paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ed50646e-6b11-46be-9a0e-59437d846299 | iifl-implicit-interactive-fleet-learning-from | 2306.15228 | null | https://arxiv.org/abs/2306.15228v1 | https://arxiv.org/pdf/2306.15228v1.pdf | IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors | Imitation learning has been applied to a range of robotic tasks, but can struggle when (1) robots encounter edge cases that are not represented in the training data (distribution shift) or (2) the human demonstrations are heterogeneous: taking different paths around an obstacle, for instance (multimodality). Interactiv... | ['Ken Goldberg', 'Eugen Solowjow', 'Anrui Gu', 'Ryan Hoque', 'Gaurav Datta'] | 2023-06-27 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 8.71636420e-02 3.21564257e-01 -2.83716351e-01 2.04880908e-02
-6.20281696e-01 -5.78784585e-01 7.42920220e-01 -1.04900993e-01
-9.22764719e-01 1.08508837e+00 -3.05869879e-04 -3.37724060e-01
-5.15908301e-01 -5.09787165e-02 -1.10117841e+00 -6.09598935e-01
-6.66004062e-01 7.43165433e-01 1.18841209e-01 -3.39396596... | [4.494185447692871, 1.0423370599746704] |
64a3f49a-c545-40e0-a3ce-1902c8ee7a67 | phonocardiogram-classification-using-1 | null | null | https://www.researchgate.net/publication/363503972_Phonocardiogram_Classification_Using_1-Dimensional_Inception_Time_Convolutional_Neural_Networks?channel=doi&linkId=63202e0c0a70852150eda8fc&showFulltext=true | https://cinc.org/2022/Program/accepted/108_Preprint.pdf | Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks | Murmurs are sounds caused by turbulent blood flow that are often the first sign of structural heart disease. These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG). We aim to identify the presence, absence, or unclear cases of murmurs, as well as predict nor... | ['Henrik Schirmer', 'Lars Ailo Bongo', 'Johan Ravn', 'Markus Kreutzer Johnsen', 'Antony M. Gitau', 'Bjørn-Jostein Singstad'] | 2022-09-07 | null | null | null | computing-in-cardiology-2022-9 | ['predict-clinical-outcome', 'classify-murmurs', 'phonocardiogram-classification'] | ['time-series', 'time-series', 'time-series'] | [ 2.54122883e-01 4.13652241e-01 3.49058270e-01 -2.00953260e-01
-7.30891824e-01 -3.12080920e-01 -1.79258838e-01 3.46544236e-01
-1.57622248e-01 7.72821844e-01 3.68161172e-01 -6.14703298e-01
-2.69549906e-01 -5.24302602e-01 -2.74231374e-01 -3.77811372e-01
-7.62892663e-01 8.11179519e-01 -3.32631245e-02 4.59602982... | [14.352355003356934, 3.286165714263916] |
8075dff4-4dd9-4552-8dbc-e67704ecd8e3 | neural-improvement-heuristics-for-preference | 2206.00383 | null | https://arxiv.org/abs/2206.00383v2 | https://arxiv.org/pdf/2206.00383v2.pdf | Neural Improvement Heuristics for Graph Combinatorial Optimization Problems | In recent years, methods based on deep neural networks, and especially Neural Improvement (NI) models, have led to a revolution in the field of combinatorial optimization. Given an instance of a graph-based problem and a candidate solution, they are able to propose a modification rule that improves its quality. However... | ['Alexander Mendiburu', 'Josu Ceberio', 'Andoni I. Garmendia'] | 2022-06-01 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 2.52487898e-01 3.15577835e-02 -6.28657222e-01 -2.67802685e-01
-2.30961516e-01 -2.36433774e-01 -3.55033875e-02 7.42187023e-01
-5.52604735e-01 8.65805805e-01 -1.17181316e-01 -4.63850051e-01
-1.06458938e+00 -1.32773769e+00 -7.64072955e-01 -6.96855903e-01
-3.45024943e-01 7.33921230e-01 5.04792966e-02 -5.51685035... | [5.3391523361206055, 3.174720048904419] |
b4aa3c8c-e9da-45c7-a888-e91ceb768253 | deep-joint-transmission-recognition-for-power | 2003.02027 | null | https://arxiv.org/abs/2003.02027v2 | https://arxiv.org/pdf/2003.02027v2.pdf | Joint Device-Edge Inference over Wireless Links with Pruning | We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge. Our goal is to enable efficient and reliable inference at the edge server assuming limited computational resources at the edge device. Previous work focused mainly on feature compression, ignoring the co... | ['Krystian Mikolajczyk', 'Mikolaj Jankowski', 'Deniz Gunduz'] | 2020-03-04 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 5.47468364e-01 1.25581697e-01 -2.92470366e-01 -1.52967304e-01
-4.34058845e-01 -3.38398144e-02 3.20667326e-02 -3.58948600e-03
-4.25022691e-01 5.51187336e-01 1.59269087e-02 -4.79790390e-01
-3.47255141e-01 -8.27402711e-01 -7.76007295e-01 -4.43722069e-01
-5.87000430e-01 1.12895720e-01 1.50983602e-01 3.51285100... | [8.4058256149292, 2.8675856590270996] |
79600d25-8974-4827-9ee8-dc0675061549 | a-collection-of-scholarly-book-reviews-from | null | null | https://aclanthology.org/L14-1660 | https://aclanthology.org/L14-1660.pdf | A Collection of Scholarly Book Reviews from the Platforms of electronic sources in Humanities and Social Sciences OpenEdition.org | In this paper, we present our contribution for the automatic construction of the Scholarly Book Reviews corpora from two different sources, the OpenEdition platform which is dedicated to electronic resources in the humanities and social sciences, and the Web. The main target is the collect of reviews in order to provid... | ["Fr{\\'e}d{\\'e}ric B{\\'e}chet", 'Patrice Bellot', 'Elodie Faath', 'Chahinez Benkoussas', 'Hussam Hamdan'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['genre-classification'] | ['computer-vision'] | [-2.82950133e-01 8.02880153e-02 -8.48599732e-01 4.98713963e-02
-7.08498418e-01 -7.67603099e-01 1.24469960e+00 7.35505998e-01
-4.47162151e-01 8.79467547e-01 5.17955124e-01 -3.80077958e-01
-2.57214934e-01 -8.74018192e-01 -1.63283333e-01 -1.80395946e-01
3.99928957e-01 6.34487212e-01 1.14347599e-01 -3.84277254... | [12.064170837402344, 9.520589828491211] |
75f17e0b-de09-46da-9465-fc806f14a6d6 | learning-to-race-through-coordinate-descent | 1802.06179 | null | http://arxiv.org/abs/1802.06179v1 | http://arxiv.org/pdf/1802.06179v1.pdf | Learning to Race through Coordinate Descent Bayesian Optimisation | In the automation of many kinds of processes, the observable outcome can
often be described as the combined effect of an entire sequence of actions, or
controls, applied throughout its execution. In these cases, strategies to
optimise control policies for individual stages of the process might not be
applicable, and in... | ['Vitor Guizilini', 'Valdir Grassi Jr', 'Rafael Oliveira', 'Lionel Ott', 'Fernando H. M. Rocha', 'Fabio Ramos'] | 2018-02-17 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 2.73065746e-01 2.43701547e-01 7.61650354e-02 -1.67725325e-01
-2.30486006e-01 -4.25185174e-01 7.64470875e-01 3.07088494e-01
-8.55614603e-01 7.70108104e-01 -4.30104077e-01 -5.34658492e-01
-5.84271193e-01 -7.08089471e-01 -6.29273057e-01 -1.02685595e+00
-1.70670807e-01 7.91464090e-01 4.20914143e-01 -2.66966015... | [4.8166823387146, 1.826172947883606] |
c1bbc701-93d5-4961-8b32-5967031acb52 | video-language-co-attention-with-multimodal | null | null | https://aclanthology.org/2022.repl4nlp-1.15 | https://aclanthology.org/2022.repl4nlp-1.15.pdf | Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA | We propose the Video Language Co-Attention Network (VLCN) – a novel memory-enhanced model for Video Question Answering (VideoQA). Our model combines two original contributions”:" A multi-modal fast-learning feature fusion (FLF) block and a mechanism that uses self-attended language features to separately guide neural a... | ['Andreas Bulling', 'Ekta Sood', 'Adnen Abdessaied'] | null | null | null | null | repl4nlp-acl-2022-5 | ['video-question-answering'] | ['computer-vision'] | [-1.20867290e-01 -3.50990444e-01 -3.24681103e-02 -2.83056468e-01
-1.28823924e+00 -6.68342173e-01 4.46974069e-01 -1.83711186e-01
-6.88965082e-01 4.15982187e-01 5.10268152e-01 -2.64948308e-01
3.87565419e-02 -4.18162584e-01 -9.35902655e-01 -2.76210308e-01
1.50854036e-01 1.82113707e-01 2.63478309e-01 -2.96171218... | [10.40866470336914, 1.0262707471847534] |
43e2f2a0-c944-4137-9f33-88af9cdd8caa | gessure-a-robust-face-authentication-enabled | 2207.11033 | null | https://arxiv.org/abs/2207.11033v2 | https://arxiv.org/pdf/2207.11033v2.pdf | GesSure- A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application | Using physical interactive devices like mouse and keyboards hinders naturalistic human-machine interaction and increases the probability of surface contact during a pandemic. Existing gesture-recognition systems do not possess user authentication, making them unreliable. Static gestures in current gesture-recognition t... | ['Piyush Modi', 'Ayush Batra', 'Pratham G. Shenwai', 'Ishita', 'Siddharth Kotian', 'Ankit Jha'] | 2022-07-22 | null | null | null | null | ['gesture-recognition', 'face-model'] | ['computer-vision', 'computer-vision'] | [ 1.96651667e-01 -4.97695506e-01 -3.71937424e-01 -4.68178749e-01
2.09007189e-02 -5.63155353e-01 5.59966981e-01 -6.49502218e-01
-9.46626842e-01 2.32937858e-01 -7.97122121e-02 -7.99397409e-01
-3.92862409e-02 -3.54459673e-01 7.63487220e-02 -6.28267407e-01
-2.30444726e-02 3.53465192e-02 -6.94209859e-02 -1.49282683... | [6.540956974029541, -0.21750518679618835] |
398e70cb-1056-495b-96a7-9c93f633804f | rffnet-scalable-and-interpretable-kernel | 2211.06410 | null | https://arxiv.org/abs/2211.06410v1 | https://arxiv.org/pdf/2211.06410v1.pdf | RFFNet: Scalable and interpretable kernel methods via Random Fourier Features | Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning. While memory requirements hinder their applicability to large datasets, many approximate solvers were recently developed for scaling up kernel methods, such as random Fourier features. However, these scalable ... | ['Rafael Izbicki', 'Mateus P. Otto'] | 2022-11-11 | null | null | null | null | ['variable-selection'] | ['methodology'] | [-1.92724526e-01 -1.97872937e-01 -3.29080015e-01 -2.81066775e-01
-8.50295782e-01 -3.60477746e-01 3.22517276e-01 9.79178250e-02
-3.31675291e-01 1.06072569e+00 -1.45355850e-01 -2.15706006e-01
-6.80412173e-01 -5.65850139e-01 -5.05491674e-01 -7.86533833e-01
-3.71873289e-01 3.85754138e-01 1.80800423e-01 1.16613001... | [7.599112510681152, 4.1388773918151855] |
883cd051-4d5f-4164-ae5c-0afe7a6012f6 | learning-to-bound-counterfactual-inference-in | 2212.02932 | null | https://arxiv.org/abs/2212.02932v2 | https://arxiv.org/pdf/2212.02932v2.pdf | Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data | We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models. We start from the case of a single observational dataset affected by a selection bias. We show that the likelihood of the available data... | ['Rafael Cabañas', 'David Huber', 'Alessandro Antonucci', 'Marco Zaffalon'] | 2022-12-06 | null | null | null | null | ['counterfactual-inference', 'selection-bias'] | ['miscellaneous', 'natural-language-processing'] | [ 6.48440182e-01 7.13639438e-01 -6.15810990e-01 -1.42447069e-01
-8.10796320e-01 -4.32933629e-01 7.08773136e-01 1.24198601e-01
-5.68392217e-01 1.51941383e+00 7.26119578e-01 -7.09624767e-01
-8.27211618e-01 -6.03011549e-01 -9.28898573e-01 -7.64449000e-01
-3.98342520e-01 5.59949815e-01 -2.45453149e-01 2.26375222... | [7.924848556518555, 5.283501625061035] |
0083d8d0-0ea7-4faa-a22c-90211020dccb | a-simple-global-neural-discourse-parser | 2009.01312 | null | https://arxiv.org/abs/2009.01312v2 | https://arxiv.org/pdf/2009.01312v2.pdf | A Simple Global Neural Discourse Parser | Discourse parsing is largely dominated by greedy parsers with manually-designed features, while global parsing is rare due to its computational expense. In this paper, we propose a simple chart-based neural discourse parser that does not require any manually-crafted features and is based on learned span representations... | ['Vivek Srikumar', 'Yichu Zhou', 'Omri Koshorek', 'Jonathan Berant'] | 2020-09-02 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.51667516e-02 6.99317217e-01 -3.05706292e-01 -3.55857670e-01
-1.12880504e+00 -7.40052760e-01 4.33122307e-01 6.01656020e-01
-2.70982057e-01 6.47039771e-01 5.96190512e-01 -6.18971825e-01
4.35329527e-02 -8.85599017e-01 -5.34644961e-01 -4.12605196e-01
-1.91717848e-01 3.98448825e-01 4.01337713e-01 -1.17837258... | [10.559646606445312, 9.478950500488281] |
1ac99487-9603-4f4f-9243-0a0371f5b278 | lightweight-improved-residual-network-for | 2307.03998 | null | https://arxiv.org/abs/2307.03998v1 | https://arxiv.org/pdf/2307.03998v1.pdf | Lightweight Improved Residual Network for Efficient Inverse Tone Mapping | The display devices like HDR10 televisions are increasingly prevalent in our daily life for visualizing high dynamic range (HDR) images. But the majority of media images on the internet remain in 8-bit standard dynamic range (SDR) format. Therefore, converting SDR images to HDR ones by inverse tone mapping (ITM) is cru... | ['Jun Xu', 'XianTong Zhen', 'Lei Zhang', 'Yan Liu', 'Yongbao Song', 'Tianyi Xu', 'Liqi Xue'] | 2023-07-08 | null | null | null | null | ['image-reconstruction', 'tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.35080534e-01 -3.50405633e-01 -3.32068026e-01 -3.76265049e-01
-5.88202119e-01 -2.11166799e-01 3.73198450e-01 -7.31499612e-01
-1.03214934e-01 4.92450655e-01 2.90137529e-01 -4.20221657e-01
1.05008200e-01 -7.81782031e-01 -7.79209554e-01 -4.54963535e-01
1.25943959e-01 -2.95923084e-01 3.75818044e-01 -4.08541054... | [10.860194206237793, -2.118788003921509] |
20f2b4a1-d094-4828-a106-be94d80c794c | evaluating-graph-generative-models-with | 2206.06234 | null | https://arxiv.org/abs/2206.06234v1 | https://arxiv.org/pdf/2206.06234v1.pdf | Evaluating Graph Generative Models with Contrastively Learned Features | A wide range of models have been proposed for Graph Generative Models, necessitating effective methods to evaluate their quality. So far, most techniques use either traditional metrics based on subgraph counting, or the representations of randomly initialized Graph Neural Networks (GNNs). We propose using representatio... | ['Danica J. Sutherland', 'Kaveh Hassani', 'Hamed Shirzad'] | 2022-06-13 | null | null | null | null | ['subgraph-counting'] | ['graphs'] | [ 2.38531530e-01 3.85988951e-01 -1.72025293e-01 -2.56483674e-01
-1.74641833e-01 -6.74081802e-01 1.21913278e+00 1.28383785e-01
4.43730280e-02 6.40871882e-01 2.05419511e-01 -5.36501706e-01
-5.06499887e-01 -1.43907297e+00 -4.63471860e-01 -5.55830002e-01
-4.28906560e-01 8.11487794e-01 2.56695122e-01 -3.98119509... | [6.9526519775390625, 6.240691184997559] |
5ab37a86-4536-4a14-885a-1a1504c99f4a | linear-transformations-for-cross-lingual | 1807.04172 | null | http://arxiv.org/abs/1807.04172v1 | http://arxiv.org/pdf/1807.04172v1.pdf | Linear Transformations for Cross-lingual Semantic Textual Similarity | Cross-lingual semantic textual similarity systems estimate the degree of the
meaning similarity between two sentences, each in a different language.
