1 | https://arxiv.org/pdf/1903.00955v1.pdf | Artificial Counselor System for Stock Investment | This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor shou... | ['Mark Crowley', 'Ali Saheb Pasand', 'Benyamin Ghojogh', 'Hadi NekoeiQachkanloo'] | 2019-03-03 | artificial-counselor-system-for-stock | https://www.aaai.org/ojs/index.php/AAAI/article/view/5016 | https://arxiv.org/pdf/1903.00955.pdf | proceedings-of-the-aaai-conference-on | ['stock-market-prediction', 'stock-price-prediction', 'stock-prediction'] | ['time-series', 'time-series', 'time-series'] | [-6.59546673e-01 -8.77588913e-02 -3.40063810e-01 -3.48977655e-01
3.41422111e-01 -6.23565972e-01 2.78195739e-01 -1.39050916e-01
-3.41578692e-01 9.13247883e-01 -1.60025477e-01 -5.24138331e-01
-5.04124701e-01 -1.16055357e+00 -3.35437544e-02 -4.32143778e-01
2.53632516e-01 4.09524918e-01 3.20291698e-01 -6.33626938... | [4.754644393920898, 4.02319860458374] |
a56d4ea7-0686-4875-8295-211b80773ce4 | can-sam-boost-video-super-resolution | 2305.06524 | null | https://arxiv.org/abs/2305.06524v2 | https://arxiv.org/pdf/2305.06524v2.pdf | Can SAM Boost Video Super-Resolution? | The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple frames. Existing works either adopt deformable convolutions or estimate optical flow as a prior to establish correspondences between frames for... | ['Xinchao Wang', 'Zhiwei Xiong', 'Jiawang Bai', 'Zeyu Xiao', 'Zhihe Lu'] | 2023-05-11 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 9.87014100e-02 -4.32933986e-01 -1.79699257e-01 -2.38816857e-01
-5.13904631e-01 -4.60381776e-01 4.61190104e-01 -3.02849710e-01
-4.06670749e-01 6.43102944e-01 4.38048661e-01 3.93760651e-02
5.73265702e-02 -8.13610494e-01 -4.91601110e-01 -5.05555809e-01
2.83627391e-01 -1.66833490e-01 7.14910746e-01 -3.79689246... | [10.904471397399902, -1.7194281816482544] |
cfd72d43-664d-4b86-9c86-022fe9007b99 | deep-learning-for-hand-gesture-recognition-on | null | null | https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771/file/DeepLearning-HandSkeletalGestureRecognition_MINES-ParisTech_FG2018.pdf | https://hal-mines-paristech.archives-ouvertes.fr/hal-01737771/file/DeepLearning-HandSkeletalGestureRecognition_MINES-ParisTech_FG2018.pdf | Deep Learning for Hand Gesture Recognition on Skeletal Data | In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
We introduce a new Convolutional Neural Network (CNN) where sequences of hand-skeletal joints’ positions are processed by parallel convolutions; we then investigate the performance of this model on hand gesture seque... | ['Guillaume Devineau', 'Fabien Moutarde', 'Jie Yang', 'Wang Xi'] | 2018-05-15 | null | null | null | ieee-fg-2018-2018-5 | ['3d-shape-retrieval', 'temporal-information-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 1.64098926e-02 -5.07207632e-01 -3.35697651e-01 -2.18005925e-01
-3.93486768e-01 -4.78106976e-01 8.95187259e-01 -6.62398756e-01
-8.67165685e-01 7.38102645e-02 1.34249687e-01 -1.86014295e-01
1.33432940e-01 -4.46257234e-01 -4.96775299e-01 -8.05128694e-01
-2.30058476e-01 7.78138041e-01 3.63364518e-01 9.02619734... | [6.652824878692627, -0.4239647388458252] |
