paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
eaae0e60-d4d5-4d02-a84d-4a445d4b08f7 | melanoma-diagnosis-with-spatio-temporal | 2006.10950 | null | https://arxiv.org/abs/2006.10950v1 | https://arxiv.org/pdf/2006.10950v1.pdf | Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images | Existing studies for automated melanoma diagnosis are based on single-time point images of lesions. However, melanocytic lesions de facto are progressively evolving and, moreover, benign lesions can progress into malignant melanoma. Ignoring cross-time morphological changes of lesions thus may lead to misdiagnosis in b... | ['ZongYuan Ge', 'Xiaojun Chang', 'Jennifer Nguyen', 'Zhen Yu', 'Lei Zhang', 'John Kelly', 'Victoria Mar', 'Catriona Mclean'] | 2020-06-19 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 8.61583889e-01 -3.16651583e-01 -2.05586955e-01 -7.51955733e-02
-4.59397644e-01 -5.09203970e-01 5.10284305e-01 2.25746363e-01
-6.53891265e-01 6.47820592e-01 -3.94892573e-01 -4.32642132e-01
-2.03822792e-01 -7.79850662e-01 -2.83657253e-01 -9.48875010e-01
-1.17922708e-01 -4.05036137e-02 1.94480270e-01 1.30966261... | [15.655463218688965, -3.000121593475342] |
c2bed743-5ee2-44d7-b69c-100d8926188d | a-step-towards-the-applicability-of | 2304.02286 | null | https://arxiv.org/abs/2304.02286v1 | https://arxiv.org/pdf/2304.02286v1.pdf | A step towards the applicability of algorithms based on invariant causal learning on observational data | Machine learning can benefit from causal discovery for interpretation and from causal inference for generalization. In this line of research, a few invariant learning algorithms for out-of-distribution (OOD) generalization have been proposed by using multiple training environments to find invariant relationships. Some ... | ['Borja Guerrero Santillan'] | 2023-04-05 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [ 5.36737025e-01 4.46444303e-01 -4.41906512e-01 -5.05434990e-01
-5.55937946e-01 -2.07621902e-01 8.69622588e-01 4.18006927e-01
-1.13614634e-01 1.12289500e+00 5.94570220e-01 -4.20569241e-01
-9.50672746e-01 -1.11319661e+00 -1.21441400e+00 -7.36414373e-01
-7.96988368e-01 6.45131230e-01 3.95477600e-02 1.92959204... | [7.8523030281066895, 5.28935432434082] |
3ec77407-fa19-4cfd-992e-5c5346c57693 | split-brain-autoencoders-unsupervised | 1611.09842 | null | http://arxiv.org/abs/1611.09842v3 | http://arxiv.org/pdf/1611.09842v3.pdf | Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction | We propose split-brain autoencoders, a straightforward modification of the
traditional autoencoder architecture, for unsupervised representation learning.
The method adds a split to the network, resulting in two disjoint sub-networks.
Each sub-network is trained to perform a difficult task -- predicting one
subset of t... | ['Alexei A. Efros', 'Richard Zhang', 'Phillip Isola'] | 2016-11-29 | split-brain-autoencoders-unsupervised-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Split-Brain_Autoencoders_Unsupervised_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Split-Brain_Autoencoders_Unsupervised_CVPR_2017_paper.pdf | cvpr-2017-7 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.08842236e-01 4.04695004e-01 -1.02711417e-01 -4.18377161e-01
-7.36292064e-01 -4.46119845e-01 3.45945507e-01 -4.52639878e-01
-3.63478392e-01 8.04946542e-01 2.27216542e-01 -1.26232682e-02
2.89561003e-01 -5.91060042e-01 -1.03057098e+00 -7.44488895e-01
-1.70277700e-01 5.30612051e-01 5.14649153e-02 1.08465813... | [9.433745384216309, 2.6292405128479004] |
e6ac3ccb-5b5f-4c60-987f-3e4c491c86c7 | neural-network-based-on-chip-spectroscopy | 2012.00878 | null | https://arxiv.org/abs/2012.00878v1 | https://arxiv.org/pdf/2012.00878v1.pdf | Neural network-based on-chip spectroscopy using a scalable plasmonic encoder | Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to gra... | ['Aydogan Ozcan', 'Yair Rivenson', 'Yunzhe Qiu', 'Ashley Clemens', 'Mason Fordham', 'Zachary Ballard', 'Artem Goncharov', 'Calvin Brown'] | 2020-12-01 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 8.58548701e-01 -3.71511906e-01 5.24415433e-01 6.93319738e-03
-6.23018682e-01 -6.04806721e-01 -1.00184083e-01 1.64237931e-01
-6.39699697e-01 8.05421591e-01 -3.56692195e-01 -7.20449165e-02
8.69759843e-02 -8.74467194e-01 -1.01730525e+00 -1.08489490e+00
1.82184488e-01 3.78547817e-01 2.54599929e-01 3.08895290... | [10.224894523620605, -2.476801872253418] |
61bedacb-c440-4049-b474-ea7b291aa2a9 | unibuckernel-geolocating-swiss-german-jodels | 2102.09379 | null | https://arxiv.org/abs/2102.09379v3 | https://arxiv.org/pdf/2102.09379v3.pdf | UnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning | In this work, we describe our approach addressing the Social Media Variety Geolocation task featured in the 2021 VarDial Evaluation Campaign. We focus on the second subtask, which is based on a data set formed of approximately 30 thousand Swiss German Jodels. The dialect identification task is about accurately predicti... | ['Radu Tudor Ionescu', 'Sebastian Cojocariu', 'Mihaela Gaman'] | 2021-02-18 | null | https://aclanthology.org/2021.vardial-1.10 | https://aclanthology.org/2021.vardial-1.10.pdf | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-6.26176715e-01 6.90668002e-02 -2.54118949e-01 -5.13835013e-01
-1.04300129e+00 -7.12508738e-01 1.22794700e+00 4.95124131e-01
-7.56658375e-01 7.26455748e-01 4.32006776e-01 -3.23633462e-01
-3.77847075e-01 -9.66217160e-01 -5.40179789e-01 -6.38337076e-01
-3.93339666e-03 5.86471438e-01 -1.89844653e-01 -3.69860530... | [9.613792419433594, 10.28873348236084] |
426e89fa-14d2-479c-ad2b-7e39aea268bf | curve-your-enthusiasm-concurvity | 2305.11475 | null | https://arxiv.org/abs/2305.11475v1 | https://arxiv.org/pdf/2305.11475v1.pdf | Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models | Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features. Despite the current enthusiasm for GAMs, their susceptibility to concurvity - i.e., (possibly non-l... | ['Martin Genzel', 'Johannes S. Otterbach', 'Maximilian Schambach', 'Alma Lindborg', 'Winfried Ripken', 'Konstantin Ditschuneit', 'Julien Siems'] | 2023-05-19 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.94162273e-01 1.58810303e-01 2.28172481e-01 -7.24174798e-01
-4.24251646e-01 -5.76272666e-01 6.38943791e-01 -1.09105734e-02
-1.96499228e-01 7.58031249e-01 2.77857989e-01 -3.01173031e-01
-5.56451082e-01 -6.81193769e-01 -5.11117220e-01 -6.65070415e-01
-2.02608705e-01 2.94662654e-01 -3.13602805e-01 -3.05348575... | [8.683663368225098, 5.4773406982421875] |
2e256162-44d7-4c44-b9d9-2f2df346b0ce | vitaa-visual-textual-attributes-alignment-in | 2005.07327 | null | https://arxiv.org/abs/2005.07327v2 | https://arxiv.org/pdf/2005.07327v2.pdf | ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language | Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches the given textual descriptions. While most of the current methods treat the task as a holistic visual and textual feature matching one, we approach it from an attribute-aligning perspective that allows ground... | ['Jun Wang', 'Zhiyuan Fang', 'Zhe Wang', 'Yezhou Yang'] | 2020-05-15 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1593_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570392.pdf | eccv-2020-8 | ['nlp-based-person-retrival', 'person-search'] | ['computer-vision', 'computer-vision'] | [ 3.00442457e-01 -5.35261407e-02 -3.47003073e-01 -8.34729671e-01
-1.24085462e+00 -8.56528759e-01 1.05376482e+00 1.98962405e-01
-6.89145088e-01 3.54604900e-01 3.17242056e-01 2.27842659e-01
-7.52358511e-02 -4.07636136e-01 -5.92135608e-01 -5.46293974e-01
3.41871649e-01 1.23940742e+00 -2.87705421e-01 1.08270101... | [14.558941841125488, 0.9375434517860413] |
34b85369-c382-4646-917b-96dc1581cd1e | hunsum-1-an-abstractive-summarization-dataset | 2302.00455 | null | https://arxiv.org/abs/2302.00455v1 | https://arxiv.org/pdf/2302.00455v1.pdf | HunSum-1: an Abstractive Summarization Dataset for Hungarian | We introduce HunSum-1: a dataset for Hungarian abstractive summarization, consisting of 1.14M news articles. The dataset is built by collecting, cleaning and deduplicating data from 9 major Hungarian news sites through CommonCrawl. Using this dataset, we build abstractive summarizer models based on huBERT and mT5. We d... | ['Judit Ács', 'Milán Konor Nyist', 'Attila Nagy', 'Dorina Lakatos', 'Botond Barta'] | 2023-02-01 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [-3.57773215e-01 3.70297462e-01 -8.79459620e-01 -1.17257744e-01
-1.37112832e+00 -6.51280820e-01 1.08035254e+00 8.40915024e-01
-4.77822721e-01 1.10668266e+00 1.54084277e+00 2.67461818e-02
-9.95583236e-02 -6.38011336e-01 -8.58346879e-01 -5.45342825e-02
8.13325867e-03 7.21368253e-01 1.01661466e-01 -4.36058253... | [12.484601020812988, 9.493569374084473] |
71a79836-b7b7-47b2-825c-a4c217c7621b | a-fast-and-accurate-vietnamese-word-segmenter | 1709.06307 | null | http://arxiv.org/abs/1709.06307v2 | http://arxiv.org/pdf/1709.06307v2.pdf | A Fast and Accurate Vietnamese Word Segmenter | We propose a novel approach to Vietnamese word segmentation. Our approach is
based on the Single Classification Ripple Down Rules methodology (Compton and
Jansen, 1990), where rules are stored in an exception structure and new rules
are only added to correct segmentation errors given by existing rules.