State-of-the-art algorithms usually employ machine translation and combine vast
amount of features, making the approach strongly supervised, resource rich, and
difficult t... | ['Tomáš Brychcín'] | 2018-07-11 | null | null | null | null | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.39453754e-02 -4.60742801e-01 -3.79018456e-01 -6.09605968e-01
-9.10869658e-01 -7.83663690e-01 8.99531364e-01 3.65967959e-01
-6.72741294e-01 6.35735035e-01 6.51189506e-01 -2.16980502e-01
-4.22284976e-02 -7.91521072e-01 -2.40373686e-01 -4.71766829e-01
7.01630354e-01 5.47193408e-01 2.53935069e-01 -7.92066693... | [10.940764427185059, 9.78098201751709] |
194e973d-b5bf-4c8c-9298-52258ef14fbf | particle-filter-recurrent-neural-networks | 1905.12885 | null | https://arxiv.org/abs/1905.12885v2 | https://arxiv.org/pdf/1905.12885v2.pdf | Particle Filter Recurrent Neural Networks | Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN re... | ['Wee Sun Lee', 'Xiao Ma', 'Peter Karkus', 'David Hsu'] | 2019-05-30 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [ 1.10011384e-01 3.78433168e-02 -3.23567063e-01 -1.52049940e-02
-3.44484091e-01 -8.17299113e-02 9.15771961e-01 -7.07035810e-02
-4.95869458e-01 1.02403665e+00 3.65847021e-01 -3.48307848e-01
-1.64693266e-01 -7.51763582e-01 -9.62878883e-01 -7.50759900e-01
-1.07508220e-01 9.52260196e-01 2.00837821e-01 -4.91106361... | [6.982832431793213, 3.4141311645507812] |
a9647eb5-cc09-44ae-896e-9d4dc0b1f393 | dectecting-invasive-ductal-carcinoma-with | 1911.06216 | null | https://arxiv.org/abs/1911.06216v2 | https://arxiv.org/pdf/1911.06216v2.pdf | Detecting Invasive Ductal Carcinoma with Semi-Supervised Conditional GANs | Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers. The detection of IDC is a necessary preprocessing step in determining the aggressiveness of the cancer, determining treatment protocols, and predicting patient outcomes, and is usually performed manually by an expert pathologist. Here, we descr... | ['Jeremiah W. Johnson'] | 2019-11-14 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 4.63119477e-01 4.12967801e-01 -4.17797565e-01 -3.65882993e-01
-1.17507732e+00 -5.91161728e-01 5.65963507e-01 3.71468544e-01
-4.61377710e-01 4.43909645e-01 2.35540494e-01 -9.06735420e-01
3.17529857e-01 -7.10428417e-01 -5.10542095e-01 -9.79291737e-01
-5.04781418e-02 8.48462164e-01 -3.98696028e-03 9.77272093... | [15.160795211791992, -2.9843456745147705] |
fe0468be-7e40-4faa-9ef3-f4cc5e145d5b | the-emergence-of-compositional-languages-for | 1910.05291 | null | https://arxiv.org/abs/1910.05291v1 | https://arxiv.org/pdf/1910.05291v1.pdf | The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents | Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined eleme... | ['Serhii Havrylov', 'Yi Ren', 'Ivan Titov', 'Stella Frank', 'Shangmin Guo', 'Kenny Smith'] | 2019-10-11 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [-1.58878081e-02 9.02496576e-02 2.69055218e-01 -1.35484844e-01
-1.05764873e-01 -6.54236138e-01 7.99467027e-01 3.50391537e-01
-6.79769158e-01 8.62958670e-01 5.16627841e-02 -3.44809920e-01
-9.18816924e-02 -1.00639641e+00 -5.67887604e-01 -7.30403602e-01
-5.04713118e-01 8.52753222e-01 -2.03933567e-01 -7.48745918... | [4.413423538208008, 1.6376776695251465] |
ddfe97f2-82e9-420f-828c-c841b98b7c1e | semantic-segmentation-on-vspw-dataset-through-1 | 2306.03508 | null | https://arxiv.org/abs/2306.03508v1 | https://arxiv.org/pdf/2306.03508v1.pdf | Semantic Segmentation on VSPW Dataset through Contrastive Loss and Multi-dataset Training Approach | Video scene parsing incorporates temporal information, which can enhance the consistency and accuracy of predictions compared to image scene parsing. The added temporal dimension enables a more comprehensive understanding of the scene, leading to more reliable results. This paper presents the winning solution of the CV... | ['Qian Wang', 'Qianxiong Ning', 'Min Yan'] | 2023-06-06 | null | null | null | null | ['scene-parsing', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.29963177e-01 1.76070005e-01 -5.09798050e-01 -6.37661994e-01
-8.47360849e-01 -4.38130945e-01 5.35193622e-01 -1.28992677e-01
-5.81458092e-01 3.91417682e-01 2.18031645e-01 -1.08166032e-01
8.54222402e-02 -5.00279307e-01 -8.81020665e-01 -3.08574200e-01
4.80111167e-02 2.36274332e-01 9.48660433e-01 1.31547228... | [9.186338424682617, 0.04475310444831848] |
56000e75-111d-4c2c-aa25-2d88dde48fda | co-optimization-of-adaptive-cruise-control | 2303.01218 | null | https://arxiv.org/abs/2303.01218v2 | https://arxiv.org/pdf/2303.01218v2.pdf | Co-Optimization of Adaptive Cruise Control and Hybrid Electric Vehicle Energy Management via Model Predictive Mixed Integer Control | In this paper, a model predictive mixed integer control method for BYD Qin Plus DM-i (Dual Model intelligent) plug-in hybrid electric vehicle (PHEV) is proposed for co-optimization to reduce fuel consumption during car following. First, the adaptive cruise control (ACC) model for energy-saving driving is established. T... | ['Yuan Lin', 'Changfu Gong', 'Qitao Li'] | 2023-03-02 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.31801531e-01 2.44589701e-01 -8.51553679e-01 1.78771354e-02
-6.48850575e-02 -2.62713253e-01 5.19324005e-01 3.84868905e-02
-1.22450531e-01 8.67977440e-01 -5.91356039e-01 -6.69506848e-01
-9.25202966e-01 -9.69308615e-01 -5.34931600e-01 -8.11450005e-01
1.11363910e-01 4.41892385e-01 -4.20455247e-01 -1.75290734... | [5.565412521362305, 2.2616865634918213] |
ab5ea0c0-a7c4-4582-93ec-09f04d980a78 | cumulative-progress-in-language-models-for | null | null | https://aclanthology.org/U13-1013 | https://aclanthology.org/U13-1013.pdf | Cumulative Progress in Language Models for Information Retrieval | null | ['Antti Puurula'] | 2013-12-01 | cumulative-progress-in-language-models-for-1 | https://aclanthology.org/U13-1013 | https://aclanthology.org/U13-1013.pdf | alta-2013-12 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.426943778991699, 3.609996795654297] |
4d4e720e-172c-4328-8b82-ff52fd23d2ca | using-twitter-to-predict-football-outcomes | 1411.1243 | null | http://arxiv.org/abs/1411.1243v1 | http://arxiv.org/pdf/1411.1243v1.pdf | Using Twitter to predict football outcomes | Twitter has been proven to be a notable source for predictive modelling on
various domains such as the stock market, the dissemination of diseases or
sports outcomes. However, such a study has not been conducted in football
(soccer) so far. The purpose of this research was to study whether data mined
from Twitter can b... | ['Andreas Adamides', 'Stylianos Kampakis'] | 2014-11-05 | null | null | null | null | ['game-of-football'] | ['playing-games'] | [-4.73080903e-01 -1.29158407e-01 -7.25995302e-01 2.91328738e-03
-6.55144691e-01 -1.16903782e-01 8.53686929e-01 1.04107606e+00
-9.11892116e-01 9.69098032e-01 4.34880823e-01 -2.62627423e-01
-3.06678712e-01 -1.25080943e+00 -6.12081587e-01 -4.51028347e-01
-1.45362198e-01 3.52774054e-01 5.27066410e-01 -6.28567100... | [8.312126159667969, 9.931244850158691] |
27f1fd52-d812-4e27-b269-cd501d7c0957 | all-you-need-in-sign-language-production | 2201.01609 | null | https://arxiv.org/abs/2201.01609v2 | https://arxiv.org/pdf/2201.01609v2.pdf | All You Need In Sign Language Production | Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundam... | ['Mohammad Sabokrou', 'Vassilis Athitsos', 'Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo'] | 2022-01-05 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation', 'sign-language-production'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [-2.85318419e-02 -8.43775049e-02 -1.64759889e-01 -3.08552057e-01
-6.69426203e-01 -4.90898818e-01 3.44051689e-01 -8.80136371e-01
-3.70738000e-01 6.51945472e-01 7.64612496e-01 -1.89583346e-01
-5.13770664e-03 -5.46733856e-01 -3.20596069e-01 -9.70758438e-01
-3.55717749e-03 1.72624514e-01 -4.17641401e-02 -4.86272454... | [9.088021278381348, -6.396602153778076] |
5b62abaa-5cba-4a6d-a612-e458db685e5f | my-way-of-telling-a-story-persona-based-1 | null | null | https://aclanthology.org/W19-3402 | https://aclanthology.org/W19-3402.pdf | ``My Way of Telling a Story'': Persona based Grounded Story Generation | Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is ... | ['Alan W. black', 'Ch', 'Shrimai Prabhumoye', 'Ruslan Salakhutdinov', 'Khyathi u'] | 2019-08-01 | null | null | null | ws-2019-8 | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.81071782e-01 5.23934066e-01 2.70480335e-01 -5.01848936e-01
-5.94605744e-01 -5.43815911e-01 1.23037577e+00 -2.71173716e-01
1.06303710e-02 7.34109044e-01 9.62773085e-01 8.21076706e-02
4.40427154e-01 -7.97850311e-01 -6.85039818e-01 -3.81282240e-01
5.54488897e-01 7.40644574e-01 6.68406412e-02 -3.86208177... | [11.140279769897461, 0.811137318611145] |
d35ce362-9f3b-4672-861a-c0466e7cafbd | backtranslation-in-neural-morphological | null | null | https://aclanthology.org/2021.insights-1.13 | https://aclanthology.org/2021.insights-1.13.pdf | Backtranslation in Neural Morphological Inflection | Backtranslation is a common technique for leveraging unlabeled data in low-resource scenarios in machine translation. The method is directly applicable to morphological inflection generation if unlabeled word forms are available. This paper evaluates the potential of backtranslation for morphological inflection using d... | ['Mans Hulden', 'Ling Liu'] | null | null | null | null | emnlp-insights-2021-11 | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.47734994e-01 1.02954894e-01 -6.62988007e-01 -5.77162504e-01
-1.23039377e+00 -1.16495216e+00 6.37783349e-01 2.70967990e-01
-8.93055141e-01 1.22188962e+00 6.34779692e-01 -7.32576251e-01
4.78445530e-01 -3.98383975e-01 -5.45943677e-01 -1.31349280e-01
4.70574021e-01 9.29364145e-01 -4.06533569e-01 -6.24403715... | [11.439751625061035, 10.261609077453613] |
977bb868-8923-43dc-9dd5-ad2dcfc026a5 | acquire-augment-segment-enjoy-weakly | 1807.02001 | null | http://arxiv.org/abs/1807.02001v2 | http://arxiv.org/pdf/1807.02001v2.pdf | Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products | Grocery stores have thousands of products that are usually identified using
barcodes with a human in the loop. For automated checkout systems, it is
necessary to count and classify the groceries efficiently and robustly. One
possibility is to use a deep learning algorithm for instance-aware semantic
segmentation. Such ... | ['Tobias Böttger', 'Bertram Drost', 'Patrick Follmann'] | 2018-07-05 | null | null | null | null | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 2.48483196e-01 2.58597076e-01 -3.11212271e-01 -5.13374090e-01
-5.63560963e-01 -7.18953967e-01 3.51287961e-01 6.87915564e-01
-5.49150229e-01 3.77304435e-01 -8.50990355e-01 -1.16277315e-01
3.63051593e-01 -1.12219322e+00 -9.51178610e-01 -6.09264374e-01
2.99451321e-01 1.19607460e+00 4.14776087e-01 1.68797541... | [9.437297821044922, 0.7150502800941467] |
bad3ae7d-db60-4f2b-a7e2-b5aba015acfb | contextualized-diachronic-word | null | null | https://aclanthology.org/W19-4705 | https://aclanthology.org/W19-4705.pdf | Contextualized Diachronic Word Representations | Diachronic word embeddings play a key role in capturing interesting patterns about how language evolves over time. Most of the existing work focuses on studying corpora spanning across several decades, which is understandably still not a possibility when working on social media-based user-generated content. In this wor... | ["Djam{\\'e} Seddah", 'Ganesh Jawahar'] | 2019-08-01 | null | null | null | ws-2019-8 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-2.52141535e-01 -2.61375219e-01 -4.56890404e-01 -1.08226866e-01
-6.39241859e-02 -5.34796536e-01 1.28319097e+00 9.22224879e-01
-1.02235413e+00 6.18334711e-01 7.26553321e-01 -3.07516962e-01
-2.38120943e-01 -1.08679473e+00 -4.73313183e-01 -5.98006308e-01
-3.03341120e-01 3.17323565e-01 4.69803452e-01 -5.54808199... | [10.115984916687012, 8.899441719055176] |
07370d1d-8838-43ce-8402-5bd32fed17cd | forecasting-individualized-disease | 1810.10489 | null | http://arxiv.org/abs/1810.10489v1 | http://arxiv.org/pdf/1810.10489v1.pdf | Forecasting Individualized Disease Trajectories using Interpretable Deep Learning | Disease progression models are instrumental in predicting individual-level
health trajectories and understanding disease dynamics. Existing models are
capable of providing either accurate predictions of patients prognoses or
clinically interpretable representations of disease pathophysiology, but not
