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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d1810bc0-dcf3-4bad-bad1-b7a8abdbfefb | aspect-based-emotion-analysis-and-multimodal | null | null | https://aclanthology.org/2022.lrec-1.61 | https://aclanthology.org/2022.lrec-1.61.pdf | Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts | While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content... | ['Veronique Hoste', 'Andrea Prati', 'Orphee De Clercq', 'Akbar Karimi', 'Luna De Bruyne'] | null | null | null | null | lrec-2022-6 | ['emotion-classification', 'aspect-based-sentiment-analysis', 'coreference-resolution', 'emotion-classification'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.51944870e-01 2.70101726e-01 -2.98594180e-02 -5.74213743e-01
-6.99320436e-01 -8.39482665e-01 7.82869279e-01 7.53792167e-01
-4.90369529e-01 2.98033446e-01 3.77979159e-01 7.19669983e-02
-7.55321160e-02 -4.58741039e-01 3.78841497e-02 -6.06368065e-01
9.30565372e-02 2.94071972e-01 -1.60325870e-01 -4.60562259... | [13.005955696105957, 5.305651664733887] |
ee4088e9-2c21-4553-9a20-5ca27feb272a | vocabulary-free-image-classification | 2306.00917 | null | https://arxiv.org/abs/2306.00917v1 | https://arxiv.org/pdf/2306.00917v1.pdf | Vocabulary-free Image Classification | Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when th... | ['Elisa Ricci', 'Yiming Wang', 'Paolo Rota', 'Massimiliano Mancini', 'Enrico Fini', 'Alessandro Conti'] | 2023-06-01 | null | null | null | null | ['vocabulary-free-image-classification', 'semantic-textual-similarity', 'semantic-similarity'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 5.00916362e-01 -2.36618146e-01 -2.35420987e-01 -3.39292258e-01
-7.43800998e-01 -8.86091352e-01 1.05544221e+00 1.73977718e-01
-6.04651392e-01 2.63074070e-01 -1.86358124e-01 -2.71323472e-01
5.79130612e-02 -8.22776735e-01 -7.39526153e-01 -5.32173216e-01
6.08241916e-01 6.74077988e-01 4.89634275e-01 7.75619177... | [10.015271186828613, 1.789754867553711] |
bef9f599-f22a-49ad-bca6-e2a6c0a7e60f | natural-language-processing-and-sentiment | null | null | https://www.worldscientific.com/doi/abs/10.1142/S2196888823500021 | https://www.worldscientific.com/doi/epdf/10.1142/S2196888823500021 | Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers | The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It... | ['Rashedur M. Rahman', 'Sabrina Mannan Meem', 'Ismat Jahan', 'Labiba Islam', 'Mahmud Hasan'] | 2023-05-04 | null | null | null | vietnam-journal-of-computer-science-2023-5 | ['hyperparameter-optimization', 'text-classification', 'sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-4.56989259e-01 -3.98124307e-01 -1.39805436e-01 -6.71793461e-01
-9.36601758e-01 -8.77480090e-01 6.76633418e-01 3.99996698e-01
-6.81114912e-01 7.88026869e-01 4.67602313e-01 -5.93572378e-01
1.45790532e-01 -5.83318293e-01 -1.20668173e-01 -7.13405550e-01
1.78213105e-01 6.17951214e-01 -1.17283262e-01 -8.40173244... | [11.163507461547852, 7.02237606048584] |
50fc3101-f11f-4c48-8c3a-d5a61dd7d483 | a-simple-and-effective-method-to-improve-zero-1 | 2210.09934 | null | https://arxiv.org/abs/2210.09934v1 | https://arxiv.org/pdf/2210.09934v1.pdf | A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resour... | ['Yiren Chen', 'Rong Tian', 'Haoyan Liu', 'Tao Zhu', 'Zhe Zhao', 'Weiquan Mao', 'Yuejian Fang', 'Weijie Liu', 'Kunbo Ding'] | 2022-10-18 | a-simple-and-effective-method-to-improve-zero | https://aclanthology.org/2022.coling-1.385 | https://aclanthology.org/2022.coling-1.385.pdf | coling-2022-10 | ['xlm-r'] | ['natural-language-processing'] | [-4.46152329e-01 -3.66755426e-01 -7.02043593e-01 -3.72253686e-01
-9.79726136e-01 -6.45677507e-01 4.58223253e-01 -9.11878049e-03
-8.88487697e-01 8.54224563e-01 3.63779038e-01 -5.25978625e-01
4.44781870e-01 -6.40340805e-01 -6.57730222e-01 -2.29230851e-01
3.61998200e-01 5.30641675e-01 -1.11902736e-01 -3.30575615... | [10.997236251831055, 9.918323516845703] |
89f13c67-e07f-427f-b3ec-8a8d5b46e90e | cal-ql-calibrated-offline-rl-pre-training-for | 2303.05479 | null | https://arxiv.org/abs/2303.05479v2 | https://arxiv.org/pdf/2303.05479v2.pdf | Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning | A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the con... | ['Sergey Levine', 'Aviral Kumar', 'Chelsea Finn', 'Yi Ma', 'Max Sobol Mark', 'Anikait Singh', 'Yuexiang Zhai', 'Mitsuhiko Nakamoto'] | 2023-03-09 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-4.79824036e-01 2.42342040e-01 -6.04286969e-01 -2.27475062e-01
-1.20517695e+00 -1.00301445e+00 2.90926337e-01 1.46202058e-01
-7.73221672e-01 1.15833390e+00 1.28494976e-02 -5.52152038e-01
-1.53967306e-01 -6.64768338e-01 -9.64247644e-01 -8.15652370e-01
-1.19595066e-01 5.91122329e-01 2.07351536e-01 -3.52111042... | [4.052394390106201, 2.2090022563934326] |
470cdd68-adef-4b26-933a-2ce196a97c3e | deep-auxiliary-learning-for-visual-1 | 2107.00222 | null | https://arxiv.org/abs/2107.00222v1 | https://arxiv.org/pdf/2107.00222v1.pdf | Deep auxiliary learning for visual localization using colorization task | Visual localization is one of the most important components for robotics and autonomous driving. Recently, inspiring results have been shown with CNN-based methods which provide a direct formulation to end-to-end regress 6-DoF absolute pose. Additional information like geometric or semantic constraints is generally int... | ['Xiahua Xia', 'Hao Shen', 'Qiong Nie', 'Mi Tian'] | 2021-07-01 | null | null | null | null | ['camera-localization', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 9.20739863e-03 -1.42500296e-01 -3.80509228e-01 -8.05650830e-01
-8.42724979e-01 -4.25039500e-01 4.31347698e-01 -1.17869698e-01
-6.50269985e-01 4.96550202e-01 2.02691946e-02 -5.23665920e-02
1.77895352e-01 -4.48959976e-01 -1.01029921e+00 -6.89459980e-01
3.28088015e-01 2.36372575e-02 1.32273689e-01 -2.23507673... | [7.795708656311035, -2.1928603649139404] |
52914536-256c-4212-a857-867a620298c8 | raum-vo-rotational-adjusted-unsupervised | 2203.07162 | null | https://arxiv.org/abs/2203.07162v1 | https://arxiv.org/pdf/2203.07162v1.pdf | RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry | Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, relying on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale-drift, and deg... | ['Holger Voos', 'Jose Luis Sanchez-Lopez', 'Hriday Bavle', 'Claudio Cimarelli'] | 2022-03-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.20757353e-02 -1.57189116e-01 -2.69432604e-01 -2.54167885e-01
-4.32672650e-01 -5.32584608e-01 4.38684106e-01 -5.02029359e-01
-4.29468572e-01 4.44952875e-01 1.09378202e-02 -7.04189762e-02
1.19577684e-01 -4.02702004e-01 -9.51733470e-01 -7.29884744e-01
1.38409972e-01 2.81276703e-01 2.60648370e-01 2.20313013... | [8.24329662322998, -2.1489739418029785] |
0f0d131f-3cc9-42c1-918a-aafe43f2a23e | a-novel-framework-for-image-forgery | 1311.6932 | null | http://arxiv.org/abs/1311.6932v1 | http://arxiv.org/pdf/1311.6932v1.pdf | A novel framework for image forgery localization | Image forgery localization is a very active and open research field for the
difficulty to handle the large variety of manipulations a malicious user can
perform by means of more and more sophisticated image editing tools. Here, we
propose a localization framework based on the fusion of three very different
tools, based... | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Davide Cozzolino'] | 2013-11-27 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.94149807e-01 -3.92323673e-01 -8.78981277e-02 6.58078194e-02
-5.92023253e-01 -7.69242764e-01 7.62468696e-01 3.19724172e-01
-4.46666479e-01 5.90694308e-01 -4.80095297e-01 -3.44503880e-01
-2.64718533e-01 -5.56701183e-01 -3.80095065e-01 -7.12656319e-01
-2.29554847e-02 4.25715819e-02 5.60885429e-01 -2.57944882... | [12.337950706481934, 0.9147510528564453] |
e37fb19a-2c3f-47ce-bc83-1f883139b996 | visual-place-recognition | 2211.14533 | null | https://arxiv.org/abs/2211.14533v1 | https://arxiv.org/pdf/2211.14533v1.pdf | Visual Place Recognition | Visual position recognition affects the safety and accuracy of automatic driving. To accurately identify the location, this paper studies a visual place recognition algorithm based on HMM filter and HMM smoother. Firstly, we constructed the traffic situations in Canberra city. Then the mathematical models of the HMM fi... | ['Zishun Zhou', 'Boyu Zhao', 'Bailu Guo'] | 2022-11-26 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-4.56060082e-01 -2.68771619e-01 -2.59162992e-01 -3.35832298e-01
-2.17149317e-01 -1.37939602e-01 5.44362962e-01 -2.83257905e-02
-3.00715327e-01 6.21777356e-01 1.05133113e-02 -7.42867351e-01
3.19315791e-01 -6.95771575e-01 -4.96562213e-01 -6.25044703e-01
-2.91721709e-02 -3.23425420e-03 6.45136058e-01 1.26559749... | [7.850645065307617, -0.9318030476570129] |
72a253c3-e0a5-4aa8-87ad-eaa6adb346a9 | fast-fourier-convolution-based-remote-sensor | 2209.00551 | null | https://arxiv.org/abs/2209.00551v1 | https://arxiv.org/pdf/2209.00551v1.pdf | Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation | Remote sensor image object detection is an important technology for Earth observation, and is used in various tasks such as forest fire monitoring and ocean monitoring. Image object detection technology, despite the significant developments, is struggling to handle remote sensor images and small-scale objects, due to t... | ['Dong Ge', 'Eugene Popov', 'Gu Lingyun'] | 2022-09-01 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 5.17670333e-01 -5.53651750e-01 6.32654876e-02 -3.15214694e-01
-5.54238111e-02 -2.12885588e-01 4.47519541e-01 -1.77634031e-01
-5.00810504e-01 3.82471412e-01 1.36517370e-02 -3.01474363e-01
-3.39550495e-01 -1.21378899e+00 -3.54728699e-01 -8.44031215e-01
-3.84125471e-01 -5.88638842e-01 6.11394703e-01 -4.28077310... | [9.30534553527832, -1.145542025566101] |
ade49172-c9e7-4563-b21c-fdf60c5511b4 | dynamic-boundary-time-warping-for-sub | 2010.14464 | null | https://arxiv.org/abs/2010.14464v1 | https://arxiv.org/pdf/2010.14464v1.pdf | Dynamic Boundary Time Warping for Sub-sequence Matching with Few Examples | The paper presents a novel method of finding a fragment in a long temporal sequence similar to the set of shorter sequences. We are the first to propose an algorithm for such a search that does not rely on computing the average sequence from query examples. Instead, we use query examples as is, utilizing all of them si... | ['Tomasz Górecki', 'Filip Graliński', 'Dawid Jurkiewicz', 'Łukasz Borchmann'] | 2020-10-27 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 4.49344426e-01 -5.82724512e-01 -3.71967286e-01 -2.36734435e-01
-1.38716006e+00 -5.87724507e-01 1.03236365e+00 5.17558932e-01
-9.80339348e-01 6.34175181e-01 1.44820213e-01 2.56355792e-01
-3.26638490e-01 -5.73947549e-01 -2.66589284e-01 -6.32669389e-01
-3.07093531e-01 5.38254321e-01 8.05969417e-01 -4.69932437... | [10.075855255126953, 3.079880714416504] |
30e29e88-a8b0-4557-a904-1d874897569d | relu-neural-networks-polyhedral | 2306.17418 | null | https://arxiv.org/abs/2306.17418v1 | https://arxiv.org/pdf/2306.17418v1.pdf | ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog | A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph. We show that while this dual graph is a coarse quantization of input space, it is sufficiently robust that it can be combined with persistent homology to detect homological signals of manifolds in the ... | ['Michael Kirby', 'Chris Peterson', 'Christina M Cole', 'Yajing Liu'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.66500464e-01 6.33343875e-01 -3.97727750e-02 -4.14787196e-02
-2.40332708e-01 -5.97171664e-01 7.59918928e-01 -7.30389506e-02
-7.91612193e-02 6.06239736e-01 1.41709268e-01 -5.76168478e-01
-1.45075768e-01 -1.13784206e+00 -9.36776578e-01 -6.63197100e-01
-5.64881444e-01 4.96966958e-01 3.84449154e-01 -6.75434947... | [7.857944488525391, 3.907844305038452] |
47f9f5de-38fd-4ba2-94f2-dbe2c9b3ebeb | neuralroom-geometry-constrained-neural | 2210.06853 | null | https://arxiv.org/abs/2210.06853v2 | https://arxiv.org/pdf/2210.06853v2.pdf | NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction | We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. Ho... | ['Chunxia Xiao', 'Yanping Fu', 'Tuo Cao', 'Kaixuan Zhou', 'Yu Jiang', 'Zongcheng Li', 'Yusen Wang'] | 2022-10-13 | null | null | null | null | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 2.75791675e-01 1.11310817e-02 2.13077664e-01 -6.02524877e-01
-4.60996211e-01 -5.72859496e-02 2.37596661e-01 -4.88143146e-01
1.54330045e-01 5.25185227e-01 2.14660332e-01 1.42217486e-03