Experimental res... | ['Mark Johnson', 'Mark Dras', 'Thanh Vu', 'Dat Quoc Nguyen', 'Dai Quoc Nguyen'] | 2017-09-19 | a-fast-and-accurate-vietnamese-word-segmenter-2 | https://aclanthology.org/L18-1410 | https://aclanthology.org/L18-1410.pdf | lrec-2018-5 | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-2.88216680e-01 3.01138729e-01 -7.09512591e-01 -4.69465643e-01
-7.38947928e-01 -9.58016574e-01 3.71059865e-01 1.17034182e-01
-7.01906264e-01 1.15746629e+00 1.77664161e-01 -6.28411531e-01
3.04004312e-01 -5.70023775e-01 -3.47060561e-01 -1.46459401e-01
4.38186705e-01 7.17754722e-01 7.10857093e-01 -5.71543515... | [10.38459587097168, 10.073356628417969] |
cdd4f445-3cd7-4fef-ad3f-9ae424c5cf5a | geometric-feature-based-face-sketch | 1312.1462 | null | https://arxiv.org/abs/1312.1462v1 | https://arxiv.org/pdf/1312.1462v1.pdf | Geometric Feature Based Face-Sketch Recognition | This paper presents a novel facial sketch image or face-sketch recognition approach based on facial feature extraction. To recognize a face-sketch, we have concentrated on a set of geometric face features like eyes, nose, eyebrows, lips, etc and their length and width ratio because it is difficult to match photos and s... | ['Debotosh Bhattacharjee', 'Sourav Pramanik'] | 2013-12-05 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 3.04834694e-01 -2.33758032e-01 -1.88247472e-01 -5.13645470e-01
-5.80795519e-02 -5.11048079e-01 5.52899957e-01 -3.65266979e-01
-1.77259594e-01 4.90417093e-01 4.46362831e-02 2.42798343e-01
-2.97495365e-01 -6.01401150e-01 -6.40388727e-02 -8.25343490e-01
2.64763921e-01 9.28289071e-03 -1.21991029e-02 -4.60130423... | [13.176111221313477, 0.7015678882598877] |
e4e6a8c7-6bdd-478c-b8ef-e44ced4c69bd | few-shot-controllable-style-transfer-for-low-1 | null | null | https://openreview.net/forum?id=iU7nNeWLbA7 | https://openreview.net/pdf?id=iU7nNeWLbA7 | Few-shot Controllable Style Transfer for Low-Resource Multilinugal Settings | Style transfer is the task of rewriting an input sentence into a target style while approximately preserving its content. While most prior literature assumes access to large style-labelled corpora, recent work (Riley et al. 2021) has attempted "few-shot" style transfer using only 3-10 sentences at inference for extract... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-anonymization'] | ['natural-language-processing'] | [ 3.52235287e-01 2.47342527e-01 -3.60871479e-02 -6.54541969e-01
-1.19990969e+00 -1.02602088e+00 7.04955101e-01 -1.40404522e-01
-6.63819611e-01 1.02216518e+00 4.88432914e-01 -2.96251774e-01
3.62440318e-01 -4.77505326e-01 -7.67731667e-01 -6.36565462e-02
6.99705780e-01 7.51924217e-01 -2.45264873e-01 -7.43515491... | [11.509256362915039, 9.658592224121094] |
989520ce-e504-4138-ab9c-cfdf2ef7a2f0 | deep-fusion-an-attention-guided-factorized | 1901.04889 | null | http://arxiv.org/abs/1901.04889v1 | http://arxiv.org/pdf/1901.04889v1.pdf | Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition | Automatic emotion recognition (AER) is a challenging task due to the abstract
concept and multiple expressions of emotion. Although there is no consensus on
a definition, human emotional states usually can be apperceived by auditory and
visual systems. Inspired by this cognitive process in human beings, it's
natural to... | ['Zi-Rui Wang', 'Yuanyuan Zhang', 'Jun Du'] | 2019-01-15 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [ 8.56160745e-03 -5.27769685e-01 4.48993355e-01 -4.27793264e-01
-9.09220338e-01 -2.42885917e-01 3.52213055e-01 1.71386033e-01
-6.04931653e-01 2.73723125e-01 2.65913397e-01 3.00134867e-01
1.47562519e-01 -3.02323490e-01 -4.36253846e-01 -6.66998506e-01
3.81892808e-02 -3.63237858e-01 -1.13646835e-01 -1.84111878... | [13.303610801696777, 5.040400505065918] |
12db982e-ec75-410e-b43d-410c37535321 | lid-2020-the-learning-from-imperfect-data | 2010.11724 | null | https://arxiv.org/abs/2010.11724v1 | https://arxiv.org/pdf/2010.11724v1.pdf | LID 2020: The Learning from Imperfect Data Challenge Results | Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotated datasets. The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel app... | ['Jun He', 'Huanqing Yan', 'Chen Gong', 'Zhenyuan Chen', 'Zhendong Wang', 'Oles Dobosevych', 'Ostap Viniavskyi', 'Mariia Dobko', 'Yao Zhao', 'Shikui Wei', 'Guanghua Gu', 'Tao Ruan', 'Chuangchuang Tan', 'Li Zhang', 'Yurong Chen', 'Yiwen Guo', 'Anbang Yao', 'Ming Lu', 'Hao Zhao', 'Gunhee Kim', 'Jinhwan Seo', 'Junhyug Noh... | 2020-10-17 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 2.04348654e-01 3.08061242e-02 -3.38697225e-01 -6.12095535e-01
-1.41052735e+00 -9.50171232e-01 1.45061255e-01 -2.09243566e-01
-4.14789379e-01 6.12863362e-01 -2.07291275e-01 -3.14819783e-01
1.51239604e-01 -3.04092586e-01 -1.22500026e+00 -6.44293427e-01
1.91372424e-01 4.45669264e-01 5.82540393e-01 2.37246603... | [9.485994338989258, 0.5862497091293335] |
a9991cac-421c-40ca-aaf5-df35ae77aae2 | on-predictive-information-sub-optimality-of | null | null | https://openreview.net/forum?id=HklsHyBKDr | https://openreview.net/pdf?id=HklsHyBKDr | On Predictive Information Sub-optimality of RNNs | Certain biological neurons demonstrate a remarkable capability to optimally compress the history of sensory inputs while being maximally informative about the future. In this work, we investigate if the same can be said of artificial neurons in recurrent neural networks (RNNs) trained with maximum likelihood. In experi... | ['Alexander A. Alemi', 'Ben Poole', 'Deniz Oktay', 'Zhe Dong'] | 2019-09-25 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 6.10679686e-01 3.49619657e-01 -1.91287100e-01 -3.93078953e-01
-1.10891007e-01 -2.92742461e-01 8.06746423e-01 -1.03717536e-01
-7.90828347e-01 9.93934631e-01 5.14560819e-01 -4.00579572e-01
-2.95781583e-01 -8.04703355e-01 -6.80134714e-01 -6.63420916e-01
-3.95755976e-01 2.34034657e-01 9.06332210e-02 -5.70459338... | [7.7275896072387695, 3.49001407623291] |
2867de83-12ca-474a-9893-abebead939ee | personalized-substitution-ranking-for-lexical | null | null | https://aclanthology.org/W19-8634 | https://aclanthology.org/W19-8634.pdf | Personalized Substitution Ranking for Lexical Simplification | A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user{'}s vocabulary p... | ['John Lee', 'Chak Yan Yeung'] | 2019-10-01 | null | null | null | ws-2019-10 | ['lexical-simplification'] | ['natural-language-processing'] | [ 6.46373406e-02 2.02690616e-01 6.77075163e-02 -2.41106957e-01
-4.90420401e-01 -7.21541882e-01 2.13699967e-01 1.08410048e+00
-8.64994526e-01 4.41886067e-01 4.19290870e-01 -3.37876260e-01
-1.47680238e-01 -8.30494940e-01 -3.94565344e-01 -1.09443709e-01
8.27540517e-01 5.96442163e-01 5.03484786e-01 -7.13948488... | [10.863243103027344, 10.272683143615723] |
7e45e9fb-b395-4a2c-bc36-00332fc90d2a | a-unifying-framework-for-differentially | 2307.04733 | null | https://arxiv.org/abs/2307.04733v1 | https://arxiv.org/pdf/2307.04733v1.pdf | A unifying framework for differentially private quantum algorithms | Differential privacy is a widely used notion of security that enables the processing of sensitive information. In short, differentially private algorithms map "neighbouring" inputs to close output distributions. Prior work proposed several quantum extensions of differential privacy, each of them built on substantially ... | ['Elham Kashefi', 'Mina Doosti', 'Armando Angrisani'] | 2023-07-10 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 5.66185117e-01 2.26793289e-01 3.94994915e-01 -2.79980689e-01
-1.11864471e+00 -1.21346366e+00 1.15602866e-01 2.15159774e-01
-5.41315913e-01 6.95245445e-01 -5.58739193e-02 -4.31709796e-01
-4.56807554e-01 -1.07767975e+00 -8.02090824e-01 -1.27463126e+00
5.55301942e-02 -3.13788466e-02 -1.06855750e-01 -5.74958444... | [5.788342475891113, 5.983330249786377] |
99508462-e746-4d98-a72d-ed8c2406d09c | mining-mid-level-features-for-action | 1409.4014 | null | http://arxiv.org/abs/1409.4014v1 | http://arxiv.org/pdf/1409.4014v1.pdf | Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation | Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect sk... | ['Pichao Wang', 'Zhimin Gao', 'Philip Ogunbona', 'Wanqing Li', 'Hanling Zhang'] | 2014-09-14 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 1.48506895e-01 -2.34925568e-01 -8.46544921e-01 -4.34301764e-01
-7.21094966e-01 -1.01342112e-01 6.91198111e-01 -6.74435273e-02
-2.15029478e-01 5.35289407e-01 7.26692140e-01 6.35847509e-01
-2.29551524e-01 -4.56322789e-01 -3.60402942e-01 -7.61603117e-01
-4.73423690e-01 1.93160713e-01 6.57480717e-01 -3.81336734... | [7.798377990722656, 0.31536900997161865] |
b9d9374a-1a14-43d6-9330-257d923fe0d1 | surface-representation-for-point-clouds | 2205.05740 | null | https://arxiv.org/abs/2205.05740v2 | https://arxiv.org/pdf/2205.05740v2.pdf | Surface Representation for Point Clouds | Most prior work represents the shapes of point clouds by coordinates. However, it is insufficient to describe the local geometry directly. In this paper, we present \textbf{RepSurf} (representative surfaces), a novel representation of point clouds to \textbf{explicitly} depict the very local structure. We explore two v... | ['Chengjie Wang', 'Jun Liu', 'Haoxi Ran'] | 2022-05-11 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-point-cloud-classification'] | ['computer-vision'] | [ 9.05342773e-02 1.60586491e-01 2.27838993e-01 -7.28518665e-02
-1.00015843e+00 -7.29095578e-01 4.75435734e-01 1.17913619e-01
-4.11003470e-01 3.68272036e-01 -8.64001095e-01 -4.94224131e-01
-6.27934653e-03 -1.29510164e+00 -1.28351080e+00 -2.49192417e-01
-2.69304752e-01 6.96119368e-01 8.86083484e-01 -1.75659209... | [7.980206489562988, -3.2728707790374756] |
31b31d19-db07-4b32-8893-10600c653284 | distributed-stochastic-bandit-learning-with | 2207.14391 | null | https://arxiv.org/abs/2207.14391v2 | https://arxiv.org/pdf/2207.14391v2.pdf | Distributed Stochastic Bandit Learning with Delayed Context Observation | We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K>>M. The goal of the agents is to simultaneously minimize the cumulative regret over all the agents over a time horizon T. We consider a setting where the exact context is observed after a ... | ['Shana Moothedath', 'Jiabin Lin'] | 2022-07-28 | null | null | null | null | ['weather-forecasting', 'stock-market-prediction'] | ['miscellaneous', 'time-series'] | [-1.34753156e-02 -3.93472202e-02 -2.18092009e-01 -2.89667726e-01
-5.92505395e-01 -9.77643311e-01 4.64722574e-01 4.87827450e-01
-9.44424927e-01 9.09205675e-01 5.44710457e-02 -3.53509516e-01
-5.07276237e-01 -6.03935778e-01 -7.71057427e-01 -9.37691450e-01
-3.77658337e-01 7.89740324e-01 -1.05564892e-01 2.61720121... | [4.4788689613342285, 3.20560359954834] |
b83c954e-7130-4bc2-9992-5ab45233daf1 | logdet-rank-minimization-with-application-to | 1507.00908 | null | http://arxiv.org/abs/1507.00908v1 | http://arxiv.org/pdf/1507.00908v1.pdf | LogDet Rank Minimization with Application to Subspace Clustering | Low-rank matrix is desired in many machine learning and computer vision
problems. Most of the recent studies use the nuclear norm as a convex surrogate
of the rank operator. However, all singular values are simply added together by
the nuclear norm, and thus the rank may not be well approximated in practical
problems. ... | ['Qiang Chen', 'Zhao Kang', 'Chong Peng', 'Jie Cheng'] | 2015-07-03 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-1.03541970e-01 -1.86670601e-01 -1.69242412e-01 -3.06792885e-01
-6.94329202e-01 -6.22802556e-01 1.79064810e-01 -3.10646832e-01
-3.28740090e-01 4.88724649e-01 1.55686110e-01 -3.94002981e-02
-3.80257219e-01 -8.50359872e-02 -4.26143706e-01 -1.01673269e+00
-5.55360988e-02 3.12242121e-01 4.95805293e-02 2.83868790... | [7.714892864227295, 4.456671237945557] |
e1a80d99-50b6-44d9-b9d3-8d037aec9721 | solving-soft-clustering-ensemble-via-k-sparse | null | null | http://proceedings.neurips.cc/paper/2021/hash/07a4e20a7bbeeb7a736682b26b16ebe8-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/07a4e20a7bbeeb7a736682b26b16ebe8-Paper.pdf | Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter | Clustering ensemble is one of the most important problems in ensemble learning. Though it has been extensively studied in the past decades, the existing methods often suffer from the issues like high computational complexity and the difficulty on understanding the consensus. In this paper, we study the more general so... | ['Hu Ding', 'Mengying Li', 'Ruizhe Qin'] | 2021-12-01 | null | https://openreview.net/forum?id=yAIYc7YjGbd | https://openreview.net/pdf?id=yAIYc7YjGbd | neurips-2021-12 | ['clustering-ensemble'] | ['graphs'] | [-9.81624201e-02 -1.70174956e-01 3.52930158e-01 -3.33580136e-01
-8.94524515e-01 -5.72050214e-01 2.12214857e-01 8.29051733e-02
-8.05089995e-02 6.93726957e-01 1.43516599e-03 -1.37603477e-01
-7.77186155e-01 -4.40092117e-01 -3.92601073e-01 -1.51206172e+00
-9.07934830e-02 5.61572373e-01 2.25089910e-03 -5.29011376... | [7.9371490478515625, 4.627134799957275] |