both. In this pape... | ['Mihaela van der Schaar', 'Ahmed M. Alaa'] | 2018-10-24 | null | null | null | null | ['disease-trajectory-forecasting'] | ['medical'] | [ 3.21667314e-01 3.06707054e-01 -4.10847574e-01 -2.24903688e-01
-6.07958257e-01 -2.95384154e-02 6.76583648e-01 4.59876478e-01
-4.65838006e-03 7.04155385e-01 6.40137851e-01 -5.28748155e-01
-5.22226155e-01 -6.55866086e-01 -1.96837679e-01 -7.08494842e-01
-7.71017969e-01 9.75630224e-01 -1.38184696e-01 -6.88562123... | [7.8302531242370605, 5.911077976226807] |
7f90d7ba-f84a-4af6-97da-5cff11d0c686 | resunet-an-advanced-architecture-for-medical | 1911.07067 | null | https://arxiv.org/abs/1911.07067v1 | https://arxiv.org/pdf/1911.07067v1.pdf | ResUNet++: An Advanced Architecture for Medical Image Segmentation | Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUN... | ['Thomas de Lange', 'Michael A. Riegler', 'Pal Halvorsen', 'Dag Johansen', 'Debesh Jha', 'Pia H. Smedsrud', 'Havard D. Johansen'] | 2019-11-16 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [ 7.77212111e-03 2.68587530e-01 -2.40979642e-01 -1.02162413e-01
-8.48788798e-01 -6.55406952e-01 -5.47075970e-03 4.49049294e-01
-4.83078271e-01 4.52326894e-01 -1.72329053e-01 -9.20978785e-01
1.71138719e-01 -7.64015019e-01 -7.94797003e-01 -5.75768948e-01
-3.01045477e-01 2.30924621e-01 3.86588871e-01 2.67148137... | [14.492087364196777, -2.848069667816162] |
06c4bd54-64dd-4eab-99bf-f651b008747e | paradise-exploiting-parallel-data-for | 2108.01887 | null | https://arxiv.org/abs/2108.01887v1 | https://arxiv.org/pdf/2108.01887v1.pdf | PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining | Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel & Denoising Integration in SEquence-to-sequence models), which exten... | ['Mikel Artetxe', 'Machel Reid'] | 2021-08-04 | null | https://aclanthology.org/2022.naacl-main.58 | https://aclanthology.org/2022.naacl-main.58.pdf | naacl-2022-7 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 3.51978183e-01 -3.16574812e-01 -1.73497915e-01 -3.44150662e-01
-1.45859349e+00 -1.00308669e+00 8.32699060e-01 -8.03617612e-02
-8.10302556e-01 1.00982630e+00 2.79787391e-01 -7.37924755e-01
6.08418882e-01 -3.00719142e-01 -1.14325392e+00 -6.49063647e-01
5.06715119e-01 6.28659487e-01 -1.15737736e-01 -3.76261741... | [11.649006843566895, 10.272026062011719] |
e8fc4957-eecd-41a2-8abd-2af3ea62c46d | a-lifetime-extended-energy-management | 2302.06236 | null | https://arxiv.org/abs/2302.06236v1 | https://arxiv.org/pdf/2302.06236v1.pdf | A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning | Modeling difficulty, time-varying model, and uncertain external inputs are the main challenges for energy management of fuel cell hybrid electric vehicles. In the paper, a fuzzy reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles is proposed to reduce fuel consumption, maintai... | ['Rachid Outbib', 'Zhongliang Li', 'Liang Guo'] | 2023-02-13 | null | null | null | null | ['self-learning', 'energy-management'] | ['natural-language-processing', 'time-series'] | [-5.37073553e-01 6.52469248e-02 -4.81650054e-01 -1.02082640e-01
1.72885448e-01 -5.30662775e-01 1.27607867e-01 3.95313427e-02
-4.68379140e-01 1.31752491e+00 -6.45289302e-01 -1.84670985e-01
-3.82495612e-01 -1.12863135e+00 -7.09326327e-01 -9.42637324e-01
2.30089441e-01 2.99940109e-01 3.88922125e-01 -3.94761056... | [5.518857955932617, 2.310990810394287] |
1cb1ec47-b24e-4f40-8927-e17560c1e7be | dual-path-adaptation-from-image-to-video | 2303.09857 | null | https://arxiv.org/abs/2303.09857v1 | https://arxiv.org/pdf/2303.09857v1.pdf | Dual-path Adaptation from Image to Video Transformers | In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters. Previous adaptation methods have simultaneously considered spatial and temporal modeling with a unified learnable module but sti... | ['Kwanghoon Sohn', 'Jiyoung Lee', 'Jungin Park'] | 2023-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification', 'activity-recognition-in-videos', 'video-understanding', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.85300404e-01 -5.06939255e-02 -4.70378786e-01 -4.50595886e-01
-5.36419034e-01 -5.56351364e-01 7.50795126e-01 -5.73044538e-01
-3.94708991e-01 3.58949125e-01 3.91144156e-01 -3.61930549e-01
5.14933057e-02 -5.89345217e-01 -1.26547706e+00 -5.45895815e-01
1.33506238e-01 2.91103512e-01 6.20260656e-01 -1.42922729... | [9.186174392700195, 0.7394708395004272] |
45c9c74d-dbbc-4be7-a240-977a3dee34bd | bridging-spectral-embedding-and-matrix | 2305.19818 | null | https://arxiv.org/abs/2305.19818v1 | https://arxiv.org/pdf/2305.19818v1.pdf | Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning | Self-supervised methods received tremendous attention thanks to their seemingly heuristic approach to learning representations that respect the semantics of the data without any apparent supervision in the form of labels. A growing body of literature is already being published in an attempt to build a coherent and theo... | ['Ivan Oseledets', 'Marina Munkhoeva'] | 2023-05-31 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 4.87286597e-01 4.23222959e-01 -4.67206448e-01 -6.73247099e-01
-5.43583810e-01 -3.67729634e-01 6.29914820e-01 3.77619952e-01
-2.39189535e-01 4.72213417e-01 4.06876713e-01 -3.06281865e-01
-2.86500514e-01 -5.36840796e-01 -6.31469965e-01 -5.56729436e-01
-4.35124636e-02 2.15167731e-01 -4.23735529e-01 -3.38707566... | [9.095806121826172, 3.149845600128174] |
d040fd42-3527-4265-b06c-538070a734d8 | adversarial-discriminative-heterogeneous-face | 1709.03675 | null | http://arxiv.org/abs/1709.03675v1 | http://arxiv.org/pdf/1709.03675v1.pdf | Adversarial Discriminative Heterogeneous Face Recognition | The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This fram... | ['Man Zhang', 'Xiang Wu', 'Ran He', 'Lingxiao Song'] | 2017-09-12 | null | null | null | null | ['heterogeneous-face-recognition', 'face-hallucination'] | ['computer-vision', 'computer-vision'] | [ 5.52083194e-01 -9.67764482e-02 3.39906543e-01 -3.59195650e-01
-1.16146100e+00 -3.00599128e-01 4.42778885e-01 -7.68230855e-01
-6.98051527e-02 7.69647658e-01 7.86042213e-02 2.76639134e-01
-2.12219238e-01 -8.65791917e-01 -7.34374344e-01 -1.08367348e+00
4.12865490e-01 -1.28708124e-01 -2.42293045e-01 -1.04482256... | [13.048041343688965, 0.19379930198192596] |
166e60d4-f6ad-40fc-9217-7023539cdbe4 | sentiment-analysis-on-brazilian-portuguese | 2112.05459 | null | https://arxiv.org/abs/2112.05459v1 | https://arxiv.org/pdf/2112.05459v1.pdf | Sentiment Analysis on Brazilian Portuguese User Reviews | Sentiment Analysis is one of the most classical and primarily studied natural language processing tasks. This problem had a notable advance with the proposition of more complex and scalable machine learning models. Despite this progress, the Brazilian Portuguese language still disposes only of limited linguistic resour... | ['João Filho', 'Frederico Souza'] | 2021-12-10 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 6.52332380e-02 5.12052923e-02 -2.72149086e-01 -4.63077784e-01
-3.99436176e-01 -6.92533255e-01 8.82544875e-01 9.36919212e-01
-8.82652879e-01 6.33182645e-01 2.28319541e-01 -3.08218241e-01
-2.60902643e-01 -8.97226989e-01 8.38577151e-02 -5.90610802e-01
1.61101595e-01 5.27775228e-01 -4.05506231e-02 -3.98715913... | [11.100207328796387, 7.139816761016846] |
d0affa41-b0b0-4116-9db6-a823dadc3428 | unsupervised-multi-view-object-segmentation | 2210.00489 | null | https://arxiv.org/abs/2210.00489v2 | https://arxiv.org/pdf/2210.00489v2.pdf | Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation | We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is ... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Huai Yu', 'Jiaben Chen', 'Xinhang Liu'] | 2022-10-02 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 9.0656877e-01 1.5702368e-01 1.2853917e-01 -8.2738829e-01
-5.5059534e-01 -6.0395622e-01 4.1925862e-01 1.3044107e-01
-2.7865791e-01 1.8008524e-01 -2.6647702e-01 -7.7840246e-02
-3.9287083e-02 -9.3675810e-01 -9.3082231e-01 -6.2307250e-01
2.7426425e-01 4.4679502e-01 7.0896339e-01 -9.9984042e-02
2.7707776e-01... | [8.841636657714844, -2.9120452404022217] |
085587f5-f145-43f7-aa7e-04400a97ab4d | credit-card-fraud-detection-using | null | null | https://link.springer.com/chapter/10.1007/978-3-319-46675-0_53 | https://link.springer.com/chapter/10.1007/978-3-319-46675-0_53 | Credit Card Fraud Detection Using Convolutional Neural Networks | Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing. Conventional methods use rule-based expert systems to detect fraud behaviors, neglecting diverse situations, extreme imbalance of positive and negative samples. In this paper, we propose a CNN-based fra... | ['and Liqing Zhang', 'Yi Tu', 'Dawei Cheng', 'Kang Fu'] | 2016-10-16 | null | null | null | international-conference-on-neural-3 | ['fraud-detection'] | ['miscellaneous'] | [-5.01164019e-01 -7.34273791e-01 -3.35905492e-01 -6.03165507e-01
5.16486503e-02 -4.95709255e-02 1.44094899e-02 1.63117632e-01
-3.63623321e-01 6.35625482e-01 -1.43910617e-01 -2.85682768e-01
2.36388907e-01 -1.06658852e+00 -2.38721341e-01 -3.74432176e-01
-2.45045707e-01 4.92087454e-01 -2.00551450e-01 -2.56067902... | [7.436163902282715, 5.716022491455078] |
8cec2074-8127-4c42-8be4-9bf7613c068b | automatic-tagging-and-retrieval-of-e-commerce | null | null | https://aclanthology.org/N16-2004 | https://aclanthology.org/N16-2004.pdf | Automatic tagging and retrieval of E-Commerce products based on visual features | null | ['Vasu Sharma', 'Harish Karnick'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['product-categorization'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.408280849456787, 3.6767075061798096] |
b1f359ab-8dfc-445a-8f66-5806aad6db09 | socialdial-a-benchmark-for-socially-aware | 2304.12026 | null | https://arxiv.org/abs/2304.12026v1 | https://arxiv.org/pdf/2304.12026v1.pdf | SocialDial: A Benchmark for Socially-Aware Dialogue Systems | Dialogue systems have been widely applied in many scenarios and are now more powerful and ubiquitous than ever before. With large neural models and massive available data, current dialogue systems have access to more knowledge than any people in their life. However, current dialogue systems still do not perform at a hu... | ['Gholamreza Haffari', 'Zhaleh Semnani-Azad', 'Ingrid Zukerman', 'Suraj Sharma', 'Lay-Ki Soon', 'Lizhen Qu', 'Yuncheng Hua', 'Xiaoxi Kang', 'Tao Feng', 'Linhao Luo', 'YuFei Wang', 'Zhuang Li', 'Haolan Zhan'] | 2023-04-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'culture'] | ['medical', 'miscellaneous', 'speech'] | [-1.93647936e-01 6.69243276e-01 9.13598388e-02 -6.99818611e-01
-2.42496803e-01 -4.15891171e-01 9.88301218e-01 -2.65107691e-01
-4.25587416e-01 1.21883726e+00 6.75935090e-01 4.92943451e-02
1.89433411e-01 -7.95676947e-01 -1.31491289e-01 -3.30564797e-01
9.74656492e-02 8.95050764e-01 1.16109669e-01 -9.45102692... | [12.901918411254883, 8.13329792022705] |
486b7c2a-96f2-4d03-b3bc-fd8fa00c905e | deep-and-confident-prediction-for-time-series | 1709.01907 | null | http://arxiv.org/abs/1709.01907v1 | http://arxiv.org/pdf/1709.01907v1.pdf | Deep and Confident Prediction for Time Series at Uber | Reliable uncertainty estimation for time series prediction is critical in
many fields, including physics, biology, and manufacturing. At Uber,
probabilistic time series forecasting is used for robust prediction of number
of trips during special events, driver incentive allocation, as well as
real-time anomaly detection... | ['Lingxue Zhu', 'Nikolay Laptev'] | 2017-09-06 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-5.36874950e-01 -2.36741498e-01 1.06689334e-02 -6.85063362e-01
-9.37101662e-01 -4.78358418e-01 5.32834411e-01 4.18127835e-01
-1.90812662e-01 7.65252590e-01 4.16385174e-01 -2.94977039e-01
-6.51062846e-01 -7.59447992e-01 -1.08436680e+00 -3.79970223e-01
-2.54447997e-01 6.04780853e-01 1.37517765e-01 -1.32544756... | [6.889160633087158, 3.1603000164031982] |
aba7d13f-f1c5-470d-a14c-8030be42c9f0 | causal-reasoning-and-large-language-models | 2305.00050 | null | https://arxiv.org/abs/2305.00050v2 | https://arxiv.org/pdf/2305.00050v2.pdf | Causal Reasoning and Large Language Models: Opening a New Frontier for Causality | The causal capabilities of large language models (LLMs) is a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We further our understanding of LLMs and their causal implications, considering the distinctions between d... | ['Chenhao Tan', 'Amit Sharma', 'Robert Ness', 'Emre Kiciman'] | 2023-04-28 | null | null | null | null | ['causal-discovery', 'common-sense-reasoning'] | ['knowledge-base', 'reasoning'] | [ 4.76587147e-01 7.91777372e-01 -8.95909131e-01 -2.03767613e-01