bccce757-a7fb-4055-bf7f-7fc53bf6150e | learning-an-adaptation-function-to-assess | 2206.01417 | null | https://arxiv.org/abs/2206.01417v1 | https://arxiv.org/pdf/2206.01417v1.pdf | Learning an Adaptation Function to Assess Image Visual Similarities | Human perception is routinely assessing the similarity between images, both for decision making and creative thinking. But the underlying cognitive process is not really well understood yet, hence difficult to be mimicked by computer vision systems. State-of-the-art approaches using deep architectures are often based o... | ['Nicolas Lomenie', 'Camille Kurtz', 'Hala Djeghim', 'Amine Marzouki', 'Olivier Risser-Maroix'] | 2022-06-03 | null | null | null | null | ['image-similarity-search', 'image-categorization'] | ['computer-vision', 'computer-vision'] | [ 2.02099428e-01 -6.45659268e-02 3.20467591e-01 -5.54297447e-01
-5.94758727e-02 -4.96699870e-01 8.76246512e-01 3.74138981e-01
-7.84340978e-01 3.25681746e-01 9.35319960e-02 -5.97288162e-02
-4.69613671e-01 -6.53134108e-01 -6.30717814e-01 -3.62911433e-01
-1.08002990e-01 3.48377705e-01 2.53939688e-01 -2.96451092... | [9.821967124938965, 2.204354763031006] |
21139517-6f7a-46bb-a78f-9972f6b53b04 | lexmae-lexicon-bottlenecked-pretraining-for | 2208.14754 | null | https://arxiv.org/abs/2208.14754v2 | https://arxiv.org/pdf/2208.14754v2.pdf | LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval | In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency. Despite it deeply exploiting the lexicon-representing capability of pre-trained language models, a crucial gap remains between language m... | ['Daxin Jiang', 'Linjun Yang', 'Binxing Jiao', 'Xiaolong Huang', 'Can Xu', 'Chongyang Tao', 'Xiubo Geng', 'Tao Shen'] | 2022-08-31 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.60980895e-01 -2.97488064e-01 -5.59774280e-01 -6.82086647e-02
-1.42279375e+00 -2.89739817e-01 5.87223709e-01 2.60032743e-01
-8.60849500e-01 4.76792872e-01 5.88896632e-01 -1.58247858e-01
-7.02124685e-02 -8.06556225e-01 -4.45945084e-01 -5.72165668e-01
-4.95478213e-02 7.70188630e-01 1.56380475e-01 -7.81341851... | [11.458114624023438, 7.767665386199951] |
347caac6-934a-4a8a-b849-b1da859821bc | lessons-learned-in-multilingual-grounded | 1809.07615 | null | http://arxiv.org/abs/1809.07615v1 | http://arxiv.org/pdf/1809.07615v1.pdf | Lessons learned in multilingual grounded language learning | Recent work has shown how to learn better visual-semantic embeddings by
leveraging image descriptions in more than one language. Here, we investigate
in detail which conditions affect the performance of this type of grounded
language learning model. We show that multilingual training improves over
bilingual training, a... | ['Grzegorz Chrupała', 'Marc-Alexandre Côté', 'Ákos Kádár', 'Desmond Elliott', 'Afra Alishahi'] | 2018-09-20 | lessons-learned-in-multilingual-grounded-1 | https://aclanthology.org/K18-1039 | https://aclanthology.org/K18-1039.pdf | conll-2018-10 | ['grounded-language-learning'] | ['natural-language-processing'] | [-1.88416958e-01 1.12042181e-01 -4.68705356e-01 -5.69354594e-01
-1.29914284e+00 -7.28531122e-01 9.92282033e-01 2.09410023e-02
-1.01771712e+00 7.81360209e-01 7.12938070e-01 -3.27758133e-01
5.43959022e-01 -3.95445198e-01 -9.61663008e-01 -1.86317056e-01
8.83529037e-02 5.44573188e-01 -8.34317505e-02 -2.24364772... | [11.226873397827148, 1.6178866624832153] |
da9a5f15-6514-41b5-baa6-eac3b48000fb | tablex-a-benchmark-dataset-for-structure-and | 2105.06400 | null | https://arxiv.org/abs/2105.06400v1 | https://arxiv.org/pdf/2105.06400v1.pdf | TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables | Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text. This paper presents TabLeX, a large-scale benchmark dataset comprising table images generated from scientific articles. TabLeX consists of two subsets, one for ... | ['Mayank Singh', 'Pratik Kayal', 'Harsh Desai'] | 2021-05-12 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [ 3.89775455e-01 6.88470826e-02 -1.78226292e-01 -9.36418325e-02
