paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b07e52a5-7ced-4aef-8c29-4e0276fd67f3 | improving-the-transferability-of-time-series | 2307.00066 | null | https://arxiv.org/abs/2307.00066v1 | https://arxiv.org/pdf/2307.00066v1.pdf | Improving the Transferability of Time Series Forecasting with Decomposition Adaptation | Due to effective pattern mining and feature representation, neural forecasting models based on deep learning have achieved great progress. The premise of effective learning is to collect sufficient data. However, in time series forecasting, it is difficult to obtain enough data, which limits the performance of neural f... | ['Qiang Wang', 'Yan Wang', 'Yan Gao'] | 2023-06-30 | null | null | null | null | ['transfer-learning', 'time-series-forecasting', 'multivariate-time-series-forecasting'] | ['miscellaneous', 'time-series', 'time-series'] | [-1.08843446e-01 -7.34710097e-01 -1.48680508e-01 -5.66962123e-01
-2.60499984e-01 -5.42816699e-01 3.07655007e-01 -5.27815163e-01
-7.55483061e-02 6.28055871e-01 3.39458972e-01 -3.86914611e-01
-2.11936221e-01 -7.92001784e-01 -5.00625610e-01 -9.89170849e-01
-1.42829910e-01 1.10677443e-01 -4.12743799e-02 -2.94339389... | [6.924373626708984, 2.93058443069458] |
888d49e2-bd45-4ff4-8c77-85b131a6baae | lwsis-lidar-guided-weakly-supervised-instance | 2212.03504 | null | https://arxiv.org/abs/2212.03504v2 | https://arxiv.org/pdf/2212.03504v2.pdf | LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving | Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations for training. In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Ins... | ['Jianbing Shen', 'Ruigang Yang', 'Yikang Li', 'Botian Shi', 'Junbo Yin', 'Xiang Li'] | 2022-12-07 | null | null | null | null | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 9.87017304e-02 4.20500904e-01 -4.40899640e-01 -5.57503879e-01
-8.65223408e-01 -5.43734670e-01 4.40590113e-01 8.84241760e-02
-3.65748525e-01 3.13078195e-01 -4.36615735e-01 -4.74185616e-01
3.25203300e-01 -8.28671396e-01 -1.15331960e+00 -6.51528597e-01
3.28967780e-01 6.28108025e-01 6.74758673e-01 -3.99406642... | [8.065104484558105, -2.9501547813415527] |
e34938a1-dd63-4e58-91f5-a0bfc944edb0 | a-fully-spiking-hybrid-neural-network-for | 2104.10719 | null | https://arxiv.org/abs/2104.10719v2 | https://arxiv.org/pdf/2104.10719v2.pdf | A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection | This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on Convolutional SNN using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) le... | ['Saibal Mukhopadhyay', 'Xueyuan She', 'Biswadeep Chakraborty'] | 2021-04-21 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 2.27000669e-01 -4.45053518e-01 2.13208437e-01 -1.44182533e-01
-3.74582082e-01 -1.63823813e-01 5.56439996e-01 1.74806431e-01
-1.02961481e+00 1.16978049e+00 -3.89333278e-01 1.94915086e-01
2.34891176e-02 -8.74581277e-01 -9.89548326e-01 -9.46981668e-01
1.66225851e-01 3.52534354e-01 1.02826321e+00 1.59863830... | [8.203936576843262, 2.4740843772888184] |
01f5fcc8-96c3-42a2-b8db-34e5b8a4a665 | multi-agent-collaborative-inference-via-dnn | 2205.11854 | null | https://arxiv.org/abs/2205.11854v2 | https://arxiv.org/pdf/2205.11854v2.pdf | Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning | Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectively, becomes attractive. However, the large intermediate feature of DNN impedes flexible decoupling, and existing approaches... | ['Shiwen Mao', 'Jianping An', 'Han Hu', 'Yong Luo', 'Guanyu Xu', 'Zhiwei Hao'] | 2022-05-24 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-2.10438296e-01 1.60491914e-01 -2.49105260e-01 -2.71329969e-01
-4.84369308e-01 -5.03586948e-01 3.04977447e-01 -4.94810641e-01
-5.70082724e-01 9.16100860e-01 -6.41714483e-02 -5.96663177e-01
-2.55092770e-01 -8.28442633e-01 -8.33636999e-01 -9.69289660e-01
2.08923072e-01 5.82380295e-01 9.94354784e-02 3.60644639... | [8.115602493286133, 2.8405816555023193] |