-5.39558947e-01 -7.00302184e-01 1.06537664e+00 6.45466745e-01
-2.20849231e-01 1.03992176e+00 9.12038863e-01 -1.04250574e+00
-7.89101183e-01 -9.20370460e-01 -8.83539796e-01 -9.20031443e-02
-3.92752975e-01 4.12762105e-01 -8.21854547e-02 -1.06459528... | [8.134244918823242, 5.504613876342773] |
03715cd4-3490-4e7d-9c50-952b7ce7e8db | fast-text-conditional-discrete-denoising-on | 2211.07292 | null | https://arxiv.org/abs/2211.07292v2 | https://arxiv.org/pdf/2211.07292v2.pdf | A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces | Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity. Contemporary techniques enable the generation of highly intricate visuals which rapidly approach near-photorealistic quality. Nevertheless, as progress is achieved,... | ['Marc Aubreville', 'Pablo Pernias', 'Dominic Rampas'] | 2022-11-14 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.54329157e-01 4.07741666e-02 2.05008358e-01 -8.83622915e-02
-6.22245491e-01 -6.26544595e-01 8.65635991e-01 -1.29049182e-01
2.14340109e-02 6.56839788e-01 3.42488080e-01 -2.55468339e-01
1.69493273e-01 -5.95840514e-01 -7.06126630e-01 -4.47710067e-01
2.56926209e-01 1.84097588e-02 1.38231060e-02 -1.67242363... | [11.363227844238281, -0.3856285810470581] |
e204b3c3-5c51-4342-8aca-292b0c06a539 | event-based-simultaneous-localization-and | 2304.09793 | null | https://arxiv.org/abs/2304.09793v1 | https://arxiv.org/pdf/2304.09793v1.pdf | Event-based Simultaneous Localization and Mapping: A Comprehensive Survey | In recent decades, visual simultaneous localization and mapping (vSLAM) has gained significant interest in both academia and industry. It estimates camera motion and reconstructs the environment concurrently using visual sensors on a moving robot. However, conventional cameras are limited by hardware, including motion ... | ['DaCheng Tao', 'Jing Zhang', 'Sen Zhang', 'Kunping Huang'] | 2023-04-19 | null | null | null | null | ['simultaneous-localization-and-mapping', 'motion-compensation'] | ['computer-vision', 'computer-vision'] | [ 1.57231297e-02 -8.65195870e-01 -2.09065333e-01 -8.51751640e-02
-5.52848756e-01 -5.00551820e-01 6.11396968e-01 1.16512544e-01
-4.81292844e-01 6.59030735e-01 4.50491644e-02 3.12960505e-01
-3.83817926e-02 -4.39977199e-01 -6.63378298e-01 -8.35568845e-01
-1.18297659e-01 -1.21202826e-01 5.92882991e-01 3.80194396... | [8.52540397644043, -1.299190878868103] |
52e5f085-60b9-4e95-adb9-521efa220022 | misc210k-a-large-scale-dataset-for-multi | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sun_MISC210K_A_Large-Scale_Dataset_for_Multi-Instance_Semantic_Correspondence_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sun_MISC210K_A_Large-Scale_Dataset_for_Multi-Instance_Semantic_Correspondence_CVPR_2023_paper.pdf | MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence | Semantic correspondence have built up a new way for object recognition. However current single-object matching schema can be hard for discovering commonalities for a category and far from the real-world recognition tasks. To fill this gap, we design the multi-instance semantic correspondence task which aims at cons... | ['Wenqiang Zhang', 'Weifeng Ge', 'Yizhou Yu', 'Runmin Wu', 'Yuzhou Zhao', 'Haijing Guo', 'Yiwen Huang', 'Yixuan Sun'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-recognition', 'semantic-correspondence'] | ['computer-vision', 'computer-vision'] | [ 2.61639744e-01 -5.25045320e-02 -2.19366342e-01 -7.36346185e-01
-1.11331117e+00 -7.43558347e-01 7.52035558e-01 2.93481201e-01
-1.90243557e-01 2.02410277e-02 -5.87572306e-02 -4.23679426e-02
-2.93900132e-01 -6.47633314e-01 -9.91943002e-01 -2.91057229e-01
1.16469443e-01 8.67774367e-01 5.03467143e-01 2.05403909... | [9.53165054321289, 1.5626201629638672] |
8937ab0f-79bd-4352-a2b9-bacb4bc8c279 | diffusion-based-conditional-ecg-generation | 2301.08227 | null | https://arxiv.org/abs/2301.08227v2 | https://arxiv.org/pdf/2301.08227v2.pdf | Diffusion-based Conditional ECG Generation with Structured State Space Models | Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for generative models for different data modalities. Also very recently, structured state space models emerged as a powerful modeling paradigm to ... | ['Nils Strodthoff', 'Juan Miguel Lopez Alcaraz'] | 2023-01-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.80274981e-01 4.52562720e-01 2.09056720e-01 -6.42883301e-01
-1.40276408e+00 -5.17867029e-01 5.73630512e-01 -9.90887135e-02
-1.45075604e-01 1.13181436e+00 3.26791555e-01 -1.47215858e-01
1.07385464e-01 -6.06780171e-01 -6.81266129e-01 -5.62359631e-01
-2.51505733e-01 6.70293272e-01 -3.87139618e-01 9.62735340... | [14.311424255371094, 3.0419936180114746] |
dc4e819e-2150-4dd5-86ec-78be50b3d972 | eyelovegan-exploiting-domain-shifts-to-boost | 2203.05344 | null | https://arxiv.org/abs/2203.05344v1 | https://arxiv.org/pdf/2203.05344v1.pdf | EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs | This paper presents our contribution to the REFUGE challenge 2020. The challenge consisted of three tasks based on a dataset of retinal images: Segmentation of optic disc and cup, classification of glaucoma, and localization of fovea. We propose employing convolutional neural networks for all three tasks. Segmentation ... | ['Jakob Mølkjær Slipsager', 'Kristine Aavild Juhl', 'Josefine Vilsbøll Sundgaard'] | 2022-03-10 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 3.56541723e-01 3.92732292e-01 1.40489832e-01 -3.31281960e-01
-4.41414177e-01 -4.83566642e-01 4.54993159e-01 -5.65588355e-01
-2.86418051e-01 6.08772039e-01 2.27358826e-02 -5.67031264e-01
2.23729372e-01 -6.27917528e-01 -7.48450398e-01 -5.90307415e-01
-6.41861185e-02 -1.64708961e-02 4.77659583e-01 8.09513032... | [15.81045150756836, -3.985072612762451] |
93794d28-18f3-4b4b-96c6-c069fd7f47bb | text-is-text-no-matter-what-unifying-text | 2107.12087 | null | https://arxiv.org/abs/2107.12087v2 | https://arxiv.org/pdf/2107.12087v2.pdf | Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation | Text recognition remains a fundamental and extensively researched topic in computer vision, largely owing to its wide array of commercial applications. The challenging nature of the very problem however dictated a fragmentation of research efforts: Scene Text Recognition (STR) that deals with text in everyday scenes, a... | ['Yi-Zhe Song', 'Pinaki Nath Chowdhury', 'Aneeshan Sain', 'Ayan Kumar Bhunia'] | 2021-07-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bhunia_Text_Is_Text_No_Matter_What_Unifying_Text_Recognition_Using_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bhunia_Text_Is_Text_No_Matter_What_Unifying_Text_Recognition_Using_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-text-recognition', 'handwriting-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.94007254e-01 -2.95039713e-01 -7.27100298e-02 -2.24724114e-01
-5.60021281e-01 -6.96799397e-01 1.05715513e+00 -9.32637602e-02
-6.66060686e-01 5.14996290e-01 1.00303337e-01 -4.05551553e-01
-2.60883272e-01 -2.45693743e-01 -4.48529720e-01 -7.05992460e-01
4.39632177e-01 6.42757535e-01 4.82331902e-01 -3.08016658... | [11.83598518371582, 2.4036309719085693] |
506979c9-2678-4828-a95d-5169f7b414cf | deep-packgen-a-deep-reinforcement-learning | 2305.11039 | null | https://arxiv.org/abs/2305.11039v1 | https://arxiv.org/pdf/2305.11039v1.pdf | Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation | Recent advancements in artificial intelligence (AI) and machine learning (ML) algorithms, coupled with the availability of faster computing infrastructure, have enhanced the security posture of cybersecurity operations centers (defenders) through the development of ML-aided network intrusion detection systems (NIDS). C... | ['Nathaniel D. Bastian', 'Tapas K. Das', 'Ankit Shah', 'Diwas Paudel', 'Jalal Ghadermazi', 'Soumyadeep Hore'] | 2023-05-18 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 2.23114341e-01 -1.21560045e-01 -2.11536303e-01 1.44152135e-01
-1.92277536e-01 -1.11978197e+00 7.01462030e-01 -1.18844375e-01
-3.59844536e-01 6.65813506e-01 -4.57311124e-01 -9.81046915e-01
-1.42699759e-02 -1.01742303e+00 -7.73103178e-01 -7.69480944e-01
-4.56070632e-01 2.38256499e-01 1.98504746e-01 -5.38076282... | [5.508810520172119, 7.513881206512451] |
95ded325-2243-4064-ad94-ba12d7c522e5 | instant-domain-augmentation-for-lidar | 2303.14378 | null | https://arxiv.org/abs/2303.14378v1 | https://arxiv.org/pdf/2303.14378v1.pdf | Instant Domain Augmentation for LiDAR Semantic Segmentation | Despite the increasing popularity of LiDAR sensors, perception algorithms using 3D LiDAR data struggle with the 'sensor-bias problem'. Specifically, the performance of perception algorithms significantly drops when an unseen specification of LiDAR sensor is applied at test time due to the domain discrepancy. This paper... | ['Jaesik Park', 'Soonmin Hwang', 'Kwonyoung Ryu'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ryu_Instant_Domain_Augmentation_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ryu_Instant_Domain_Augmentation_for_LiDAR_Semantic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.93322289e-01 1.69230085e-02 -1.48191795e-01 -5.70375860e-01
-7.43536890e-01 -7.02704549e-01 3.75759780e-01 3.16053838e-01
-5.57792425e-01 5.57269156e-01 -5.90500474e-01 -2.30890676e-01
-8.82698596e-03 -9.10385728e-01 -8.98331583e-01 -4.59574819e-01
3.24095726e-01 1.10686994e+00 9.20067072e-01 -1.41524881... | [8.1012544631958, -2.650712490081787] |
9973abe0-a2f5-4cfd-90e1-21b34ee6cbcf | rethinking-online-action-detection-in | 2003.12041 | null | https://arxiv.org/abs/2003.12041v1 | https://arxiv.org/pdf/2003.12041v1.pdf | Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol | The Online Action Detection (OAD) problem needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find very few works and no consensus on the evaluation protocols to be used. In this work we propose to rethink the ... | ['S. Maldonado-Bascón', 'F. Javier Acevedo-Rodríguez', 'Roberto J. López-Sastre', 'Marcos Baptista Rios', 'Jan van Gemert', 'Fabian Caba Heilbron'] | 2020-03-26 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 3.23750675e-02 -1.32353902e-01 -3.99147272e-01 -2.91161507e-01
-7.36915529e-01 -6.26111507e-01 7.16563225e-01 2.45622620e-01
-6.75982118e-01 5.85404813e-01 8.15196633e-02 -1.70676261e-01
-3.26574683e-01 -4.13733453e-01 -2.95517564e-01 -4.89298612e-01
-3.04790288e-01 2.55360901e-01 7.75618255e-01 -2.56744623... | [8.18074893951416, 0.5016990303993225] |
0e639aea-65fa-43e5-bdb5-8b87436cf9ae | mocha-a-multi-task-training-approach-for | 2210.14650 | null | https://arxiv.org/abs/2210.14650v1 | https://arxiv.org/pdf/2210.14650v1.pdf | MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective | Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap between human written texts and machine-generated outputs. In this work, we propose a novel multi-task training strategy for coherent text gener... | ['Lifu Huang', 'Hou Pong Chan', 'Zhe Hu'] | 2022-10-26 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.95795858e-01 6.11176968e-01 -2.91742027e-01 -1.67088956e-01
-1.18758500e+00 -4.82435137e-01 1.33977234e+00 -7.64729604e-02
-1.21047787e-01 1.17732036e+00 1.35485637e+00 -1.25508532e-01
2.53068924e-01 -9.97051954e-01 -5.83504975e-01 -1.13347378e-02
7.56105304e-01 7.68913507e-01 -1.43758431e-01 -6.24581158... | [11.689026832580566, 8.90648365020752] |
e3233783-92d8-4f18-9fde-9915ae0307a2 | real-time-emotion-recognition-via-attention | 1911.09075 | null | https://arxiv.org/abs/1911.09075v1 | https://arxiv.org/pdf/1911.09075v1.pdf | Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network | Real-time emotion recognition (RTER) in conversations is significant for developing emotionally intelligent chatting machines. Without the future context in RTER, it becomes critical to build the memory bank carefully for capturing historical context and summarize the memories appropriately to retrieve relevant informa... | ['Michael R. Lyu', 'Irwin King', 'Wenxiang Jiao'] | 2019-11-20 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-4.01443131e-02 1.26526833e-01 7.16107711e-02 -4.40696269e-01
-5.17920196e-01 -6.27782494e-02 3.73421609e-01 -5.07355817e-02
-3.29968125e-01 6.41293824e-01 7.58656502e-01 -3.57543007e-02
4.23041165e-01 -7.30834603e-01 -4.98517901e-01 -7.43892014e-01
1.38415456e-01 3.39684300e-02 2.61776913e-02 -5.35589516... | [13.041813850402832, 6.066465377807617] |