-1.65684938e-01 -1.22620702e+00 -1.13592827e+00 -7.47264385e-01
5.52731693e-01 2.41845459e-01 1.81471393e-01 -2.38464400... | [8.958061218261719, -3.0278565883636475] |
efb1f666-2602-4efe-bf5e-b855a0740b58 | voxel-mae-masked-autoencoders-for-pre | 2206.09900 | null | https://arxiv.org/abs/2206.09900v6 | https://arxiv.org/pdf/2206.09900v6.pdf | Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders | Current perception models in autonomous driving rely heavily on large-scale labeled LiDAR data, which is costly and time-consuming to annotate. In this work, we aim to facilitate research on self-supervised masked learning using the vast amount of unlabeled LiDAR data available in autonomous driving. However, existing ... | ['Bin Dai', 'Xinli Xu', 'Yiming Nie', 'Liang Xiao', 'Dawei Zhao', 'Chen Min'] | 2022-06-20 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.21337223e-01 1.52531683e-01 -3.70554626e-01 -6.43379390e-01
-1.14896286e+00 -4.80340689e-01 2.76089072e-01 1.10760137e-01
-5.40143371e-01 5.68413675e-01 -3.77649724e-01 -3.67770702e-01
3.21198493e-01 -8.76733422e-01 -1.22088993e+00 -4.52648789e-01
-6.03267625e-02 7.79360890e-01 8.32697809e-01 -3.53110954... | [8.115767478942871, -2.5760631561279297] |
fae811f8-a8cd-4fd5-8cc5-0cf4bbfc14d4 | temporal-action-localization-with-multi | 2208.07493 | null | https://arxiv.org/abs/2208.07493v1 | https://arxiv.org/pdf/2208.07493v1.pdf | Temporal Action Localization with Multi-temporal Scales | Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions on a feature space of a single-temporal scale. However, the temporal features of a low-level scale lack enough semantics for action classifi... | ['Shenyong Chen', 'Meng Wang', 'An-An Liu', 'Zhiyong Cheng', 'Tao Zhuo', 'Xinglei Cui', 'Zan Gao'] | 2022-08-16 | null | null | null | null | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 2.92042494e-01 -2.51471609e-01 -4.34012949e-01 -2.36263022e-01
-4.79481995e-01 -4.96401675e-02 3.68650913e-01 -4.76281233e-02
-3.50025624e-01 6.79009497e-01 4.31272298e-01 1.72057346e-01
-6.96399286e-02 -6.94574952e-01 -6.12026453e-01 -5.90145171e-01
-2.95299530e-01 -3.36029023e-01 1.06342006e+00 -1.72448680... | [8.362161636352539, 0.5150885581970215] |
775a9ea7-8b1d-43c1-821b-4eabb05e1cb8 | transition-based-dependency-parsing-using | 2001.08279 | null | https://arxiv.org/abs/2001.08279v2 | https://arxiv.org/pdf/2001.08279v2.pdf | Transition-Based Dependency Parsing using Perceptron Learner | Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models that can be trained on syntactically annotated corpora. In this paper, we tackle transition-based dependency parsing using a Perceptron Learne... | ['Miguel Ballesteros', 'Chris Dyer', 'Rahul Radhakrishnan Iyer', 'Robert Frederking'] | 2020-01-22 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 1.83955640e-01 6.45428300e-01 -1.39711678e-01 -1.01005828e+00
-7.89810598e-01 -6.52349651e-01 3.80682528e-01 4.10622597e-01
-7.83294141e-01 7.63865471e-01 5.64403653e-01 -7.80673683e-01
1.95468903e-01 -1.02009189e+00 -5.24678469e-01 -3.20181251e-01
-2.45417744e-01 5.63033104e-01 6.24462366e-01 -2.66126484... | [10.31791877746582, 9.73066520690918] |
35ac8747-c66b-4bbc-aa05-24e56e317bcb | fully-convolutional-recurrent-network-for | 1604.04953 | null | http://arxiv.org/abs/1604.04953v1 | http://arxiv.org/pdf/1604.04953v1.pdf | Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition | This paper proposes an end-to-end framework, namely fully convolutional
recurrent network (FCRN) for handwritten Chinese text recognition (HCTR).
Unlike traditional methods that rely heavily on segmentation, our FCRN is
trained with online text data directly and learns to associate the pen-tip
trajectory with a sequenc... | ['Lianwen Jin', 'Ziyong Feng', 'Zenghui Sun', 'Zecheng Xie', 'Shuye Zhang'] | 2016-04-18 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 4.97641861e-01 -2.92502433e-01 -1.86292216e-01 -7.24126399e-01
-1.05894399e+00 -7.35864460e-01 2.92683303e-01 -4.27287787e-01
-5.89064896e-01 4.11130339e-01 8.29815343e-02 -5.42682528e-01
3.32904190e-01 -3.34488690e-01 -6.93860590e-01 -6.48480773e-01
2.60515273e-01 6.96143329e-01 1.82540044e-01 1.50856555... | [11.957856178283691, 2.4249353408813477] |
00d86297-8273-4b2a-a779-de1780fadb3a | cure-code-aware-neural-machine-translation | 2103.00073 | null | https://arxiv.org/abs/2103.00073v4 | https://arxiv.org/pdf/2103.00073v4.pdf | CURE: Code-Aware Neural Machine Translation for Automatic Program Repair | Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to fix software bugs automatically. While promising, these approaches have two major limitations. Their search space often does not contain the correct fix, and their search st... | ['Lin Tan', 'Thibaud Lutellier', 'Nan Jiang'] | 2021-02-26 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 5.97255789e-02 -5.50601743e-02 -6.14962876e-01 -7.28149563e-02
-1.23523092e+00 -5.48812628e-01 -2.27737695e-01 2.71389961e-01
1.84806779e-01 5.29806495e-01 -1.68121174e-01 -7.12640822e-01
2.88213074e-01 -7.45205998e-01 -1.17535448e+00 -1.12879323e-02
-8.95454884e-02 -7.98543617e-02 4.66578424e-01 -4.85592782... | [7.611617565155029, 7.724489212036133] |
b5a93333-2ebf-43c9-ad97-e30ab279af99 | embedding-based-silhouette-community | 1908.02556 | null | https://arxiv.org/abs/1908.02556v1 | https://arxiv.org/pdf/1908.02556v1.pdf | Embedding-based Silhouette Community Detection | Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. In this work, we propose Silhouette Community Detection (SCD), an approach for detecting ... | ['Nada Lavrač', 'Blaž Škrlj', 'Jan Kralj'] | 2019-07-17 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.64117113e-01 4.89436358e-01 6.54116645e-02 -2.76807621e-02
3.04097801e-01 -6.57868862e-01 6.66870475e-01 9.03342128e-01
-2.10947230e-01 6.13800883e-01 1.63472176e-01 -5.36992073e-01
-5.00962317e-01 -1.07092309e+00 -5.20413697e-01 -4.79198605e-01
-7.46635199e-01 8.88582766e-01 6.46122158e-01 -2.55696088... | [6.991089344024658, 5.861876487731934] |
24aeee7b-3f6e-4d05-b0f5-203e89526cd4 | evalne-a-framework-for-evaluating-network-1 | null | null | https://openreview.net/forum?id=H1eJH3IaLN | https://openreview.net/pdf?id=H1eJH3IaLN | EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction | Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space. These representations are then used for a variety of downstream prediction tasks. Link prediction is one of the most popular choices for assessing the performance of NE methods. However... | ['Anonymous'] | 2019-03-06 | null | null | null | iclr-workshop-rml-2019-5 | ['network-embedding'] | ['methodology'] | [-4.95626293e-02 7.36895502e-02 -2.73522437e-01 -6.37077726e-03
-4.42128062e-01 -8.02529752e-01 8.32920313e-01 6.22279882e-01
-3.50029737e-01 6.76749408e-01 2.09711999e-01 -6.61795080e-01
-4.65907395e-01 -7.96831429e-01 -4.26594138e-01 -4.57010120e-01
-4.39087987e-01 5.04418552e-01 2.92405456e-01 -3.57635841... | [7.007401943206787, 6.052452087402344] |
04f36980-177a-4f10-97f8-66c7aa6c6992 | tandem-multitask-training-of-speaker | 2207.03852 | null | https://arxiv.org/abs/2207.03852v1 | https://arxiv.org/pdf/2207.03852v1.pdf | Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription | Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker dia... | ['Philip C. Woodland', 'Chao Zhang', 'Xianrui Zheng'] | 2022-07-08 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-6.55360240e-03 1.28848538e-01 7.58696571e-02 -3.87515008e-01
-1.12407720e+00 -5.09614468e-01 5.77374339e-01 -8.56575221e-02
-5.61230004e-01 1.09759346e-01 5.09447873e-01 -5.58935165e-01
2.20820829e-01 -1.44677386e-01 -1.70577958e-01 -7.57813573e-01
-1.97439753e-02 4.24260110e-01 1.52995884e-01 -3.45418155... | [14.49393081665039, 6.168959140777588] |
9f722fe5-e4c5-498a-9cca-7bbd99e818df | deception-detection-in-videos | 1712.04415 | null | http://arxiv.org/abs/1712.04415v1 | http://arxiv.org/pdf/1712.04415v1.pdf | Deception Detection in Videos | We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show ... | ['Zhe Wu', 'V. S. Subrahmanian', 'Larry S. Davis', 'Bharat Singh'] | 2017-12-12 | null | null | null | null | ['deception-detection-in-videos', 'deception-detection-in-videos', 'deception-detection'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 9.15075094e-03 -1.18902571e-01 -9.16305259e-02 -4.19586450e-01
-1.08843708e+00 -6.12250566e-01 5.34665704e-01 -1.98269319e-02
-5.65993547e-01 5.83777726e-01 3.92997026e-01 -7.64326155e-02
3.84463429e-01 -1.84180856e-01 -3.73962253e-01 -4.89164948e-01
-1.87840294e-02 -2.59027123e-01 7.07587674e-02 -1.49560854... | [13.269737243652344, 2.03741455078125] |
de5e343c-19b2-42b1-b7b6-74303bbb504a | effectively-modeling-time-series-with-simple | 2303.09489 | null | https://arxiv.org/abs/2303.09489v1 | https://arxiv.org/pdf/2303.09489v1.pdf | Effectively Modeling Time Series with Simple Discrete State Spaces | Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep lea... | ['Christopher Ré', 'Karan Goel', 'Tri Dao', 'Michael Poli', 'Khaled K. Saab', 'Michael Zhang'] | 2023-03-16 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 3.38032037e-01 -2.85520554e-01 2.18385682e-02 -2.45760426e-01
-9.23441648e-01 -5.52532136e-01 6.87769115e-01 -2.41840303e-01
-2.56131977e-01 4.48036641e-01 9.23753623e-03 -1.03552079e+00
-3.42256784e-01 -4.20534849e-01 -7.95768857e-01 -7.13233173e-01
-8.98526192e-01 1.89127058e-01 -2.90844589e-01 -1.82114735... | [7.243439674377441, 3.2593929767608643] |
667312ff-7cf1-4765-b142-9792b0f7fda2 | learning-fast-and-slow-a-goal-directed-memory | 2301.13758 | null | https://arxiv.org/abs/2301.13758v2 | https://arxiv.org/pdf/2301.13758v2.pdf | Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments | Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-based planning using a parallel memory retrieval system (which we term the slow mechanism); ii) Instead of learning state values, we guide the ... | ['John Chong Min Tan', 'Mehul Motani'] | 2023-01-31 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-3.62468744e-03 5.09885550e-01 -3.89818609e-01 1.27457455e-01
-6.40798509e-01 -2.59253055e-01 8.68809104e-01 1.24037363e-01
-7.15342879e-01 1.03838551e+00 3.03491205e-01 -1.78256154e-01
-1.24325410e-01 -8.06310952e-01 -7.43513703e-01 -7.29256332e-01
-5.14056027e-01 8.60408664e-01 3.08112234e-01 -4.33957338... | [4.1521430015563965, 1.6294862031936646] |
ed4171d8-165a-4fe0-b13e-8f8fa88c8379 | pie-net-photometric-invariant-edge-guided | 2203.16670 | null | https://arxiv.org/abs/2203.16670v2 | https://arxiv.org/pdf/2203.16670v2.pdf | PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition | Intrinsic image decomposition is the process of recovering the image formation components (reflectance and shading) from an image. Previous methods employ either explicit priors to constrain the problem or implicit constraints as formulated by their losses (deep learning). These methods can be negatively influenced by ... | ['Theo Gevers', 'Sezer Karaoglu', 'Partha Das'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.40298408e-01 7.30881542e-02 1.53840810e-01 -3.44192564e-01
-3.99734467e-01 -3.15732777e-01 5.09856164e-01 -3.09363633e-01
-4.78198946e-01 5.73486924e-01 9.24557373e-02 3.60918045e-02
-4.75112684e-02 -6.11572146e-01 -6.95403874e-01 -9.12731528e-01
1.84803486e-01 1.40490858e-02 1.67238727e-01 -2.42146626... | [10.224120140075684, -2.7201757431030273] |
d150c834-2a52-4d48-89ea-029a7d9f8748 | joint-estimation-of-age-and-gender-from | 1806.02023 | null | http://arxiv.org/abs/1806.02023v1 | http://arxiv.org/pdf/1806.02023v1.pdf | Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications | Automatic age and gender classification based on unconstrained images has
become essential techniques on mobile devices. With limited computing power,
how to develop a robust system becomes a challenging task. In this paper, we
present an efficient convolutional neural network (CNN) called lightweight
multi-task CNN fo... | ['Chu-Song Chen', 'Ting-Yen Chen', 'Yi-Ming Chan', 'Jia-Hong Lee'] | 2018-06-06 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-2.60136843e-01 -6.99094608e-02 -2.90201247e-01 -7.94635832e-01
-3.35195094e-01 -3.73149179e-02 3.59294146e-01 1.84525102e-01
-9.32167828e-01 8.19876850e-01 -2.97366064e-02 -2.10294977e-01
1.98735103e-01 -7.18911350e-01 -2.73600996e-01 -5.71112394e-01
1.50987446e-01 4.57777858e-01 -4.72908348e-01 1.40638128... | [13.567463874816895, 0.9431411623954773] |