c6b5d272-9dd8-47c0-a304-f50bdd467231 | xinggan-for-person-image-generation | 2007.09278 | null | https://arxiv.org/abs/2007.09278v1 | https://arxiv.org/pdf/2007.09278v1.pdf | XingGAN for Person Image Generation | We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i.e., translating the pose of a given person to a desired one. The proposed Xing generator consists of two generation branches that model the person's appearance and shape information, respectively. Moreover, w... | ['Philip H. S. Torr', 'Li Zhang', 'Song Bai', 'Hao Tang', 'Nicu Sebe'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5192_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700715.pdf | eccv-2020-8 | ['pose-transfer'] | ['computer-vision'] | [-2.01650374e-02 7.13420063e-02 3.54696006e-01 -2.83923447e-01
-3.80653858e-01 -4.38323021e-01 8.22100699e-01 -5.72268546e-01
-1.59323037e-01 8.92740488e-01 6.28398880e-02 1.57032371e-01
4.81016248e-01 -1.03724432e+00 -7.35966742e-01 -7.65175760e-01
6.09057367e-01 3.82871956e-01 -1.19381376e-01 -2.16759488... | [12.013676643371582, -0.8299381732940674] |
5e1dc150-f35e-4c52-b8c7-e17618d50978 | efficient-tms-based-motor-cortex-mapping | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9515996 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9515996 | Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning | Transcranial Magnetic Stimulation (TMS) can be used to map cortical motor topography by spatially sampling the sensorimotor cortex while recording Motor Evoked Potentials (MEP) with surface electromyography (EMG). Traditional sampling strategies are time-consuming and inefficient, as they ignore the fact that responsiv... | ['D Erdoğmuş', 'D Brooks', 'E Tunik', 'T Imbiriba', 'M Yarossi', 'R Faghihpirayesh'] | 2021-08-18 | null | null | null | ieee-transactions-on-neural-systems-and-1 | ['electromyography-emg'] | ['medical'] | [ 4.21824098e-01 1.53493345e-01 -2.37794593e-01 1.85101017e-01
-1.31584597e+00 -4.80769187e-01 4.19082224e-01 -1.75980493e-01
-7.35698342e-01 1.02726114e+00 3.59503448e-01 -1.19830489e-01
-4.20525879e-01 -3.52264196e-01 -5.38780212e-01 -1.03512549e+00
-2.23855883e-01 7.52567947e-01 3.08649689e-01 1.91845357... | [12.880169868469238, 3.367546319961548] |
82e65b6f-dcb3-47e4-9069-310834fe8a14 | towards-a-real-time-demand-response-framework | 2303.00186 | null | https://arxiv.org/abs/2303.00186v2 | https://arxiv.org/pdf/2303.00186v2.pdf | Targeted demand response for flexible energy communities using clustering techniques | The present study explores the use of clustering techniques for the design and implementation of a demand response (DR) program for commercial and residential prosumers. The goal of the program is to alter the consumption behavior of the prosumers pertaining to a distributed energy community in Italy. This aggregation ... | ['Dimitris Askounis', 'Mohammad Ghoreishi', 'Francesca Santori', 'Spiros Mouzakitis', 'Evangelos Karakolis', 'Angelos Pipergias', 'Sotiris Pelekis'] | 2023-03-01 | null | null | null | null | ['dynamic-time-warping'] | ['time-series'] | [-3.62452388e-01 -1.19072311e-01 -9.73866582e-02 -1.70246020e-01
-4.35164779e-01 -9.54903185e-01 4.58253264e-01 5.15410662e-01
2.31053069e-01 4.65575784e-01 3.31068397e-01 -2.78004944e-01
-9.01616991e-01 -1.06947899e+00 3.56153250e-01 -1.25568354e+00
-4.24049973e-01 8.27492297e-01 -3.72887403e-01 -2.20080629... | [5.728696823120117, 2.575523614883423] |
2b33745c-c64d-48ed-b7ef-d991bd552fa3 | uct-learning-unified-convolutional-networks | 1711.04661 | null | http://arxiv.org/abs/1711.04661v1 | http://arxiv.org/pdf/1711.04661v1.pdf | UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking | Convolutional neural networks (CNN) based tracking approaches have shown
favorable performance in recent benchmarks. Nonetheless, the chosen CNN
features are always pre-trained in different task and individual components in