0c09b606-3a65-4b27-8d5b-e5fa4b623b03 | 0-1-constrained-optimization-solving-sample | 2210.11889 | null | https://arxiv.org/abs/2210.11889v3 | https://arxiv.org/pdf/2210.11889v3.pdf | 0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming | Sample average approximation (SAA) is a tractable approach to deal with the chance constrained programming, a challenging issue in stochastic programming. The constraint is usually characterized by the 0/1 loss function which results in enormous difficulties in designing numerical algorithms. Most existing methods have... | [] | 2022-10-21 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 1.17487617e-01 8.54979530e-02 -4.21620756e-01 -2.44507954e-01
-8.01349342e-01 -6.09008372e-01 1.88955277e-01 7.79723078e-02
-2.96418130e-01 1.09356701e+00 -1.84553295e-01 -3.86830688e-01
-6.78941011e-01 -6.39236808e-01 -6.46169484e-01 -1.17429519e+00
-7.16078728e-02 5.87144256e-01 -1.36420131e-01 -1.54642060... | [6.514327049255371, 4.266393184661865] |
f8e97778-61a1-4f4d-b32a-7eb0fd215c44 | group-sampling-for-unsupervised-person-re | 2107.03024 | null | https://arxiv.org/abs/2107.03024v3 | https://arxiv.org/pdf/2107.03024v3.pdf | Rethinking Sampling Strategies for Unsupervised Person Re-identification | Unsupervised person re-identification (re-ID) remains a challenging task. While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role. We analyze the reasons for the performance differences between various sampling strategies un... | ['Jianbin Jiao', 'Gang Pan', 'Jian Zhao', 'Guorong Li', 'Zhenjun Han', 'Qixiang Ye', 'Xuehui Yu', 'Xumeng Han'] | 2021-07-07 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.08689331e-02 -1.48209676e-01 -3.75797451e-01 -6.51956737e-01
-6.96246743e-01 -3.52942854e-01 6.98111296e-01 3.38676795e-02
-7.28049219e-01 7.62884259e-01 2.75724828e-01 1.57185867e-01
4.07306701e-02 -5.27733088e-01 -5.31937242e-01 -6.66591525e-01
1.80649802e-01 6.53524637e-01 2.57068351e-02 2.32938021... | [14.745272636413574, 1.0548495054244995] |
6af6b043-2000-46e4-8e5c-8bedb117044d | towards-an-imu-based-pen-online-handwriting | 2105.12434 | null | https://arxiv.org/abs/2105.12434v1 | https://arxiv.org/pdf/2105.12434v1.pdf | Towards an IMU-based Pen Online Handwriting Recognizer | Most online handwriting recognition systems require the use of specific writing surfaces to extract positional data. In this paper we present a online handwriting recognition system for word recognition which is based on inertial measurement units (IMUs) for digitizing text written on paper. This is obtained by means o... | ['Bjoern Eskofier', 'Dario Zanca', 'Peter Kaempf', 'Jens Barth', 'Tim Hamann', 'Mohamad Wehbi'] | 2021-05-26 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.12458986e-01 -4.27202910e-01 -2.54368305e-01 -8.24408308e-02
-6.11443445e-02 -7.16038287e-01 5.18073797e-01 1.18543811e-01
-9.41801727e-01 6.05922461e-01 -3.32052588e-01 -6.98837459e-01
-1.42931014e-01 -6.44864202e-01 -9.26739693e-01 -3.06225687e-01
3.93674046e-01 5.34620464e-01 1.16677545e-01 -6.24958724... | [11.872705459594727, 2.539796829223633] |
6250c7c2-2a4c-4bf0-affb-0fee8967c993 | docdiff-document-enhancement-via-residual | 2305.03892 | null | https://arxiv.org/abs/2305.03892v1 | https://arxiv.org/pdf/2305.03892v1.pdf | DocDiff: Document Enhancement via Residual Diffusion Models | Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks. However, existing regression-based methods optimized for pixel-level distortion reduction tend to suffer from significant los... | ['Xing Zhang', 'Junjie Zhou', 'Ziqi Liu', 'Xiaojun Tang', 'Guibin Wu', 'Lan Yi', 'Yongping Xiong', 'Baolin Liu', 'Zongyuan Yang'] | 2023-05-06 | null | null | null | null | ['document-enhancement', 'deblurring'] | ['computer-vision', 'computer-vision'] | [ 3.05232048e-01 -5.51835239e-01 4.66046147e-02 2.94287712e-03
-7.78854847e-01 -2.49913722e-01 5.15047669e-01 -1.65449128e-01
-6.31047711e-02 3.63768756e-01 5.71870267e-01 -1.05183549e-01
5.82454912e-02 -5.58599234e-01 -4.27418619e-01 -1.05927753e+00
1.37863621e-01 -3.00290734e-01 6.06331453e-02 1.12797059... | [11.276101112365723, -2.2823166847229004] |
04c79104-12d6-4b92-a037-b7beac94daf9 | neural-crossbreed-neural-based-image | 2009.00905 | null | https://arxiv.org/abs/2009.00905v1 | https://arxiv.org/pdf/2009.00905v1.pdf | Neural Crossbreed: Neural Based Image Metamorphosis | We propose Neural Crossbreed, a feed-forward neural network that can learn a semantic change of input images in a latent space to create the morphing effect. Because the network learns a semantic change, a sequence of meaningful intermediate images can be generated without requiring the user to specify explicit corresp... | ['Sanghun Park', 'Kwanggyoon Seo', 'Junyong Noh'] | 2020-09-02 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 5.92630684e-01 2.63952911e-01 -1.98080689e-02 -4.21306074e-01
-4.50550854e-01 -5.32331109e-01 5.35901070e-01 -5.45542538e-01
-1.01886056e-01 4.61775959e-01 2.45038643e-02 2.20655650e-02
3.94397527e-01 -1.07287931e+00 -1.29399419e+00 -6.70126081e-01
3.41001689e-01 3.07274163e-01 7.47411251e-02 -2.93118864... | [11.655019760131836, -0.5109860301017761] |
1e8aff4f-69fd-4237-89bf-a3c2e18faa59 | betrayed-by-captions-joint-caption-grounding | 2301.00805 | null | https://arxiv.org/abs/2301.00805v1 | https://arxiv.org/pdf/2301.00805v1.pdf | Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation | In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover in... | ['Chen Change Loy', 'Yunhai Tong', 'Guangliang Cheng', 'Xia Li', 'Henghui Ding', 'Xiangtai Li', 'Jianzong Wu'] | 2023-01-02 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 6.07652843e-01 5.44341624e-01 -1.61061957e-01 -5.40005088e-01
-1.30777550e+00 -7.86831975e-01 6.87342465e-01 -1.70420278e-02
-4.30005819e-01 4.92223591e-01 7.94737339e-02 -2.20051005e-01
3.29796582e-01 -6.46136880e-01 -1.14862621e+00 -5.23647070e-01
1.81115568e-01 7.22882450e-01 4.04602110e-01 -1.55517027... | [9.708316802978516, 0.7708637714385986] |
dcbd4f0c-3b9a-49dc-9f41-24266b8eeac1 | adversarial-examples-detection-with-enhanced | 2305.04436 | null | https://arxiv.org/abs/2305.04436v1 | https://arxiv.org/pdf/2305.04436v1.pdf | Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization | Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptible perturbations can greatly mislead DNNs even if the full underlying model parameters are not accessible. Various defense methods have been ... | ['Bin Luo', 'Wanli Lyu', 'Jianteng Peng', 'Hang Su', 'Shaowei Zhu', 'Zhaoxia Yin'] | 2023-05-08 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 2.19047323e-01 -2.65520096e-01 2.50459373e-01 -1.64820075e-01
-2.88738489e-01 -7.69685566e-01 7.73327827e-01 -2.20707104e-01
-3.77243310e-01 4.42252964e-01 1.17798448e-02 -1.89966902e-01
1.00459859e-01 -9.98648405e-01 -5.33373058e-01 -9.83288288e-01
-1.10226534e-01 -3.99580181e-01 4.16215301e-01 -4.97092783... | [5.510412693023682, 7.914054870605469] |
4bd3a483-4051-4abb-85b4-e2db2e193e1d | distinguishing-natural-and-computer-generated | 2110.09428 | null | https://arxiv.org/abs/2110.09428v2 | https://arxiv.org/pdf/2110.09428v2.pdf | Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet | The problem of distinguishing natural images from photo-realistic computer-generated ones either addresses natural images versus computer graphics or natural images versus GAN images, at a time. But in a real-world image forensic scenario, it is highly essential to consider all categories of image generation, since in ... | ['Lajish V L', 'Anoop K', 'Manjary P Gangan'] | 2021-10-18 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.65777004e-01 2.58265942e-01 4.38069165e-01 -6.88546523e-02
-5.91096818e-01 -8.54649067e-01 7.74404049e-01 -1.13139749e-01
-4.80228812e-01 3.86416852e-01 -4.07994501e-02 -5.83015800e-01
7.42284209e-02 -5.83511591e-01 -7.86596179e-01 -6.28366232e-01
3.17275614e-01 2.88284630e-01 -8.77177864e-02 -9.07273293... | [11.861541748046875, 0.7496405243873596] |
28825fd7-5dd2-4f6a-aa20-1cf902b75c1e | a-distance-aware-multi-task-framework-for | null | null | https://aclanthology.org/2022.coling-1.76 | https://aclanthology.org/2022.coling-1.76.pdf | A Distance-Aware Multi-Task Framework for Conversational Discourse Parsing | Conversational discourse parsing aims to construct an implicit utterance dependency tree to reflect the turn-taking in a multi-party conversation. Existing works are generally divided into two lines: graph-based and transition-based paradigms, which perform well for short-distance and long-distance dependency links, re... | ['Qiaoming Zhu', 'Fang Kong', 'Peifeng Li', 'Yaxin Fan'] | null | null | null | null | coling-2022-10 | ['discourse-parsing'] | ['natural-language-processing'] | [ 9.76835042e-02 3.75028908e-01 2.41686795e-02 -5.79847634e-01
-6.38564706e-01 -3.98241490e-01 5.96518397e-01 4.91008162e-04
-6.87630624e-02 4.10856724e-01 7.38787711e-01 -5.63131213e-01
4.48605083e-02 -1.03550553e+00 -4.47314382e-01 -3.93295288e-01
6.67707901e-03 5.41416287e-01 4.19113427e-01 -6.62206590... | [12.358169555664062, 7.878538131713867] |
5d722b89-788d-449e-a3c7-994f109f22c0 | training-set-cleansing-of-backdoor-poisoning | 2210.10272 | null | https://arxiv.org/abs/2210.10272v2 | https://arxiv.org/pdf/2210.10272v2.pdf | Training set cleansing of backdoor poisoning by self-supervised representation learning | A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small number of samples that each possess the backdoor pattern (usually a pattern that is either imperceptible or innocuous) and which are mislabeled t... | ['G. Kesidis', 'D. J. Miller', 'Z. Xiang', 'J. Chen', 'E. Emamjomeh-Zadeh', 'H. Ritter', 'O. Dia', 'S. Karami', 'H. Wang'] | 2022-10-19 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.00930190e-01 3.73713598e-02 -5.24514198e-01 -2.63697773e-01
-4.65400457e-01 -9.63204622e-01 6.32139027e-01 2.63263881e-01
-3.11788797e-01 5.45579433e-01 -7.55823776e-02 -5.59843659e-01
1.75119236e-01 -1.10714006e+00 -1.18120015e+00 -1.09541500e+00
-4.93226685e-02 1.15536146e-01 1.44819811e-01 3.36549804... | [5.745940208435059, 7.769559383392334] |
dc147c12-1451-485e-9a7f-9509e8df4af9 | streaming-hypergraph-partitioning-algorithms | 2103.05394 | null | https://arxiv.org/abs/2103.05394v1 | https://arxiv.org/pdf/2103.05394v1.pdf | Streaming Hypergraph Partitioning Algorithms on Limited Memory Environments | Many well-known, real-world problems involve dynamic data which describe the relationship among the entities. Hypergraphs are powerful combinatorial structures that are frequently used to model such data. For many of today's data-centric applications, this data is streaming; new items arrive continuously, and the data ... | ['Bora Uçar', 'Kamer Kaya', 'Berkay Demireller', 'Fatih Taşyaran'] | 2021-03-09 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 2.40369756e-02 -2.75401399e-02 -2.76818544e-01 1.05029523e-01
-4.59956110e-01 -8.94922316e-01 1.07373968e-01 8.17643166e-01
-3.11965168e-01 4.16801840e-01 -3.60143743e-02 -4.06722337e-01
-3.45869839e-01 -1.42183065e+00 -6.00665629e-01 -3.90602767e-01
-6.84233487e-01 1.05528593e+00 7.34830976e-01 -1.49948180... | [6.945619106292725, 5.179756164550781] |
70fa2362-c6c3-460d-92d1-46baeea92c40 | egru-event-based-gru-for-activity-sparse | 2206.06178 | null | https://arxiv.org/abs/2206.06178v3 | https://arxiv.org/pdf/2206.06178v3.pdf | Efficient recurrent architectures through activity sparsity and sparse back-propagation through time | Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application re... | ['David Kappel', 'Christian Mayr', 'Mark Schöne', 'Khaleelulla Khan Nazeer', 'Anand Subramoney'] | 2022-06-13 | null | null | null | null | ['gesture-recognition', 'sequential-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.52550852e-01 -1.75727159e-01 3.20909508e-02 1.31789416e-01
1.68694615e-01 -3.49279583e-01 5.83116889e-01 -5.15367873e-02
-7.76719928e-01 8.25850666e-01 -7.38776987e-03 -2.68950224e-01
7.64840171e-02 -1.02308309e+00 -8.22161555e-01 -9.64294851e-01
-2.04874620e-01 8.55871364e-02 4.84635204e-01 -1.23988777... | [8.184164047241211, 2.6009700298309326] |