-1.24514186e+00 -1.34603465e+00 7.13570654e-01 5.82170904e-01
-2.12887768e-02 8.01252782e-01 2.30804488e-01 -6.46460235e-01
8.37153718e-02 -7.35363483e-01 -7.68904328e-01 -1.66816667e-01
1.59133196e-01 6.80402517e-01 1.57583177e-01 -3.66401039... | [11.688565254211426, 2.989166021347046] |
33f07143-faa8-4270-a974-5ba97836ecd1 | auditory-separation-of-a-conversation-from | 1905.10751 | null | https://arxiv.org/abs/1905.10751v1 | https://arxiv.org/pdf/1905.10751v1.pdf | Auditory Separation of a Conversation from Background via Attentional Gating | We present a model for separating a set of voices out of a sound mixture containing an unknown number of sources. Our Attentional Gating Network (AGN) uses a variable attentional context to specify which speakers in the mixture are of interest. The attentional context is specified by an embedding vector which modifies ... | ['Bruno Olshausen', 'Shariq Mobin'] | 2019-05-26 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 4.52593088e-01 2.24352390e-01 3.02458197e-01 -1.69809565e-01
-7.54950345e-01 -6.16728365e-01 6.31086290e-01 -1.57319948e-01
-4.11722094e-01 3.74124050e-01 2.71777302e-01 -7.92641938e-02
-6.14935113e-03 -1.55764788e-01 -3.54867220e-01 -9.51508224e-01
-7.30041936e-02 6.30652547e-01 3.16740334e-01 -2.42403626... | [14.904731750488281, 5.969191074371338] |
7eb1df3d-c380-48c6-ab1c-9db632cff5e6 | binding-language-models-in-symbolic-languages | 2210.02875 | null | https://arxiv.org/abs/2210.02875v2 | https://arxiv.org/pdf/2210.02875v2.pdf | Binding Language Models in Symbolic Languages | Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) fu... | ['Tao Yu', 'Noah A. Smith', 'Luke Zettlemoyer', 'Mari Ostendorf', 'Dragomir Radev', 'Caiming Xiong', 'Yushi Hu', 'Rahul Nadkarni', 'Chengzu Li', 'Peng Shi', 'Tianbao Xie', 'Zhoujun Cheng'] | 2022-10-06 | null | null | null | null | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 6.39307722e-02 4.39774454e-01 -1.54677108e-01 -5.57245791e-01
-1.06050706e+00 -1.12489212e+00 2.16372564e-01 4.68056835e-02
-1.67671204e-01 4.95583892e-01 -2.54583895e-01 -8.92478287e-01
9.77202281e-02 -8.63181233e-01 -1.23803878e+00 -3.66487540e-02
3.78204435e-01 7.65055418e-01 1.07698657e-01 -1.57921627... | [9.626266479492188, 7.63092041015625] |
391c22c5-5eb8-4ac7-8856-00832894570c | heterogeneous-graph-transformer-for-graph-to | null | null | https://aclanthology.org/2020.acl-main.640 | https://aclanthology.org/2020.acl-main.640.pdf | Heterogeneous Graph Transformer for Graph-to-Sequence Learning | The graph-to-sequence (Graph2Seq) learning aims to transduce graph-structured representations to word sequences for text generation. Recent studies propose various models to encode graph structure. However, most previous works ignore the indirect relations between distance nodes, or treat indirect relations and direct ... | ['Xiaojun Wan', 'Tianming Wang', 'Shaowei Yao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 4.53244448e-01 7.01645613e-01 -3.78039241e-01 -2.75287569e-01
-4.17632580e-01 -6.90262616e-01 8.58924091e-01 1.47473723e-01
4.73508686e-02 1.12098265e+00 6.54089987e-01 -8.57577682e-01
2.62733698e-01 -1.43375564e+00 -7.16198623e-01 -3.17904860e-01
6.10387735e-02 8.59209538e-01 9.16526914e-02 -8.12030554... | [10.2379732131958, 8.319182395935059] |