tracking systems are learned separately, thus the achieved tracking performance
may be suboptima... | ['Chang Huang', 'Wei Zou', 'Guan Huang', 'Dalong Du', 'Zheng Zhu'] | 2017-11-10 | null | null | null | null | ['real-time-visual-tracking'] | ['computer-vision'] | [-3.77777249e-01 -6.68014646e-01 -2.49680743e-01 -1.12462685e-01
-4.81424391e-01 -6.26764715e-01 4.49338078e-01 -9.61963907e-02
-8.25616896e-01 5.17583370e-01 -2.20160261e-01 -3.56381275e-02
-8.60571209e-03 -4.23713356e-01 -8.22069407e-01 -6.20438457e-01
-1.06341459e-01 1.09721050e-01 6.32313669e-01 1.54191419... | [6.309696197509766, -2.1380774974823] |
0a547910-2330-420f-aa0a-8eeb4b3b6639 | model-agnostic-vs-model-intrinsic | 2108.05317 | null | https://arxiv.org/abs/2108.05317v2 | https://arxiv.org/pdf/2108.05317v2.pdf | Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search | Product retrieval systems have served as the main entry for customers to discover and purchase products online. With increasing concerns on the transparency and accountability of AI systems, studies on explainable information retrieval has received more and more attention in the research community. Interestingly, in th... | ['Lakshmi Narayanan Ramasamy', 'Qingyao Ai'] | 2021-08-11 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-7.58555755e-02 5.71234167e-01 -8.37936223e-01 -5.61427295e-01
-2.01014549e-01 -5.90477943e-01 6.44523859e-01 3.15891594e-01
2.46161800e-02 1.92316756e-01 2.62795120e-01 -4.68730897e-01
-5.86541891e-01 -3.57027143e-01 -4.90496904e-01 -1.45792454e-01
4.59375888e-01 7.42248893e-01 -1.85086265e-01 -3.76942217... | [9.409615516662598, 5.98390007019043] |
6991e46e-03de-43e0-bae4-f495a0a39a36 | sampling-of-bayesian-posteriors-with-a-non | 1910.12717 | null | https://arxiv.org/abs/1910.12717v1 | https://arxiv.org/pdf/1910.12717v1.pdf | Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset | This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics problem under the following assumptions: 1) neither the prior model nor the likelihood function are Gau... | ['Christian Soize', 'Roger Ghanem'] | 2019-10-28 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.62437159e-01 5.22302449e-01 1.42872423e-01 2.16634795e-01
-7.06407607e-01 -6.03503198e-04 4.69657570e-01 1.33162215e-01
-4.59499091e-01 1.02348602e+00 -2.87090689e-01 -1.20498940e-01
-7.09590018e-01 -4.34532106e-01 -6.65119946e-01 -1.23893189e+00
-5.16813993e-01 7.16495395e-01 1.39457658e-01 -1.06701568... | [6.919426918029785, 4.014157295227051] |
3e019778-6535-4357-a6a7-6b97e72bbff1 | curiosity-killed-the-cat-and-the | 2006.03357 | null | https://arxiv.org/abs/2006.03357v2 | https://arxiv.org/pdf/2006.03357v2.pdf | Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent | Reinforcement learners are agents that learn to pick actions that lead to high reward. Ideally, the value of a reinforcement learner's policy approaches optimality--where the optimal informed policy is the one which maximizes reward. Unfortunately, we show that if an agent is guaranteed to be "asymptotically optimal" i... | ['Marcus Hutter', 'Elliot Catt', 'Michael K. Cohen'] | 2020-06-05 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-2.34143883e-01 3.99095178e-01 -1.99540406e-01 8.52713659e-02
-6.91511273e-01 -6.53552532e-01 4.02913809e-01 3.42571437e-02
-9.69356239e-01 1.27790773e+00 1.17395535e-01 -7.25007296e-01
-3.33433896e-01 -8.05318773e-01 -8.24420154e-01 -1.02388632e+00
-5.49687266e-01 6.09211802e-01 -8.97614956e-02 -3.47891062... | [4.1863508224487305, 2.310831308364868] |