da08708c-d03e-44f4-add9-7c661b952f53 | intent-segmentation-of-user-queries-via | null | null | https://aclanthology.org/2020.iwdp-1.7 | https://aclanthology.org/2020.iwdp-1.7.pdf | Intent Segmentation of User Queries Via Discourse Parsing | In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation. Our target texts are user queries from a real-world chatbot. Our results show the feasibility of our approach with an F1-score of 82.97 points, and some advantages and disadvantages compared to two machine learning... | ['Changjian Hu', 'Xiaohua Wang', 'Ruosen Li', 'Ziyue Wen', 'Yibing Yang', 'Vicente Ivan Sanchez Carmona'] | null | null | null | null | aacl-iwdp-2020-12 | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.02937308e-02 6.73891246e-01 -1.72425255e-01 -4.69950616e-01
-1.03253567e+00 -3.97549301e-01 7.66472042e-01 -2.80411452e-01
-6.67698503e-01 8.70639980e-01 5.12780190e-01 -4.16968495e-01
4.88626629e-01 -2.99423456e-01 2.59328224e-02 -2.50695795e-01
7.68712536e-02 5.83259404e-01 3.61604691e-01 -5.05907178... | [12.75971508026123, 7.853498458862305] |
13c07323-1d57-4fe1-9401-178ebb4d0e9b | training-deep-boltzmann-networks-with-sparse | 2303.10728 | null | https://arxiv.org/abs/2303.10728v1 | https://arxiv.org/pdf/2303.10728v1.pdf | Training Deep Boltzmann Networks with Sparse Ising Machines | The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization problems. In this paper, we show a new application domain for probabilistic bit (p-bit) based Ising machines by training deep generative AI mo... | ['Kerem Y. Camsari', 'Yao Qin', 'Shuvro Chowdhury', 'Masoud Mohseni', 'Navid Anjum Aadit', 'Shaila Niazi'] | 2023-03-19 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 2.29298130e-01 -9.76220742e-02 3.34778965e-01 -2.36756042e-01
-7.63979137e-01 -5.18800557e-01 8.18786263e-01 -2.47633472e-01
-7.02097714e-01 9.89598274e-01 -4.30136353e-01 -5.68238080e-01
5.22804745e-02 -1.24586070e+00 -9.97399092e-01 -1.23892367e+00
-9.00665298e-02 1.22987711e+00 2.88489312e-01 -1.03150241... | [5.583192825317383, 4.876694679260254] |
24a18e08-ecbd-4262-a8ce-49fdda873a32 | synthetic-combinations-a-causal-inference | 2303.14226 | null | https://arxiv.org/abs/2303.14226v1 | https://arxiv.org/pdf/2303.14226v1.pdf | Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions | We consider a setting with $N$ heterogeneous units and $p$ interventions. Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i.e., $N \times 2^p$ causal parameters. Choosing combinations of interventions is a problem that naturally arises in many applications such as f... | ['Suhas Vijaykumar', 'Anish Agarwal', 'Abhineet Agarwal'] | 2023-03-24 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 4.24160093e-01 -6.07961006e-02 -8.38943243e-01 3.95414466e-03
-5.92893422e-01 -6.56477034e-01 1.37347460e-01 2.02450439e-01
-3.69363755e-01 8.68572533e-01 2.33160958e-01 -5.36243021e-01
-7.04976797e-01 -9.48282480e-01 -1.00114465e+00 -7.13696420e-01
-5.86306691e-01 2.24122033e-01 -4.85682815e-01 2.19274625... | [7.60601282119751, 5.055535793304443] |
0c4e3a67-89f3-46ea-adf2-f8774e44ba28 | herald-an-annotation-efficient-method-to | 2106.00162 | null | https://arxiv.org/abs/2106.00162v2 | https://arxiv.org/pdf/2106.00162v2.pdf | HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations | Open-domain dialog systems have a user-centric goal: to provide humans with an engaging conversation experience. User engagement is one of the most important metrics for evaluating open-domain dialog systems, and could also be used as real-time feedback to benefit dialog policy learning. Existing work on detecting user... | ['Zhou Yu', 'Kai-Hui Liang', 'Weixin Liang'] | 2021-06-01 | null | https://aclanthology.org/2021.acl-long.283 | https://aclanthology.org/2021.acl-long.283.pdf | acl-2021-5 | ['open-domain-dialog'] | ['natural-language-processing'] | [-6.21959716e-02 5.49185753e-01 -2.43430212e-01 -7.23213673e-01
-8.72361541e-01 -9.83200133e-01 7.89269745e-01 2.23043617e-02
-5.28435826e-01 7.15324640e-01 8.25412273e-01 -2.70643592e-01
3.79320621e-01 -3.29482496e-01 2.40075022e-01 -3.68625849e-01
3.76528382e-01 1.01894546e+00 1.22588277e-01 -4.74938273... | [12.861246109008789, 7.982020378112793] |
47c19db3-dc47-4d20-af0b-ad3f15ae594d | align-and-attend-multimodal-summarization | 2303.07284 | null | https://arxiv.org/abs/2303.07284v3 | https://arxiv.org/pdf/2303.07284v3.pdf | Align and Attend: Multimodal Summarization with Dual Contrastive Losses | The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimodal summarization task explicitly leverages cross-modal information to help generate more reliable and high-quality summaries. However, exis... | ['Zhaowen Wang', 'Abhinav Shrivastava', 'Trung Bui', 'JieLin Qiu', 'Jun Wang', 'Bo He'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/He_Align_and_Attend_Multimodal_Summarization_With_Dual_Contrastive_Losses_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/He_Align_and_Attend_Multimodal_Summarization_With_Dual_Contrastive_Losses_CVPR_2023_paper.pdf | cvpr-2023-1 | ['supervised-video-summarization', 'extractive-document-summarization'] | ['computer-vision', 'natural-language-processing'] | [ 2.68730044e-01 -6.68696389e-02 -2.79700518e-01 -4.21210647e-01
-1.59124935e+00 -6.22290850e-01 8.05540979e-01 3.45001191e-01
-1.16440922e-01 8.52549911e-01 1.16602433e+00 2.42177814e-01
2.57511418e-02 -3.09806913e-01 -6.20668113e-01 -5.58773100e-01
1.86796322e-01 1.65752053e-01 -1.41663194e-01 -1.27802297... | [10.671018600463867, 0.6856414675712585] |
d50f574d-49e7-45c7-b78c-e26e39ab364c | analysing-the-effectiveness-of-a-generative | 2211.01886 | null | https://arxiv.org/abs/2211.01886v1 | https://arxiv.org/pdf/2211.01886v1.pdf | Analysing the effectiveness of a generative model for semi-supervised medical image segmentation | Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. However, training these models... | ['Ben Glocker', 'Daniel Coelho de Castro', 'Miguel Monteiro', 'Fabio De Sousa Ribeiro', 'Margherita Rosnati'] | 2022-11-03 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 6.62123501e-01 3.15715998e-01 -3.64938557e-01 -6.20999813e-01
-1.13700330e+00 -5.13428807e-01 3.02086800e-01 2.18019247e-01
-3.81025165e-01 6.03905678e-01 5.54351173e-02 -2.33007163e-01
7.56983012e-02 -6.79285705e-01 -4.19854254e-01 -9.03422713e-01
3.63969177e-01 9.65664625e-01 1.40939742e-01 1.29867971... | [14.628130912780762, -2.253631591796875] |
9b83e92c-a2ff-4d1b-9d44-272e343b9029 | forgetting-to-learn-logic-programs | 1911.06643 | null | https://arxiv.org/abs/1911.06643v1 | https://arxiv.org/pdf/1911.06643v1.pdf | Forgetting to learn logic programs | Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other prog... | ['Andrew Cropper'] | 2019-11-15 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.13480651e-01 1.35117158e-01 -5.52410424e-01 -2.97002643e-01
-8.59545708e-01 -5.94466865e-01 2.10185140e-01 3.77694368e-01
-5.42535484e-01 1.20260906e+00 -2.54707754e-01 -6.40086949e-01
-3.99894565e-02 -1.04057884e+00 -1.41255510e+00 -3.98439169e-01
-9.58840251e-02 5.06453753e-01 5.52115202e-01 1.90632671... | [8.635811805725098, 7.230319499969482] |
01fa2f33-82d4-40be-9872-0bfdaf122814 | cogview-mastering-text-to-image-generation | 2105.13290 | null | https://arxiv.org/abs/2105.13290v3 | https://arxiv.org/pdf/2105.13290v3.pdf | CogView: Mastering Text-to-Image Generation via Transformers | Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding. We propose CogView, a 4-billion-parameter Transformer with VQ-VAE tokenizer to advance this problem. We also demonstrate the finetuning strategies for various down... | ['Jie Tang', 'Hongxia Yang', 'Zhou Shao', 'Xu Zou', 'Junyang Lin', 'Da Yin', 'Chang Zhou', 'Wendi Zheng', 'Wenyi Hong', 'Zhuoyi Yang', 'Ming Ding'] | 2021-05-26 | null | http://proceedings.neurips.cc/paper/2021/hash/a4d92e2cd541fca87e4620aba658316d-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Paper.pdf | neurips-2021-12 | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 2.24577412e-01 1.44596204e-01 -1.56507418e-01 -5.32329142e-01
-1.20695674e+00 -6.52248979e-01 7.03150630e-01 -8.83752823e-01
-1.50615692e-01 9.68070626e-01 6.12039983e-01 -1.86956033e-01
1.47948056e-01 -5.81866026e-01 -9.66987967e-01 -5.64987838e-01
5.58729053e-01 8.37502420e-01 -3.05901468e-01 -2.91352332... | [11.44284439086914, -0.24534466862678528] |
e7dd430d-e3ea-48b7-807d-d6b491e07908 | saaformer-spectral-spatial-axial-aggregation | 2306.16759 | null | https://arxiv.org/abs/2306.16759v2 | https://arxiv.org/pdf/2306.16759v2.pdf | SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification | Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc. Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset ... | ['Dazhi Zhang', 'Yao Li', 'Zhichang Guo', 'Enzhe Zhao'] | 2023-06-29 | null | null | null | null | ['hyperspectral-image-classification'] | ['computer-vision'] | [ 7.20537007e-01 -5.72681248e-01 -3.11316699e-01 -1.48473769e-01
-4.67295915e-01 -5.47109008e-01 1.94057763e-01 -1.17693255e-02
2.56483834e-02 6.09778345e-01 -2.65779704e-01 -3.59412283e-01
-5.37552238e-01 -1.12899649e+00 -4.59555298e-01 -1.13441300e+00
-8.76185969e-02 -3.24120671e-01 -2.27761358e-01 2.74992473... | [9.940954208374023, -1.5981656312942505] |
4125b20c-1bad-470e-9584-9fd71878027e | real-time-simultaneous-localization-and | 2301.09257 | null | https://arxiv.org/abs/2301.09257v2 | https://arxiv.org/pdf/2301.09257v2.pdf | Real-Time Simultaneous Localization and Mapping with LiDAR intensity | We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method , which addresses the geometry degeneracy problem in unstructured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features ... | ['Giovanni Beltrame', 'Wenqiang Du'] | 2023-01-23 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 1.28189832e-01 -2.55104542e-01 2.19795736e-03 -4.54103380e-01
-6.05015457e-01 -3.79744500e-01 2.70737618e-01 2.89849490e-01
-6.03204608e-01 5.74705005e-01 -4.22879130e-01 -4.68387790e-02
-1.53971210e-01 -1.10222852e+00 -7.70716548e-01 -2.46097028e-01
3.74701852e-03 1.25397348e+00 6.22068107e-01 -2.57119358... | [7.4362030029296875, -2.265137195587158] |
98af7da0-d289-4c5b-a380-d72ba3c61a43 | stvgbert-a-visual-linguistic-transformer | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Su_STVGBert_A_Visual-Linguistic_Transformer_Based_Framework_for_Spatio-Temporal_Video_Grounding_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Su_STVGBert_A_Visual-Linguistic_Transformer_Based_Framework_for_Spatio-Temporal_Video_Grounding_ICCV_2021_paper.pdf | STVGBert: A Visual-Linguistic Transformer Based Framework for Spatio-Temporal Video Grounding | Spatio-temporal video grounding (STVG) aims to localize a spatio-temporal tube of a target object in an untrimmed video based on a query sentence. In this work, we propose a one-stage visual-linguistic transformer based framework called STVGBert for the STVG task, which can simultaneously localize the target object... | ['Dong Xu', 'Qian Yu', 'Rui Su'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [-4.35127616e-02 -1.14125490e-01 -8.77238214e-02 -1.46283388e-01
-8.59404683e-01 -4.62133259e-01 6.44187570e-01 -2.76778992e-02
-4.08636063e-01 2.82216072e-01 2.05172431e-02 -1.15768112e-01
2.73532093e-01 -5.97398579e-01 -9.13592219e-01 -5.41791499e-01
3.13495159e-01 2.66216844e-01 9.33815479e-01 -9.63135809... | [9.667292594909668, 0.6526778340339661] |
13ad19c7-43dc-44d4-991a-932ccc357eb4 | document-level-event-argument-extraction-by | 2104.05919 | null | https://arxiv.org/abs/2104.05919v1 | https://arxiv.org/pdf/2104.05919v1.pdf | Document-Level Event Argument Extraction by Conditional Generation | Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information-seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as co... | ['Jiawei Han', 'Heng Ji', 'Sha Li'] | 2021-04-13 | null | https://aclanthology.org/2021.naacl-main.69 | https://aclanthology.org/2021.naacl-main.69.pdf | naacl-2021-4 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 5.28485537e-01 1.16680396e+00 -3.77095282e-01 -4.98498440e-01
-1.57031262e+00 -7.01022685e-01 1.00013864e+00 6.01483643e-01
-7.75817811e-01 1.05244684e+00 8.45907807e-01 -3.09806019e-01
3.68111581e-02 -7.01391876e-01 -9.67367232e-01 -3.46898846e-02
9.05869752e-02 5.98082304e-01 2.53396899e-01 -5.85044362... | [9.079776763916016, 9.192380905151367] |
d2ab8e1f-79d2-47fe-9086-1ad64e610c53 | stock-index-prediction-with-multi-task | 2008.07605 | null | https://arxiv.org/abs/2008.07605v1 | https://arxiv.org/pdf/2008.07605v1.pdf | Stock Index Prediction with Multi-task Learning and Word Polarity Over Time | Sentiment-based stock prediction systems aim to explore sentiment or event signals from online corpora and attempt to relate the signals to stock price variations. Both the feature-based and neural-networks-based approaches have delivered promising results. However, the frequently minor fluctuations of the stock prices... | ['Kerstin Voigt', 'Yue Zhou'] | 2020-08-17 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-3.51433992e-01 -5.38318217e-01 -6.18965387e-01 -6.15573049e-01