08ab96c6-044b-41ea-93a7-86d411be3117 | dehazed-image-quality-evaluation-from-partial | 2211.12636 | null | https://arxiv.org/abs/2211.12636v1 | https://arxiv.org/pdf/2211.12636v1.pdf | Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception | Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference ... | ['Huiyan Chen', 'Hantao Liu', 'Leida Li', 'Ruizeng Zhang', 'Wei Zhou'] | 2022-11-22 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.25904816e-01 -6.03985012e-01 4.18510884e-01 -2.44261011e-01
-5.70565522e-01 -1.46401018e-01 5.39126158e-01 -1.03647090e-01
-2.16455415e-01 5.64307034e-01 3.15668374e-01 -1.14020415e-01
-3.17136019e-01 -1.05904329e+00 -3.09440613e-01 -1.15186191e+00
1.13828436e-01 -8.03382337e-01 4.17577684e-01 -4.62752163... | [10.94222640991211, -3.045412540435791] |
3e5919d3-5e26-4318-a039-43675f02d6a2 | emt-nas-transferring-architectural-knowledge | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liao_EMT-NASTransferring_Architectural_Knowledge_Between_Tasks_From_Different_Datasets_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liao_EMT-NASTransferring_Architectural_Knowledge_Between_Tasks_From_Different_Datasets_CVPR_2023_paper.pdf | EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different Datasets | The success of multi-task learning (MTL) can largely be attributed to the shared representation of related tasks, allowing the models to better generalise. In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights. However, the joint training of weig... | ['Wenli Du', 'Yaochu Jin', 'Peng Liao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['architecture-search'] | ['methodology'] | [ 2.20702186e-01 -3.44224691e-01 2.98692554e-01 -3.41265768e-01
-6.30097091e-01 -2.72571027e-01 2.21275195e-01 -2.34564468e-02
-7.81517565e-01 6.86487317e-01 -2.43030384e-01 2.43373085e-02
-5.47688365e-01 -2.96225786e-01 -5.67775607e-01 -8.52768004e-01
1.58430729e-02 4.89647359e-01 8.08294341e-02 -1.66949719... | [9.27874755859375, 3.370607376098633] |
ba31bb2c-9e1b-4088-90a2-c9a4f68d1fdd | towards-reasoning-aware-explainable-vqa | 2211.05190 | null | https://arxiv.org/abs/2211.05190v1 | https://arxiv.org/pdf/2211.05190v1.pdf | Towards Reasoning-Aware Explainable VQA | The domain of joint vision-language understanding, especially in the context of reasoning in Visual Question Answering (VQA) models, has garnered significant attention in the recent past. While most of the existing VQA models focus on improving the accuracy of VQA, the way models arrive at an answer is oftentimes a bla... | ['Govind Thattai', 'Abhinav Mathur', 'Feng Gao', 'Rakesh Vaideeswaran'] | 2022-11-09 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 2.32764423e-01 7.85510898e-01 1.06743105e-01 -8.14563394e-01
-8.51191640e-01 -5.76543391e-01 7.25681782e-01 -2.37060994e-01
9.99448355e-03 6.62973046e-01 5.87477088e-01 -7.97953129e-01
1.85304463e-01 -8.10533285e-01 -9.00941133e-01 -1.16007254e-02
6.21401608e-01 7.60554016e-01 -8.28465819e-02 -2.09129408... | [10.785514831542969, 1.8441369533538818] |
fe8540ef-0b69-4549-93fe-028626032590 | audio-visual-contrastive-learning-with | 2302.07702 | null | https://arxiv.org/abs/2302.07702v1 | https://arxiv.org/pdf/2302.07702v1.pdf | Audio-Visual Contrastive Learning with Temporal Self-Supervision | We propose a self-supervised learning approach for videos that learns representations of both the RGB frames and the accompanying audio without human supervision. In contrast to images that capture the static scene appearance, videos also contain sound and temporal scene dynamics. To leverage the temporal and aural dim... | ['John Collomosse', 'Alexander Black', 'Simon Jenni'] | 2023-02-15 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 4.57067907e-01 -3.48857194e-01 -2.81360596e-01 -4.34004515e-01
-1.02451360e+00 -7.59741783e-01 6.73155129e-01 -3.83614510e-01
-4.67660576e-01 3.57169718e-01 3.77758086e-01 4.85011637e-01
-1.40224323e-01 -1.99850500e-01 -1.05089462e+00 -6.61082327e-01
-7.15959251e-01 -1.17810957e-01 2.49721527e-01 1.42336205... | [9.48346996307373, 1.0403611660003662] |
0597a1a1-ebd7-4aa7-91bf-032fe35f27cf | container-few-shot-named-entity-recognition | 2109.07589 | null | https://arxiv.org/abs/2109.07589v2 | https://arxiv.org/pdf/2109.07589v2.pdf | CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning | Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances.... | ['Rui Zhang', 'Rebecca J. Passonneau', 'Arzoo Katiyar', 'Sarkar Snigdha Sarathi Das'] | 2021-09-15 | null | https://aclanthology.org/2022.acl-long.439 | https://aclanthology.org/2022.acl-long.439.pdf | acl-2022-5 | ['few-shot-ner'] | ['natural-language-processing'] | [-3.10104400e-01 -9.93754566e-02 -7.62102827e-02 -4.62268412e-01
-1.25478160e+00 -8.71186018e-01 5.37554026e-01 4.04175043e-01
-9.48710680e-01 8.86204898e-01 3.20020288e-01 1.72027498e-02
7.56166577e-02 -6.05811775e-01 -1.74479023e-01 -3.50045085e-01
-2.06745982e-01 4.62930709e-01 2.53431886e-01 3.94574180... | [9.6933012008667, 9.408905029296875] |
d6f34da9-b3a3-406f-90ea-907660bde98c | single-image-reflection-removal-beyond | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wen_Single_Image_Reflection_Removal_Beyond_Linearity_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wen_Single_Image_Reflection_Removal_Beyond_Linearity_CVPR_2019_paper.pdf | Single Image Reflection Removal Beyond Linearity | Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from t... | [' Shengfeng He', ' Guoqiang Han', ' Wenxi Liu', ' Jing Qin', ' Yinjie Tan', 'Qiang Wen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['reflection-removal'] | ['computer-vision'] | [ 5.70123792e-01 9.30180103e-02 4.22157258e-01 -8.31593871e-02
-3.30066800e-01 -2.22168133e-01 5.46618223e-01 -7.34445512e-01
-1.93318591e-01 4.40032661e-01 2.17548773e-01 -3.43940645e-01
2.46316224e-01 -7.56769180e-01 -9.19685423e-01 -1.09062994e+00
5.18313527e-01 -7.77793974e-02 9.74918157e-02 -2.62197375... | [10.645275115966797, -2.762925148010254] |
0ab977c5-789d-4043-9bd5-8399c96c5bec | gaussian-process-gradient-maps-for-loop | 2009.00221 | null | https://arxiv.org/abs/2009.00221v1 | https://arxiv.org/pdf/2009.00221v1.pdf | Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments | The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition ... | ['Teresa Vidal-Calleja', 'Wolfgang Stürzl', 'Rudolph Triebel', 'Mallikarjuna Vayugundla', 'Cedric Le Gentil', 'Riccardo Giubilato'] | 2020-09-01 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 9.79619622e-02 -1.42556936e-01 4.85542595e-01 -3.36229771e-01
-7.06054330e-01 -7.64577627e-01 9.90514815e-01 2.91596204e-01
-6.48034215e-01 6.69552863e-01 -5.29379189e-01 -7.62469321e-02
-3.47361833e-01 -1.02765191e+00 -4.93715227e-01 -5.84849775e-01
-4.20728058e-01 1.02804995e+00 5.49638212e-01 -4.06089962... | [7.326570510864258, -2.087146043777466] |
fc4af598-14bb-4117-afaf-00d70efc8f90 | novel-features-for-the-detection-of-bearing | 2304.08249 | null | https://arxiv.org/abs/2304.08249v1 | https://arxiv.org/pdf/2304.08249v1.pdf | Novel features for the detection of bearing faults in railway vehicles | {In this paper, we address the challenging problem of detecting bearing faults from vibration signals. For this, several time- and frequency-domain features have been proposed already in the past. However, these features are usually evaluated on data originating from relatively simple scenarios and a significant perfor... | ['Walter Kellermann', 'Alexander Schmidt', 'Matthias Kreuzer'] | 2023-04-14 | null | null | null | null | ['audio-signal-processing', 'fault-detection'] | ['audio', 'miscellaneous'] | [ 5.74534118e-01 7.47948512e-02 3.36332142e-01 -1.61733747e-01
-8.05902421e-01 -4.43643890e-02 3.84224862e-01 7.07029164e-01
-3.02165419e-01 6.85990870e-01 -4.19587970e-01 2.53367648e-02
-4.87148404e-01 -5.33013403e-01 -4.03150350e-01 -8.87800395e-01
-3.23090911e-01 1.72514036e-01 2.95328051e-01 -4.65637207... | [6.734221458435059, 2.4240124225616455] |
b3ac9184-c514-480b-8812-c1ca1e8dc412 | affordancenet-an-end-to-end-deep-learning | 1709.07326 | null | http://arxiv.org/abs/1709.07326v3 | http://arxiv.org/pdf/1709.07326v3.pdf | AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection | We propose AffordanceNet, a new deep learning approach to simultaneously
detect multiple objects and their affordances from RGB images. Our
AffordanceNet has two branches: an object detection branch to localize and
classify the object, and an affordance detection branch to assign each pixel in
the object to its most pr... | ['Ian Reid', 'Thanh-Toan Do', 'Anh Nguyen'] | 2017-09-21 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [-7.43003935e-02 -9.10933688e-02 -1.03337526e-01 -4.15776551e-01
-2.66927958e-01 -2.76591778e-01 3.72367293e-01 -1.02733582e-01
-5.45937240e-01 2.62161255e-01 -7.28932247e-02 -3.58949229e-02
-1.09750733e-01 -4.20746714e-01 -8.68713796e-01 -6.57011509e-01
-2.00558960e-01 1.54829726e-01 4.15458113e-01 -3.87357026... | [5.161718845367432, -0.11632885783910751] |
105bbcc7-9f29-4178-a1be-f6e109a44035 | gci-a-g-raph-c-oncept-i-nterpretation | 2302.04899 | null | https://arxiv.org/abs/2302.04899v1 | https://arxiv.org/pdf/2302.04899v1.pdf | GCI: A (G)raph (C)oncept (I)nterpretation Framework | Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks. An important challenge facing concept extraction approaches is the difficulty of interpreting and evaluating discovered concepts, especially for complex tasks s... | ['Pietro Lio', 'Mateja Jamnik', 'Pietro Barbiero', 'Lucie Charlotte Magister', 'Botty Dimanov', 'Dmitry Kazhdan'] | 2023-02-09 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 1.14684749e+00 7.87539840e-01 -2.68046021e-01 -3.22298914e-01
-2.40971819e-01 -9.65719044e-01 6.71870291e-01 9.39806223e-01
-4.72551771e-02 1.09923768e+00 2.15169877e-01 -8.24096918e-01
-5.99958062e-01 -9.17676568e-01 -8.65625679e-01 -4.80276704e-01
-5.01693785e-01 8.12273502e-01 -3.85936081e-01 -8.54231566... | [7.762241363525391, 6.195655345916748] |
dbf5f118-dca3-4691-ac2e-dfa7416689a5 | scribble-supervised-lidar-semantic | 2203.08537 | null | https://arxiv.org/abs/2203.08537v2 | https://arxiv.org/pdf/2203.08537v2.pdf | Scribble-Supervised LiDAR Semantic Segmentation | Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised performance, developing efficient methods that take advantage of realistic weak supervision have yet to be explored. In this paper, we prop... | ['Luc van Gool', 'Dengxin Dai', 'Ozan Unal'] | 2022-03-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Unal_Scribble-Supervised_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Unal_Scribble-Supervised_LiDAR_Semantic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.11618856e-01 2.95033365e-01 -2.34889880e-01 -6.61055803e-01
-1.00860322e+00 -8.32303286e-01 3.04298759e-01 4.13431704e-01
-3.89653444e-01 5.93042195e-01 -3.21058661e-01 -3.09415370e-01
2.40787142e-03 -6.71141565e-01 -9.85725105e-01 -9.88627821e-02
2.08116278e-01 1.16156125e+00 7.71298110e-01 2.27289468... | [8.049899101257324, -3.023550510406494] |
b07139c6-ecc7-4e58-be40-f9f9dc7a9d57 | using-supervised-bigram-based-ilp-for | null | null | https://aclanthology.org/P13-1099 | https://aclanthology.org/P13-1099.pdf | Using Supervised Bigram-based ILP for Extractive Summarization | null | ['Chen Li', 'Yang Liu', 'Xian Qian'] | 2013-08-01 | null | null | null | acl-2013-8 | ['extractive-document-summarization'] | ['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... | [-7.353213787078857, 3.7914838790893555] |
5ddb14b9-cf87-46fd-9b29-f5aaa6f000db | graph-attention-with-hierarchies-for-multi | 2301.11792 | null | https://arxiv.org/abs/2301.11792v1 | https://arxiv.org/pdf/2301.11792v1.pdf | Graph Attention with Hierarchies for Multi-hop Question Answering | Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents. In recent years, a number of Deep Learning-based approaches have been proposed to tackle this complex task, as well as a few standard benchmarks to assess models Multi-hop QA capabilities. In this paper, we focu... | ['Pontus Stenetorp', 'Ieva Staliunaite', 'Philip John Gorinski', 'Yunjie He'] | 2023-01-27 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 9.88087356e-02 6.49305105e-01 2.46886969e-01 -4.38363463e-01
-9.13232744e-01 -5.67950964e-01 4.70417351e-01 6.31877065e-01
-1.85502648e-01 6.21442020e-01 4.15537030e-01 -6.23969257e-01
-3.10385436e-01 -1.09205830e+00 -7.01501548e-01 -2.75249451e-01
3.12839337e-02 9.59773183e-01 6.71873271e-01 -6.58325613... | [10.770428657531738, 7.947452068328857] |
465fb2e6-e569-4a9b-a3f3-abc144efe8b2 | federated-ensemble-directed-offline | 2305.03097 | null | https://arxiv.org/abs/2305.03097v1 | https://arxiv.org/pdf/2305.03097v1.pdf | Federated Ensemble-Directed Offline Reinforcement Learning | We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control policy only using small pre-collected datasets generated according to different unknown behavior policies. Naively combining a standard offline R... | ['Srinivas Shakkottai', 'Dileep Kalathil', 'Nitin Ragothaman', 'Desik Rengarajan'] | 2023-05-04 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [-5.32810450e-01 7.58852884e-02 -2.78210584e-02 -1.94954559e-01