7c917a82-9838-40ac-abc5-4ec25edc71b6 | bridging-the-gap-between-events-and-frames | 2109.02618 | null | https://arxiv.org/abs/2109.02618v2 | https://arxiv.org/pdf/2109.02618v2.pdf | Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation | Reliable perception during fast motion maneuvers or in high dynamic range environments is crucial for robotic systems. Since event cameras are robust to these challenging conditions, they have great potential to increase the reliability of robot vision. However, event-based vision has been held back by the shortage of ... | ['Davide Scaramuzza', 'Mathias Gehrig', 'Daniel Gehrig', 'Nico Messikommer'] | 2021-09-06 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 6.06379330e-01 -7.85188675e-02 -2.00576931e-01 -2.72380888e-01
-8.23032975e-01 -5.41027844e-01 9.56384838e-01 -2.35612512e-01
-6.58037484e-01 6.34738028e-01 1.43999949e-01 1.03216274e-02
4.28793468e-02 -5.59705615e-01 -1.07888138e+00 -7.29207695e-01
2.52447277e-01 1.66988954e-01 6.94645584e-01 1.31283030... | [8.436281204223633, -0.8754507899284363] |
cb522fbe-98ca-497b-a3a0-9ee553976ba4 | extending-monocular-visual-odometry-to-stereo | 1905.12723 | null | https://arxiv.org/abs/1905.12723v3 | https://arxiv.org/pdf/1905.12723v3.pdf | Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization | This paper proposes a novel approach for extending monocular visual odometry to a stereo camera system. The proposed method uses an additional camera to accurately estimate and optimize the scale of the monocular visual odometry, rather than triangulating 3D points from stereo matching. Specifically, the 3D points gene... | ['Junaed Sattar', 'Jiawei Mo'] | 2019-05-29 | null | null | null | null | ['stereo-matching', 'monocular-visual-odometry'] | ['computer-vision', 'robots'] | [ 2.45367941e-02 7.62954578e-02 2.72256106e-01 -3.49171281e-01
-2.36559808e-01 -7.22069621e-01 4.44703609e-01 -2.75203109e-01
-3.45839292e-01 4.64795172e-01 1.45124933e-02 3.22692841e-02
3.51460636e-01 -6.88169599e-01 -7.74917603e-01 -5.34719646e-01
2.43309081e-01 9.09672499e-01 5.90333819e-01 -8.61396194... | [7.688779354095459, -2.241863489151001] |
5df53652-6242-4723-ba0a-7e87d4e3f5e7 | fast-model-based-policy-search-for-universal | 2202.05843 | null | https://arxiv.org/abs/2202.05843v1 | https://arxiv.org/pdf/2202.05843v1.pdf | Fast Model-based Policy Search for Universal Policy Networks | Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. Although recent approaches such as universal policy networks partially address this issue by enabling the storage of multiple policies trained in simulation on a wide range of dynamic/laten... | ['Svetha Venkatesh', 'Santu Rana', 'Thommen George Karimpanal', 'Buddhika Laknath Semage'] | 2022-02-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-5.00832163e-02 -3.79282832e-01 -2.34842569e-01 8.12206045e-03
-5.75954854e-01 -5.79749763e-01 9.08244252e-01 2.58345634e-01
-8.43556941e-01 1.05550873e+00 1.70881823e-01 -3.12023669e-01
-3.64377737e-01 -6.00516915e-01 -9.78914857e-01 -7.41223514e-01
-1.27224192e-01 7.93767989e-01 5.12148201e-01 -9.42883920... | [4.283660888671875, 1.8487696647644043] |
00f02bd0-985d-4cfc-a1cf-32271de2a669 | semantically-grounded-object-matching-for | 2111.07975 | null | https://arxiv.org/abs/2111.07975v1 | https://arxiv.org/pdf/2111.07975v1.pdf | Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement | Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning. Goal-images describing a desired scene configuration are a promising and increasingly used mode of instruction. A key outstan... | ['Ingmar Posner', 'Ioannis Havoutis', 'Sagar Vaze', 'Walter Goodwin'] | 2021-11-15 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.51576620e-01 -3.12448829e-01 -1.60786986e-01 -4.24932241e-01
-6.78365827e-01 -6.29884779e-01 3.64336610e-01 4.74568844e-01
-4.17001039e-01 1.79336250e-01 -2.81140894e-01 -1.51136816e-01
-3.82148862e-01 -4.68012691e-01 -1.20687950e+00 -2.62145996e-01
-5.59647754e-02 7.61183500e-01 5.55119336e-01 -5.83718836... | [4.959063529968262, 0.20806580781936646] |