fbe545c1-5f54-4dbc-9573-2a7bf1e21d79 | convolutional-neural-network-with-median | 1908.06452 | null | https://arxiv.org/abs/1908.06452v1 | https://arxiv.org/pdf/1908.06452v1.pdf | Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations | We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neura... | ['Jing Qin', 'Luming Liang', 'Xinming Wu', 'Sen Deng', 'Mingqiang Wei', 'Lionel Gueguen'] | 2019-08-18 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 1.17388263e-01 -2.90121526e-01 3.21186423e-01 -4.99049664e-01
-7.10495472e-01 -4.88694310e-01 4.24499273e-01 -1.54490247e-01
-7.19980717e-01 3.82605493e-01 3.70855749e-01 -3.71809065e-01
1.43914521e-01 -8.63054216e-01 -1.00025129e+00 -8.43563020e-01
1.63922887e-02 -5.40477395e-01 1.01028748e-01 -3.18043500... | [11.453877449035645, -2.381481647491455] |
99fd0d53-3112-45c2-aa09-972231f3525a | mantis-at-tsar-2022-shared-task-improved | 2212.09855 | null | https://arxiv.org/abs/2212.09855v1 | https://arxiv.org/pdf/2212.09855v1.pdf | MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders | In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system in the following ... | ['Elma Kerz', 'Yu Qiao', 'Daniel Wiechmann', 'Xiaofei Li'] | 2022-12-19 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 2.53484905e-01 6.93667054e-01 -1.16294228e-01 -1.63986564e-01
-1.00382054e+00 -2.05881238e-01 6.43027008e-01 5.49875140e-01
-8.68054330e-01 8.38876486e-01 9.42257404e-01 -2.34417871e-01
-2.81059116e-01 -6.38158262e-01 -4.90409702e-01 1.59045279e-01
7.12926269e-01 9.35633957e-01 1.86849535e-01 -7.96516597... | [10.95309829711914, 10.381580352783203] |
34c05465-12fd-4c03-b73f-eb29dd950f46 | a-survey-on-intrinsic-images-delving-deep | 2112.03842 | null | https://arxiv.org/abs/2112.03842v1 | https://arxiv.org/pdf/2112.03842v1.pdf | A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond | Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry. Deep learning techniques have been broadly applied... | ['Jorge Lopez-Moreno', 'Dan Casas', 'Carlos Rodriguez-Pardo', 'Elena Garces'] | 2021-12-07 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 7.97442675e-01 -7.92236403e-02 1.95125267e-01 -3.29487324e-01
-2.37661764e-01 -3.70745212e-01 8.41249406e-01 -2.97283947e-01
-2.24928975e-01 5.31205952e-01 1.82890341e-01 -1.84756294e-02
-2.92421252e-01 -8.51550519e-01 -5.71911156e-01 -1.14127600e+00
2.84105480e-01 3.81385833e-01 -1.33105561e-01 -6.33458495... | [10.006396293640137, -2.8840718269348145] |
25da69f5-9d7f-41ff-bb86-010e83157823 | open-set-semi-supervised-learning-for-3d | 2205.01006 | null | https://arxiv.org/abs/2205.01006v1 | https://arxiv.org/pdf/2205.01006v1.pdf | Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding | Semantic understanding of 3D point cloud relies on learning models with massively annotated data, which, in many cases, are expensive or difficult to collect. This has led to an emerging research interest in semi-supervised learning (SSL) for 3D point cloud. It is commonly assumed in SSL that the unlabeled data are dra... | ['Kui Jia', 'Chuan Sheng Foo', 'Xiatian Zhu', 'Wanyue Zhang', 'Xun Xu', 'Xian Shi'] | 2022-05-02 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 8.05928484e-02 -2.07948666e-02 -2.68313229e-01 -5.92746913e-01
-7.17148602e-01 -5.34392297e-01 1.89681217e-01 1.48644984e-01
-4.17976379e-01 6.31997824e-01 -2.57595927e-01 -3.81605685e-01
-1.01340346e-01 -5.39658189e-01 -6.73402488e-01 -8.98091435e-01
2.63339400e-01 5.86991549e-01 1.14401221e-01 4.06678349... | [8.038986206054688, -3.1953253746032715] |
848acf54-e64f-4a12-9c44-761761d4df35 | crosshuman-learning-cross-guidance-from-multi | 2207.09735 | null | https://arxiv.org/abs/2207.09735v1 | https://arxiv.org/pdf/2207.09735v1.pdf | CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction | We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction. To recover geometry details and texture even in invisible regions, we design a reconstruction pipeline combined with tracking-based methods and trackin... | ['Yandong Guo', 'Han Huang', 'Jiaqi Li', 'Liliang Chen'] | 2022-07-20 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 7.20877498e-02 3.74718904e-02 2.54814237e-01 -2.73053963e-02
-6.40782058e-01 -1.72937512e-01 2.68830538e-01 -6.37198389e-01
-2.57964641e-01 5.86896777e-01 -4.64721769e-03 5.45550704e-01
3.73166859e-01 -6.80673003e-01 -7.58015335e-01 -6.30322456e-01
2.58253723e-01 7.63163209e-01 7.95660377e-01 -2.70880669... | [7.162333965301514, -1.2824246883392334] |
81664b58-5a70-4890-9fc9-2718c64ce4ff | weakly-supervised-instance-segmentation-using-1 | 1804.00880 | null | http://arxiv.org/abs/1804.00880v1 | http://arxiv.org/pdf/1804.00880v1.pdf | Weakly Supervised Instance Segmentation using Class Peak Response | Weakly supervised instance segmentation with image-level labels, instead of
expensive pixel-level masks, remains unexplored. In this paper, we tackle this
challenging problem by exploiting class peak responses to enable a
classification network for instance mask extraction. With image labels
supervision only, CNN class... | ['Yi Zhu', 'Jianbin Jiao', 'Qixiang Ye', 'Qiang Qiu', 'Yanzhao Zhou'] | 2018-04-03 | weakly-supervised-instance-segmentation-using-2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Weakly_Supervised_Instance_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Weakly_Supervised_Instance_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.10453415e-01 4.46868122e-01 -4.02585059e-01 -5.92958152e-01
-1.13984871e+00 -7.02917457e-01 6.08189285e-01 3.15697104e-01
-3.27479780e-01 6.87724829e-01 -4.02667135e-01 1.51242362e-02
1.31238312e-01 -7.83150911e-01 -1.33068919e+00 -7.84035861e-01
2.54597701e-02 2.52663046e-01 6.52736545e-01 2.45240390... | [9.54993724822998, 0.5172655582427979] |
c21369e1-3f00-4ec2-854b-02e594ced59a | robust-subgroup-discovery | 2103.13686 | null | https://arxiv.org/abs/2103.13686v4 | https://arxiv.org/pdf/2103.13686v4.pdf | Robust subgroup discovery | We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pa... | ['Peter Grünwald', 'Thomas Bäck', 'Matthijs van Leeuwen', 'Hugo Manuel Proença'] | 2021-03-25 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 6.24759436e-01 4.47461873e-01 -5.01015484e-01 -4.97446626e-01
-1.05633509e+00 -7.09978759e-01 5.94563484e-01 4.46784854e-01
-1.03666425e-01 9.53164935e-01 1.52810872e-01 -2.03163728e-01
-1.15599906e+00 -6.99918330e-01 -5.62383890e-01 -8.09793532e-01
-4.75338846e-01 8.77227902e-01 1.63802445e-01 3.32284302... | [7.732905864715576, 4.837463855743408] |
82c655d5-cbf9-44e8-9cd0-f1501d4edcc2 | expert-guided-symmetry-detection-in-markov | 2111.10297 | null | https://arxiv.org/abs/2111.10297v1 | https://arxiv.org/pdf/2111.10297v1.pdf | Expert-Guided Symmetry Detection in Markov Decision Processes | Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space. Yet, many MDPs are endowed with invariant reward and transition functions with respect to some transfor... | ['Caroline P. C. Chanel', 'Nicolas Drougard', 'Giorgio Angelotti'] | 2021-11-19 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-2.59388592e-02 1.16447687e-01 -1.21563412e-01 -8.68249312e-02
-5.55506349e-01 -6.33657515e-01 1.10607481e+00 2.56054997e-01
-4.24632400e-01 1.03419781e+00 1.70354061e-02 -1.50795013e-01
-4.17842418e-01 -6.82411611e-01 -6.86313450e-01 -1.12042964e+00
-4.42492396e-01 1.13349509e+00 3.90813082e-01 1.02566639... | [4.37091064453125, 2.0630836486816406] |
737f3510-661f-412a-bc32-8088bca355af | uncertain-facial-expression-recognition-via | 2212.07144 | null | https://arxiv.org/abs/2212.07144v1 | https://arxiv.org/pdf/2212.07144v1.pdf | Uncertain Facial Expression Recognition via Multi-task Assisted Correction | Deep models for facial expression recognition achieve high performance by training on large-scale labeled data. However, publicly available datasets contain uncertain facial expressions caused by ambiguous annotations or confusing emotions, which could severely decline the robustness. Previous studies usually follow th... | ['Guoying Zhao', 'Janne Kauttonen', 'Xingming Zhang', 'Yang Liu'] | 2022-12-14 | null | null | null | null | ['facial-expression-recognition', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.25185984e-01 1.29223153e-01 -1.99148655e-01 -1.17745757e+00
-8.89777780e-01 -1.59712628e-01 2.72229642e-01 -2.12373376e-01
-2.39772215e-01 7.40072489e-01 2.63539076e-01 5.80041766e-01
-2.75932997e-03 -3.27166468e-01 -4.81790394e-01 -1.02783298e+00
3.58978093e-01 2.53018916e-01 -5.18703878e-01 -9.90003496... | [13.633140563964844, 1.6603196859359741] |
057827f2-b4ff-437f-9f19-b5927a35be77 | dense-hybrid-recurrent-multi-view-stereo-net | 2007.10872 | null | https://arxiv.org/abs/2007.10872v1 | https://arxiv.org/pdf/2007.10872v1.pdf | Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking | In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel hybrid recurrent multi-view stereo net consists of two core modules: 1) a light DRENet (Dense Reception ... | ['Jian-Feng Yan', 'Yu-Wing Tai', 'Yisong Chen', 'Zizhuang Wei', 'Runze Zhang', 'Mingyu Ding', 'Hongwei Yi', 'Guoping Wang'] | 2020-07-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2666_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490647.pdf | eccv-2020-8 | ['point-cloud-reconstruction'] | ['computer-vision'] | [-2.66376436e-01 -1.92744583e-01 3.89108837e-01 -3.08960617e-01
-1.02342308e+00 -3.58347863e-01 5.09297401e-02 -2.99183697e-01
-2.00709924e-01 4.50830400e-01 -1.81691602e-01 -2.57641315e-01
-5.64484261e-02 -1.25182831e+00 -1.13593888e+00 -5.46810865e-01
1.36076272e-01 6.25660181e-01 5.73037803e-01 -2.28896722... | [8.733799934387207, -2.8161916732788086] |
2282fe0d-00de-4251-907f-cdca293ca3ac | weakly-supervised-visualbert-pre-training | 2010.12831 | null | https://arxiv.org/abs/2010.12831v2 | https://arxiv.org/pdf/2010.12831v2.pdf | Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions | Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel image-caption data for pre-training. Such data are costly to collect and require cumbersome curation. Inspired by unsupervised machine translati... | ['Kai-Wei Chang', 'Shih-Fu Chang', 'Alireza Zareian', 'Zhecan Wang', 'Haoxuan You', 'Liunian Harold Li'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.420 | https://aclanthology.org/2021.naacl-main.420.pdf | naacl-2021-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 6.82502925e-01 3.22991222e-01 -4.47989851e-01 -5.97326398e-01
-1.25165272e+00 -7.54470289e-01 9.35588121e-01 1.99086106e-04
-7.24326491e-01 4.33080077e-01 6.10747077e-02 -5.24207830e-01
6.17193401e-01 -3.77657086e-01 -1.37290084e+00 -2.95249283e-01
5.16369522e-01 6.00137830e-01 5.31186908e-02 2.73647290... | [10.829463005065918, 1.5099990367889404] |
11998215-04f5-49c5-a4e0-40ded8a6b38c | anomalous-subdiffusion-in-living-cells | 2004.01114 | null | https://arxiv.org/abs/2004.01114v1 | https://arxiv.org/pdf/2004.01114v1.pdf | Anomalous subdiffusion in living cells: bridging the gap between experiments and realistic models through collaborative challenges | The life of a cell is governed by highly dynamical microscopic processes. Two notable examples are the diffusion of membrane receptors and the kinetics of transcription factors governing the rates of gene expression. Different fluorescence imaging techniques have emerged to study molecular dynamics. Among them, fluores... | ['Hugues Berry', 'Cyril Favard', 'Ignacio Izeddin', 'Maxime Woringer'] | 2020-04-02 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.54196882e-01 -5.76489508e-01 1.84513420e-01 -1.86492622e-01
-3.47618371e-01 -7.12903559e-01 7.10231960e-01 5.44893026e-01
-8.04539919e-01 1.06181264e+00 -3.64391565e-01 -5.65974526e-02
-1.43391892e-01 -4.28655565e-01 -2.50210881e-01 -1.45402348e+00
-1.82916328e-01 8.04663599e-01 7.61147559e-01 -9.04885158... | [13.905354499816895, -3.134878635406494] |
00185735-5b2f-4f49-b6bc-6ff115b176a6 | tr-misr-multiimage-super-resolution-based-on | null | null | https://ieeexplore.ieee.org/abstract/document/9684717 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9684717 | TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers | Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with... | ['Chunhong Pan', 'Lingfeng Wang', 'Bin Xue', 'Chunlei Huo', 'Xin Zhang', 'Tai An'] | 2022-02-05 | null | null | null | ieee-journal-of-selected-topics-in-applied-3 | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.23177040e-01 -4.56078649e-01 3.11771035e-01 -4.03592378e-01
-8.20058107e-01 -2.67715484e-01 5.25991797e-01 -4.64315563e-01
-2.99970001e-01 6.04088724e-01 8.70302096e-02 -7.22435638e-02
-2.82824159e-01 -9.41827476e-01 -7.27835417e-01 -8.89392614e-01