-8.68610799e-01 -7.71358311e-01 6.20999157e-01 -1.81565836e-01
-4.36004221e-01 1.29532230e+00 5.81163839e-02 -2.31869802e-01
-2.42898494e-01 -8.50990057e-01 -1.00042641e+00 -9.29743946e-01
-4.92930561e-01 8.05819392e-01 -1.20899200e-01 -1.59142911... | [3.954127073287964, 1.9103909730911255] |
abbd3517-fd6c-4dd8-81f7-0a2153191621 | feature-rich-named-entity-recognition-for | 2109.15121 | null | https://arxiv.org/abs/2109.15121v1 | https://arxiv.org/pdf/2109.15121v1.pdf | Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields | The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other languages with language-specific lexical, syntactic and morphological inform... | ['Kiril Ivanov Simov', 'Petya Osenova', 'Kuzman Ganchev', 'Preslav Nakov', 'Georgi Georgiev'] | 2021-09-26 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-6.16896391e-01 6.33039400e-02 -2.17076853e-01 -5.45704424e-01
-8.21906328e-01 -7.99713016e-01 9.54038918e-01 7.49553144e-01
-1.06439590e+00 1.12895107e+00 6.40262365e-01 -2.19184682e-01
-4.43480089e-02 -7.93482363e-01 -1.53384104e-01 -5.33599317e-01
-4.99085821e-02 7.14096069e-01 3.44953269e-01 -2.65836298... | [9.750544548034668, 9.620210647583008] |
d0fcf07c-7d82-4abf-9556-ebc74ff67c62 | acoustic-gait-based-person-identification | 1406.2895 | null | http://arxiv.org/abs/1406.2895v1 | http://arxiv.org/pdf/1406.2895v1.pdf | Acoustic Gait-based Person Identification using Hidden Markov Models | We present a system for identifying humans by their walking sounds. This
problem is also known as acoustic gait recognition. The goal of the system is
to analyse sounds emitted by walking persons (mostly the step sounds) and
identify those persons. These sounds are characterised by the gait pattern and
are influenced b... | ['Björn Schuller', 'Maximilian Kneißl', 'Jürgen T. Geiger', 'Gerhard Rigoll'] | 2014-06-11 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.32852942e-01 -4.79851931e-01 1.72563940e-01 7.26970583e-02
-2.82298237e-01 -1.13634929e-01 3.85546684e-01 -8.58879834e-02
-4.99814451e-01 4.63485569e-01 8.69607404e-02 5.35503387e-01
-1.65658221e-02 -7.35920846e-01 -1.30832657e-01 -8.88024628e-01
-5.00684440e-01 4.55320626e-01 6.78628623e-01 -3.21457148... | [14.143410682678223, 1.511596441268921] |
f486384a-10ac-454d-909f-cdd171ef271e | semantic-instance-segmentation-of-3d-scenes | 2206.01203 | null | https://arxiv.org/abs/2206.01203v2 | https://arxiv.org/pdf/2206.01203v2.pdf | Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes | Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which are notoriously laborious to annotate. Few attempts have been made to circumvent the need for dense per-point annotations. In this work, we look at weakly-supervised 3D semantic instance segmentation. The key idea is to leverage 3D ... | ['Gerard Pons-Moll', 'Tuan Anh Tran', 'Francis Engelmann', 'Julian Chibane'] | 2022-06-02 | null | null | null | null | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.54062840e-02 4.46455568e-01 -2.45389044e-01 -4.02536511e-01
-1.16804850e+00 -1.05697155e+00 6.41873121e-01 6.33912235e-02
-3.34401727e-01 2.36502618e-01 -2.18525320e-01 -5.14576375e-01
2.82302320e-01 -5.76115549e-01 -8.77773523e-01 -4.33467627e-01
6.81106374e-02 9.78984296e-01 6.78374648e-01 -4.46623489... | [8.084687232971191, -3.103752374649048] |
b693b11e-ba82-490c-b20c-4db9da025102 | projb-an-improved-bilinear-biased-proje-model | 2209.02390 | null | https://arxiv.org/abs/2209.02390v2 | https://arxiv.org/pdf/2209.02390v2.pdf | ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion | Knowledge Graph Embedding (KGE) methods have gained enormous attention from a wide range of AI communities including Natural Language Processing (NLP) for text generation, classification and context induction. Embedding a huge number of inter-relationships in terms of a small number of dimensions, require proper modeli... | ['Farhana Zulkernine', 'Sahar Vahdati', 'Mojtaba Moattari'] | 2022-08-15 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-1.73058659e-01 2.60632455e-01 -1.06386051e-01 -1.35610044e-01
-6.18111640e-02 -4.35020238e-01 6.64767921e-01 5.32001197e-01
-4.92054611e-01 8.25944841e-01 1.67549655e-01 -2.09140852e-01
-7.72778511e-01 -1.07278609e+00 -6.41709328e-01 -4.69083160e-01
-2.28218362e-01 6.83190584e-01 1.11457154e-01 -3.00656468... | [8.718070030212402, 7.854051113128662] |
1c9def60-da36-4de6-9206-90386dc2f1ef | seo-safety-aware-energy-optimization | 2302.12493 | null | https://arxiv.org/abs/2302.12493v1 | https://arxiv.org/pdf/2302.12493v1.pdf | SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge | Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints. Typical for such systems, however, is to have their controllers designed with formal guarantees on safety that precede in priority such optimizations, which ... | ['Mohammad Abdullah Al Faruque', 'Yasser Shoukry', 'James Ferlez', 'Mohanad Odema'] | 2023-02-24 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 7.00061694e-02 4.33571011e-01 -4.72077906e-01 -2.31951416e-01
-2.97192454e-01 -4.92084742e-01 4.19158787e-01 1.80593103e-01
-3.51358622e-01 4.53731596e-01 -1.65735006e-01 -7.01651275e-01
3.05161793e-02 -7.11486399e-01 -6.16541564e-01 -5.39480507e-01
-1.80778816e-01 2.83434242e-02 4.25466597e-01 -2.19054922... | [5.645238876342773, 2.916300058364868] |
ca735186-a4e7-4157-907d-2de731103338 | square-a-large-scale-dataset-of-sensitive | 2305.17696 | null | https://arxiv.org/abs/2305.17696v1 | https://arxiv.org/pdf/2305.17696v1.pdf | SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration | The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those who explicitly make hate speech or elicit harmful responses. However, ... | ['Jung-Woo Ha', 'Sangchul Park', 'Alice Oh', 'Yong Lim', 'Eun-Ju Lee', 'Gunhee Kim', 'Byoung Pil Kim', 'Yejin Choi', 'Meeyoung Cha', 'Takyoung Kim', 'Joonsuk Park', 'Seokhee Hong', 'Hwaran Lee'] | 2023-05-28 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-7.22412392e-02 3.68858218e-01 -2.27740929e-01 -2.96888232e-01
-9.22851503e-01 -7.84296453e-01 4.67165381e-01 9.98546258e-02
-3.40078682e-01 6.76125109e-01 9.26529109e-01 -2.63658047e-01
1.42792061e-01 -5.12000799e-01 -1.23189270e-01 -2.19946966e-01
3.09684336e-01 -6.31809756e-02 -5.59778325e-02 -6.60691261... | [8.836612701416016, 10.355944633483887] |
5afc6724-c4db-453b-a569-73bdaa5eaa3f | improved-counting-and-localization-from | 2203.15691 | null | https://arxiv.org/abs/2203.15691v1 | https://arxiv.org/pdf/2203.15691v1.pdf | Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging | Object counting and localization are key steps for quantitative analysis in large-scale microscopy applications. This procedure becomes challenging when target objects are overlapping, are densely clustered, and/or present fuzzy boundaries. Previous methods producing density maps based on deep learning have reached a h... | ['Guido Gerig', 'Thomas Ach', 'Shijie Li'] | 2022-03-29 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.03020236e-01 -3.87864202e-01 2.55827725e-01 -3.09555858e-01
-5.56486845e-01 -4.10266250e-01 5.51401436e-01 7.09284902e-01
-1.18612444e+00 9.33520794e-01 -4.28245038e-01 1.72893003e-01
-6.58036843e-02 -8.69248986e-01 -7.20487475e-01 -9.07036960e-01
-1.17653064e-01 1.01921606e+00 5.88448882e-01 6.28086388... | [14.655692100524902, -3.1959433555603027] |
aabea63f-aa77-4571-a9ba-5215cc4a97ad | forecasting-localized-weather-impacts-on | 2303.16198 | null | https://arxiv.org/abs/2303.16198v1 | https://arxiv.org/pdf/2303.16198v1.pdf | Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction | We present a novel approach for modeling vegetation response to weather in Europe as measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus on photorealistic quality of the multispectral images, while derived vegetation dynamics have not yet received as much attention. We leverage... | ['Markus Reichstein', 'Mélanie Weynants', 'Nora Linscheid', 'Zhihan Gao', 'José Cortés', 'Lazaro Alonso', 'Claire Robin', 'Christian Requena-Mesa', 'Vitus Benson'] | 2023-03-28 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.81694949e-01 -3.79743159e-01 -2.44019434e-01 -2.75998533e-01
-1.73536703e-01 -1.04938090e+00 8.83204401e-01 1.41213670e-01
-9.17015299e-02 6.71421766e-01 2.17222765e-01 -6.41698182e-01
1.21223137e-01 -1.27868307e+00 -5.99478245e-01 -6.94987178e-01
-5.51526129e-01 -1.22220524e-01 2.10662082e-01 -7.16152787... | [9.5361328125, -1.5697654485702515] |
87ac747e-37df-4858-bb55-bb1ee25cfee4 | contactless-human-activity-recognition-using | 2304.09756 | null | https://arxiv.org/abs/2304.09756v1 | https://arxiv.org/pdf/2304.09756v1.pdf | Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio | Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates th... | ['Qammer H. Abbasi', 'Anis Koubaa', 'Syed Aziz Shah', 'Matthew Broadbent', 'Wadii Boulila', 'Jawad Ahmad', 'Muhammad Zakir Khan'] | 2023-04-18 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.93200421e-01 -1.94327012e-01 9.10854191e-02 -1.44452751e-01
-5.48572421e-01 -2.05232114e-01 1.46823332e-01 -1.49943650e-01
-5.16868591e-01 1.09234703e+00 3.78491879e-01 -3.88228565e-01
-2.47562006e-01 -6.12200975e-01 -1.46190539e-01 -9.64623511e-01
-3.63099486e-01 -5.78302383e-01 -1.46649048e-01 1.63056731... | [6.951259136199951, 0.6648915410041809] |
4fdf690f-5f7a-40bc-8f1e-7b58ff804c81 | partial-counterfactual-identification-of | 2306.01424 | null | https://arxiv.org/abs/2306.01424v1 | https://arxiv.org/pdf/2306.01424v1.pdf | Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model | Counterfactual inference aims to answer retrospective ''what if'' questions and thus belongs to the most fine-grained type of inference in Pearl's causality ladder. Existing methods for counterfactual inference with continuous outcomes aim at point identification and thus make strong and unnatural assumptions about the... | ['Stefan Feuerriegel', 'Dennis Frauen', 'Valentyn Melnychuk'] | 2023-06-02 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 1.61841869e-01 3.33429873e-01 -5.91531694e-01 -5.66274188e-02
-7.17337668e-01 -7.10366249e-01 8.41082633e-01 -1.51773870e-01
-2.11279720e-01 1.18043971e+00 6.01726711e-01 -8.28959227e-01
-3.95307094e-01 -9.22110438e-01 -1.32975137e+00 -5.94547212e-01
-2.55379230e-01 5.73123693e-01 -2.77217507e-01 -7.60758147... | [8.110467910766602, 5.4063920974731445] |
682ea29a-4707-4c39-816a-9fdaac3bfb14 | unsupervised-action-segmentation-with-self | 2105.14158 | null | https://arxiv.org/abs/2105.14158v3 | https://arxiv.org/pdf/2105.14158v3.pdf | SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation | Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset. Thus in this work we propose an unsupervised method, namely SSCAP, that operates o... | ['Charless Fowlkes', 'Joseph Tighe', 'Yuanjun Xiong', 'Chunhui Liu', 'Xinyu Li', 'Hao Chen', 'Zhe Wang'] | 2021-05-29 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 4.98775989e-01 5.64374216e-03 -7.46544480e-01 -5.05809069e-01
-8.22415709e-01 -7.99922109e-01 6.19198859e-01 -1.31026268e-01
-1.94861740e-01 5.24098694e-01 6.17404401e-01 1.00945912e-01
-6.98394999e-02 -2.77977288e-01 -6.89914525e-01 -4.71892506e-01
-4.89356220e-01 2.33407721e-01 7.07980573e-01 2.00957999... | [8.42308521270752, 0.5774403214454651] |
e134e020-1092-4577-9156-6a688c482127 | eclare-extreme-classification-with-label | 2108.00261 | null | https://arxiv.org/abs/2108.00261v1 | https://arxiv.org/pdf/2108.00261v1.pdf | ECLARE: Extreme Classification with Label Graph Correlations | Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels that are rarely seen during training. Such rare labels hold the key to personalized recommendations ... | ['Manik Varma', 'Purushottam Kar', 'Sumeet Agarwal', 'Sheshansh Agrawal', 'Noveen Sachdeva', 'Anshul Mittal'] | 2021-07-31 | null | null | null | null | ['extreme-multi-label-classification', 'product-recommendation', 'short-text-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-2.30251282e-01 -1.37709498e-01 -5.04491270e-01 -9.82333958e-01
-8.47964227e-01 -8.31875861e-01 4.19962615e-01 3.78302634e-01
2.58130562e-02 4.01603341e-01 9.00272802e-02 -2.21685156e-01
-2.84575373e-01 -6.30113542e-01 -5.19865334e-01 -4.77123499e-01
-9.86428708e-02 7.89129615e-01 -3.50223035e-01 -1.55700579... | [9.596043586730957, 4.408268928527832] |
8f3ba328-f596-4a65-9f77-a2eb92d69562 | on-the-role-of-depth-predictions-for-3d-human | 2103.02521 | null | https://arxiv.org/abs/2103.02521v1 | https://arxiv.org/pdf/2103.02521v1.pdf | On the role of depth predictions for 3D human pose estimation | Following the successful application of deep convolutional neural networks to 2d human pose estimation, the next logical problem to solve is 3d human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2d inputs. With the goal... | ['Stephen Baek', 'Dmitriy Shin', 'Mitchell Messmore', 'Alec Diaz-Arias'] | 2021-03-03 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.99609622e-01 1.46944672e-01 1.95371971e-01 -5.09269118e-01