2632cd88-3162-47b0-94d4-50bb14eccce3 | how-to-ask-better-questions-a-large-scale | 1911.09247 | null | https://arxiv.org/abs/1911.09247v1 | https://arxiv.org/pdf/1911.09247v1.pdf | How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions | We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. ... | ['Manaal Faruqui', 'Xiance Si', 'Zewei Chu', 'Mingda Chen', 'Kevin Gimpel', 'Miaosen Wang', 'Jing Chen'] | 2019-11-21 | null | null | null | null | ['question-rewriting'] | ['natural-language-processing'] | [ 3.91657829e-01 4.27998543e-01 2.34550506e-01 -5.88311255e-01
-1.65117788e+00 -1.00498116e+00 5.11890948e-01 8.18310529e-02
-5.66446781e-01 7.81224847e-01 5.25095940e-01 -5.14083743e-01
2.28565380e-01 -6.18920982e-01 -1.04823983e+00 2.54367381e-01
6.58471704e-01 4.74366397e-01 6.73719227e-01 -7.25444555... | [11.409646034240723, 8.072209358215332] |
abe7bd98-d5b7-4707-be35-97e3e81fed6a | finbert-mrc-financial-named-entity | 2205.15485 | null | https://arxiv.org/abs/2205.15485v1 | https://arxiv.org/pdf/2205.15485v1.pdf | FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm | Financial named entity recognition (FinNER) from literature is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts. It is widely accepted to use sequence tagging frameworks to implement FinNER tasks. However, such ... | ['Hong Zhang', 'Yuzhe Zhang'] | 2022-05-31 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [-2.47780696e-01 1.35301873e-01 -2.03166142e-01 -5.94138205e-01
-1.03728843e+00 -8.57188880e-01 4.12311822e-01 1.66062012e-01
-7.85441577e-01 9.28209960e-01 3.00344139e-01 -5.53929150e-01
4.80203003e-01 -6.49469733e-01 -7.08620846e-01 -2.42342681e-01
2.20803827e-01 4.36649501e-01 2.23515764e-01 9.38909352... | [9.82951545715332, 9.649425506591797] |
02d31dbe-0e3a-4c07-926c-8e9a6c2c3133 | concept-extraction-using-pointer-generator | 2008.11295 | null | https://arxiv.org/abs/2008.11295v1 | https://arxiv.org/pdf/2008.11295v1.pdf | Concept Extraction Using Pointer-Generator Networks | Concept extraction is crucial for a number of downstream applications. However, surprisingly enough, straightforward single token/nominal chunk-concept alignment or dictionary lookup techniques such as DBpedia Spotlight still prevail. We propose a generic open-domain OOV-oriented extractive model that is based on dista... | ['Alexander Shvets', 'Leo Wanner'] | 2020-08-25 | null | null | null | null | ['concept-alignment'] | ['computer-vision'] | [-2.51874894e-01 6.76825702e-01 -4.83650565e-01 2.77805813e-02
-8.95043850e-01 -7.12677240e-01 9.93064165e-01 6.28304064e-01
-7.57676780e-01 1.09640527e+00 3.96361321e-01 -5.89275897e-01
4.01160233e-02 -1.11837661e+00 -1.10188520e+00 -1.89645439e-01
-5.77021837e-01 8.12514067e-01 4.65863615e-01 -4.80978161... | [9.502586364746094, 8.572388648986816] |
94914d7c-0888-43f8-a2c8-6487a0999e4d | ticket-bert-labeling-incident-management | 2307.00108 | null | https://arxiv.org/abs/2307.00108v1 | https://arxiv.org/pdf/2307.00108v1.pdf | Ticket-BERT: Labeling Incident Management Tickets with Language Models | An essential aspect of prioritizing incident tickets for resolution is efficiently labeling tickets with fine-grained categories. However, ticket data is often complex and poses several unique challenges for modern machine learning methods: (1) tickets are created and updated either by machines with pre-defined algorit... | ['Siduo Jiang', 'Cris Benge', 'Zhexiong Liu'] | 2023-06-30 | null | null | null | null | ['active-learning', 'management', 'active-learning'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 1.99910015e-01 -1.14413515e-01 -6.41346753e-01 -8.96189570e-01
-8.68202090e-01 -3.72369081e-01 4.78228509e-01 3.93264979e-01
-5.89709997e-01 9.02911007e-01 8.53457768e-03 -2.96988279e-01
-5.32287478e-01 -6.33713365e-01 -4.78425294e-01 -2.72461712e-01
-2.27894157e-01 1.53560460e+00 4.28937107e-01 -2.75744945... | [9.527668952941895, 4.457243919372559] |