7bba5a64-6211-4fe0-abd1-e0989611146e | mc-bert-efficient-language-pre-training-via-a | 2006.05744 | null | https://arxiv.org/abs/2006.05744v2 | https://arxiv.org/pdf/2006.05744v2.pdf | MC-BERT: Efficient Language Pre-Training via a Meta Controller | Pre-trained contextual representations (e.g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks. However, large-scale pre-training is computationally expensive. ELECTRA, an early attempt to accelerate pre-training, trains a discriminative model that predicts whether each input token... | ['Li-Wei Wang', 'Tie-Yan Liu', 'Shuxin Zheng', 'Zhenhui Xu', 'Linyuan Gong', 'Guolin Ke', 'Di He', 'Jiang Bian'] | 2020-06-10 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 3.02767068e-01 2.04532459e-01 -4.07396913e-01 -5.35067320e-01
-1.17977679e+00 -4.01282519e-01 7.23043859e-01 1.74088359e-01
-5.11225581e-01 6.44546032e-01 2.50020236e-01 -1.86465129e-01
5.18058956e-01 -1.01808059e+00 -1.15052760e+00 -4.73017186e-01
3.04043472e-01 7.18359768e-01 1.20306462e-01 -7.93442130... | [10.721213340759277, 8.565399169921875] |
09e4706b-bff1-40e4-84b5-d2aee0518ea0 | multi-step-entity-centric-information | 1909.07598 | null | https://arxiv.org/abs/1909.07598v1 | https://arxiv.org/pdf/1909.07598v1.pdf | Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering | Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evide... | ['Abhishek Singhal', 'Dilip Kavarthapu', 'Ameya Godbole', 'Manzil Zaheer', 'Andrew McCallum', 'Zhiyu Gong', 'Xiaoxiao Guo', 'Rajarshi Das', 'Hamed Zamani', 'Mo Yu', 'Tian Gao'] | 2019-09-17 | multi-step-entity-centric-information-1 | https://aclanthology.org/D19-5816 | https://aclanthology.org/D19-5816.pdf | ws-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 8.54423922e-03 5.71657658e-01 -2.20365852e-01 -1.59870133e-01
-2.10168052e+00 -1.02268219e+00 7.01954126e-01 6.61696196e-01
-8.44283104e-01 1.20411253e+00 2.12371632e-01 -5.33141494e-01
-6.96594596e-01 -8.11455488e-01 -1.07110071e+00 -7.82984123e-02
1.91191941e-01 1.02359366e+00 9.15490270e-01 -8.39299142... | [11.01725959777832, 7.883810520172119] |
941751a0-7534-44d9-8029-ae09ff2ae1e4 | hierarchical-dynamic-image-harmonization | 2211.08639 | null | https://arxiv.org/abs/2211.08639v3 | https://arxiv.org/pdf/2211.08639v3.pdf | Hierarchical Dynamic Image Harmonization | Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global transformations (i.e., normalization and color curve rendering) to achieve visual consistency. However, these models ignore local visual con... | ['Huaxiong Li', 'Weiqiang Wang', 'Changhua Meng', 'Jun Lan', 'Yaohui Li', 'Zhangxuan Gu', 'Haoxing Chen'] | 2022-11-16 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 1.03128783e-01 -5.50264657e-01 -8.11147615e-02 -2.01532707e-01
-1.62277207e-01 -5.87312765e-02 2.54943788e-01 -3.48059863e-01
-2.68456519e-01 2.70042360e-01 -5.65346330e-02 1.26624778e-01
5.94031513e-02 -8.71269584e-01 -4.80715960e-01 -9.78035688e-01
4.70399588e-01 -6.42538667e-02 6.80427730e-01 -2.70826340... | [11.180508613586426, -1.3152751922607422] |
550c0e15-2ea0-42f8-a5cf-ee4be176c94a | large-scale-unsupervised-audio-pre-training | 2306.15464 | null | https://arxiv.org/abs/2306.15464v1 | https://arxiv.org/pdf/2306.15464v1.pdf | Large-scale unsupervised audio pre-training for video-to-speech synthesis | Video-to-speech synthesis is the task of reconstructing the speech signal from a silent video of a speaker. Most established approaches to date involve a two-step process, whereby an intermediate representation from the video, such as a spectrogram, is extracted first and then passed to a vocoder to produce the raw aud... | ['Maja Pantic', 'Yannis Panagakis', 'Triantafyllos Kefalas'] | 2023-06-27 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 5.87888241e-01 1.76676303e-01 7.43070692e-02 -1.64273560e-01