2.27591679e-01 -3.55495624e-02 3.46290857e-01 -4.33041990... | [10.40264892578125, -1.924184799194336] |
902bbe8e-a5de-437e-82b9-1d74d62b647b | unsupervised-representation-learning-for-3d | 2110.06632 | null | https://arxiv.org/abs/2110.06632v2 | https://arxiv.org/pdf/2110.06632v2.pdf | Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data | Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has received much less attention to date. In this paper, we propose a simple yet effective approach for unsup... | ['Meili Wang', 'Wanli Ouyang', 'Xuequan Lu', 'Jincen Jiang'] | 2021-10-13 | null | null | null | null | ['3d-object-classification', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.05810100e-01 1.11512788e-01 -3.04622263e-01 -7.59017348e-01
-9.17814434e-01 -6.15954757e-01 4.81889576e-01 1.42444462e-01
-1.58020258e-01 2.15095028e-01 -3.86351198e-01 -2.32388496e-01
5.84785547e-03 -7.42362022e-01 -9.06652868e-01 -4.90776867e-01
4.23527621e-02 7.66448498e-01 3.28206450e-01 1.05792344... | [8.02275562286377, -3.2908995151519775] |
7842f3e1-7814-4e2e-8033-a5d579edc656 | automated-stitching-of-coral-reef-images-and | 2006.15478 | null | https://arxiv.org/abs/2006.15478v1 | https://arxiv.org/pdf/2006.15478v1.pdf | Automated Stitching of Coral Reef Images and Extraction of Features for Damselfish Shoaling Behavior Analysis | Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness... | ['Kristofer delas Peñas', 'Riza Rae Pineda', 'Dana Manogan'] | 2020-06-28 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 1.58042163e-02 -6.01688087e-01 6.32087469e-01 -3.02180886e-01
4.08693880e-01 -6.71587229e-01 7.98365101e-02 4.34150606e-01
-9.05182958e-01 5.95944166e-01 1.99732244e-01 1.25615209e-01
-3.46020341e-01 -7.43159890e-01 -5.79453766e-01 -1.00116313e+00
-6.49353147e-01 -5.58728814e-01 4.43466336e-01 -3.18851829... | [8.43889045715332, -1.2528647184371948] |
2eba7be4-63e0-427d-9ef3-0492d32e45bf | autoencoder-based-iterative-modeling-and | 2209.04213 | null | https://arxiv.org/abs/2209.04213v2 | https://arxiv.org/pdf/2209.04213v2.pdf | Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm | This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the increase of availability in many industrial fields. Labeling, sorting ... | ['Clemens Gühmann', 'Lars Henning', 'Jonas Köhne'] | 2022-09-09 | null | null | null | null | ['clustering-algorithms-evaluation', 'clustering-multivariate-time-series'] | ['methodology', 'time-series'] | [ 2.38708243e-01 -5.26986599e-01 2.28384405e-01 -3.06225181e-01
-3.32482129e-01 -4.33012515e-01 3.18855286e-01 6.84444904e-01
-1.78427398e-01 3.57844502e-01 -3.15803647e-01 -3.62683445e-01
-7.88245618e-01 -7.25738764e-01 -3.33092988e-01 -9.51992214e-01
-4.24006343e-01 5.36834419e-01 2.19997585e-01 -1.23614892... | [6.970816135406494, 2.8025310039520264] |
2ede77d0-f534-408c-a2ec-dbe5e8d266db | computer-vision-with-deep-learning-for-plant | 2006.11391 | null | https://arxiv.org/abs/2006.11391v1 | https://arxiv.org/pdf/2006.11391v1.pdf | Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey | In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitori... | ['Vineeth N. Balasubramanian', 'Sai Vikas Desai', 'Wei Guo', 'Akshay L Chandra'] | 2020-06-18 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.26999569e-01 -3.49161327e-01 -5.60584843e-01 2.39273198e-02
3.00408036e-01 -9.34856534e-01 -2.77400136e-01 9.34366763e-01
1.61762327e-01 6.32151306e-01 -3.20431083e-01 -7.32172489e-01
-2.36381188e-01 -1.28245687e+00 -3.93865258e-01 -8.31409037e-01
-5.38340099e-02 1.21729616e-02 -2.20700026e-01 -4.82441127... | [9.230631828308105, -1.5880417823791504] |
3224b739-f8f8-4d55-8d15-a44ed4546a58 | curriculum-guided-abstractive-summarization | 2302.00954 | null | https://arxiv.org/abs/2302.00954v1 | https://arxiv.org/pdf/2302.00954v1.pdf | Curriculum-guided Abstractive Summarization for Mental Health Online Posts | Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state. Recent Transformers-based summarization models have presented a promising approac... | ['Franck Dernoncourt', 'Hanieh Deilamsalehy', 'Nazli Goharian', 'Sajad Sotudeh'] | 2023-02-02 | null | null | null | null | ['abstractive-text-summarization', 'extreme-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.98429883e-01 8.25198233e-01 -4.48605269e-01 -3.96604091e-01
-1.32714510e+00 -1.28958315e-01 3.03378910e-01 8.12976539e-01
-5.40885389e-01 1.00412560e+00 7.71317303e-01 -1.63846180e-01
-1.67045072e-01 -5.69555461e-01 -2.42321014e-01 -1.91454470e-01
3.03523451e-01 4.56340879e-01 -5.57765365e-02 -5.29709995... | [12.373883247375488, 9.469683647155762] |
299ffb8b-6ae3-4605-9a15-ca8567dbc21b | generative-pointnet-energy-based-learning-on | 2004.01301 | null | https://arxiv.org/abs/2004.01301v2 | https://arxiv.org/pdf/2004.01301v2.pdf | Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification | We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual poin... | ['Song-Chun Zhu', 'Zilong Zheng', 'Ying Nian Wu', 'Jianwen Xie', 'Yifei Xu'] | 2020-04-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-reconstruction', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-3.33726220e-02 1.81417286e-01 5.85011691e-02 -4.26507860e-01
-9.68962193e-01 -6.55646205e-01 8.80886495e-01 -1.36042073e-01
2.57250424e-02 4.96324211e-01 -1.78261697e-01 -2.77954608e-01
-8.61985758e-02 -1.46394038e+00 -1.37675679e+00 -7.84018397e-01
1.40584648e-01 1.02455163e+00 -6.16971478e-02 -1.95850059... | [8.772919654846191, -3.71879506111145] |
b445cf36-9350-4a79-914b-c2def2b4b6dd | hierarchical-integration-diffusion-model-for | 2305.12966 | null | https://arxiv.org/abs/2305.12966v2 | https://arxiv.org/pdf/2305.12966v2.pdf | Hierarchical Integration Diffusion Model for Realistic Image Deblurring | Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computat... | ['Xin Yuan', 'Linghe Kong', 'Jinjin Gu', 'Bin Xia', 'Ding Liu', 'Yulun Zhang', 'Zheng Chen'] | 2023-05-22 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [-1.46222219e-01 -6.44201934e-01 2.24945083e-01 -5.71370982e-02
-5.17854214e-01 -3.26356918e-01 5.07671714e-01 -6.74107254e-01
5.52530847e-02 5.49653769e-01 6.10203147e-01 -9.94628072e-02
-1.57576308e-01 -4.30660933e-01 -4.25772488e-01 -1.13503695e+00
3.55109930e-01 -3.20164204e-01 8.05262551e-02 2.83138365... | [11.483701705932617, -2.6275222301483154] |
834a095f-5618-4347-af2d-c029e785c52e | dlue-benchmarking-document-language | 2305.09520 | null | https://arxiv.org/abs/2305.09520v1 | https://arxiv.org/pdf/2305.09520v1.pdf | DLUE: Benchmarking Document Language Understanding | Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark do... | ['Le Sun', 'Yingfei Sun', 'Xianpei Han', 'Xinyan Guan', 'Hongyu Lin', 'Ruoxi Xu'] | 2023-05-16 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 4.96093273e-01 -1.06823459e-01 -3.06480885e-01 -5.04372001e-01
-7.07901239e-01 -1.07956672e+00 8.87507260e-01 2.65409112e-01
-2.37063095e-01 5.00455976e-01 4.25488293e-01 -5.76740682e-01
-4.01581019e-01 -4.39115018e-01 -4.44456905e-01 -2.93088466e-01
2.44255736e-01 9.45244551e-01 -1.58120304e-01 -2.01150611... | [11.102917671203613, 8.288763046264648] |
b85e7064-a3f8-4fe1-9081-bfab3fec0678 | temporal-relation-extraction-with-a-graph | 2201.06125 | null | https://arxiv.org/abs/2201.06125v1 | https://arxiv.org/pdf/2201.06125v1.pdf | Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model | Temporal information extraction plays a critical role in natural language understanding. Previous systems have incorporated advanced neural language models and have successfully enhanced the accuracy of temporal information extraction tasks. However, these systems have two major shortcomings. First, they fail to make u... | ['Amarnath Gupta', 'Kuan-Yin Lai', 'Shang-Ling Hsu', 'Bo-Ying Su'] | 2022-01-16 | null | null | null | null | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.53875118e-02 3.89352649e-01 -5.30950010e-01 -5.26512980e-01
-4.56939578e-01 -4.67443317e-01 8.80757630e-01 3.94126624e-01
-5.12325525e-01 6.78828537e-01 2.25782990e-01 -6.27282023e-01
-2.06368372e-01 -1.03283179e+00 -6.36147141e-01 -1.94562927e-01
-6.55292213e-01 5.16984701e-01 5.78679144e-01 -2.42594346... | [9.053898811340332, 9.114568710327148] |
e9d8d381-d346-452f-a42a-54f5938870a6 | few-shot-video-to-video-synthesis | 1910.12713 | null | https://arxiv.org/abs/1910.12713v1 | https://arxiv.org/pdf/1910.12713v1.pdf | Few-shot Video-to-Video Synthesis | Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous ... | ['Ming-Yu Liu', 'Ting-Chun Wang', 'Jan Kautz', 'Andrew Tao', 'Guilin Liu', 'Bryan Catanzaro'] | 2019-10-28 | few-shot-video-to-video-synthesis-1 | http://papers.nips.cc/paper/8746-few-shot-video-to-video-synthesis | http://papers.nips.cc/paper/8746-few-shot-video-to-video-synthesis.pdf | neurips-2019-12 | ['video-to-video-synthesis'] | ['computer-vision'] | [ 3.38689357e-01 5.03874719e-02 -3.44131291e-02 -3.22905689e-01
-6.08782053e-01 -3.30347002e-01 5.41923225e-01 -8.48718941e-01
-2.84559488e-01 6.48959816e-01 2.00165331e-01 2.47005016e-01
3.91988277e-01 -4.55744028e-01 -1.00557566e+00 -5.07740736e-01
2.45340660e-01 4.07470405e-01 5.39562285e-01 -1.61931902... | [10.818865776062012, -0.5043867826461792] |
44c9566a-b267-4665-9ba1-7e43bc47150b | connecting-metrics-for-shape-texture | 2301.10608 | null | https://arxiv.org/abs/2301.10608v1 | https://arxiv.org/pdf/2301.10608v1.pdf | Connecting metrics for shape-texture knowledge in computer vision | Modern artificial neural networks, including convolutional neural networks and vision transformers, have mastered several computer vision tasks, including object recognition. However, there are many significant differences between the behavior and robustness of these systems and of the human visual system. Deep neural ... | ['Arlindo L. Oliveira', 'Tiago Marques', 'Tiago Oliveira'] | 2023-01-25 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 7.33603463e-02 -1.36557490e-01 1.92609355e-01 -4.98230040e-01
5.99441051e-01 -6.45930052e-01 8.70408833e-01 1.47004053e-01
-7.07591951e-01 1.95676669e-01 -7.35569596e-02 -1.43509164e-01
-6.64785728e-02 -9.98703301e-01 -6.64682209e-01 -7.16384590e-01
-3.00083309e-03 2.21940339e-01 5.92122912e-01 -3.65033031... | [9.90003776550293, 2.337444305419922] |
0ee93638-81df-4d23-9791-4b2d76c470df | improving-frame-level-classifier-for-word | 2306.07949 | null | https://arxiv.org/abs/2306.07949v1 | https://arxiv.org/pdf/2306.07949v1.pdf | Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition | End-to-end (E2E) systems have shown comparable performance to hybrid systems for automatic speech recognition (ASR). Word timings, as a by-product of ASR, are essential in many applications, especially for subtitling and computer-aided pronunciation training. In this paper, we improve the frame-level classifier for wor... | ['Zejun Ma', 'Yi He', 'Kang Wang', 'Yist Y. Lin', 'Xianzhao Chen'] | 2023-06-09 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 2.60699280e-02 -1.65451556e-01 -3.25698182e-02 -3.04890931e-01
-1.31803155e+00 -4.86474156e-01 5.50310850e-01 -3.99723416e-03
-9.47546124e-01 5.23520648e-01 1.59826338e-01 -7.06508815e-01
3.52068782e-01 -1.04650125e-01 -3.39947045e-01 -5.65785408e-01
8.93499851e-02 -5.12043647e-02 2.65656143e-01 -2.21847266... | [14.526080131530762, 6.60904598236084] |
595b60f6-e860-4b27-9d5a-ac5bad444b47 | wizard-of-tasks-a-novel-conversational | null | null | https://aclanthology.org/2022.coling-1.310 | https://aclanthology.org/2022.coling-1.310.pdf | Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings | Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such con... | ['Eugene Agichtein', 'Oleg Rokhlenko', 'Shervin Malmasi', 'Marcus Collins', 'Giuseppe Castellucci', 'Jie Zhao', 'Nikhita Vedula', 'Saar Kuzi', 'Jason Ingyu Choi'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [ 7.52028599e-02 4.91679937e-01 2.82769650e-01 -6.21903181e-01
-8.27210963e-01 -7.39436805e-01 6.85252368e-01 4.15044874e-01
-5.04277050e-01 6.31320477e-01 8.13210130e-01 -4.59010035e-01
-2.31857062e-03 -4.46599215e-01 -3.34308594e-01 -3.23022306e-01
1.26163405e-03 9.14125144e-01 1.74391866e-01 -8.56172383... | [12.553722381591797, 7.96472692489624] |
1f8b34a2-d54d-4cc1-9cf4-7f59662fe946 | image-stylization-for-robust-features | 2008.06959 | null | https://arxiv.org/abs/2008.06959v1 | https://arxiv.org/pdf/2008.06959v1.pdf | Image Stylization for Robust Features | Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization improves robustness of local features to not only day-night, but also weather and season variations. We show that image stylization in addit... | ['Juho Kannala', 'Iaroslav Melekhov', 'Daniyar Turmukhambetov', 'Gabriel J. Brostow'] | 2020-08-16 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [-2.12290704e-01 -3.54852140e-01 -1.80260345e-01 -4.54656363e-01