80462f27-e593-4cbd-9f32-2806e98d119a | language-driven-region-pointer-advancement | 2011.14901 | null | https://arxiv.org/abs/2011.14901v1 | https://arxiv.org/pdf/2011.14901v1.pdf | Language-Driven Region Pointer Advancement for Controllable Image Captioning | Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-u... | ['John D. Kelleher', 'Robert J. Ross', 'Annika Lindh'] | 2020-11-30 | null | https://aclanthology.org/2020.coling-main.174 | https://aclanthology.org/2020.coling-main.174.pdf | coling-2020-8 | ['controllable-image-captioning'] | ['computer-vision'] | [ 4.70458299e-01 4.31138158e-01 -4.80214745e-01 -6.76385880e-01
-1.05355406e+00 -9.18262184e-01 8.72184992e-01 2.38128260e-01
-4.71808791e-01 5.25549889e-01 7.27669239e-01 -3.28817874e-01
3.69608641e-01 -5.32902896e-01 -1.22881377e+00 -4.82771903e-01
1.71175793e-01 4.86176938e-01 4.75372039e-02 -2.58673638... | [10.979716300964355, 0.9775854349136353] |
672f7b71-0cb7-4882-a702-bce821e6b398 | a-deep-learning-model-for-forecasting-global | 2202.09967 | null | https://arxiv.org/abs/2202.09967v1 | https://arxiv.org/pdf/2202.09967v1.pdf | A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies | Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Ni\~{n}o-Southern Oscillation regarded as a major source of interannual climate variability at the global scale. The ability to be able to make long-range forecasts of sea surface temperature a... | ['Ming Feng', 'John Taylor'] | 2022-02-21 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.48149112e-01 -4.84279156e-01 3.04166079e-01 -2.98644155e-01
-5.30563593e-01 -4.72685635e-01 7.39111841e-01 -1.30838454e-01
-3.62618506e-01 1.08661997e+00 -3.01760924e-03 -1.09278524e+00
-2.65703350e-02 -1.09451973e+00 -4.30005193e-01 -1.09588301e+00
-7.58762896e-01 9.44928378e-02 -4.13155228e-01 -8.42181504... | [6.521899700164795, 2.9653947353363037] |
57afddf5-9317-47e0-a503-aa3049a360a0 | interactive-generative-adversarial-networks | 1801.09092 | null | http://arxiv.org/abs/1801.09092v2 | http://arxiv.org/pdf/1801.09092v2.pdf | Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions | A social interaction is a social exchange between two or more
individuals,where individuals modify and adjust their behaviors in response to
their interaction partners. Our social interactions are one of most fundamental
aspects of our lives and can profoundly affect our mood, both positively and
negatively. With growi... | ['Yuchi Huang', 'Behnaz Nojavanasghari', 'Saad Khan'] | 2018-01-27 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [-1.22271866e-01 6.07579052e-01 1.97617501e-01 -8.88101339e-01
4.19274390e-01 -5.13825893e-01 7.35015213e-01 -2.88811564e-01
2.71966904e-02 9.60358024e-01 2.68964231e-01 5.43260872e-01
2.83477426e-01 -8.01117480e-01 -1.92773208e-01 -4.59830523e-01
-6.96124062e-02 3.54644209e-01 -1.13692023e-02 -6.54461741... | [13.145583152770996, -0.34196510910987854] |
0c1ebf6c-3fa5-4ee5-a0d0-905d00f26b85 | the-neural-architecture-of-language | null | null | https://www.pnas.org/content/118/45/e2105646118 | https://www.pnas.org/doi/epdf/10.1073/pnas.2105646118 | The neural architecture of language: Integrative modeling converges on predictive processing | The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models. By revealing trends across models, this approach yields novel insights into cognitive and neural mechani... | ['Evelina Fedorenko', 'Joshua Tenenbaum', 'Nancy Kanwisher', 'Eghbal Hosseini', 'Carina Kauf', 'Greta Tuckute', 'Idan Blank', 'Martin Schrimpf'] | 2021-11-04 | null | null | null | proceedings-of-the-national-academy-of-1 | ['probing-language-models'] | ['natural-language-processing'] | [ 3.36427540e-01 -1.16328493e-01 2.22161204e-01 -4.49861079e-01
-2.68062145e-01 -5.89323044e-01 9.57824647e-01 1.97374523e-01
-7.39790797e-01 1.42851800e-01 5.33396304e-01 -4.05665338e-01
-3.89464855e-01 -4.89667624e-01 -3.62690359e-01 -2.24695474e-01
-4.29956876e-02 1.31603628e-01 -1.51643157e-01 -1.59117803... | [10.219429969787598, 8.331756591796875] |