-4.41181660e-01 -6.40840888e-01 7.49100924e-01 2.46053800e-01
-3.76823723e-01 7.89979935e-01 7.15941846e-01 -1.33826479e-01
2.88592018e-02 -1.03007388e+00 -4.25820589e-01 -4.76782441e-01
1.00169100e-01 -7.18708634e-02 2.46721670e-01 -5.99278033... | [4.418448448181152, 4.332669258117676] |
83497d4d-ef71-4ece-9281-e11d4b3354e3 | visual-entailment-a-novel-task-for-fine | 1901.06706 | null | http://arxiv.org/abs/1901.06706v1 | http://arxiv.org/pdf/1901.06706v1.pdf | Visual Entailment: A Novel Task for Fine-Grained Image Understanding | Existing visual reasoning datasets such as Visual Question Answering (VQA),
often suffer from biases conditioned on the question, image or answer
distributions. The recently proposed CLEVR dataset addresses these limitations
and requires fine-grained reasoning but the dataset is synthetic and consists
of similar object... | ['Asim Kadav', 'Ning Xie', 'Derek Doran', 'Farley Lai'] | 2019-01-20 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [-1.69340502e-02 2.17718527e-01 1.35229632e-01 -7.72558510e-01
-7.65500665e-01 -6.89127564e-01 8.53357017e-01 -1.62840217e-01
-7.62194544e-02 5.20593584e-01 4.11385000e-01 -7.21647620e-01
4.34782624e-01 -6.67734861e-01 -1.27767050e+00 5.04962541e-02
5.07304847e-01 5.81067324e-01 3.88017036e-02 -8.61797184... | [10.858098983764648, 1.8121591806411743] |
3c1af40e-94ce-4e35-89e8-823c857a1bd6 | adaptive-representation-selection-in | 1802.00981 | null | https://arxiv.org/abs/1802.00981v4 | https://arxiv.org/pdf/1802.00981v4.pdf | Contextual Bandit with Adaptive Feature Extraction | We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of co... | ['Djallel Bouneffouf', 'Baihan Lin', 'Irina Rish', 'Guillermo Cecchi'] | 2018-02-03 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 6.29054546e-01 -2.12056190e-01 -9.04349923e-01 -4.63103175e-01
-1.12354255e+00 -5.26273608e-01 6.53664887e-01 1.85518533e-01
-5.78978598e-01 9.71576095e-01 1.59149587e-01 -5.12110293e-01
-5.51771164e-01 -6.32833481e-01 -9.74607885e-01 -1.01183200e+00
-6.29387200e-02 5.37654757e-01 -3.41123603e-02 3.86669785... | [4.503096103668213, 3.0958240032196045] |
c9e172f6-51a4-4716-a600-dcb9b73925e3 | avatar-adversarial-self-supervised-domain | 2305.00082 | null | https://arxiv.org/abs/2305.00082v2 | https://arxiv.org/pdf/2305.00082v2.pdf | AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain | This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the ... | ['Hyunsoo Yoon', 'Jun Kataoka'] | 2023-04-28 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 3.02702814e-01 1.13695867e-01 -5.48707545e-01 -4.24136013e-01
-1.16867602e+00 -8.13357294e-01 7.44731665e-01 -1.22115515e-01
-1.80401251e-01 9.31018651e-01 1.20068155e-03 -1.40205279e-01
-4.09948193e-02 -6.89743459e-01 -7.49841034e-01 -6.02980673e-01
3.17137361e-01 1.04330790e+00 1.66212007e-01 -2.24338815... | [10.335722923278809, 3.0928754806518555] |
1b16d068-7880-4917-ab0a-f17f99d2aa9c | sketch-qnet-a-quadruplet-convnet-for-color | 2104.11130 | null | https://arxiv.org/abs/2104.11130v1 | https://arxiv.org/pdf/2104.11130v1.pdf | Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval | Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate ... | ['Jose M. Saavedra', 'Anibal Fuentes'] | 2021-04-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.58578986e-01 -7.50483274e-01 -4.36363399e-01 -1.81062892e-01
-8.80151570e-01 -6.53488457e-01 5.63346446e-01 2.09924370e-01
-6.36953950e-01 7.91423023e-01 -4.85737801e-01 -7.55566210e-02
-5.43963730e-01 -8.72080624e-01 -5.80727518e-01 -6.64806843e-01
2.00444572e-02 5.39715111e-01 4.29753482e-01 -5.37223458... | [11.360757827758789, 0.6696395874023438] |
b7c0e398-e48a-4f06-a951-aa8487a06a27 | partial-explainer-abductive-natural-language | 2105.03417 | null | https://arxiv.org/abs/2105.03417v2 | https://arxiv.org/pdf/2105.03417v2.pdf | Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference | This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization. Specifically, Diff-Explainer allows for the fine-tuning of neural representations within a constrained optimizatio... | ['André Freitas', 'Julia Rozanova', 'Deborah Ferreira', 'Marco Valentino', 'Mokanarangan Thayaparan'] | 2021-05-07 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 9.76087525e-02 9.38645244e-01 -1.82715982e-01 -6.18201554e-01
-1.24621177e+00 -5.85888386e-01 5.06821573e-01 -1.05793968e-01
1.96024701e-01 9.81793582e-01 2.11359203e-01 -7.73908973e-01
-5.18736899e-01 -7.55301893e-01 -1.09973371e+00 8.96374285e-02
1.85458824e-01 9.40010548e-01 -3.69788259e-01 -4.34011370... | [9.700113296508789, 7.30706262588501] |
6c50fc89-a80a-4bb6-b539-87436b6c9b38 | monotonic-neural-additive-models-pursuing | 2209.10070 | null | https://arxiv.org/abs/2209.10070v1 | https://arxiv.org/pdf/2209.10070v1.pdf | Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring | The forecasting of credit default risk has been an active research field for several decades. Historically, logistic regression has been used as a major tool due to its compliance with regulatory requirements: transparency, explainability, and fairness. In recent years, researchers have increasingly used complex and ad... | ['Weicheng Ye', 'Dangxing Chen'] | 2022-09-21 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 9.62719172e-02 2.56469458e-01 -5.33475101e-01 -9.03940678e-01
-2.94533134e-01 -2.02115923e-01 2.28700846e-01 1.01391479e-01
-3.75203043e-01 9.81304049e-01 -2.49111801e-02 -5.64121485e-01
-2.08726659e-01 -8.97616744e-01 -5.38992465e-01 -4.53385770e-01
2.49962062e-01 3.32135588e-01 -2.94462413e-01 -9.89016239... | [8.852985382080078, 5.326560020446777] |
42e965ff-a3b6-45f9-a0df-480bd4f80817 | interformer-real-time-interactive-image | 2304.02942 | null | https://arxiv.org/abs/2304.02942v1 | https://arxiv.org/pdf/2304.02942v1.pdf | InterFormer: Real-time Interactive Image Segmentation | Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators' later click is based on mode... | ['Liujuan Cao', 'Rongrong Ji', 'Guannan Jiang', 'Shengchuan Zhang', 'Ke Sun', 'Hao Yang', 'You Huang'] | 2023-04-06 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.81708652e-01 -3.16067436e-03 6.61175232e-03 -3.62894714e-01
-9.41050529e-01 -6.21359825e-01 -1.79018840e-01 3.44352536e-02
-7.71113932e-01 -1.13931753e-01 -3.70953172e-01 -4.18171376e-01
4.51867640e-01 -5.08211315e-01 -4.61714387e-01 -4.81862754e-01
5.90825617e-01 4.75314975e-01 9.97672558e-01 3.14425856... | [9.526150703430176, -0.01348385401070118] |
788465b4-bf36-451a-94ea-88a99e4b56eb | tracking-multiple-deformable-objects-in | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Tracking_Multiple_Deformable_Objects_in_Egocentric_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Tracking_Multiple_Deformable_Objects_in_Egocentric_Videos_CVPR_2023_paper.pdf | Tracking Multiple Deformable Objects in Egocentric Videos | Most existing multiple object tracking (MOT) methods that solely rely on appearance features struggle in tracking highly deformable objects. Other MOT methods that use motion clues to associate identities across frames have difficulty handling egocentric videos effectively or efficiently. In this work, we propose D... | ['Siwei Lyu', 'Honghong Peng', 'Jun Hu', 'Xiaoxing Li', 'Mingzhen Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['multiple-object-tracking', 'motion-disentanglement', 'multi-object-tracking', 'disentanglement'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [-3.87942493e-01 -3.00834984e-01 -2.04744548e-01 6.73523396e-02
-6.18901908e-01 -5.92649758e-01 1.93152487e-01 -5.55520892e-01
-3.69282216e-01 5.73032796e-01 5.58206737e-02 5.36307037e-01
4.05057333e-02 -2.29774624e-01 -9.46069658e-01 -7.74263680e-01
-9.53851342e-02 4.81187135e-01 7.76828110e-01 2.77642936... | [6.289285182952881, -1.9994313716888428] |
633fa174-0f48-4ab5-a901-16dfface87aa | sparse-coding-approach-for-multi-frame-image | 1402.3926 | null | http://arxiv.org/abs/1402.3926v1 | http://arxiv.org/pdf/1402.3926v1.pdf | Sparse Coding Approach for Multi-Frame Image Super Resolution | An image super-resolution method from multiple observation of low-resolution
images is proposed. The method is based on sub-pixel accuracy block matching
for estimating relative displacements of observed images, and sparse signal
representation for estimating the corresponding high-resolution image. Relative
displaceme... | ['Hideitsu Hino', 'Noboru Murata', 'Toshiyuki Kato'] | 2014-02-17 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 8.73205721e-01 -3.22919011e-01 -2.20298424e-01 -8.26718733e-02
-1.35662568e+00 3.01507348e-03 2.79872566e-01 -3.55053037e-01
-1.09026909e-01 9.35880303e-01 3.48683596e-01 7.39396513e-01
-1.67477980e-01 -8.47066700e-01 -5.72596431e-01 -1.14773667e+00
1.05891079e-01 8.77767727e-02 5.19798636e-01 -1.41403407... | [11.049928665161133, -2.176532030105591] |
d7f7e794-78ab-4550-a548-582dcf326328 | systematic-generalization-with-edge-1 | 2112.00578 | null | https://arxiv.org/abs/2112.00578v1 | https://arxiv.org/pdf/2112.00578v1.pdf | Systematic Generalization with Edge Transformers | Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, we propose Edge Transformer, a new model that combines inspiration from Transformers and rule-based s... | ['Dzmitry Bahdanau', "Timothy J. O'Donnell", 'Leon Bergen'] | 2021-12-01 | systematic-generalization-with-edge | http://proceedings.neurips.cc/paper/2021/hash/0a4dc6dae338c9cb08947c07581f77a2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/0a4dc6dae338c9cb08947c07581f77a2-Paper.pdf | neurips-2021-12 | ['relational-reasoning', 'systematic-generalization'] | ['natural-language-processing', 'reasoning'] | [ 3.73794258e-01 7.97854066e-01 -4.39826787e-01 -3.32478911e-01
-9.12543014e-02 -6.69163883e-01 7.24789977e-01 2.99859196e-01
6.33755233e-03 3.64698857e-01 5.65209150e-01 -9.52997506e-01
3.53220850e-02 -1.42336297e+00 -1.19401574e+00 -1.02721937e-01
1.59001574e-01 8.80972266e-01 3.92171621e-01 -7.48456001... | [9.140504837036133, 7.52302360534668] |
211289f8-63c9-43f0-9bb2-d83e07483ea2 | qfa2sr-query-free-adversarial-transfer | 2305.14097 | null | https://arxiv.org/abs/2305.14097v1 | https://arxiv.org/pdf/2305.14097v1.pdf | QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems | Current adversarial attacks against speaker recognition systems (SRSs) require either white-box access or heavy black-box queries to the target SRS, thus still falling behind practical attacks against proprietary commercial APIs and voice-controlled devices. To fill this gap, we propose QFA2SR, an effective and imperce... | ['Fu Song', 'Zhe Zhao', 'Yedi Zhang', 'Guangke Chen'] | 2023-05-23 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.93640366e-01 -8.11544061e-02 5.51609062e-02 -1.12734877e-01
-1.38024986e+00 -1.18725133e+00 3.69995594e-01 -3.78048033e-01
-3.41018379e-01 3.90535951e-01 1.90879062e-01 -6.36279047e-01
-1.67686090e-01 -3.92536372e-01 -3.72378320e-01 -5.17883360e-01
-2.15972319e-01 -3.10307950e-01 2.95414388e-01 -4.73336041... | [13.971423149108887, 5.826022148132324] |
c1dfb67b-9327-46bc-bfeb-3737fda5f8b8 | sapa-similarity-aware-point-affiliation-for | 2209.12866 | null | https://arxiv.org/abs/2209.12866v2 | https://arxiv.org/pdf/2209.12866v2.pdf | SAPA: Similarity-Aware Point Affiliation for Feature Upsampling | We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels ... | ['Zhiguo Cao', 'Yuliang Liu', 'Hongtao Fu', 'Zixuan Ye', 'Wenze Liu', 'Hao Lu'] | 2022-09-26 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 1.60582438e-01 2.31640100e-01 -2.40080401e-01 -5.75900018e-01
-8.49657238e-01 -2.64477879e-01 6.37998581e-01 2.19733357e-01
-1.23333866e-02 3.46794724e-01 3.00101042e-01 3.09725493e-01
4.06062696e-03 -8.65442693e-01 -9.77674007e-01 -5.10879934e-01
8.72274861e-02 3.84792507e-01 5.75140178e-01 4.63888422... | [9.603850364685059, 0.3909594416618347] |
58091173-b880-4ab7-9641-35d243a8902a | hallucinated-iqa-no-reference-image-quality | 1804.01681 | null | http://arxiv.org/abs/1804.01681v1 | http://arxiv.org/pdf/1804.01681v1.pdf | Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning | No-reference image quality assessment (NR-IQA) is a fundamental yet
challenging task in low-level computer vision community. The difficulty is