-1.29830718e+00 -6.31869197e-01 8.53625953e-01 -1.50859624e-01
-1.00502558e-01 6.23096228e-01 4.03756291e-01 -2.34644681e-01
4.66856211e-01 -4.27838951e-01 -9.06422794e-01 -5.18943131e-01
8.72011185e-02 2.13548884e-01 2.09961772e-01 2.23927889... | [15.112496376037598, 5.127927303314209] |
76ab64f8-a61b-4d69-b573-721d3ab5baf4 | multi-directional-multi-level-dual-cross | 1401.5311 | null | https://arxiv.org/abs/1401.5311v2 | https://arxiv.org/pdf/1401.5311v2.pdf | Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition | To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduc... | ['DaCheng Tao', 'Larry S. Davis', 'Changxing Ding', 'Jonghyun Choi'] | 2014-01-21 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.06117013e-02 -7.46693909e-01 -1.53351068e-01 -5.56089997e-01
-5.69295704e-01 -3.79305869e-01 5.63105524e-01 -2.94770926e-01
-1.41151631e-02 4.81836736e-01 -2.31804028e-01 2.03869551e-01
-4.64155674e-01 -6.79786682e-01 -4.15848881e-01 -1.16188693e+00
-3.27372164e-01 -8.44262689e-02 -3.72215696e-02 -2.00861946... | [12.944083213806152, 0.6121460795402527] |
22534386-dee3-4b34-8740-1b28b6645b7d | inductive-topic-variational-graph-auto | null | null | https://aclanthology.org/2021.naacl-main.333 | https://aclanthology.org/2021.naacl-main.333.pdf | Inductive Topic Variational Graph Auto-Encoder for Text Classification | Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance. However, existing GCN-based methods do not assume an explicit latent semantic structure of documents, making learned representations less effective and difficult to interpret. They are also trans... | ['Jian-Yun Nie', 'Min Peng', 'Pan Du', 'Jimin Huang', 'Qianqian Xie'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [-8.66230801e-02 4.77525771e-01 -2.35011265e-01 -3.63625228e-01
-2.17556581e-01 -4.25805420e-01 8.83709967e-01 5.09336889e-01
6.89389408e-02 3.76713544e-01 4.72055316e-01 -3.30475241e-01
-1.92643330e-01 -1.12035358e+00 -7.27091610e-01 -7.31846631e-01
9.93121639e-02 8.82269025e-01 2.35662106e-02 -1.69181600... | [10.142773628234863, 6.843301773071289] |
8807d9e6-a1a7-4302-a73f-30280ef774fe | exploring-the-adjugate-matrix-approach-to | 2205.09116 | null | https://arxiv.org/abs/2205.09116v1 | https://arxiv.org/pdf/2205.09116v1.pdf | Exploring the Adjugate Matrix Approach to Quaternion Pose Extraction | Quaternions are important for a wide variety of rotation-related problems in computer graphics, machine vision, and robotics. We study the nontrivial geometry of the relationship between quaternions and rotation matrices by exploiting the adjugate matrix of the characteristic equation of a related eigenvalue problem to... | ['Sonya M. Hanson', 'Andrew J. Hanson'] | 2022-05-17 | null | null | null | null | ['3d-point-cloud-matching'] | ['computer-vision'] | [ 6.25406951e-02 4.38186973e-02 -8.24346542e-02 5.99810742e-02
-1.78495407e-01 -8.09091568e-01 5.35481811e-01 1.14259191e-01
-4.74708945e-01 3.12460899e-01 -3.34946096e-01 -3.95626873e-01
-1.06733516e-01 -5.24674416e-01 -7.02037692e-01 -5.39406061e-01
-1.22831516e-01 8.74345243e-01 -2.81965099e-02 -7.14596450... | [7.96394157409668, -2.3183233737945557] |