da9fab5b-0ca7-44a9-8921-cc0bf757eba5 | abhe-all-attention-based-homography | 2212.03029 | null | https://arxiv.org/abs/2212.03029v3 | https://arxiv.org/pdf/2212.03029v3.pdf | AbHE: All Attention-based Homography Estimation | Homography estimation is a basic computer vision task, which aims to obtain the transformation from multi-view images for image alignment. Unsupervised learning homography estimation trains a convolution neural network for feature extraction and transformation matrix regression. While the state-of-theart homography met... | ['Xianqiang Yang', 'Xinyang Ren', 'Zhihao Zhang', 'Mingxiao Huo'] | 2022-12-06 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-5.47211953e-02 -2.57807404e-01 4.71415259e-02 -3.14160258e-01
-4.25742567e-01 -3.00544035e-02 6.93342149e-01 -6.65329695e-01
-2.93720961e-01 2.15946555e-01 2.70915180e-01 2.77502626e-01
-1.05599694e-01 -8.64565015e-01 -8.62393916e-01 -7.74253368e-01
3.89142215e-01 5.62452137e-01 2.47102603e-01 -2.89685398... | [8.64319133758545, -2.2339982986450195] |
a294adb6-f6b4-4f2f-a639-4679caaf64c8 | stationarity-analysis-of-the-stock-market | 2112.12459 | null | https://arxiv.org/abs/2112.12459v3 | https://arxiv.org/pdf/2112.12459v3.pdf | Stationarity analysis of the stock market data and its application to mechanical trading | This study proposes a scheme for stationarity analysis of stock price fluctuations based on KM$_2$O-Langevin theory. Using this scheme, we classify the time-series data of stock price fluctuations into three periods: stationary, non-stationary, and intermediate. We then suggest an example of a low-risk stock trading st... | ['Norikazu Todoroki', 'Kazuki Kanehira'] | 2021-12-23 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.93334317e-01 -4.71804023e-01 -1.19309552e-01 3.00547779e-01
-1.92721635e-01 -8.62600684e-01 5.48565447e-01 -2.77262747e-01
-4.14180070e-01 1.01140690e+00 -2.37899661e-01 -4.37128216e-01
-1.58495262e-01 -1.06520557e+00 -4.48354572e-01 -8.76924217e-01
-6.32981420e-01 1.98177949e-01 5.66038430e-01 -4.41399246... | [4.793015480041504, 4.106166839599609] |
b99e89a8-6f62-4cd4-a04e-45af486149e4 | face-recognition-by-fusion-of-local-and | 1002.00382 | null | http://arxiv.org/abs/1002.0382v1 | http://arxiv.org/pdf/1002.0382v1.pdf | Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm | Faces are highly deformable objects which may easily change their appearance
over time. Not all face areas are subject to the same variability. Therefore
decoupling the information from independent areas of the face is of paramount
importance to improve the robustness of any face recognition technique. This
paper prese... | ['Jamuna Kanta Sing', 'Phalguni Gupta', 'Massimo Tistarelli', 'Dakshina Ranjan Kisku'] | 2010-02-02 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.53425530e-01 -1.56688914e-01 -7.74287106e-03 -6.02236271e-01
-2.28809580e-01 -3.91500294e-01 6.18412852e-01 -1.72521710e-01
-2.13302732e-01 4.19798434e-01 1.53353736e-02 4.84816819e-01
-3.78924042e-01 -6.31084681e-01 -2.70185083e-01 -1.06369615e+00
7.82886147e-02 9.57146287e-02 1.77273616e-01 -4.56456952... | [13.110581398010254, 0.6071850657463074] |
40fd0181-bd71-413f-b8f5-6f46c4477763 | opera-attention-regularized-transformers-for | 2103.03873 | null | https://arxiv.org/abs/2103.03873v1 | https://arxiv.org/pdf/2103.03873v1.pdf | OperA: Attention-Regularized Transformers for Surgical Phase Recognition | In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the model to focus on high-quality frames during training. Moreover, the attention weights are utilized to identify characteristic high attenti... | ['Nassir Navab', 'Benjamin Busam', 'Seong Tae Kim', 'Daniel Ostler', 'Magdalini Paschali', 'Tobias Czempiel'] | 2021-03-05 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 1.80946797e-01 4.24809158e-01 -6.30382359e-01 -7.76316524e-02
-6.95261478e-01 4.63016145e-02 4.33646232e-01 2.02583313e-01
-3.64238560e-01 4.00457710e-01 7.14377999e-01 -1.07305288e-01