particularly pronounced for the limited information, for which the
corresponding reference for comparison is typically absent. Although various
feature extraction mechanisms hav... | ['Kwan-Yee Lin', 'Guanxiang Wang'] | 2018-04-05 | hallucinated-iqa-no-reference-image-quality-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lin_Hallucinated-IQA_No-Reference_Image_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lin_Hallucinated-IQA_No-Reference_Image_CVPR_2018_paper.pdf | cvpr-2018-6 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.82868832e-01 -2.19519109e-01 -7.09612817e-02 -2.77040601e-01
-1.23835039e+00 -1.88434809e-01 4.23724532e-01 -2.10697457e-01
-6.33389503e-02 6.23387277e-01 4.17180061e-01 2.93709747e-02
-5.82288727e-02 -5.18282473e-01 -5.67933500e-01 -7.90594757e-01
3.80789369e-01 -9.00033042e-02 6.52534701e-03 -5.65470122... | [11.823880195617676, -1.8305304050445557] |
a24a484f-c912-4cd2-8b60-8a1d3a032efd | triplere-knowledge-graph-embeddings-via | 2209.08271 | null | https://arxiv.org/abs/2209.08271v1 | https://arxiv.org/pdf/2209.08271v1.pdf | TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors | Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based approaches have achieved some progress in recent years, the performance was still unsatisfactory. This paper proposes a novel knowledge gra... | ['Yafeng Deng', 'Hongzhu Li', 'Deng Lin', 'Huanyong Liu', 'Zhicong Luo', 'Long Yu'] | 2022-09-17 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-3.34978253e-01 8.92674550e-02 -6.25259399e-01 2.89444383e-02
-4.04768258e-01 -3.88525009e-01 5.91067433e-01 1.58657327e-01
-4.61456597e-01 6.79359078e-01 1.89536303e-01 -2.27307752e-01
-3.58709127e-01 -1.05370080e+00 -3.60211968e-01 -5.13456762e-01
-1.70277193e-01 5.54276645e-01 4.37863231e-01 -4.53306377... | [8.735809326171875, 7.849104404449463] |
8ca03873-8c13-4777-89e7-1d8ed585a35f | motion-puzzle-arbitrary-motion-style-transfer | 2202.05274 | null | https://arxiv.org/abs/2202.05274v2 | https://arxiv.org/pdf/2202.05274v2.pdf | Motion Puzzle: Arbitrary Motion Style Transfer by Body Part | This paper presents Motion Puzzle, a novel motion style transfer network that advances the state-of-the-art in several important respects. The Motion Puzzle is the first that can control the motion style of individual body parts, allowing for local style editing and significantly increasing the range of stylized motion... | ['Sung-Hee Lee', 'Soomin Park', 'Deok-Kyeong Jang'] | 2022-02-10 | null | null | null | null | ['motion-style-transfer'] | ['computer-code'] | [-5.45529127e-02 -1.46425650e-01 -3.01100850e-01 -3.42764631e-02
-1.49751693e-01 -8.05413902e-01 5.59366465e-01 -6.62533164e-01
-3.02269131e-01 6.87179148e-01 3.75502586e-01 8.89964178e-02
2.82227963e-01 -8.98042023e-01 -5.98187625e-01 -6.49061382e-01
2.27869779e-01 4.62644637e-01 4.06846344e-01 -4.96417701... | [10.762748718261719, -0.6841039061546326] |
69da6d59-b6b0-4647-b643-6f656c93f0f2 | gradient-based-learning-applied-to-document | null | null | https://ieeexplore.ieee.org/document/726791 | https://ieeexplore.ieee.org/document/726791 | Gradient-based learning applied to document recognition | Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patt... | ['P. Haffner', 'Y. Bengio', 'L. Bottou', 'Y. LeCun'] | 1998-11-01 | null | null | null | proceedings-of-the-ieee-1998-11 | ['handwriting-recognition', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.45094350e-01 -3.56983274e-01 -2.45059267e-01 -5.80209494e-01
-1.43023342e-01 -4.80689913e-01 3.33108723e-01 -1.56433195e-01
-3.97871941e-01 4.59162265e-01 -4.70262468e-01 -8.08381319e-01
-1.46772295e-01 -9.95787323e-01 -4.22448218e-01 -4.68147606e-01
1.21111432e-02 6.92595959e-01 3.10012847e-01 -3.10529947... | [11.799497604370117, 2.642153739929199] |
b90a5986-e47f-41dc-9f05-342cbe21bf19 | to-catch-a-chorus-verse-intro-or-anything | 2205.14700 | null | https://arxiv.org/abs/2205.14700v1 | https://arxiv.org/pdf/2205.14700v1.pdf | To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions | Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., 'A', 'B', and 'C'). However, explicitly identifying the function of each segment (e.g., 'verse' or 'chorus') is rarely attempted, but has many applications. We introduce a multi-task deep le... | ['Jordan B. L. Smith', 'Yun-Ning Hung', 'Ju-Chiang Wang'] | 2022-05-29 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.22001104e-01 -4.51433510e-01 -1.57533914e-01 -2.03408405e-01
-8.32484782e-01 -8.69663715e-01 5.45406163e-01 -9.97253507e-02
-5.80309518e-02 2.29411051e-01 4.39002037e-01 1.19569302e-01
-1.20699249e-01 -3.09395820e-01 -5.39786339e-01 -6.06777906e-01
-1.24781253e-03 2.63128579e-01 1.02890059e-01 -1.51515424... | [15.812104225158691, 5.317692756652832] |
733c1a35-b20d-44c6-8201-8ab0792fa26b | litevl-efficient-video-language-learning-with | 2210.11929 | null | https://arxiv.org/abs/2210.11929v1 | https://arxiv.org/pdf/2210.11929v1.pdf | LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling | Recent large-scale video-language pre-trained models have shown appealing performance on various downstream tasks. However, the pre-training process is computationally expensive due to the requirement of millions of video-text pairs and the redundant data structure of each video. To mitigate these problems, we propose ... | ['Qun Liu', 'Xin Jiang', 'Lifeng Shang', 'Lu Hou', 'Chaofan Tao', 'Dongsheng Chen'] | 2022-10-21 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 4.67239283e-02 -3.53971243e-01 -1.70532182e-01 -5.89221120e-01
-9.69950795e-01 -3.97138149e-01 7.25350499e-01 -3.08216691e-01
-8.58120441e-01 2.43685052e-01 4.20142442e-01 -3.18861485e-01
3.31984788e-01 -2.43047327e-01 -1.03301358e+00 -5.37942469e-01
1.06608659e-01 -1.40664518e-01 3.77888024e-01 1.20963581... | [10.2954740524292, 0.9500704407691956] |
611df8e5-72ce-4747-9970-adad372b2f4a | cross-guided-optimization-of-radiance-fields | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yoon_Cross-Guided_Optimization_of_Radiance_Fields_With_Multi-View_Image_Super-Resolution_for_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yoon_Cross-Guided_Optimization_of_Radiance_Fields_With_Multi-View_Image_Super-Resolution_for_CVPR_2023_paper.pdf | Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis | Novel View Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitrary resolution as it learns a continuous volumetric representation. However, radiance fields rely hea... | ['Kuk-Jin Yoon', 'Youngho Yoon'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-super-resolution', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.02322230e-01 -2.43641630e-01 1.70264423e-01 -5.22055209e-01
-1.15930724e+00 -4.09265369e-01 5.23870528e-01 -6.55503869e-01
-7.77608762e-03 7.44083941e-01 2.02810735e-01 3.60250443e-01
-4.53566134e-01 -1.10180509e+00 -9.54784572e-01 -7.98788011e-01
5.73713422e-01 2.06065387e-01 1.81923270e-01 -3.44758153... | [9.788714408874512, -2.6280386447906494] |
e02051ea-5657-42ae-a4b6-bdcacb7859e4 | learning-soccer-juggling-skills-with-layer | null | null | https://dl.acm.org/doi/10.1145/3528233.3530735 | https://www.cs.ubc.ca/~van/papers/2022-SIGGRAPH-juggle/soccer_juggling.pdf | Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts | Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem. We present a system to learn control policies for multiple soccer juggling skills, based on deep reinforcement learning. We introduce a task-description framework for th... | ['Michiel Van de Panne', 'Hung Yu Ling', 'Sebastian Starke', 'Zhaoming Xie'] | 2022-07-24 | null | null | null | siggraph-2022-7 | ['humanoid-control'] | ['robots'] | [-2.10101381e-01 -6.07360750e-02 -3.34031790e-01 1.56499311e-01
-6.83881998e-01 -6.03682995e-01 4.24603909e-01 -2.43124872e-01
-7.82605708e-01 8.99023652e-01 -1.80935025e-01 -1.60650700e-01
-2.54891217e-01 -4.03334737e-01 -1.03311861e+00 -6.66978478e-01
-2.33169109e-01 9.63820219e-01 6.25932455e-01 -9.29968834... | [4.784039497375488, 0.8927969932556152] |
d53a568a-8c5e-4d56-8a07-0adc0dbf2b43 | towards-ghost-free-shadow-removal-via-dual | 1911.08718 | null | https://arxiv.org/abs/1911.08718v2 | https://arxiv.org/pdf/1911.08718v2.pdf | Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN | Shadow removal is an essential task for scene understanding. Many studies consider only matching the image contents, which often causes two types of ghosts: color in-consistencies in shadow regions or artifacts on shadow boundaries. In this paper, we tackle these issues in two ways. First, to carefully learn the border... | ['Chi-Man Pun', 'Xiaodong Cun', 'Cheng Shi'] | 2019-11-20 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 7.38525927e-01 5.03064431e-02 2.74961442e-01 -4.92436975e-01
-4.00136739e-01 -4.57672626e-01 3.72246027e-01 -8.21431279e-01
2.65177456e-03 7.55512834e-01 4.10322919e-02 -4.77822751e-01
4.38482553e-01 -8.08082283e-01 -1.08606291e+00 -1.00349247e+00
4.26238358e-01 4.61820513e-02 5.49742639e-01 -2.67181635... | [10.84699821472168, -4.0940728187561035] |
3206dbac-32ee-446d-ac90-e9722c73f82c | partially-shuffling-the-training-data-to-1 | 1903.04167 | null | http://arxiv.org/abs/1903.04167v2 | http://arxiv.org/pdf/1903.04167v2.pdf | Partially Shuffling the Training Data to Improve Language Models | Although SGD requires shuffling the training data between epochs, currently
none of the word-level language modeling systems do this. Naively shuffling all
sentences in the training data would not permit the model to learn
inter-sentence dependencies. Here we present a method that partially shuffles
the training data b... | ['Ofir Press'] | 2019-03-11 | partially-shuffling-the-training-data-to | null | null | arxiv-2019-3 | ['sentence-ordering'] | ['natural-language-processing'] | [-2.53282309e-01 2.92864501e-01 -3.02942663e-01 -9.04492795e-01
-6.77442729e-01 -7.27528274e-01 3.22999418e-01 2.97624767e-01
-7.86763906e-01 8.89934301e-01 3.99958193e-01 -1.06346262e+00
2.56066710e-01 -6.23970807e-01 -5.54594576e-01 -2.19318315e-01
-1.82698891e-01 7.14231372e-01 1.51349440e-01 -3.85390073... | [10.659965515136719, 9.123229026794434] |
5d82f1ab-e611-4a2b-bc9f-e4fd696c84b7 | the-neural-hype-and-comparisons-against-weak | null | null | https://dl.acm.org/citation.cfm?id=3308781 | http://sigir.org/wp-content/uploads/2019/01/p040.pdf | The Neural Hype and Comparisons Against Weak Baselines | Recently, the machine learning community paused in a moment of self-reflection. In a widely discussed paper at ICLR 2018, Sculley et al. wrote: "We observe that the rate of empirical advancement may not have been matched by consistent increase in the level of empirical rigor across the field as a whole." Their primary ... | ['Jimmy Lin'] | 2018-12-01 | null | null | null | acm-sigir-forum-volume-52-issue-2-2018-12 | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-4.41079140e-02 3.27182724e-03 -3.89400750e-01 -3.96526843e-01
-8.52756143e-01 -6.58616483e-01 8.84381711e-01 1.81319699e-01
-6.39571786e-01 7.35109985e-01 4.82274532e-01 -5.62697232e-01
-2.62111127e-01 -1.56478658e-01 -8.66564214e-01 -6.50724649e-01
3.74152839e-01 4.02723372e-01 -2.69071937e-01 -4.11283106... | [9.007275581359863, 6.348455429077148] |
3ad98b71-06d1-45de-b419-ec76e60e0ebe | avlnet-learning-audio-visual-language | 2006.09199 | null | https://arxiv.org/abs/2006.09199v2 | https://arxiv.org/pdf/2006.09199v2.pdf | AVLnet: Learning Audio-Visual Language Representations from Instructional Videos | Current methods for learning visually grounded language from videos often rely on text annotation, such as human generated captions or machine generated automatic speech recognition (ASR) transcripts. In this work, we introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared au... | ['James Glass', 'Antonio Torralba', 'Brian Kingsbury', 'Rameswar Panda', 'Hilde Kuehne', 'Kartik Audhkhasi', 'Dhiraj Joshi', 'Brian Chen', 'Samuel Thomas', 'David Harwath', 'Angie Boggust', 'Andrew Rouditchenko', 'Rogerio Feris', 'Michael Picheny'] | 2020-06-16 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 3.26467633e-01 -1.25786617e-01 -2.95770586e-01 -3.66330266e-01
-1.41410458e+00 -8.04941714e-01 6.13236964e-01 7.23036677e-02
-2.53405929e-01 3.81395131e-01 6.03992701e-01 -2.28179380e-01
1.73107997e-01 -2.17238203e-01 -1.23341465e+00 -3.08381677e-01
-1.39777884e-01 1.25411674e-02 7.52121508e-02 1.65823847... | [10.423304557800293, 1.0858670473098755] |
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