-4.68860835e-01 -3.57387900e-01 3.74510020e-01 -5.02759106e-02
-8.83761585e-01 4.75735068e-01 1.74996123e-01 1.62061587e-01
2.35963136e-01 -5.68294644e-01 -7.49910891e-01 -1.55994356e-01
-6.91652894e-02 8.35387111e-01 3.51617217e-01 -3.81923556... | [6.91553258895874, -0.9779030680656433] |
37d02c04-999d-49f2-88d5-d67c71b85e27 | semi-supervised-image-captioning-with-clip | 2306.15111 | null | https://arxiv.org/abs/2306.15111v1 | https://arxiv.org/pdf/2306.15111v1.pdf | Semi-Supervised Image Captioning with CLIP | Image captioning, a fundamental task in vision-language understanding, seeks to generate accurate natural language descriptions for provided images. The CLIP model, with its rich semantic features learned from a large corpus of image-text pairs, is well-suited for this task. In this paper, we present a two-stage semi-s... | ['Chuanyang Jin'] | 2023-06-26 | null | null | null | null | ['image-captioning', 'text-generation'] | ['computer-vision', 'natural-language-processing'] | [ 6.40172064e-01 5.53090513e-01 -1.35105669e-01 -4.80486065e-01
-1.19909072e+00 -5.33325493e-01 1.01470208e+00 1.55326482e-02
-3.27662081e-01 8.55136216e-01 5.28967977e-01 1.52892008e-01
5.72913706e-01 -3.15796643e-01 -1.14341998e+00 -3.14194560e-01
4.44314539e-01 7.24721909e-01 -1.18029229e-02 4.91800234... | [10.989714622497559, 1.018776535987854] |
a887da77-260b-4d24-aded-b26a8edfd223 | person-recognition-using-smartphones | 1711.04689 | null | http://arxiv.org/abs/1711.04689v1 | http://arxiv.org/pdf/1711.04689v1.pdf | Person Recognition using Smartphones' Accelerometer Data | Smartphones have become quite pervasive in various aspects of our daily
lives. They have become important links to a host of important data and
applications, which if compromised, can lead to disastrous results. Due to
this, today's smartphones are equipped with multiple layers of authentication
modules. However, there... | ['Rajsekhar Kumar Nath', 'A. V. Narsimhadhan', 'Thingom Bishal Singha'] | 2017-11-13 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.12649798e-01 -2.12988287e-01 -3.70827556e-01 -2.00583562e-01
-4.34759915e-01 -2.57905871e-01 4.46421176e-01 3.65144253e-01
-4.69805896e-01 7.61941314e-01 -6.21635579e-02 -4.23465401e-01
-9.71780624e-03 -1.00045323e+00 -5.85062392e-02 -5.51945448e-01
-6.95194677e-02 -4.04025972e-01 3.51260126e-01 -1.56632781... | [13.817909240722656, 1.4381335973739624] |
a025def9-3371-4f14-9e27-68a7d27950c0 | optic-disc-cup-and-fovea-detection-from | null | null | https://doi.org/10.1007/978-3-030-63419-3_10 | https://link.springer.com/content/pdf/10.1007%2F978-3-030-63419-3.pdf | Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder | The accurate detection of retinal structures like an optic disc (OD), cup, and fovea is crucial for the analysis of Age-related Macular Degeneration (AMD), Glaucoma, and other retinal conditions. Most segmentation methods rely on separate detection of these retinal structures due to which a combined analysis for comput... | ['Nitin Singhal', 'Pranab Samanta', 'Ravi Kamble'] | 2020-11-20 | null | null | null | null | ['optic-disc-detection', 'optic-cup-segmentation', 'fovea-detection', 'optic-cup-detection'] | ['medical', 'medical', 'medical', 'medical'] | [-1.57298267e-01 4.98812497e-02 1.84649780e-01 -1.98812857e-01
-4.12689716e-01 -1.61265105e-01 1.15956567e-01 -8.52623209e-02
-7.49888897e-01 6.07807994e-01 -1.85937986e-01 -4.24806178e-01
-1.02690637e-01 -4.69373584e-01 -2.16366559e-01 -5.52886546e-01
-1.94415078e-01 1.24518834e-01 6.66978359e-01 1.99765265... | [15.819473266601562, -3.9910285472869873] |
69709c54-1c7d-453c-95db-e86bb6b4cdbc | proto-program-guided-transformer-for-program | 2110.00804 | null | https://arxiv.org/abs/2110.00804v2 | https://arxiv.org/pdf/2110.00804v2.pdf | ProTo: Program-Guided Transformer for Program-Guided Tasks | Programs, consisting of semantic and structural information, play an important role in the communication between humans and agents. Towards learning general program executors to unify perception, reasoning, and decision making, we formulate program-guided tasks which require learning to execute a given program on the o... | ['Le Song', 'Binghong Chen', 'Karan Samel', 'Zelin Zhao'] | 2021-10-02 | null | http://proceedings.neurips.cc/paper/2021/hash/8d34201a5b85900908db6cae92723617-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8d34201a5b85900908db6cae92723617-Paper.pdf | neurips-2021-12 | ['learning-to-execute'] | ['computer-code'] | [ 2.26421744e-01 1.32808924e-01 -5.20355225e-01 -4.77587521e-01
-2.00242892e-01 -6.45986915e-01 1.09090304e+00 3.68703246e-01
-1.03744909e-01 -6.47959113e-02 2.67952532e-01 -6.52010679e-01
2.45282978e-01 -1.01653624e+00 -1.18939435e+00 -1.99584424e-01
-6.31352980e-03 5.27124047e-01 1.64240524e-01 -1.11974046... | [8.602160453796387, 7.105854511260986] |
e193d5ed-3e42-4ae9-a689-dfed9124c307 | hand-segmentation-and-fingertip-tracking-from | 1901.03465 | null | https://arxiv.org/abs/1901.03465v3 | https://arxiv.org/pdf/1901.03465v3.pdf | Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet | Hand segmentation and fingertip detection play an indispensable role in hand gesture-based human-machine interaction systems. In this study, we propose a method to discriminate hand components and to locate fingertips in RGB-D images. The system consists of three main steps: hand detection using RGB images providing re... | ['Soo-Hyung Kim', 'In-Seop Na', 'Tai Nhu Do', 'Duong Hai Nguyen'] | 2019-01-11 | null | null | null | null | ['hand-detection', 'fingertip-detection', 'hand-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.52814046e-01 -5.05948126e-01 -2.95057148e-01 -1.48215130e-01
-5.07491827e-02 -7.40972936e-01 3.22650850e-01 -4.01073426e-01
-8.10714960e-01 1.34517372e-01 -2.92907983e-01 -4.61690903e-01
-4.95193452e-02 -6.32192612e-01 1.57945171e-01 -6.50230467e-01
1.74518332e-01 6.74584627e-01 8.77075613e-01 -8.86095315... | [6.464465618133545, -0.35355231165885925] |
a0eb0212-4b58-4ef3-8b82-529086e83d6d | experts-in-the-loop-conditional-variable | 2209.15249 | null | https://arxiv.org/abs/2209.15249v1 | https://arxiv.org/pdf/2209.15249v1.pdf | Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning | Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devices is key to enable post-silicon tuning and debugging. This analysis is typically performed by experienced human experts. However, with the... | ['Bin Yang', 'Raphaël Latty', 'Yiwen Liao'] | 2022-09-30 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.73598158e-01 -1.29440621e-01 -2.45865837e-01 -4.88152623e-01
-4.06445384e-01 -3.88582736e-01 9.71335396e-02 1.59351885e-01
9.07646418e-02 1.00326467e+00 -7.70626664e-01 -4.96765018e-01
-2.83615410e-01 -7.45953143e-01 -5.98764002e-01 -5.53230286e-01
2.38991916e-01 4.94106591e-01 2.12466151e-01 1.46611646... | [7.0144853591918945, 2.3962554931640625] |
24b08a1f-b769-40c5-809b-642ac82b28dd | a-meta-evaluation-of-c-w-l-a-metrics-system | 2307.02936 | null | https://arxiv.org/abs/2307.02936v1 | https://arxiv.org/pdf/2307.02936v1.pdf | A Meta-Evaluation of C/W/L/A Metrics: System Ranking Similarity, System Ranking Consistency and Discriminative Power | Recently, Moffat et al. proposed an analytic framework, namely C/W/L/A, for offline evaluation metrics. This framework allows information retrieval (IR) researchers to design evaluation metrics through the flexible combination of user browsing models and user gain aggregations. However, the statistical stability of C/W... | ['Tetsuya Sakai', 'Nuo Chen'] | 2023-07-06 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-3.66981000e-01 -3.32373261e-01 -1.97036028e-01 -1.17124766e-01
-5.92210054e-01 -8.81806016e-01 7.34075665e-01 6.11276090e-01
-4.91893381e-01 5.23507714e-01 -1.43101573e-01 -3.43071997e-01
-1.22413540e+00 -7.25990713e-01 -1.38239309e-01 -5.94301462e-01
-3.78791988e-01 3.33588332e-01 5.75243771e-01 -4.46642399... | [9.740206718444824, 5.760509490966797] |
9202230e-a913-44d4-890c-13a1f419ef7a | deep-filter-banks-for-texture-recognition-and | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf | Deep Filter Banks for Texture Recognition and Segmentation | Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface tex... | ['Subhransu Maji', 'Mircea Cimpoi', 'Andrea Vedaldi'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['material-recognition'] | ['computer-vision'] | [ 5.05263567e-01 -3.28263044e-01 2.05442324e-01 -4.33977604e-01
-9.31062400e-01 -4.98801619e-01 6.20495498e-01 4.04612236e-02
-1.79994002e-01 4.26218390e-01 -1.78487763e-01 9.49609801e-02
-2.14337677e-01 -9.86989737e-01 -9.17506158e-01 -9.96459007e-01
-1.81369185e-01 4.41978782e-01 1.85877651e-01 -1.23357125... | [10.194026947021484, -0.19641128182411194] |
44fc4f1e-ca6d-432d-8700-700859c2d286 | deep-convolutional-poses-for-human | 1612.03982 | null | http://arxiv.org/abs/1612.03982v1 | http://arxiv.org/pdf/1612.03982v1.pdf | Deep Convolutional Poses for Human Interaction Recognition in Monocular Videos | Human interaction recognition is a challenging problem in computer vision and
has been researched over the years due to its important applications. With the
development of deep models for the human pose estimation problem, this work
aims to verify the effectiveness of using the human pose in order to recognize
the huma... | ['Neil M. Robertson', 'Sankha Mukherjee', 'Marcel Sheeny de Moraes'] | 2016-12-13 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [-5.19096106e-02 -3.24424624e-01 3.69616866e-01 -3.04026216e-01
-2.65229512e-02 -3.24441612e-01 6.52082205e-01 -4.71901029e-01
-7.72624135e-01 4.65710461e-01 2.49873281e-01 3.95606816e-01
-3.01171821e-02 -3.74114841e-01 -2.54775047e-01 -5.76772690e-01
1.33958548e-01 7.95778811e-01 1.05607204e-01 -2.33159110... | [7.820623874664307, 0.14783835411071777] |
55367960-fd51-4620-a0bd-770b74c42b22 | proactive-image-manipulation-detection | 2203.15880 | null | https://arxiv.org/abs/2203.15880v2 | https://arxiv.org/pdf/2203.15880v2.pdf | Proactive Image Manipulation Detection | Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contra... | ['Xiaoming Liu', 'Sijia Liu', 'Tal Hassner', 'Xi Yin', 'Vishal Asnani'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Asnani_Proactive_Image_Manipulation_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Asnani_Proactive_Image_Manipulation_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-manipulation-detection'] | ['computer-vision'] | [ 1.04614723e+00 1.59886211e-01 -2.29042873e-01 1.86340019e-01
-7.04788506e-01 -8.64771545e-01 8.27665806e-01 -2.36545518e-01
-1.51717186e-01 2.33945340e-01 -2.52366096e-01 -6.12238906e-02
3.07831705e-01 -7.20417023e-01 -9.80511785e-01 -7.74041295e-01
-1.15170581e-02 1.24967165e-01 4.32792842e-01 2.07543912... | [12.410158157348633, 1.0637781620025635] |
9e999c43-f8a8-45eb-b3cc-59dbf82afdeb | memebot-towards-automatic-image-meme | 2004.14571 | null | https://arxiv.org/abs/2004.14571v1 | https://arxiv.org/pdf/2004.14571v1.pdf | memeBot: Towards Automatic Image Meme Generation | Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-base... | ['Yezhou Yang', 'Kausic Gunasekar', 'Aadhavan Sadasivam', 'Hasan Davulcu'] | 2020-04-30 | null | null | null | null | ['meme-classification', 'meme-captioning'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.37529325e-01 3.32445204e-01 3.38604718e-01 -4.87146139e-01
-7.74992168e-01 -4.31686491e-01 1.01198304e+00 -1.57651916e-01
-5.32986164e-01 7.82625854e-01 6.22048020e-01 3.05944532e-01
8.59019876e-01 -1.06070983e+00 -1.12886953e+00 -3.53112429e-01
5.12423575e-01 5.75447083e-01 1.86539199e-02 -3.08828115... | [11.100171089172363, 0.9403026103973389] |
47744946-d484-424b-a64b-800b74f9b5fa | implicit-diffusion-models-for-continuous | 2303.16491 | null | https://arxiv.org/abs/2303.16491v1 | https://arxiv.org/pdf/2303.16491v1.pdf | Implicit Diffusion Models for Continuous Super-Resolution | Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-reso... | ['Baochang Zhang', 'XianTong Zhen', 'Jianzhuang Liu', 'Xiaoyan Luo', 'Yanjing Li', 'Sheng Xu', 'Bohan Zeng', 'Xuhui Liu', 'Sicheng Gao'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gao_Implicit_Diffusion_Models_for_Continuous_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 5.61896920e-01 -8.40230733e-02 -1.04386501e-01 -3.31312239e-01
-8.12966406e-01 2.73362640e-02 5.54305971e-01 -6.35638356e-01
-8.22250620e-02 5.59916854e-01 5.91172695e-01 2.71658927e-01
-7.74181336e-02 -7.13331819e-01 -6.36460781e-01 -7.26182938e-01
3.12993944e-01 -3.57327849e-01 2.17046276e-01 -2.53805131... | [11.26628303527832, -1.9717836380004883] |