-6.60885155e-01 -8.84808779e-01 1.20648336e+00 -1.21521764e-01
-7.34528780e-01 6.92722559e-01 1.74420014e-01 -1.14257028e-02
4.11605030e-01 -4.50558424e-01 -1.13556802e+00 -3.79664719e-01
1.52082741e-01 3.39725539e-02 2.95210332e-01 -5.08369267... | [7.862860202789307, -1.9241291284561157] |
de9cfe3e-8e86-4c8e-9a12-cf49a7bee6e2 | avoid-overthinking-in-self-supervised-models | 2211.08989 | null | https://arxiv.org/abs/2211.08989v1 | https://arxiv.org/pdf/2211.08989v1.pdf | Avoid Overthinking in Self-Supervised Models for Speech Recognition | Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result in slow inference and network overthinking, where predictions made from the last layer of large models is worse than those made from intermed... | ['Shinji Watanabe', 'Brian Yan', 'Dan Berrebbi'] | 2022-11-01 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.63499910e-01 3.52399051e-01 4.19529490e-02 -4.59364355e-01
-5.30257940e-01 -5.56941509e-01 8.32838655e-01 -1.36034787e-01
-7.13245213e-01 4.92799878e-01 -6.84503615e-02 -6.56447947e-01
-3.38789783e-02 -3.40057164e-01 -6.59586906e-01 -5.09607136e-01
-2.88039837e-02 6.19867086e-01 6.04981005e-01 -3.17720231... | [14.20895004272461, 6.470616817474365] |
67bc4996-e9cf-4ada-a350-de757ba5cecf | causality-aided-trade-off-analysis-for | 2305.13057 | null | https://arxiv.org/abs/2305.13057v1 | https://arxiv.org/pdf/2305.13057v1.pdf | Causality-Aided Trade-off Analysis for Machine Learning Fairness | There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essentia... | ['Yanhui Li', 'Shuai Wang', 'Pingchuan Ma', 'Zhenlan Ji'] | 2023-05-22 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [-2.66162734e-02 4.64481562e-02 -5.30635297e-01 -7.41348505e-01
-2.78846264e-01 -6.51284158e-01 5.25350630e-01 2.93400943e-01
-3.38551819e-01 7.20060170e-01 3.47215474e-01 -7.02985942e-01
-3.42209071e-01 -5.94619274e-01 -5.28863072e-01 -1.82312265e-01
-1.13844357e-01 6.41589016e-02 -1.09321102e-01 -7.30177015... | [8.907405853271484, 5.438151836395264] |
8ee14943-7e1e-41bd-ad0c-ae43b38ff6a2 | semeval-2017-task-6-hashtagwars-learning-a | null | null | https://aclanthology.org/S17-2004 | https://aclanthology.org/S17-2004.pdf | SemEval-2017 Task 6: \#HashtagWars: Learning a Sense of Humor | This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the {`}Hashtag Wars{'} segment... | ['Anna Rumshisky', 'Peter Potash', 'Alexey Romanov'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-1.75445508e-02 -5.38674695e-03 4.32394415e-01 -2.99589664e-01
-7.21284568e-01 -6.75708175e-01 9.37346518e-01 4.49084878e-01
-3.35349083e-01 7.17452109e-01 6.20563805e-01 -1.12853058e-01
1.06672838e-01 -7.01263785e-01 -8.38259086e-02 -4.73387629e-01
8.42360109e-02 4.40401018e-01 3.03723752e-01 -1.09980762... | [8.873466491699219, 11.073596954345703] |
b7356580-9abc-4dd6-b816-4a65c3498bd0 | one-model-to-rule-them-all-multitask-and | 1711.01100 | null | http://arxiv.org/abs/1711.01100v1 | http://arxiv.org/pdf/1711.01100v1.pdf | One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis | When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native la... | ['Johannes Bjerva'] | 2017-11-03 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 2.81533688e-01 2.44084597e-01 -1.90241545e-01 -1.77995756e-01
-6.76865101e-01 -7.11292088e-01 3.99903834e-01 5.70354223e-01
-8.92441094e-01 8.62382233e-01 2.38480747e-01 -5.30891061e-01
-2.90834725e-01 -9.68381703e-01 -5.59158981e-01 -4.44740355e-01
2.86365896e-01 4.78278637e-01 6.37283474e-02 -4.96135473... | [10.579010963439941, 9.934558868408203] |
429473af-25e6-4935-8213-6fcc0cd9bc86 | real-time-steganalysis-for-stream-media-based | 1902.01286 | null | http://arxiv.org/abs/1902.01286v1 | http://arxiv.org/pdf/1902.01286v1.pdf | Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows | Previous VoIP steganalysis methods face great challenges in detecting speech
signals at low embedding rates, and they are also generally difficult to
perform real-time detection, making them hard to truly maintain cyberspace
security. To solve these two challenges, in this paper, combined with the
sliding window detect... | ['Yu-Jin Zhang', 'Yuting Hu', 'Yongfeng Huang', 'Hao Yang', 'Zhongliang Yang'] | 2019-02-04 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 2.52748847e-01 -7.40819275e-01 -9.10254568e-02 4.48016316e-01
-1.85939878e-01 -2.89139539e-01 1.06076404e-01 -4.70177442e-01
-2.69131422e-01 1.43072397e-01 -8.85958299e-02 -5.46868384e-01
2.76030272e-01 -8.19271684e-01 -1.22417130e-01 -8.67644429e-01
-3.49217921e-01 -5.39192200e-01 4.16824758e-01 3.70068736... | [4.301290035247803, 8.054553031921387] |
f6db9621-1a01-4d63-896e-0582b75162ae | human-motion-detection-based-on-dual-graph | 2304.04879 | null | https://arxiv.org/abs/2304.04879v1 | https://arxiv.org/pdf/2304.04879v1.pdf | Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm Regularizations | Motion detection has been widely used in many applications, such as surveillance and robotics. Due to the presence of the static background, a motion video can be decomposed into a low-rank background and a sparse foreground. Many regularization techniques that preserve low-rankness of matrices can therefore be imposed... | ['Biyun Xie', 'Jing Qin'] | 2023-04-10 | null | null | null | null | ['motion-detection', 'moving-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.83398062e-01 -2.63361722e-01 -1.13102242e-01 1.69115942e-02
-1.99464992e-01 -2.34857038e-01 2.81905532e-01 -4.42146480e-01
-2.87194252e-01 5.15123308e-01 6.55442476e-02 3.92159335e-02
-1.47032171e-01 -4.45490837e-01 -4.03020352e-01 -1.07890999e+00
-2.96948235e-02 -1.84883207e-01 6.75401509e-01 1.08173437... | [9.007619857788086, -0.8162215948104858] |
a9999dad-5aac-43aa-9f0f-ebca689eabea | transformerg2g-adaptive-time-stepping-for | 2307.02588 | null | https://arxiv.org/abs/2307.02588v1 | https://arxiv.org/pdf/2307.02588v1.pdf | TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers | Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varyin... | ['George Em Karniadakis', 'Mengjia Xu', 'Aniruddha Bora', 'Alan John Varghese'] | 2023-07-05 | null | null | null | null | ['graph-embedding', 'node-classification', 'link-prediction', 'graph-generation', 'dynamic-graph-embedding', 'anomaly-detection', 'recommendation-systems'] | ['graphs', 'graphs', 'graphs', 'graphs', 'graphs', 'methodology', 'miscellaneous'] | [-3.11353058e-01 7.33887181e-02 -2.43923917e-01 5.18550165e-02
-1.54870987e-01 -6.40467465e-01 6.30325675e-01 5.02150655e-01
2.18060434e-01 5.30263901e-01 2.19379932e-01 -4.59800363e-01
-4.80923653e-01 -1.03971076e+00 -5.68794608e-01 -6.47491515e-01
-9.35874462e-01 2.71725744e-01 9.59342122e-02 -2.73758382... | [7.146629333496094, 6.034050464630127] |
1b2fe7c1-9623-4f89-84e2-bc54d8da093d | little-red-riding-hood-goes-around-the-globe | 2212.10471 | null | https://arxiv.org/abs/2212.10471v2 | https://arxiv.org/pdf/2212.10471v2.pdf | Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models | Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. We consider whether planning brings advantages to automatic story generation across languages. We propose a new task of cross-lingual story generation with planning and p... | ['Shashi Narayan', 'Mirella Lapata', 'Annie Louis', 'Joshua Maynez', 'Evgeniia Razumovskaia'] | 2022-12-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 1.86929718e-01 6.37923539e-01 -2.69518703e-01 -1.30693704e-01
-9.47324991e-01 -7.77847469e-01 1.48780739e+00 -5.04561476e-02
2.00181324e-02 1.01985610e+00 1.47472894e+00 8.07100162e-02
2.63833702e-01 -9.39200282e-01 -5.70357323e-01 8.94760415e-02
4.85891098e-04 7.70098507e-01 -2.61057526e-01 -6.78494036... | [11.721237182617188, 8.817631721496582] |
104ddfad-a8a1-42b0-ae35-0d30db38afbc | large-scale-visual-font-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Large-Scale_Visual_Font_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Large-Scale_Visual_Font_2014_CVPR_paper.pdf | Large-Scale Visual Font Recognition | This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although visual font recognition has many practical applications, it has largely been neglected by the ... | ['Eli Shechtman', 'Tony X. Han', 'Aseem Agarwala', 'Jianchao Yang', 'Hailin Jin', 'Guang Chen', 'Jonathan Brandt'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['font-recognition', 'image-categorization'] | ['computer-vision', 'computer-vision'] | [ 3.93919885e-01 -5.33328533e-01 -2.17063397e-01 -4.64061707e-01
-5.57860374e-01 -1.02542186e+00 5.75489223e-01 -1.44500539e-01
-3.39648239e-02 3.19028556e-01 -1.84483618e-01 -5.22604406e-01
7.64112398e-02 -4.69446480e-01 -5.33713579e-01 -6.48122668e-01
2.43803427e-01 3.03904682e-01 2.74209082e-01 -3.70087735... | [11.940791130065918, 2.067129135131836] |
9f4b300a-5ecd-48be-aeef-34bd45838f28 | a-time-series-graph-cut-image-segmentation | 1809.05210 | null | http://arxiv.org/abs/1809.05210v1 | http://arxiv.org/pdf/1809.05210v1.pdf | A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors | Tumor detection in biomedical imaging is a time-consuming process for medical
professionals and is not without errors. Thus in recent decades, researchers
have developed algorithmic techniques for image processing using a wide variety
of mathematical methods, such as statistical modeling, variational techniques,
and ma... | ['Yu-An Wang', 'Yufeng Cao', 'Brian Hobbs', 'Laramie Paxton', 'Kevin R. Vixie', 'Chaan Ng'] | 2018-09-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 2.53773153e-01 1.13437489e-01 1.48103178e-01 -3.78570795e-01
-6.31103933e-01 -3.08395803e-01 2.78320730e-01 6.08626842e-01
-6.50030494e-01 5.98428130e-01 -1.46385834e-01 -3.32757950e-01
-8.98052678e-02 -6.62586212e-01 -1.76294520e-01 -1.12171674e+00
-1.03039980e-01 5.71235001e-01 1.85339838e-01 4.03983474... | [14.24261474609375, -2.6197025775909424] |
d6f55c74-4de8-403d-a0a8-b937d80d768d | retrieving-and-highlighting-action-with | 2005.09183 | null | https://arxiv.org/abs/2005.09183v1 | https://arxiv.org/pdf/2005.09183v1.pdf | Retrieving and Highlighting Action with Spatiotemporal Reference | In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlightin... | ['Seito Kasai', 'Yuchi Ishikawa', 'Masaki Hayashi', 'Yoshimitsu Aoki', 'Hirokatsu Kataoka', 'Kensho Hara'] | 2020-05-19 | null | null | null | null | ['explainable-models', 'video-text-retrieval'] | ['computer-vision', 'computer-vision'] | [ 4.19796765e-01 -2.38154352e-01 -3.69229615e-01 -7.81745836e-02
-1.10515141e+00 -4.87226874e-01 9.34059143e-01 4.63495888e-02
-4.11496133e-01 4.86746311e-01 9.63089347e-01 -9.54730611e-04
9.09136236e-02 -2.61314273e-01 -7.99312055e-01 -5.25828302e-01
-2.02875078e-01 8.94516036e-02 5.17571807e-01 -1.18902810... | [10.049508094787598, 0.7239444851875305] |
50fe402b-765a-48dc-afce-ef0cba0b3da7 | situation-aware-deep-reinforcement-learning | 2301.00124 | null | https://arxiv.org/abs/2301.00124v1 | https://arxiv.org/pdf/2301.00124v1.pdf | Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems | According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of D... | ['Joongheon Kim', 'Soyi Jung', 'Won Joon Yun', 'Soohyun Park', 'Hyunsoo Lee'] | 2022-12-31 | null | null | null | null | ['unity'] | ['computer-vision'] | [-2.27348953e-01 -3.67419660e-01 5.57147898e-02 1.33077502e-01
2.95473486e-01 -7.42931485e-01 5.73637664e-01 4.92087454e-02
-6.36775315e-01 9.55399215e-01 -2.98277080e-01 -6.81092024e-01
-8.00137281e-01 -1.16765797e+00 -5.72109371e-02 -7.19834924e-01
-6.39367759e-01 5.85298240e-01 5.96378446e-01 -1.22524023... | [5.238036632537842, 2.0135364532470703] |
d22d40b1-f555-4b96-91ea-468876ab7931 | full-body-cardiovascular-sensing-with-remote | 2303.09638 | null | https://arxiv.org/abs/2303.09638v1 | https://arxiv.org/pdf/2303.09638v1.pdf | Full-Body Cardiovascular Sensing with Remote Photoplethysmography | Remote photoplethysmography (rPPG) allows for noncontact monitoring of blood volume changes from a camera by detecting minor fluctuations in reflected light. Prior applications of rPPG focused on face videos. In this paper we explored the feasibility of rPPG from non-face body regions such as the arms, legs, and hands.... | ['Patrick Flynn', 'Adam Czajka', 'Ben Sporrer', 'Nathan Vance', 'Jeremy Speth', 'Lu Niu'] | 2023-03-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 2.31631264e-01 1.09609313e-01 1.79416627e-01 -9.86350998e-02