c14a9dda-f166-4886-94a5-5bce22dc33b5 | overcoming-the-domain-gap-in-contrastive | 2111.14595 | null | https://arxiv.org/abs/2111.14595v1 | https://arxiv.org/pdf/2111.14595v1.pdf | Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations | A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. For example, the ability to extract behavioral intentions from neural data, or neural decoding, is critical for developing effective brain machine interfaces. Although simple linear models have been applied to thi... | ['Pascal Fua', 'Pavan Ramdya', 'Sina Honari', 'Florian Aymanns', 'Semih Günel'] | 2021-11-29 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 5.62149942e-01 -2.63721973e-01 -2.32282132e-01 -4.72059578e-01
-1.64950699e-01 -7.74437785e-01 6.47790372e-01 2.48878356e-02
-9.05230522e-01 7.39850163e-01 1.70763418e-01 2.79665768e-01
-6.69788793e-02 -2.73178875e-01 -6.76153839e-01 -8.07616293e-01
-1.84571147e-01 3.98937792e-01 1.72208503e-01 -2.65725106... | [9.575722694396973, 2.5106472969055176] |
67e11188-64df-465c-ab9a-30cc61d593b8 | deepfn-towards-generalizable-facial-action | 2103.02484 | null | https://arxiv.org/abs/2103.02484v1 | https://arxiv.org/pdf/2103.02484v1.pdf | DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization | Facial action unit recognition has many applications from market research to psychotherapy and from image captioning to entertainment. Despite its recent progress, deployment of these models has been impeded due to their limited generalization to unseen people and demographics. This work conducts an in-depth analysis o... | ['Mary Czerwinski', 'Alberto Fung', 'Rudovic', 'Ognjen', 'Daniel McDuff', 'Javier Hernandez'] | 2021-03-03 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 3.08677942e-01 7.60822445e-02 -8.20683911e-02 -6.63980842e-01
-3.14991981e-01 -3.63525480e-01 5.61267316e-01 -4.89236981e-01
-6.06702745e-01 4.44906503e-01 3.39324236e-01 2.42226735e-01
2.76164263e-01 -6.89896643e-01 -4.20606494e-01 -8.34073544e-01
8.69486183e-02 -1.16458967e-01 -4.33765352e-01 -2.83598602... | [13.346035957336426, 1.4717254638671875] |
80371a28-a232-4113-905d-295671a9c6fd | real-time-variational-fisheye-stereo-without | 1909.07545 | null | https://arxiv.org/abs/1909.07545v1 | https://arxiv.org/pdf/1909.07545v1.pdf | Real-Time Variational Fisheye Stereo without Rectification and Undistortion | Dense 3D maps from wide-angle cameras is beneficial to robotics applications such as navigation and autonomous driving. In this work, we propose a real-time dense 3D mapping method for fisheye cameras without explicit rectification and undistortion. We extend the conventional variational stereo method by constraining t... | ['Takeshi Oishi', 'Menandro Roxas'] | 2019-09-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 2.17419311e-01 4.81407419e-02 4.44029689e-01 -3.64037365e-01
-2.30123937e-01 -5.18600583e-01 8.70014191e-01 -3.14910680e-01
-7.74968147e-01 7.65605152e-01 -5.91167472e-02 -1.89407229e-01
-2.83483695e-02 -9.54385221e-01 -9.20971155e-01 -3.16157103e-01
5.43340147e-01 9.07048166e-01 6.02912724e-01 -4.34539080... | [8.817781448364258, -2.567304849624634] |
3a49b367-405e-4b97-aec7-88b6dff4c643 | cae-mechanism-to-diminish-the-class | null | null | https://link.springer.com/chapter/10.1007/978-3-031-16210-7_12 | https://link.springer.com/content/pdf/10.1007/978-3-031-16210-7_12.pdf | CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task | Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance. However, classed imbalance problem in NLU has not been carefully inv... | ['Nguyen Le Minh', 'Tung Le', 'Nguyen Minh Phuong'] | 2022-09-21 | null | null | null | advances-in-computational-collective | ['spoken-language-understanding', 'semantic-parsing', 'intent-detection', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [-6.49417564e-02 5.18423080e-01 -1.44546539e-01 -8.56714427e-01