83f25537-6cae-4ca9-8abf-3c72b5a1da0c | learning-disentangled-representations-in | null | null | https://openreview.net/forum?id=rm893aOESdu | https://openreview.net/pdf?id=rm893aOESdu | Learning Disentangled Representations in Natural Language Definitions with Semantic Role Labeling Supervision | Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. However, most disentanglement methods are unsupervised or rely on synthetic datasets with known generative factors. We argue that recurren... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.15630299e-01 4.50145334e-01 -4.17322725e-01 -6.56199276e-01
-6.34077191e-01 -8.40687931e-01 1.09298599e+00 5.68668731e-02
-2.83298671e-01 6.03401601e-01 1.04239666e+00 -2.02121139e-01
-1.31579399e-01 -8.14426184e-01 -6.65417373e-01 -6.27159417e-01
4.08456236e-01 7.27125347e-01 -5.63401043e-01 -2.69433409... | [9.341492652893066, 5.052163124084473] |
c9d571f5-9490-42bb-97eb-1fcb610bfcb0 | sequence-to-sequence-model-with-transformer | 2306.05012 | null | https://arxiv.org/abs/2306.05012v1 | https://arxiv.org/pdf/2306.05012v1.pdf | Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring | This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq mod... | ['Abouzar Estebsari', 'Roozbeh Rajabi', 'Mohammad Irani Azad'] | 2023-06-08 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.28406577e-03 -3.98828954e-01 1.14080094e-01 -4.97204304e-01
-7.43107557e-01 -1.47341415e-01 5.15057862e-01 -1.03108548e-01
-6.43843561e-02 7.03508735e-01 3.35507900e-01 -1.52988583e-01
-1.27483279e-01 -7.03098357e-01 -4.06868905e-01 -7.43977427e-01
-2.38281831e-01 2.13217944e-01 1.69217631e-01 -2.82695651... | [16.060401916503906, 7.577633857727051] |
28aef99b-40c8-42c0-95bf-57fc3742cdd6 | prompt-based-conservation-learning-for-multi | 2209.06923 | null | https://arxiv.org/abs/2209.06923v1 | https://arxiv.org/pdf/2209.06923v1.pdf | Prompt-based Conservation Learning for Multi-hop Question Answering | Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a model has performed the desired reasoning to reach the correct answer. Most existing multi-h... | ['Patricia Riddle', 'Michael Witbrock', 'Qianqian Qi', 'Yang Chen', 'Yonghua Zhu', 'Zhenyun Deng'] | 2022-09-14 | null | https://aclanthology.org/2022.coling-1.154 | https://aclanthology.org/2022.coling-1.154.pdf | coling-2022-10 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 9.46636647e-02 3.74748647e-01 5.97869977e-03 -4.62218553e-01
-1.37856042e+00 -6.68787658e-01 3.17522138e-01 5.42191803e-01
-4.47695374e-01 9.96231854e-01 1.54441923e-01 -6.07480407e-01
-2.72763878e-01 -1.03300488e+00 -9.91634309e-01 -1.87956870e-01
2.68125594e-01 7.69508183e-01 1.03636599e+00 -5.58955610... | [10.903515815734863, 7.901548385620117] |
7aa4a470-3403-4215-8624-8f1191dd7b4b | cdgnet-class-distribution-guided-network-for | 2111.14173 | null | https://arxiv.org/abs/2111.14173v3 | https://arxiv.org/pdf/2111.14173v3.pdf | CDGNet: Class Distribution Guided Network for Human Parsing | The objective of human parsing is to partition a human in an image into constituent parts. This task involves labeling each pixel of the human image according to the classes. Since the human body comprises hierarchically structured parts, each body part of an image can have its sole position distribution characteristic... | ['Wonjun Hwang', 'Jianming Wang', 'Ouk Choi', 'Kunliang Liu'] | 2021-11-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_CDGNet_Class_Distribution_Guided_Network_for_Human_Parsing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_CDGNet_Class_Distribution_Guided_Network_for_Human_Parsing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-parsing'] | ['computer-vision'] | [ 2.26173460e-01 4.10314083e-01 -5.19253552e-01 -5.61667800e-01
-2.23743707e-01 -3.44503164e-01 2.55693913e-01 -1.22737035e-01
-2.23981306e-01 4.06886041e-01 3.80496919e-01 4.74220403e-02
3.33266586e-01 -7.88641214e-01 -6.18935823e-01 -7.65562177e-01
3.08736563e-01 4.25440758e-01 3.30475509e-01 1.66870371... | [8.633820533752441, 0.015389771200716496] |
e2245220-5519-4831-8296-0e44ea701855 | plug-and-play-multilingual-few-shot-spoken | 2305.03058 | null | https://arxiv.org/abs/2305.03058v1 | https://arxiv.org/pdf/2305.03058v1.pdf | Plug-and-Play Multilingual Few-shot Spoken Words Recognition | As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance. The growing adoption of mobile, wearable, and other Internet of Things (IoT) devices has changed how we interact with these smart devices, making accurate spoken words recognition a cr... | ['Vasileios Tsouvalas', 'Aaqib Saeed'] | 2023-05-03 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-4.79541719e-02 -4.43018138e-01 -2.16855511e-01 -6.17254496e-01
-1.16733730e+00 -5.76626003e-01 4.07482237e-01 -6.11077659e-02
-4.88084584e-01 4.21005577e-01 5.22380650e-01 -2.07529828e-01
1.99012667e-01 -3.99084002e-01 -3.94096971e-01 -1.67842075e-01
4.54677567e-02 5.44699490e-01 2.24098176e-01 -3.48927081... | [14.276729583740234, 6.362946033477783] |
9cdc2413-2391-4c25-89f3-539ec0933e22 | 100-things-you-always-wanted-to-know-about | null | null | https://aclanthology.org/N12-4001 | https://aclanthology.org/N12-4001.pdf | 100 Things You Always Wanted to Know about Linguistics But Were Afraid to Ask* | null | ['Emily M. Bender'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [-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... | [-7.139074802398682, 3.6218371391296387] |
159aa745-20d7-4e56-8f34-86c7ca873faf | comprehensive-evaluation-of-no-reference | 2010.09414 | null | https://arxiv.org/abs/2010.09414v2 | https://arxiv.org/pdf/2010.09414v2.pdf | Comprehensive evaluation of no-reference image quality assessment algorithms on KADID-10k database | The main goal of objective image quality assessment is to devise computational, mathematical models which are able to predict perceptual image quality consistently with subjective evaluations. The evaluation of objective image quality assessment algorithms is based on experiments conducted on publicly available benchma... | ['Domonkos Varga'] | 2020-10-19 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.10494354e-01 -3.24972034e-01 -1.12872295e-01 -3.81241143e-01
-1.11691248e+00 -3.16178232e-01 5.48754156e-01 2.15638801e-01
-4.24011946e-01 7.63346493e-01 -4.24455702e-02 -4.85185459e-02
-2.75377363e-01 -5.31160414e-01 -2.57901579e-01 -6.80554271e-01
-2.47550845e-01 3.13515998e-02 3.05624306e-01 -1.75188422... | [11.750015258789062, -1.8983701467514038] |
2543d645-3d56-48bd-8d5c-fc65adcbb691 | person-re-identification-with-correspondence | 1504.06243 | null | http://arxiv.org/abs/1504.06243v1 | http://arxiv.org/pdf/1504.06243v1.pdf | Person Re-identification with Correspondence Structure Learning | This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera p... | ['Mingliang Xu', 'Yang Shen', 'Junchi Yan', 'Weiyao Lin', 'Jingdong Wang', 'Jianxin Wu'] | 2015-04-23 | person-re-identification-with-correspondence-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Shen_Person_Re-Identification_With_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Shen_Person_Re-Identification_With_ICCV_2015_paper.pdf | iccv-2015-12 | ['patch-matching'] | ['computer-vision'] | [ 1.76602185e-01 -6.08090162e-01 -1.66709006e-01 -6.64989531e-01
-7.35817671e-01 -6.39705300e-01 6.52672410e-01 -9.08614323e-02
-2.82191128e-01 2.38167286e-01 1.22745380e-01 2.57685363e-01
-2.25822791e-03 -5.45990229e-01 -7.84125686e-01 -5.49652219e-01
3.49938989e-01 2.81906635e-01 2.51432240e-01 2.49394551... | [14.747896194458008, 1.0005977153778076] |
aa14b869-a0de-40ee-ab6e-78582152eb71 | can-occupant-behaviors-affect-urban-energy | 2303.03006 | null | https://arxiv.org/abs/2303.03006v1 | https://arxiv.org/pdf/2303.03006v1.pdf | Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities | To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessar... | ['Wim Zeiler', 'Henrik Madsen', 'Dominik Franjo Dominkovic', 'Daniela Guericke', 'Amos Schledorn', 'Julien Leprince'] | 2023-03-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.84978920e-01 3.10675919e-01 1.12043016e-01 3.33676308e-01
-6.31048799e-01 -5.67310750e-01 6.36108458e-01 1.72236532e-01
-8.36375728e-02 1.00435281e+00 1.99117333e-01 -5.26597619e-01
-7.42828488e-01 -1.36134088e+00 -3.19862276e-01 -1.15314353e+00
-6.23433031e-02 4.67632353e-01 -3.63188863e-01 -2.98244685... | [5.6946234703063965, 2.444659471511841] |
890cf09b-1e4a-4660-8e3d-627be66e556b | efficient-text-based-reinforcement-learning-1 | null | null | https://openreview.net/forum?id=0McAgVlYSy | https://openreview.net/pdf?id=0McAgVlYSy | Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations | Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge -- like commonsense knowledge -- to improve the efficiency of RL agents for TBGs; and to resemb... | ['Anonymous'] | 2021-02-22 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 2.39704445e-01 6.49053276e-01 -3.60926762e-02 7.43191019e-02
-4.86617744e-01 -5.41399300e-01 7.82302618e-01 3.96796077e-01
-5.60381055e-01 7.69443452e-01 6.34310782e-01 -4.75369543e-01
-1.48683444e-01 -1.43417037e+00 -6.58548117e-01 -1.18994728e-01
1.63625367e-02 7.95168936e-01 2.52649486e-01 -1.00777233... | [3.8686068058013916, 1.2872322797775269] |
08f71be9-a86d-4359-9ef2-811b5080f8b6 | a-generalized-template-based-graph-neural | null | null | https://www.nature.com/articles/s42256-022-00526-z | https://www.nature.com/articles/s42256-022-00526-z | A generalized-template-based graph neural network for accurate organic reactivity prediction | The reliable prediction of chemical reactivity remains in the realm of knowledgeable synthetic chemists. Automating this process by using artificial intelligence could accelerate synthesis design in future digital laboratories. While several machine learning approaches have demonstrated promising results, most current ... | ['Yousung Jung', 'Shuan Chen'] | 2022-09-15 | null | null | null | nature-machine-intelligence-2022-9 | ['chemical-reaction-prediction'] | ['medical'] | [ 4.18777525e-01 4.14932907e-01 -2.88504332e-01 -3.51515740e-01
-1.85083374e-01 -8.45513642e-01 7.32700944e-01 7.35582352e-01
-7.64428303e-02 1.05897820e+00 -1.02977879e-01 -7.76656926e-01
-1.99280959e-02 -1.05415368e+00 -7.28499949e-01 -8.04025233e-01
1.83631182e-01 4.78191137e-01 2.02374071e-01 -4.33999896... | [4.641829013824463, 5.991127967834473] |
1576210f-9f94-4303-895f-83a027e61b50 | see-through-text-grouping-for-referring-image | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Chen_See-Through-Text_Grouping_for_Referring_Image_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_See-Through-Text_Grouping_for_Referring_Image_Segmentation_ICCV_2019_paper.pdf | See-Through-Text Grouping for Referring Image Segmentation | Motivated by the conventional grouping techniques to image segmentation, we develop their DNN counterpart to tackle the referring variant. The proposed method is driven by a convolutional-recurrent neural network (ConvRNN) that iteratively carries out top-down processing of bottom-up segmentation cues. Given a natural ... | [' Tyng-Luh Liu', ' Hwann-Tzong Chen', ' Yi-Chen Lo', ' Songhao Jia', 'Ding-Jie Chen'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 5.42988479e-01 5.50387919e-01 -2.19632491e-01 -4.89768177e-01
-1.08384132e+00 -2.83890098e-01 4.96494889e-01 3.66113447e-02
-3.86977017e-01 3.58489037e-01 1.74229816e-01 -2.13025615e-01
-2.47916337e-02 -8.73282313e-01 -9.89901185e-01 -7.42284894e-01
4.81921315e-01 4.23258185e-01 2.32875526e-01 -1.05594516... | [10.24178695678711, 1.2149345874786377] |
bc942edf-88ab-4378-8b3f-684012b6ebc9 | unified-language-representation-for-question | 2306.16762 | null | https://arxiv.org/abs/2306.16762v1 | https://arxiv.org/pdf/2306.16762v1.pdf | Unified Language Representation for Question Answering over Text, Tables, and Images | When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data. Previous approaches to this problem have focused on designing input features or model structure in the multi-modal space, which is inflexible for cross-modal reasoning or data-effici... | ['Yongbin Li', 'Fei Huang', 'Haiyang Yu', 'Cheng Fu', 'Bowen Yu'] | 2023-06-29 | null | null | null | null | ['retrieval', 'question-answering'] | ['methodology', 'natural-language-processing'] | [-1.61688089e-01 -1.41092286e-01 3.23513858e-02 -2.81229347e-01
-1.49054492e+00 -9.69244421e-01 7.82665312e-01 4.14943546e-01
-3.10179561e-01 7.92802513e-01 3.14498693e-01 -5.09312451e-01
-5.75653836e-03 -8.82443666e-01 -5.14547348e-01 -3.63472819e-01
4.79987293e-01 8.95845234e-01 3.21303099e-01 -6.54068291... | [11.039430618286133, 8.03691577911377] |
634f0d94-cc58-4a39-a511-7ab32c5fff94 | techniques-for-effective-and-efficient-fire | 1506.03844 | null | http://arxiv.org/abs/1506.03844v2 | http://arxiv.org/pdf/1506.03844v2.pdf | Techniques for effective and efficient fire detection from social media images | Social media could provide valuable information to support decision making in
crisis management, such as in accidents, explosions and fires. However, much of
the data from social media are images, which are uploaded in a rate that makes
it impossible for human beings to analyze them. Despite the many works on image