-4.74757016e-01 -3.12678218e-01 4.42796983e-02 -4.38057929e-01
-2.86424875e-01 7.98573911e-01 1.78038239e-01 3.95958245e-01
5.17434299e-01 -4.56703424e-01 -2.12228492e-01 -9.04902160e-01
-2.92719573e-01 -1.49946675e-01 8.54255855e-02 3.11748236... | [13.902769088745117, 2.835517168045044] |
079904c1-01dc-4fdd-8379-51512f078c48 | evaluation-of-spatial-distortion-in | 2306.08053 | null | https://arxiv.org/abs/2306.08053v1 | https://arxiv.org/pdf/2306.08053v1.pdf | Evaluation of Spatial Distortion in Multichannel Audio | Despite the recent proliferation of spatial audio technologies, the evaluation of spatial quality continues to rely on subjective listening tests, often requiring expert listeners. Based on the duplex theory of spatial hearing, it is possible to construct a signal model for frequency-independent spatial distortion by a... | ['Alexander Lerch', 'Karn N. Watcharasupat'] | 2023-06-13 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.01463634e-01 -3.43618333e-01 2.22304255e-01 -7.37314001e-02
-1.25602472e+00 -8.01994443e-01 1.92277595e-01 2.94477701e-01
-1.43615514e-01 6.56092763e-01 4.64690447e-01 -1.84423655e-01
-6.27312958e-01 -4.10804719e-01 -4.06996071e-01 -7.82492161e-01
-3.10379565e-01 -4.25327450e-01 2.45767578e-01 2.08897397... | [15.346549034118652, 5.64389705657959] |
dd572c36-179e-457c-bc81-67fb44ccb791 | dismec-distributed-sparse-machines-for | 1609.02521 | null | http://arxiv.org/abs/1609.02521v1 | http://arxiv.org/pdf/1609.02521v1.pdf | DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification | Extreme multi-label classification refers to supervised multi-label learning
involving hundreds of thousands or even millions of labels. Datasets in extreme
classification exhibit fit to power-law distribution, i.e. a large fraction of
labels have very few positive instances in the data distribution. Most
state-of-the-... | ['Bernhard Shoelkopf', 'Rohit Babbar'] | 2016-09-08 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 7.72510692e-02 -2.00121418e-01 -3.02629769e-01 -5.32886326e-01
-1.02854431e+00 -7.32916296e-01 3.41320097e-01 5.89677036e-01
-3.64353776e-01 6.04240656e-01 -2.06687432e-02 -2.42892087e-01
-4.16047692e-01 -4.85819876e-01 -5.50394714e-01 -8.18666577e-01
-6.21240065e-02 8.22847724e-01 -1.39145767e-02 1.39448360... | [9.502242088317871, 4.368603229522705] |
c2cd9074-1f69-47d7-bb39-1165ece7c251 | mmfn-multi-modal-fusion-net-for-end-to-end-1 | 2207.00186 | null | https://arxiv.org/abs/2207.00186v2 | https://arxiv.org/pdf/2207.00186v2.pdf | MMFN: Multi-Modal-Fusion-Net for End-to-End Driving | Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are... | ['Lujia Wang', 'Ren Xin', 'Feiyi Chen', 'Ruoyu Geng', 'Mingkai Tang', 'Qingwen Zhang'] | 2022-07-01 | null | null | null | null | ['carla-map-leaderboard'] | ['robots'] | [ 6.41135350e-02 -5.19002788e-02 3.83684002e-02 -8.02900732e-01
-1.97225481e-01 -4.98088956e-01 5.65376341e-01 -1.34711236e-01
-6.07656717e-01 6.53945565e-01 2.51482427e-02 -4.59971204e-02
-1.37094051e-01 -1.02682078e+00 -6.74350679e-01 -3.54356378e-01
2.51549751e-01 1.41844884e-01 5.64448118e-01 -5.91924310... | [8.205838203430176, -2.3352057933807373] |
e3f294d8-a1b4-4fdf-a750-c0209a0acaef | an-algorithm-for-the-se-3-transformation-on | 2206.08712 | null | https://arxiv.org/abs/2206.08712v1 | https://arxiv.org/pdf/2206.08712v1.pdf | An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions | Implicit representations are widely used for object reconstruction due to their efficiency and flexibility. In 2021, a novel structure named neural implicit map has been invented for incremental reconstruction. A neural implicit map alleviates the problem of inefficient memory cost of previous online 3D dense reconstru... | ['Andreas Nuechter', 'Yijun Yuan'] | 2022-06-17 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 1.61739528e-01 5.99436283e-01 -4.45308536e-02 -3.60730380e-01
-6.98452711e-01 -1.88175857e-01 7.07614481e-01 -1.21052951e-01
-3.56437951e-01 7.25692689e-01 2.38744214e-01 -2.09549572e-02
-5.91393523e-02 -1.25162578e+00 -1.26757526e+00 -3.84909779e-01
1.22271739e-01 7.50636935e-01 2.80791044e-01 -2.39189714... | [8.456287384033203, -3.0832202434539795] |
1c8fc038-a008-47a1-a7ad-03cf0c7d9b47 | folded-graph-signals-sensing-with-unlimited | 1903.03741 | null | http://arxiv.org/abs/1903.03741v2 | http://arxiv.org/pdf/1903.03741v2.pdf | Folded Graph Signals: Sensing with Unlimited Dynamic Range | Self-reset analog-to-digital converters (ADCs) are used to sample high
dynamic range signals resulting in modulo-operation based folded signal
samples. We consider the case where each vertex of a graph (e.g., sensors in a
network) is equipped with a self-reset ADC and senses a time series. Graph
sampling allows the gra... | [] | 2020-01-20 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.00960517e+00 3.76893915e-02 -2.96002001e-01 -2.25818623e-02
-6.08076870e-01 -7.49861598e-01 -7.47815371e-02 6.26290515e-02
8.75477642e-02 6.48608923e-01 -3.42685401e-01 9.90079567e-02
-4.42977875e-01 -7.24339902e-01 -7.35778093e-01 -5.84190786e-01
-4.63963360e-01 -1.18796177e-01 -1.35725960e-01 1.07902139... | [6.581774711608887, 1.6373331546783447] |
4e169490-a37a-4862-8948-5cfe0f78c99d | reply-to-garcia-et-al-common-mistakes-in | 1505.06750 | null | http://arxiv.org/abs/1505.06750v2 | http://arxiv.org/pdf/1505.06750v2.pdf | Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics | We demonstrate that the concerns expressed by Garcia et al. are misplaced,
due to (1) a misreading of our findings in [1]; (2) a widespread failure to
examine and present words in support of asserted summary quantities based on
word usage frequencies; and (3) a range of misconceptions about word usage
frequency, word r... | ['M. R. Frank', 'J. R. Williams', 'I. M. Kloumann', 'P. S. Dodds', 'M. T. McMahon', 'J. P. Bagrow', 'C. M. Danforth', 'L. Mitchell', 'K. D. Harris', 'E. M. Clark', 'S. Desu', 'K. Megerdoomian', 'B. F. Tivnan', 'A. J. Reagan'] | 2015-05-25 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.84102401e-01 -4.52913269e-02 -5.98587036e-01 -2.55276322e-01
-7.09823072e-01 -7.02021122e-01 6.72757328e-01 9.14878070e-01
-8.76964688e-01 7.33410239e-01 1.01391149e+00 -1.17674875e+00
-2.74722725e-01 -5.86397946e-01 -6.01771772e-01 -1.43474877e-01
3.10223371e-01 -1.85536608e-01 -7.54737779e-02 -2.07658872... | [9.884885787963867, 8.624430656433105] |
fd9b19b7-72e5-43c9-8758-525104118ccc | progressive-domain-independent-feature | 2003.09869 | null | https://arxiv.org/abs/2003.09869v2 | https://arxiv.org/pdf/2003.09869v2.pdf | Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space... | ['Yanhua Yang', 'Muli Yang', 'Hao Wang', 'Xinxun Xu'] | 2020-03-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.31595242e-02 -4.41743940e-01 -5.43018103e-01 -2.05272764e-01
-7.03019023e-01 -3.47829878e-01 5.99707186e-01 -5.57095289e-01
-1.67485639e-01 2.23509699e-01 1.81745365e-01 3.46412808e-01
-5.23574173e-01 -9.04291391e-01 -3.42435628e-01 -7.50084460e-01
5.99096000e-01 2.98393071e-01 2.11491168e-01 -3.68898690... | [11.606792449951172, 0.7070814967155457] |
8affc434-3446-43b4-b2cf-788458116d6d | 190807849 | 1908.07849 | null | https://arxiv.org/abs/1908.07849v1 | https://arxiv.org/pdf/1908.07849v1.pdf | Semi-supervised Sequence Modeling for Elastic Impedance Inversion | Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints - the lack of which might lead to undesirable results. To overcome this issue, we have deve... | ['Ghassan AlRegib', 'Motaz Alfarraj'] | 2019-08-19 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 3.53121281e-01 8.90718848e-02 3.49415421e-01 -3.53733003e-01
-8.19488704e-01 -1.20314643e-01 3.48153263e-01 -2.97055334e-01
-5.28002441e-01 4.59683806e-01 1.81445792e-01 -6.43784583e-01
-2.86353081e-01 -1.08672142e+00 -9.29664552e-01 -6.93653345e-01
-3.57294947e-01 5.31535387e-01 1.97284877e-01 -3.21654022... | [6.835955619812012, 2.557645320892334] |
48d0a8b0-1660-481e-be72-74429722acbd | when-source-free-domain-adaptation-meets | 2301.08413 | null | https://arxiv.org/abs/2301.08413v2 | https://arxiv.org/pdf/2301.08413v2.pdf | Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation | Source-free domain adaptation (SFDA), where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation in the real world. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supe... | ['Hong Wang', 'Wenming Cao', 'Xidong Xi', 'Yan Li', 'Guitao Cao', 'Chunwei Wu'] | 2023-01-20 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-1.52430877e-01 -5.10046817e-02 -5.55849850e-01 -3.38585705e-01
-9.11585450e-01 -5.12520194e-01 4.91767645e-01 2.98774153e-01
-9.32697952e-02 7.10305154e-01 9.73455384e-02 2.18842402e-01
-4.12824273e-01 -6.09885097e-01 -6.37408495e-01 -9.45125699e-01
5.36779466e-04 8.73573720e-01 3.41937602e-01 6.37360811... | [10.28181266784668, 3.047830581665039] |
c23b1ada-a4f8-4465-8bc1-1aa285a07648 | inspired-by-norbert-wiener-feedback-loop | 2212.02096 | null | https://arxiv.org/abs/2212.02096v1 | https://arxiv.org/pdf/2212.02096v1.pdf | Inspired by Norbert Wiener: FeedBack Loop Network Learning Incremental Knowledge for Driver Attention Prediction and Beyond | The problem of predicting driver attention from the driving perspective is gaining the increasing research focuses due to its remarkable significance for autonomous driving and assisted driving systems. Driving experience is extremely important for driver attention prediction, a skilled driver is able to effortlessly p... | ['Zhixiong Nan', 'Yilong Chen'] | 2022-12-05 | null | null | null | null | ['saliency-prediction', 'driver-attention-monitoring'] | ['computer-vision', 'computer-vision'] | [ 1.41133845e-01 1.53465450e-01 -4.31081891e-01 -3.82730037e-01
-1.50685012e-01 1.00465022e-01 5.14753401e-01 3.08017489e-02
-3.72452497e-01 4.75011051e-01 4.26159292e-01 -4.25427318e-01
-1.40062496e-01 -6.27415955e-01 -5.34240067e-01 -4.92181748e-01
2.74447292e-01 -3.15689594e-01 6.01893783e-01 -5.49852669... | [7.544142246246338, 0.01956135407090187] |
85d21be2-40a9-4934-b062-c40e1a834eec | lyapunov-driven-deep-reinforcement-learning | 2305.10931 | null | https://arxiv.org/abs/2305.10931v1 | https://arxiv.org/pdf/2305.10931v1.pdf | Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces | In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new data are continuously generated/collected by a set of devices and are handled th... | ['George C. Alexandropoulos', 'Paolo Di Lorenzo', 'Mattia Merluzzi', 'Kyriakos Stylianopoulos'] | 2023-05-18 | null | null | null | null | ['stochastic-optimization', 'data-compression'] | ['methodology', 'time-series'] | [ 3.03158581e-01 3.04770581e-02 -1.43405408e-01 3.35296802e-02
-2.85115063e-01 -2.39885822e-01 1.54011808e-02 2.09924474e-01
-4.02306467e-01 7.18975008e-01 -4.89203155e-01 -4.75834697e-01
-6.61169231e-01 -9.99102592e-01 -6.95827425e-01 -1.05094862e+00
-4.75736231e-01 4.45067465e-01 -2.29975104e-01 1.06145926... | [5.927200794219971, 1.5968371629714966] |
6df5696a-a56f-4697-8117-76fa0435f03f | filterbank-learning-for-small-footprint | 2211.10565 | null | https://arxiv.org/abs/2211.10565v2 | https://arxiv.org/pdf/2211.10565v2.pdf | Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting | In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstrate that filterbank learning outperforms handcrafted speech features for KWS whenever the number of filterbank channels is severely decreased... | ['John H. L. Hansen', 'Jesper Jensen', 'Zheng-Hua Tan', 'Ram C. M. C. Shekar', 'Iván López-Espejo'] | 2022-11-19 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [ 1.82896271e-01 -1.01478435e-01 -3.17745388e-01 -3.07755709e-01
-1.25500095e+00 -5.47123432e-01 2.52148181e-01 -4.72006015e-02
-6.57532811e-01 6.50829494e-01 2.23749638e-01 -6.58067167e-01
1.13252044e-01 -3.88282895e-01 -5.64646423e-01 -5.37541270e-01
3.48266326e-02 -4.88795221e-01 2.95925606e-02 -4.23757397... | [14.351781845092773, 6.086236953735352] |
9221b009-a82e-4d4b-aad6-20902efe5220 | legal-tech-open-diaries-lesson-learned-on-how | 2210.13086 | null | https://arxiv.org/abs/2210.13086v1 | https://arxiv.org/pdf/2210.13086v1.pdf | Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models | In the era of billion-parameter-sized Language Models (LMs), start-ups have to follow trends and adapt their technology accordingly. Nonetheless, there are open challenges since the development and deployment of large models comes with a need for high computational resources and has economical consequences. In this wor... | ['Ilias Chalkidis', 'Prodromos Malakasiotis', 'Sotiris Legkas', 'Stelios Maroudas'] | 2022-10-24 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-9.78062302e-03 4.41886395e-01 -4.28323478e-01 -3.65957409e-01