-1.03942537e+00 -5.85221946e-01 3.89377087e-01 1.97655961e-01
-7.27859318e-01 1.27266264e+00 3.02037537e-01 -3.20923805e-01
4.26092446e-01 -8.31822336e-01 -7.13628829e-01 -2.22067654e-01
9.59654078e-02 9.80055928e-01 6.40518785e-01 -3.32359940... | [12.606319427490234, 7.445695877075195] |
4481f99a-1a0b-4158-af14-fefcba622126 | deep-learning-for-predicting-metastasis-on | 2303.05752 | null | https://arxiv.org/abs/2303.05752v1 | https://arxiv.org/pdf/2303.05752v1.pdf | Deep Learning for Predicting Metastasis on Melanoma WSIs | Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based on a pathologist's subjective visual analysis of the patient's tumor. This methodology is heavily time-consuming, and the prognosis variabili... | ['Kjersti Engan', 'Emiel A. M. Janssen', 'Helga Hardardottir', 'Saul Fuster', 'Christopher Andreassen'] | 2023-03-10 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 1.17686726e-01 -5.97659014e-02 -3.06379616e-01 1.41095728e-01
-8.20294917e-01 -4.97395217e-01 3.75470340e-01 6.60608351e-01
-8.10344458e-01 8.03157210e-01 -6.40234426e-02 -6.06522322e-01
3.25827450e-02 -6.91278338e-01 3.65484320e-02 -1.15479195e+00
8.26718286e-02 5.93731005e-04 2.32078031e-01 1.42195271... | [15.310314178466797, -3.024662971496582] |
0f6b356d-c71d-4a10-b21d-388e2694e7d6 | view-dialogue-in-2d-a-two-stream-model-in | null | null | https://aclanthology.org/2022.coling-1.531 | https://aclanthology.org/2022.coling-1.531.pdf | View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond | Existing works on dialogue summarization often follow the common practice in document summarization and view the dialogue, which comprises utterances of different speakers, as a single utterance stream ordered by time. However, this single-stream approach without specific attention to the speaker-centered points has li... | ['Zhongfeng Wang', 'Siyuan Lu', 'Jiaxin Zhuang', 'Dongchen He', 'Keli Xie'] | null | null | null | null | coling-2022-10 | ['machine-reading-comprehension', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.10141623e-01 3.41325164e-01 -3.38250399e-01 -5.11889756e-01
-1.00166500e+00 -6.18928254e-01 9.62708533e-01 5.17560482e-01
-1.08307935e-01 6.82095945e-01 1.17448449e+00 -2.10065618e-01
1.37652755e-01 -4.31832850e-01 -1.58250883e-01 -3.65706533e-01
1.74986809e-01 5.85241377e-01 2.51097649e-01 -6.81849599... | [12.584975242614746, 9.178815841674805] |
2e4c8e18-4629-48fc-bbe5-2acf25e03271 | learning-non-autoregressive-models-from | null | null | https://openreview.net/forum?id=UNzc8gReN7m | https://openreview.net/pdf?id=UNzc8gReN7m | Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization | Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['headline-generation', 'abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.50255597e-01 4.87915456e-01 -1.34391636e-01 -4.02389020e-01
-1.55519617e+00 -4.77676034e-01 5.68660855e-01 4.07119840e-01
-3.32760125e-01 9.38919425e-01 7.99592257e-01 -1.41510606e-01
3.21422368e-01 -8.17853689e-01 -9.55872536e-01 -4.89328682e-01
2.64444113e-01 8.38287711e-01 8.76459554e-02 -7.01977685... | [12.464950561523438, 9.451364517211914] |
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