ana... | ['Mirela Cazzolato', 'Alceu Costa', 'Caetano Traina Jr', 'Willian Oliveira', 'Marcos Bedo', 'Jose Rodrigues', 'Gustavo Blanco', 'Agma Traina'] | 2015-06-11 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.53140771e-01 -3.93387288e-01 3.73341471e-01 -3.21801640e-02
-6.30536854e-01 -7.70990431e-01 7.85318375e-01 7.88818955e-01
-9.19137120e-01 4.92249370e-01 6.39577582e-02 -7.92562664e-02
-3.16846311e-01 -1.28585815e+00 -1.99181914e-01 -8.33448470e-01
-3.64650220e-01 1.73500881e-01 4.77084219e-01 -2.55320877... | [9.146439552307129, -1.1490392684936523] |
fc4a9ae4-b634-467a-9d7f-20c37ad984b9 | the-scattering-transform-network-with | 2206.07857 | null | https://arxiv.org/abs/2206.07857v1 | https://arxiv.org/pdf/2206.07857v1.pdf | The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification | We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal classification problems. The GMWs form a parameterized family of truly analytic wavelets while the Morlet wavelets are only approxi... | ['David Weber', 'Naoki Saito', 'Wai Ho Chak'] | 2022-06-16 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.81145567e-01 -5.59484959e-01 3.01665753e-01 4.23508920e-02
-5.43163002e-01 -4.45605397e-01 1.82314903e-01 -2.93784559e-01
-1.20716684e-01 6.16221726e-01 3.09865147e-01 -7.95070548e-03
-4.43375945e-01 -6.12651765e-01 -3.02787125e-01 -9.99006629e-01
-4.34223562e-01 -2.31010169e-01 1.47590056e-01 -3.34833533... | [15.373628616333008, 5.561366081237793] |
409d6de6-e0a0-4401-a49b-d251f9c5b566 | masked-representation-learning-for-domain | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rao_Masked_Representation_Learning_for_Domain_Generalized_Stereo_Matching_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rao_Masked_Representation_Learning_for_Domain_Generalized_Stereo_Matching_CVPR_2023_paper.pdf | Masked Representation Learning for Domain Generalized Stereo Matching | Recently, many deep stereo matching methods have begun to focus on cross-domain performance, achieving impressive achievements. However, these methods did not deal with the significant volatility of generalization performance among different training epochs. Inspired by masked representation learning and multi-task... | ['Xing Li', 'Zhelun Shen', 'Renjie He', 'Yuchao Dai', 'Mingyi He', 'Bangshu Xiong', 'Zhibo Rao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-reconstruction', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 2.11894140e-01 -2.83768535e-01 -2.54739691e-02 -5.02729714e-01
-9.69127834e-01 -2.91729033e-01 4.93427157e-01 -6.11945152e-01
-4.37427133e-01 4.39947158e-01 3.22599262e-01 -4.78037857e-02
-1.41486358e-02 -8.28739524e-01 -7.93746173e-01 -5.95722020e-01
3.01298380e-01 2.43571654e-01 6.01094246e-01 -2.75142014... | [8.738137245178223, -2.247398853302002] |
b03d31ce-27ae-4164-abe8-af8ce168f333 | companykg-a-large-scale-heterogeneous-graph | 2306.10649 | null | https://arxiv.org/abs/2306.10649v1 | https://arxiv.org/pdf/2306.10649v1.pdf | CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification | In the investment industry, it is often essential to carry out fine-grained company similarity quantification for a range of purposes, including market mapping, competitor analysis, and mergers and acquisitions. We propose and publish a knowledge graph, named CompanyKG, to represent and learn diverse company features a... | ['Dhiana Deva Cavacanti Rocha', 'Armin Catovic', 'Andrew McCornack', 'Richard Anselmo Stahl', 'Mark Granroth-Wilding', 'Vilhelm von Ehrenheim', 'Lele Cao'] | 2023-06-18 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-2.19351128e-01 3.03567909e-02 -3.89681906e-01 -7.53336996e-02
-5.48661530e-01 -8.94251883e-01 1.00807929e+00 8.29115450e-01
-2.00160444e-01 5.44748962e-01 4.14303243e-01 -8.32166821e-02
-4.33549702e-01 -1.05901337e+00 -2.29713678e-01 1.08848594e-01
-4.44111452e-02 1.00579500e+00 5.83327226e-02 -4.92348969... | [8.77233600616455, 7.8751091957092285] |
fe29338e-7bec-4dc7-acca-7f619600901c | shift-reduce-ccg-parsing-using-neural-network | null | null | https://aclanthology.org/N16-1052 | https://aclanthology.org/N16-1052.pdf | Shift-Reduce CCG Parsing using Neural Network Models | null | ['Mark Steedman', 'Bharat Ram Ambati', 'Tejaswini Deoskar'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['ccg-supertagging'] | ['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... | [-7.295064449310303, 3.880946636199951] |
686ccbce-a370-4025-8251-146b3c04ec03 | text-to-kg-alignment-comparing-current | 2306.02871 | null | https://arxiv.org/abs/2306.02871v1 | https://arxiv.org/pdf/2306.02871v1.pdf | Text-To-KG Alignment: Comparing Current Methods on Classification Tasks | In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information. This makes them attractive for systems that supplement or ground the knowledge found in pre-trained language models with an external knowledge source. This has especially been the case for class... | ['Erik Velldal', 'Lilja Øvrelid', 'Sondre Wold'] | 2023-06-05 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [ 1.49028718e-01 5.98532736e-01 -5.47247887e-01 -2.79485375e-01
-5.60820222e-01 -8.32708776e-01 8.91463041e-01 9.09694672e-01
-4.21346843e-01 8.04129660e-01 6.00340605e-01 -3.51578265e-01
-2.50318170e-01 -9.11880732e-01 -4.70321208e-01 -1.43317834e-01
9.25705954e-02 9.49153602e-01 5.95319152e-01 -4.75292951... | [9.45775032043457, 8.195188522338867] |
57c02420-5e8a-4ddb-abc8-556de3752179 | a-radiomics-approach-to-traumatic-brain | 1811.05699 | null | http://arxiv.org/abs/1811.05699v1 | http://arxiv.org/pdf/1811.05699v1.pdf | A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans | Computer Tomography (CT) is the gold standard technique for brain damage
evaluation after acute Traumatic Brain Injury (TBI). It allows identification
of most lesion types and determines the need of surgical or alternative
therapeutic procedures. However, the traditional approach for lesion
classification is restricted... | ['Thijs Vande Vyvere', 'Bjoern Menze', 'Jan S. Kirschke', 'Ezequiel de la Rosa', 'Diana M. Sima'] | 2018-11-14 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 3.37789506e-01 -2.49324307e-01 -1.45164788e-01 -1.94088116e-01
-9.22331691e-01 -1.51161283e-01 4.95788962e-01 6.07713163e-01
-7.44551718e-01 6.27587378e-01 -2.05767658e-02 -1.06793515e-01
-2.04231843e-01 -8.06109548e-01 -2.50848591e-01 -1.02167869e+00
-2.11676940e-01 1.10260856e+00 6.24551177e-01 1.81680530... | [14.364632606506348, -2.1605327129364014] |
931c68bf-dfd3-446f-be24-b96a383691ea | bringing-structure-into-summaries-a-faceted | 2106.00130 | null | https://arxiv.org/abs/2106.00130v2 | https://arxiv.org/pdf/2106.00130v2.pdf | Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents | Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization dataset... | ['Daqing He', 'Tong Wang', 'Xingdi Yuan', 'Yue Dong', 'Lei Zhang', 'Khushboo Thaker', 'Rui Meng'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-short.137 | https://aclanthology.org/2021.acl-short.137.pdf | acl-2021-5 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 2.08184153e-01 4.21318144e-01 -7.15530157e-01 -1.39744177e-01
-1.20240355e+00 -9.44209099e-01 7.77241766e-01 6.48594499e-01
1.28662691e-01 1.00320458e+00 1.62846863e+00 -1.92873567e-01
-2.08955333e-01 -4.00919229e-01 -3.58970374e-01 -5.46784960e-02
3.23534876e-01 3.97547036e-01 -1.67805195e-01 -2.13049024... | [12.506623268127441, 9.5230131149292] |
7d80ab4d-5864-423e-ac6f-d9ddebfa594c | multi-label-image-recognition-by-recurrently | 1711.02816 | null | http://arxiv.org/abs/1711.02816v1 | http://arxiv.org/pdf/1711.02816v1.pdf | Multi-label Image Recognition by Recurrently Discovering Attentional Regions | This paper proposes a novel deep architecture to address multi-label image
recognition, a fundamental and practical task towards general visual
understanding. Current solutions for this task usually rely on an extra step of
extracting hypothesis regions (i.e., region proposals), resulting in redundant
computation and s... | ['Zhouxia Wang', 'Liang Lin', 'Tianshui Chen', 'Ruijia Xu', 'Guanbin Li'] | 2017-11-08 | multi-label-image-recognition-by-recurrently-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Wang_Multi-Label_Image_Recognition_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wang_Multi-Label_Image_Recognition_ICCV_2017_paper.pdf | iccv-2017-10 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.29272974e-01 -1.85453296e-02 -2.28356063e-01 -6.02937698e-01
-1.15515041e+00 -4.45881039e-01 4.29240614e-01 3.16982538e-01
-5.29809058e-01 3.44888598e-01 -1.03648476e-01 -3.60048622e-01
3.08762848e-01 -4.20567006e-01 -8.90495002e-01 -8.47894371e-01
4.31128234e-01 2.69149214e-01 1.38446108e-01 2.85793692... | [9.82436466217041, 3.98407244682312] |
fadd0668-d18a-44d5-9f78-458f5d3ea468 | generative-imagination-elevates-machine | 2009.09654 | null | https://arxiv.org/abs/2009.09654v2 | https://arxiv.org/pdf/2009.09654v2.pdf | Generative Imagination Elevates Machine Translation | There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existing multimodal neural machine translation methods (MNMT) require triplets of bilingual sentence - image for training and tuples of source sen... | ['Lei LI', 'Quanyu Long', 'Mingxuan Wang'] | 2020-09-21 | null | https://aclanthology.org/2021.naacl-main.457 | https://aclanthology.org/2021.naacl-main.457.pdf | naacl-2021-4 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 6.23640060e-01 4.50089365e-01 -3.59254599e-01 -3.60569715e-01
-7.62695372e-01 -8.16647768e-01 1.01641762e+00 -5.05773544e-01
-9.61505622e-02 8.05879593e-01 3.42583805e-01 -5.25049388e-01
9.63833690e-01 -5.59945107e-01 -1.35561359e+00 -3.35498035e-01
8.37428987e-01 5.15451252e-01 -4.57227260e-01 -1.34970814... | [11.359959602355957, 1.423008918762207] |
bac610d0-8b3b-4df1-a4f6-dd8c513821c3 | 3d-object-class-detection-in-the-wild | 1503.05038 | null | http://arxiv.org/abs/1503.05038v1 | http://arxiv.org/pdf/1503.05038v1.pdf | 3D Object Class Detection in the Wild | Object class detection has been a synonym for 2D bounding box localization
for the longest time, fueled by the success of powerful statistical learning
techniques, combined with robust image representations. Only recently, there
has been a growing interest in revisiting the promise of computer vision from
the early day... | ['Peter Gehler', 'Michael Stark', 'Tobias Ritschel', 'Bojan Pepik', 'Bernt Schiele'] | 2015-03-17 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.24118350e-01 -2.85590559e-01 -6.41676709e-02 -3.53280932e-01
-5.70129633e-01 -8.23194265e-01 8.90927553e-01 3.80485445e-01
-4.37148809e-01 -4.67429459e-02 -2.09014192e-01 -1.32110015e-01
1.02821812e-01 -1.92851603e-01 -4.89662826e-01 -3.77155215e-01
-1.84033468e-01 6.86552882e-01 8.53836238e-01 9.43926945... | [7.63656759262085, -2.675401210784912] |
4287dcf6-9967-4914-8b29-8ae61d9005d1 | a-conceptual-framework-for-implicit | 2104.03940 | null | https://arxiv.org/abs/2104.03940v1 | https://arxiv.org/pdf/2104.03940v1.pdf | A Conceptual Framework for Implicit Evaluation of Conversational Search Interfaces | Conversational search (CS) has recently become a significant focus of the information retrieval (IR) research community. Multiple studies have been conducted which explore the concept of conversational search. Understanding and advancing research in CS requires careful and detailed evaluation. Existing CS studies have ... | ['Gareth J. F. Jones', 'Abhishek Kaushik'] | 2021-04-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.05730179e-02 -3.93022783e-02 -1.66127637e-01 -2.00509980e-01
-7.52474010e-01 -9.39930916e-01 1.11565197e+00 3.73935223e-01
-5.62890589e-01 1.97141871e-01 2.26486355e-01 -6.67081356e-01
-5.59543848e-01 1.19168513e-01 8.85591358e-02 4.34236452e-02
1.28136069e-01 2.37100080e-01 3.15317184e-01 -4.79278594... | [12.22993278503418, 7.724972248077393] |
27f46ab7-e8bc-4d0c-bd88-5031efc58f3e | dependency-based-decipherment-for-resource | null | null | https://aclanthology.org/D13-1173 | https://aclanthology.org/D13-1173.pdf | Dependency-Based Decipherment for Resource-Limited Machine Translation | null | ['Qing Dou', 'Kevin Knight'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['decipherment'] | ['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... | [-7.212493419647217, 3.53434419631958] |
eed12dce-49b0-4bf3-ae56-73e959061959 | transformer-based-neural-marked-spatio | 2302.09276 | null | https://arxiv.org/abs/2302.09276v1 | https://arxiv.org/pdf/2302.09276v1.pdf | Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis | With recently available football match event data that record the details of football matches, analysts and researchers have a great opportunity to develop new performance metrics, gain insight, and evaluate key performance. However, most sports sequential events modeling methods and performance metrics approaches coul... | ['Keisuke Fujii', 'Tony Sit', 'Calvin C. K. Yeung'] | 2023-02-18 | null | null | null | null | ['point-processes'] | ['methodology'] | [-2.66025811e-01 -7.23224103e-01 -3.19956541e-01 -1.67364761e-01