-1.42810500e+00 -5.29496431e-01 6.73157930e-01 5.34604266e-02
-7.38808215e-01 5.77545345e-01 4.70405400e-01 -8.11246753e-01
-1.96298942e-01 -5.00418901e-01 -6.21234417e-01 -8.17072019e-02
2.00077310e-01 1.04588842e+00 5.31220697e-02 -3.86648148... | [10.4169921875, 9.054981231689453] |
d2f9d40f-9286-4d23-8a9c-0e32c852e15a | protein-language-model-rescue-mutations | 2211.10000 | null | https://arxiv.org/abs/2211.10000v1 | https://arxiv.org/pdf/2211.10000v1.pdf | Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes | Despite being self-supervised, protein language models have shown remarkable performance in fundamental biological tasks such as predicting impact of genetic variation on protein structure and function. The effectiveness of these models on diverse set of tasks suggests that they learn meaningful representations of fitn... | ['Pablo Cordero', 'Onuralp Soylemez'] | 2022-11-18 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 6.26508772e-01 1.75795555e-01 -1.49127990e-01 -5.58148682e-01
-7.99188077e-01 -6.98768973e-01 1.37175366e-01 6.56063616e-01
-3.48717213e-01 9.70201254e-01 4.85637844e-01 -9.19053674e-01
-8.94064605e-02 -1.73222199e-01 -7.93101490e-01 -8.11078131e-01
-1.57110319e-01 5.27175903e-01 5.58478236e-02 -2.81055838... | [4.747972011566162, 5.585265159606934] |
4bc13f46-3679-4e5c-b0bb-eadef94e2eb2 | sparse-instance-activation-for-real-time | 2203.12827 | null | https://arxiv.org/abs/2203.12827v1 | https://arxiv.org/pdf/2203.12827v1.pdf | Sparse Instance Activation for Real-Time Instance Segmentation | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of ... | ['Wenyu Liu', 'Zhaoxiang Zhang', 'Chang Huang', 'Qian Zhang', 'Wenqiang Zhang', 'Shaoyu Chen', 'Xinggang Wang', 'Tianheng Cheng'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 3.78869683e-01 2.09787294e-01 -1.75477043e-01 -3.98866504e-01
-6.29215181e-01 -4.34440762e-01 3.68060470e-01 1.32178396e-01
-3.13409925e-01 5.39258122e-01 -3.58009428e-01 -1.83523595e-02
-3.68408374e-02 -7.71298289e-01 -9.47097600e-01 -8.18525255e-01
1.05572067e-01 4.94584173e-01 5.65469980e-01 3.49596798... | [9.503664016723633, 0.1744803786277771] |
0e097b97-62da-467f-8714-43b3ac118302 | a-simple-baseline-for-multi-camera-3d-object | 2208.10035 | null | https://arxiv.org/abs/2208.10035v1 | https://arxiv.org/pdf/2208.10035v1.pdf | A Simple Baseline for Multi-Camera 3D Object Detection | 3D object detection with surrounding cameras has been a promising direction for autonomous driving. In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Detection, to solve the problem. To incorporate multi-view information as well as build upon previous efforts on monocular 3D object detection, ... | ['Jiwen Lu', 'Jie zhou', 'Guan Huang', 'Zheng Zhu', 'Wenzhao Zheng', 'Yunpeng Zhang'] | 2022-08-22 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.56344082e-02 -2.35325903e-01 -2.74407893e-01 -4.34586823e-01
-7.65874147e-01 -6.35937691e-01 6.53535187e-01 -3.48463178e-01
-3.58491778e-01 1.56123370e-01 -4.49669966e-03 -1.56148210e-01
3.89618963e-01 -5.08922279e-01 -7.58798003e-01 -6.33341789e-01
4.32022512e-01 1.70286447e-01 1.03999257e+00 -1.01071708... | [7.849907398223877, -2.3703880310058594] |
5bfa900c-9ef8-4ca8-a87b-dc78cf703059 | camera-calibration-without-camera-access-a | 2302.06949 | null | https://arxiv.org/abs/2302.06949v1 | https://arxiv.org/pdf/2302.06949v1.pdf | Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods | A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-sh... | ['Johan Edstedt', 'Per-Erik Forssén', 'Emil Brissman'] | 2023-02-14 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 2.42810532e-01 -1.49861351e-01 2.58673906e-01 -2.96869904e-01
-7.45085597e-01 -7.32791781e-01 3.35519284e-01 -1.22934297e-01
-4.39613670e-01 5.29365778e-01 -3.17623645e-01 -3.10716510e-01
5.67027107e-02 -6.23581946e-01 -8.80904257e-01 -5.32035589e-01
5.46024323e-01 6.92184806e-01 5.64389884e-01 1.95871025... | [8.171351432800293, -2.284576654434204] |
688f34bc-2c82-490d-82f7-41048836d409 | pillarsegnet-pillar-based-semantic-grid-map | 2105.04169 | null | https://arxiv.org/abs/2105.04169v2 | https://arxiv.org/pdf/2105.04169v2.pdf | PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data | Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the spars... | ['Christoph Stiller', 'Frank Bieder', 'Philipp Heidenreich', 'Kunyu Peng', 'Juncong Fei'] | 2021-05-10 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.00696325e-01 3.02916318e-01 -5.94262481e-02 -7.63232291e-01
-6.03234828e-01 -3.26815426e-01 7.83822119e-01 1.79389343e-02
-1.63775727e-01 5.19325316e-01 -3.67858052e-01 -2.01517403e-01
-6.64171716e-03 -1.44067836e+00 -9.57953513e-01 -1.63370714e-01
-5.61669189e-03 1.51839411e+00 8.18168104e-01 -1.39056250... | [8.139134407043457, -2.722888469696045] |
612da542-ef2f-4f2d-9e42-9cfa3406b2e5 | hospital-readmission-prediction-applying | 1804.01188 | null | http://arxiv.org/abs/1804.01188v1 | http://arxiv.org/pdf/1804.01188v1.pdf | Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models | Hospital readmissions have become one of the key measures of healthcare
quality. Preventable readmissions have been identified as one of the primary
targets for reducing costs and improving healthcare delivery. However, most
data driven studies for understanding readmissions have produced black box
classification and p... | ['Jialiang Jiang', 'Varun Chandola', 'Sharon Hewner'] | 2018-04-03 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 2.32375432e-02 1.16175734e-01 -8.91262829e-01 -3.13898325e-01
-5.03902376e-01 -5.30674495e-02 6.76506534e-02 7.30922878e-01
-3.01990688e-01 6.95224047e-01 1.27008450e+00 -6.81923687e-01
-6.71871603e-01 -7.00117767e-01 -5.14222622e-01 -5.03258169e-01
-2.18946338e-01 2.75302261e-01 -6.96271002e-01 8.85498226... | [8.007780075073242, 6.200531005859375] |
3b3943c1-7ffd-485a-9061-15ce16f9e212 | making-a-long-story-short-a-multi-importance | 1711.03473 | null | http://arxiv.org/abs/1711.03473v3 | http://arxiv.org/pdf/1711.03473v3.pdf | Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects | The emergence of low-cost high-quality personal wearable cameras combined
with the increasing storage capacity of video-sharing websites have evoked a
growing interest in first-person videos, since most videos are composed of
long-running unedited streams which are usually tedious and unpleasant to
watch. State-of-the-... | ['Erickson Rangel Nascimento', 'João Pedro Klock Ferreira', 'Michel Melo Silva', 'Felipe Cadar Chamone', 'Washington Luis Souza Ramos', 'Mario Fernando Montenegro Campos'] | 2017-11-09 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-1.69800501e-02 -3.13779026e-01 -1.75985917e-01 -4.60801601e-01
-3.73011440e-01 -3.62394035e-01 3.40664625e-01 -1.73916981e-01
-5.57418585e-01 5.98982215e-01 5.46979249e-01 3.34828228e-01
-5.18013239e-02 -3.29985201e-01 -5.18651545e-01 -5.00401437e-01
-1.09758966e-01 9.20034386e-03 4.79551256e-01 -1.03753552... | [9.663887023925781, -0.566422700881958] |
d57c4677-3d20-44bd-a183-943d15050ddb | permuteattack-counterfactual-explanation-of | 2008.10138 | null | https://arxiv.org/abs/2008.10138v2 | https://arxiv.org/pdf/2008.10138v2.pdf | PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards | This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. Our proposed framework draws motivation from the field of Artificial Intelligence (AI) security and adversarial ML where the need for certifying the perf... | ['Masoud Hashemi', 'Ali Fathi'] | 2020-08-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.81352437e-01 5.32796502e-01 8.79761428e-02 -2.48345584e-01
-6.35012209e-01 -8.89205754e-01 7.52517700e-01 2.31393501e-01
-3.02801698e-01 8.70971560e-01 -2.39717022e-01 -8.29882383e-01
-4.76565212e-01 -9.17313755e-01 -9.35318172e-01 -8.18741441e-01
-1.83860555e-01 6.53704345e-01 -3.99809778e-01 -2.83606797... | [5.720148086547852, 7.673329830169678] |
74a58195-0081-48c4-a9c0-8cd3040ef2f0 | object-landmark-discovery-through | 1910.09469 | null | https://arxiv.org/abs/1910.09469v1 | https://arxiv.org/pdf/1910.09469v1.pdf | Object landmark discovery through unsupervised adaptation | This paper proposes a method to ease the unsupervised learning of object landmark detectors. Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category. Contrary to previous works, we do however assume that ... | ['Enrique Sanchez', 'Georgios Tzimiropoulos'] | 2019-10-21 | object-landmark-discovery-through-1 | http://papers.nips.cc/paper/9505-object-landmark-discovery-through-unsupervised-adaptation | http://papers.nips.cc/paper/9505-object-landmark-discovery-through-unsupervised-adaptation.pdf | neurips-2019-12 | ['unsupervised-landmark-detection'] | ['computer-vision'] | [ 4.87725213e-02 5.10532022e-01 -4.39859070e-02 -4.09345597e-01
-5.47832847e-01 -6.05840921e-01 7.86984086e-01 1.23947307e-01
-9.06395376e-01 4.73817915e-01 -1.97750498e-02 1.33007504e-02
-3.62414829e-02 -6.25256717e-01 -8.51726592e-01 -9.04262125e-01
6.77606510e-03 6.87429011e-01 4.66723025e-01 -8.22225362... | [9.463491439819336, 1.289738655090332] |
beee3f8a-9ff1-4c58-82b0-5bfa7dd74f86 | toward-stance-classification-based-on-claim | null | null | https://aclanthology.org/W17-5210 | https://aclanthology.org/W17-5210.pdf | Toward Stance Classification Based on Claim Microstructures | Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining. We propose a framework for representing claims as microstructures, which express the beliefs, judgments, and policies about the relations between domain... | ['Jan {\\v{S}}najder', "Filip Boltu{\\v{z}}i{\\'c}"] | 2017-09-01 | null | null | null | ws-2017-9 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 3.74254167e-01 8.99688005e-01 -1.07174981e+00 -3.22422296e-01
-1.04148698e+00 -9.38185096e-01 1.22062767e+00 1.11370409e+00
-2.29649410e-01 7.67416298e-01 1.06733465e+00 -1.22389174e+00
-1.06796227e-01 -7.91324615e-01 -9.57799256e-01 -5.00145229e-03
2.90887177e-01 5.88677168e-01 4.68306482e-01 -4.19297725... | [9.389873504638672, 9.617715835571289] |
84829b94-80f4-4607-a864-d1b371c3e54c | home-action-genome-cooperative-compositional | 2105.05226 | null | https://arxiv.org/abs/2105.05226v1 | https://arxiv.org/pdf/2105.05226v1.pdf | Home Action Genome: Cooperative Compositional Action Understanding | Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomic-actions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn repre... | ['Juan Carlos Niebles', 'Ehsan Adeli', 'Shun Ishizaka', 'Kazuki Kozuka', 'Rishi Desai', 'Jingwei Ji', 'Haofeng Chen', 'Nishant Rai'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.pdf | cvpr-2021-1 | ['few-shot-action-recognition', 'action-understanding'] | ['computer-vision', 'computer-vision'] | [ 6.22559905e-01 2.12687198e-02 -7.45497704e-01 -4.51340616e-01
-9.94076014e-01 -5.08707702e-01 9.11044359e-01 -3.74046266e-01
4.96394970e-02 4.34195578e-01 1.41479051e+00 4.18391258e-01
1.38248891e-01 -4.14766103e-01 -7.92690337e-01 -5.47242582e-01
-4.44523767e-02 2.58249760e-01 2.11414486e-01 -5.89642562... | [8.413121223449707, 0.6720313429832458] |
73978870-37fe-4415-84e2-d37f32d9e4fc | deepi2p-image-to-point-cloud-registration-via | 2104.03501 | null | https://arxiv.org/abs/2104.03501v1 | https://arxiv.org/pdf/2104.03501v1.pdf | DeepI2P: Image-to-Point Cloud Registration via Deep Classification | This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation bet... | ['Gim Hee Lee', 'Jiaxin Li'] | 2021-04-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-to-point-cloud-registration'] | ['computer-vision'] | [ 2.91254222e-01 -1.67264178e-01 -2.16642041e-02 -4.50478345e-01
-7.98423171e-01 -7.77039349e-01 4.84301925e-01 -1.16815560e-01
-4.19479311e-01 1.06553480e-01 -3.11546654e-01 -3.78740057e-02
4.42888252e-02 -5.61141491e-01 -9.91606116e-01 -4.30864304e-01
4.08168942e-01 8.48234951e-01 -5.50850891e-02 1.23083927... | [7.716202735900879, -2.5614120960235596] |
f5930472-da86-4e90-9837-72c5f70a947c | context-aware-taxi-dispatching-at-city-scale | null | null | https://ieeexplore.ieee.org/abstract/document/9247444 | https://ieeexplore.ieee.org/abstract/document/9247444 | Context-aware taxi dispatching at city-scale using deep reinforcement learning | Abstract— Proactive taxi dispatching is of great importance to
balance taxi demand-supply gaps among different locations in
a city. Recent advances primarily rely on deep reinforcement
learning (DRL) to directly learn the optimal dispatching policy.
These works, however, are still not sufficiently efficient because... | ['and Kaishun Wu', 'Jiangzhou Li', 'IEEE', 'Member', 'Zhidan Liu'] | 2021-05-26 | null | null | null | ieee-transactions-on-intelligent-9 | ['action-generation'] | ['computer-vision'] | [-3.99177641e-01 -1.55036941e-01 -7.81845868e-01 -3.70504647e-01
-6.88545465e-01 -4.90042567e-01 4.87974614e-01 8.45598355e-02
-1.17827304e-01 1.07255971e+00 9.56003070e-02 -7.42226660e-01
-6.10030472e-01 -1.47085035e+00 -5.41183650e-01 -7.68632948e-01
-1.41475469e-01 1.08492887e+00 2.07837567e-01 -5.78692257... | [5.7872314453125, 1.7422456741333008] |
cb9199e3-cb05-43ed-9371-e36c59f2e4f8 | biomedical-chinese-english-clir-using-an | null | null | https://aclanthology.org/L12-1149 | https://aclanthology.org/L12-1149.pdf | Biomedical Chinese-English CLIR Using an Extended CMeSH Resource to Expand Queries | Cross-lingual information retrieval (CLIR) involving the Chinese language has been thoroughly studied in the general language domain, but rarely in the biomedical domain, due to the lack of suitable linguistic resources and parsing tools. In this paper, we describe a Chinese-English CLIR system for biomedical literatur... | ["Jun{'}ichi Tsujii", 'Xinkai Wang', 'Sophia Ananiadou', 'Paul Thompson'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 1.06879093e-01 -2.01305240e-01 -1.94121093e-01 5.24433516e-02
-1.47966838e+00 -5.14413118e-01 6.92846477e-01 6.39870942e-01
-1.19514573e+00 1.00243592e+00 5.06025314e-01 -5.41894972e-01
-4.95720834e-01 -4.10912693e-01 -4.11513984e-01 -5.32154739e-01
3.54717225e-01 7.17366159e-01 6.27749383e-01 -4.75757837... | [8.854633331298828, 8.717455863952637] |
e3c07b88-dc82-4621-be56-03320c06c85b | looking-outside-the-window-wider-context | 2106.15754 | null | https://arxiv.org/abs/2106.15754v6 | https://arxiv.org/pdf/2106.15754v6.pdf | Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images | Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a Wide-Conte... | ['Lorenzo Bruzzone', 'Hao Tang', 'Yuebin Wang', 'Xiaojie Cui', 'Jing Zhang', 'Shaofu Lin', 'Dong Lin', 'Lei Ding'] | 2021-06-29 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.13823748e-01 -1.11906156e-01 -1.39386415e-01 -5.23403883e-01
-4.23733145e-01 -2.80200154e-01 3.25730383e-01 -2.64739752e-01
-3.41001004e-01 6.82986796e-01 3.83531526e-02 -5.17180204e-01
-2.00339854e-01 -1.43480098e+00 -5.65160930e-01 -8.73588145e-01
1.01030715e-01 -2.30496943e-01 2.68268019e-01 -3.99474442... | [9.605616569519043, -1.3780580759048462] |
81b35af9-d9bb-45a8-ad4b-a7a00e360361 | unified-vision-language-representation | 2302.05093 | null | https://arxiv.org/abs/2302.05093v2 | https://arxiv.org/pdf/2302.05093v2.pdf | Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval | Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be used for similar products retrieval from different suppliers or duplicate products detection of one supplier. Common methods use the image a... | ['Wei Ning', 'Wen Jiang', 'Dehong Gao', 'Xinxin Wang', 'Linbo Jin', 'Ben Chen'] | 2023-02-10 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 1.01021945e-01 -6.35470986e-01 -3.15241754e-01 -2.24050075e-01
-8.93006682e-01 -9.64282215e-01 5.19497633e-01 9.84165147e-02
-2.05960363e-01 -1.18364923e-01 -2.06776917e-01 -6.77398667e-02
-2.59790212e-01 -5.13472438e-01 -4.38256681e-01 -6.52155340e-01
2.68436462e-01 6.38665020e-01 1.22879304e-01 -4.05304283... | [10.887730598449707, 1.141183614730835] |
f2aa5013-4b78-41ce-8af6-9b2f4fc84955 | a-light-weight-model-for-active-speaker | 2303.04439 | null | https://arxiv.org/abs/2303.04439v1 | https://arxiv.org/pdf/2303.04439v1.pdf | A Light Weight Model for Active Speaker Detection | Active speaker detection is a challenging task in audio-visual scenario understanding, which aims to detect who is speaking in one or more speakers scenarios. This task has received extensive attention as it is crucial in applications such as speaker diarization, speaker tracking, and automatic video editing. The exist... | ['Liangyin Chen', 'Yanbing Yang', 'Wanbing Zhao', 'Kanghui Feng', 'Haihan Duan', 'Junhua Liao'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.29860118e-01 -2.56268531e-01 -6.33742064e-02 -3.41153532e-01
-1.18628836e+00 -4.61297184e-01 2.58140862e-01 -9.61976200e-02
-3.94737989e-01 8.82475302e-02 1.66550249e-01 -4.78021413e-01
8.99879485e-02 -3.24184150e-01 -3.64922345e-01 -7.94842958e-01
-1.71544254e-01 1.73445478e-01 3.72396797e-01 2.34483276... | [14.42644214630127, 5.343384742736816] |
810d450e-c693-496f-ad47-415bea4a4ccf | character-level-representations-improve-drs | 2011.04308 | null | https://arxiv.org/abs/2011.04308v1 | https://arxiv.org/pdf/2011.04308v1.pdf | Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT | We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to differen... | ['Johan Bos', 'Antonio Toral', 'Rik van Noord'] | 2020-11-09 | null | https://aclanthology.org/2020.emnlp-main.371 | https://aclanthology.org/2020.emnlp-main.371.pdf | emnlp-2020-11 | ['drs-parsing'] | ['natural-language-processing'] | [ 4.53330874e-01 4.36841249e-01 -7.16376007e-01 -4.98357207e-01
-1.02709985e+00 -8.05077612e-01 9.64282691e-01 8.07163537e-01
-5.89775980e-01 6.07665598e-01 1.20867860e+00 -6.07224166e-01
2.47429103e-01 -9.12379324e-01 -5.96198261e-01 2.54182015e-02
2.91983057e-02 4.68012005e-01 4.71608669e-01 -5.33365667... | [10.549723625183105, 9.258108139038086] |
204d112b-76b2-467e-b914-b4c0e63882a2 | probabilistic-neural-programmed-networks-for | null | null | http://papers.nips.cc/paper/7658-probabilistic-neural-programmed-networks-for-scene-generation | http://papers.nips.cc/paper/7658-probabilistic-neural-programmed-networks-for-scene-generation.pdf | Probabilistic Neural Programmed Networks for Scene Generation | In this paper we address the text to scene image generation problem. Generative models that capture the variability in complicated scenes containing rich semantics is a grand goal of image generation. Complicated scene images contain rich visual elements, compositional visual concepts, and complicated relations between... | ['Jiacheng Chen', 'Zhiwei Deng', 'Yifang Fu', 'Greg Mori'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['scene-generation'] | ['computer-vision'] | [ 3.60118508e-01 3.22040841e-02 2.77490616e-01 -4.15507734e-01
-3.43561590e-01 -7.16059387e-01 1.11614513e+00 -6.79261923e-01
3.08768392e-01 6.87544048e-01 6.18644178e-01 -5.38115613e-02
1.33942440e-01 -1.21932352e+00 -1.03054011e+00 -8.32926333e-01
5.14522731e-01 6.76529646e-01 -2.87637934e-02 -2.79783100... | [11.32016658782959, -0.23785781860351562] |
9dd3fc19-9a3a-404c-9aff-b10cf33af3b7 | what-can-unsupervised-machine-translation | 2106.15818 | null | https://arxiv.org/abs/2106.15818v2 | https://arxiv.org/pdf/2106.15818v2.pdf | On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation | Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions. As the performance gap between supervised and unsupervised MT narrows, it is interesting to ask whether the different training methods result in systematically different output beyond wh... | ['David Grangier', 'Markus Freitag', 'Kelly Marchisio'] | 2021-06-30 | null | https://aclanthology.org/2022.naacl-main.161 | https://aclanthology.org/2022.naacl-main.161.pdf | naacl-2022-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.15772736e-01 4.52893227e-01 -4.54611897e-01 -5.08262277e-01
-1.15438879e+00 -1.07643366e+00 9.79743958e-01 1.71038464e-01
-3.42774361e-01 1.08618736e+00 7.93681920e-01 -7.65185833e-01
1.73029765e-01 -4.91486013e-01 -4.38455433e-01 -2.94902593e-01
5.86203277e-01 8.83098900e-01 -2.25105137e-01 -5.43161094... | [11.569479942321777, 10.255558013916016] |
d591d272-0dd8-4ad0-b6e7-923447cd6b56 | ultra-light-ocr-competition-technical-report | 2110.12623 | null | https://arxiv.org/abs/2110.12623v1 | https://arxiv.org/pdf/2110.12623v1.pdf | Ultra Light OCR Competition Technical Report | Ultra Light OCR Competition is a Chinese scene text recognition competition jointly organized by CSIG (China Society of Image and Graphics) and Baidu, Inc. In addition to focusing on common problems in Chinese scene text recognition, such as long text length and massive characters, we need to balance the trade-off of m... | ['Yichao Xiong', 'Tianhe Wang', 'Yuxin Zou', 'Shuhan Zhang'] | 2021-10-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [-1.14279106e-01 -8.62209976e-01 7.87675008e-02 -1.28585830e-01
-5.29885411e-01 -4.33751315e-01 5.73212564e-01 -3.72873992e-01
-5.28437555e-01 3.05283427e-01 1.26806602e-01 -4.59295720e-01
4.53542978e-01 -4.03522432e-01 -4.00773793e-01 -5.31355619e-01
7.43732870e-01 3.07437569e-01 2.53374040e-01 4.46944823... | [11.957849502563477, 2.261159896850586] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.