-4.69735891e-01 -2.92072147e-01 5.42892933e-01 5.46613455e-01
-4.97023821e-01 4.74585712e-01 4.78800118e-01 8.29647928e-02
-6.17815077e-01 -1.19640148e+00 -6.45755231e-01 -3.46357167e-01
-3.54714692e-01 1.71347395e-01 5.52952707e-01 -4.23373580... | [6.788328170776367, 0.35883572697639465] |
ba16d0e4-5861-4e92-8721-64cdce0220db | snel-a-structured-neuro-symbolic-language-for | 2306.06036 | null | https://arxiv.org/abs/2306.06036v1 | https://arxiv.org/pdf/2306.06036v1.pdf | SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding | In the evolving landscape of artificial intelligence, multimodal and Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on the identification and interaction with entities and their relations across diverse modalities. Addressing the need for complex querying and interaction in this context, we... | ['Ivanovitch Silva', 'Allan Martins', 'Silvan Ferreira'] | 2023-06-09 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 4.07053083e-01 1.84680149e-01 -2.72335917e-01 -4.13311005e-01
-3.77145499e-01 -9.74705517e-01 7.73445129e-01 6.67948723e-01
-5.55752575e-01 3.03698719e-01 4.43741888e-01 -2.32566610e-01
-3.70399714e-01 -8.08929443e-01 -1.33080304e-01 -4.42385264e-02
-2.53630191e-01 2.83458740e-01 8.94803032e-02 -5.87334931... | [10.690797805786133, 1.845304012298584] |
5cda6aa9-c192-4f99-afe2-44464cc3a8a7 | a-prototype-malayalam-to-sign-language | 1412.7415 | null | http://arxiv.org/abs/1412.7415v2 | http://arxiv.org/pdf/1412.7415v2.pdf | A prototype Malayalam to Sign Language Automatic Translator | Sign language, which is a medium of communication for deaf people, uses
manual communication and body language to convey meaning, as opposed to using
sound. This paper presents a prototype Malayalam text to sign language
translation system. The proposed system takes Malayalam text as input and
generates corresponding S... | ['Kannan Balakrishnan', 'Jestin Joy'] | 2014-12-23 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.47079498e-01 -1.44103661e-01 8.18605274e-02 -1.75360799e-01
-6.97577372e-02 -6.56796217e-01 7.49267936e-01 -2.59485453e-01
-6.90541327e-01 1.06984103e+00 7.91308165e-01 -7.04858899e-01
1.75462484e-01 -8.17498446e-01 1.20498680e-01 -5.26393652e-01
2.19024733e-01 4.00554687e-01 4.11726385e-01 -5.74767530... | [9.077159881591797, -6.383749961853027] |
5e17d329-da5d-4ffb-b097-6ca04dea2272 | robust-high-resolution-video-matting-with | 2108.11515 | null | https://arxiv.org/abs/2108.11515v1 | https://arxiv.org/pdf/2108.11515v1.pdf | Robust High-Resolution Video Matting with Temporal Guidance | We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-fr... | ['Soumyadip Sengupta', 'Imran Saleemi', 'Linjie Yang', 'Shanchuan Lin'] | 2021-08-25 | null | null | null | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 1.95514947e-01 -3.16348642e-01 -1.33468330e-01 -2.55034983e-01
-5.81772864e-01 -1.95361644e-01 3.02502185e-01 -4.00085807e-01
-4.87773836e-01 5.03365576e-01 -3.23542207e-02 -3.53513092e-01
5.05557060e-01 -6.83914840e-01 -9.26153600e-01 -5.44069529e-01
2.43875206e-01 5.09209156e-01 5.41618049e-01 -5.52438572... | [10.628860473632812, -0.888198733329773] |
2a738d5c-9724-4531-8695-f987039b3158 | mcmlsd-a-dynamic-programming-approach-to-line | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Almazan_MCMLSD_A_Dynamic_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Almazan_MCMLSD_A_Dynamic_CVPR_2017_paper.pdf | MCMLSD: A Dynamic Programming Approach to Line Segment Detection | Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In... | ['Yiming Qian', 'Ron Tal', 'James H. Elder', 'Emilio J. Almazan'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['line-segment-detection'] | ['computer-vision'] | [ 2.45141387e-01 2.33164430e-01 -2.23082200e-01 -2.20149964e-01
-1.02978969e+00 -6.02109373e-01 5.78231394e-01 6.47069931e-01
-5.48746884e-01 4.30116594e-01 -3.17132205e-01 -1.68242618e-01
2.09400188e-02 -6.74813330e-01 -7.07641423e-01 -7.08541989e-01
6.46223128e-03 9.40950990e-01 1.09745777e+00 3.11391622... | [8.29963207244873, -1.3834388256072998] |
993f796a-e517-439c-9e6b-ee6557268a08 | last-layer-state-space-model-for | 2307.01566 | null | https://arxiv.org/abs/2307.01566v1 | https://arxiv.org/pdf/2307.01566v1.pdf | Last layer state space model for representation learning and uncertainty quantification | As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging. Efforts in quantifying uncertainty often rely on specific training procedures, and bear additional computational costs due to the dimensionality of such models. In this paper, we propose to decompose ... | ['Sylvain Le Corff', 'Maurice Charbit', 'Max Cohen'] | 2023-07-04 | null | null | null | null | ['representation-learning'] | ['methodology'] | [ 1.04021199e-01 1.69491902e-01 -9.27107856e-02 -7.02581584e-01
-1.13801956e+00 -5.63520968e-01 8.74781251e-01 1.62532330e-01
-1.27289146e-01 1.19324756e+00 1.07743531e-01 -5.91616273e-01
-3.12020689e-01 -9.99996722e-01 -8.08342814e-01 -5.79408824e-01
-1.88825816e-01 7.40753531e-01 -3.45849961e-01 5.16042709... | [7.2464280128479, 3.7531585693359375] |
8af3973d-f42e-4e91-82ca-b9abd70fbe82 | contrastive-learning-for-low-light-raw | 2305.03352 | null | https://arxiv.org/abs/2305.03352v1 | https://arxiv.org/pdf/2305.03352v1.pdf | Contrastive Learning for Low-light Raw Denoising | Image/video denoising in low-light scenes is an extremely challenging problem due to limited photon count and high noise. In this paper, we propose a novel approach with contrastive learning to address this issue. Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in... | ['Yuhan Dong', 'Taoyong Cui'] | 2023-05-05 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.30654204e-01 -5.53235888e-01 5.35195649e-01 -2.36812770e-01
-4.61848438e-01 -2.71696988e-02 4.31411535e-01 -4.00731623e-01
-6.03004336e-01 8.99246812e-01 3.65486205e-01 2.27595285e-01
-5.64175248e-02 -7.43863285e-01 -7.35472620e-01 -1.18719733e+00
7.63147101e-02 -5.29763877e-01 2.23013833e-01 -3.52223724... | [10.926766395568848, -2.5599827766418457] |
6c00d2e9-65e7-4f9e-8732-109657ec53ac | freedom-target-label-source-data-domain | 2307.02493 | null | https://arxiv.org/abs/2307.02493v1 | https://arxiv.org/pdf/2307.02493v1.pdf | FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization | From a service perspective, Multi-Source Domain Adaptation (MSDA) is a promising scenario to adapt a deployed model to a client's dataset. It can provide adaptation without a target label and support the case where a source dataset is constructed from multiple domains. However, it is impractical, wherein its training h... | ['Chan-Hyun Youn', 'Gyusang Cho', 'Eunju Yang'] | 2023-07-04 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 1.68879181e-01 1.03139249e-03 -5.10416210e-01 -5.65433025e-01
-6.33633792e-01 -8.39264750e-01 5.83688855e-01 -2.09827349e-01
-1.52272880e-01 8.22267234e-01 -1.20831020e-01 3.60100269e-02
-2.07128882e-01 -8.91507566e-01 -6.12917900e-01 -8.96685302e-01
4.27558839e-01 1.09827793e+00 3.20388168e-01 -3.29196602... | [10.378844261169434, 3.1971089839935303] |
aab964a8-7fcd-45d4-a7fc-464a4b1b7b43 | practical-lessons-on-optimizing-sponsored | 2304.09107 | null | https://arxiv.org/abs/2304.09107v1 | https://arxiv.org/pdf/2304.09107v1.pdf | Practical Lessons on Optimizing Sponsored Products in eCommerce | In this paper, we study multiple problems from sponsored product optimization in ad system, including position-based de-biasing, click-conversion multi-task learning, and calibration on predicted click-through-rate (pCTR). We propose a practical machine learning framework that provides the solutions to such problems wi... | ['Musen Men', 'Jayanth Korlimarla', 'Weizhi Du', 'Bo Liu', 'Yanbing Xue'] | 2023-04-05 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 6.28914461e-02 -4.06347275e-01 -7.81059682e-01 -7.94232011e-01
-9.21102047e-01 -3.82017404e-01 1.94993749e-01 1.05190471e-01
-2.16368228e-01 6.89156234e-01 -7.92286396e-02 -7.58881807e-01
-3.36745083e-01 -7.08916545e-01 -6.23261273e-01 -2.09074736e-01
-9.97125078e-03 4.51393872e-01 2.34004721e-01 -5.25512934... | [9.856561660766602, 5.470157623291016] |
6d3dfded-bff8-4d76-82d0-8f915f75c202 | context-free-textspotter-for-real-time-and | 2106.05611 | null | https://arxiv.org/abs/2106.05611v1 | https://arxiv.org/pdf/2106.05611v1.pdf | Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition | In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable. In concept, end-to-end (E2E) text spotting is suitable for such purposes because it performs text detection and recognition in a single model. However, current state-of-the-art E2E methods rely ... | ['Naoaki Yamashita', 'Takumi Fujino', 'Kenji Doi', 'Tomohiro Tanaka', 'Ryota Yoshihashi'] | 2021-06-10 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.49042469e-01 -4.92168814e-01 1.67221785e-01 -1.33232057e-01
-7.67818451e-01 -3.33925426e-01 6.08528018e-01 -2.07267031e-01
-6.07464433e-01 6.22937679e-02 8.43405277e-02 -7.65354753e-01
3.56294245e-01 -3.07319999e-01 -4.50497061e-01 -4.84368503e-01
5.07980049e-01 5.21395266e-01 3.55136722e-01 -3.15718591... | [11.974152565002441, 2.2451441287994385] |
6e7ddb20-a161-4583-b9fd-94e5a73139e7 | multi-exposure-image-fusion-based-on-exposure | 1806.09607 | null | http://arxiv.org/abs/1806.09607v1 | http://arxiv.org/pdf/1806.09607v1.pdf | Multi-Exposure Image Fusion Based on Exposure Compensation | This paper proposes a novel multi-exposure image fusion method based on
exposure compensation. Multi-exposure image fusion is a method to produce
images without color saturation regions, by using photos with different
exposures. However, in conventional works, it is unclear how to determine
appropriate exposure values,... | ['Sayaka Shiota', 'Hitoshi Kiya', 'Yuma Kinoshita', 'Taichi Yoshida'] | 2018-06-23 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 8.7475991e-01 -6.6748369e-01 3.4477600e-01 -2.1295339e-01
-3.0202448e-01 -3.8296840e-01 2.5073913e-01 1.6298585e-02
-5.9660548e-01 7.7134860e-01 -9.9835172e-03 -4.0327787e-02
-4.2335767e-01 -9.2056155e-01 -3.1864068e-01 -7.9511076e-01
5.0272536e-01 -4.1100556e-01 2.0535339e-01 -3.0212882e-01
4.9891284e-01... | [10.936078071594238, -2.454396963119507] |
52cd1f36-ec19-45f2-ab86-5f868c1f67c6 | a-divide-and-conquer-approach-for-multi-label | null | null | https://aclanthology.org/2021.findings-emnlp.412 | https://aclanthology.org/2021.findings-emnlp.412.pdf | A Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering | Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple r... | ['Yunbo Cao', 'Qian-Wen Zhang', 'Chenchen Ye', 'Linhai Zhang', 'Yanzheng Xiang', 'Deyu Zhou'] | null | null | null | null | findings-emnlp-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-7.31411725e-02 3.93594414e-01 -2.88107872e-01 -3.26373369e-01
-1.06109071e+00 -5.08478105e-01 3.84107292e-01 4.54151869e-01
-1.14012614e-01 1.06644678e+00 1.12625711e-01 -4.70783263e-01
-2.76464075e-01 -1.17490530e+00 -6.16367280e-01 -5.49609601e-01
3.90817314e-01 8.82822096e-01 1.03168976e+00 -2.82663912... | [9.501321792602539, 8.522119522094727] |
35a55641-2a0b-4c5e-86b5-edaf22dda58e | neural-ranking-models-with-weak-supervision | 1704.08803 | null | http://arxiv.org/abs/1704.08803v2 | http://arxiv.org/pdf/1704.08803v2.pdf | Neural Ranking Models with Weak Supervision | Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents whe... | ['Mostafa Dehghani', 'W. Bruce Croft', 'Hamed Zamani', 'Jaap Kamps', 'Aliaksei Severyn'] | 2017-04-28 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 3.3391601e-01 5.6785047e-02 -3.7462640e-01 -5.2863508e-01
-1.1517755e+00 -6.4548057e-01 9.8446226e-01 2.7606368e-01
-8.8052177e-01 6.4087796e-01 4.3994758e-01 -2.0556679e-01
-4.1159177e-01 -8.1067419e-01 -8.7548673e-01 -3.8344893e-01
-1.8894070e-01 9.1042143e-01 2.4521385e-01 -5.0466108e-01
7.2333559e-02... | [11.416539192199707, 7.563056468963623] |
1495faf6-5bdf-426d-a271-35a741751be4 | learning-through-transcription | null | null | https://aclanthology.org/2022.computel-1.11 | https://aclanthology.org/2022.computel-1.11.pdf | Learning Through Transcription | Transcribing speech for primarily oral, local languages is often a joint effort involving speakers and outsiders. It is commonly motivated by externally-defined scientific goals, alongside local motivations such as language acquisition and access to heritage materials. We explore the task of ‘learning through transcrip... | ['Steven Bird', 'Mat Bettinson'] | null | null | null | null | computel-acl-2022-5 | ['language-acquisition'] | ['natural-language-processing'] | [ 1.06556788e-01 6.93333030e-01 3.29520181e-02 -7.25143969e-01
-1.59044349e+00 -7.64923215e-01 5.03183901e-01 2.17062682e-01
-4.72611278e-01 6.72814965e-01 1.32917333e+00 -6.24221146e-01
2.92935856e-02 -8.41117129e-02 -3.35167259e-01 -4.06835347e-01
1.88642323e-01 3.59862804e-01 1.13152321e-02 -5.58318734... | [14.073956489562988, 7.042368412017822] |
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