-2.63873160e-01 -2.24356875e-01 -6.11059010e-01 -7.98618734e-01
-3.63582134e-01 2.22749442e-01 7.80251250e-02 -5.28012179... | [14.136795043945312, -3.32171368598938] |
15ca92e1-40a5-4ab3-9d16-99e7e1281a03 | beyond-greedy-search-tracking-by-multi-agent | 2205.09676 | null | https://arxiv.org/abs/2205.09676v3 | https://arxiv.org/pdf/2205.09676v3.pdf | Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search | To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the tracking result of each frame. However, we found that this may be not an optimal choice, especially when... | ['Bo Jiang', 'DaCheng Tao', 'Bin Luo', 'Jin Tang', 'Zhe Chen', 'Xiao Wang'] | 2022-05-19 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [ 1.30679831e-01 -5.11637986e-01 -4.54884946e-01 -1.00582510e-01
-5.08031666e-01 -5.75756192e-01 2.32353702e-01 2.57446259e-01
-6.37194276e-01 8.41973364e-01 -2.58084774e-01 -5.81594296e-02
-1.44672751e-01 -5.74826658e-01 -4.96744365e-01 -1.09357071e+00
1.29905224e-01 3.59632492e-01 7.08663702e-01 4.04322654... | [6.385588645935059, -2.099343776702881] |
0a543024-8c85-4b1f-b390-a693fa70d24f | meta3d-single-view-3d-object-reconstruction | 2003.03711 | null | https://arxiv.org/abs/2003.03711v3 | https://arxiv.org/pdf/2003.03711v3.pdf | Single-View 3D Object Reconstruction from Shape Priors in Memory | Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images containing noisy backgrounds and heavy occlusions because the extracted image features do not contain enough information to reconstruct high-quali... | ['Jiahao Xia', 'Stuart Perry', 'Haozhe Xie', 'Min Xu', 'Shuo Yang'] | 2020-03-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Single-View_3D_Object_Reconstruction_From_Shape_Priors_in_Memory_CVPR_2021_paper.pdf | cvpr-2021-1 | ['single-view-3d-reconstruction', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-0.09800956 -0.21140818 -0.03886572 -0.45397347 -0.5575351 -0.32716322
0.3906545 -0.3277357 0.02688159 0.31849223 0.25715947 0.091216
0.1458595 -1.0181725 -1.2022129 -0.6970538 0.58048135 0.52941924
0.26801237 0.01351733 0.26658985 0.89097 -1.6670485 0.49770024
0.44737503 1.3577267 0.79... | [8.44268798828125, -3.362149477005005] |
c2dfb984-4907-4a9d-94ff-87c23fa021d2 | conformer-and-blind-noisy-students-for | 2204.12819 | null | https://arxiv.org/abs/2204.12819v1 | https://arxiv.org/pdf/2204.12819v1.pdf | Conformer and Blind Noisy Students for Improved Image Quality Assessment | Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metri... | ['Radu Timofte', 'Maxime Burchi', 'Marcos V. Conde'] | 2022-04-27 | null | null | null | null | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 9.29147601e-02 -3.09756815e-01 1.32569402e-01 -4.60555673e-01
-1.33335996e+00 -3.85828137e-01 4.27209914e-01 -3.48871686e-02
-3.19811255e-01 5.19695699e-01 4.20488387e-01 -1.99926600e-01
-5.37040047e-02 -5.43792665e-01 -6.61280990e-01 -5.17152965e-01
3.22550654e-01 -1.06950618e-01 4.21336293e-02 -1.18491121... | [11.906563758850098, -1.8558233976364136] |
8745a33f-e355-449a-a255-0ee2a465df3a | noisetrans-point-cloud-denoising-with | 2304.11812 | null | https://arxiv.org/abs/2304.11812v1 | https://arxiv.org/pdf/2304.11812v1.pdf | NoiseTrans: Point Cloud Denoising with Transformers | Point clouds obtained from capture devices or 3D reconstruction techniques are often noisy and interfere with downstream tasks. The paper aims to recover the underlying surface of noisy point clouds. We design a novel model, NoiseTrans, which uses transformer encoder architecture for point cloud denoising. Specifically... | ['Zhonghan Zhang', 'Jie Yan', 'Yanhua Liang', 'Minghui Sun', 'Guihe Qin', 'Guangzhe Hou'] | 2023-04-24 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [-5.07780723e-02 -9.99956653e-02 4.69699353e-01 -2.37896487e-01
-7.90162325e-01 -4.82712954e-01 3.61581922e-01 -2.25188881e-01
1.46792322e-01 5.49776703e-02 3.93840820e-01 2.23408207e-01
1.47690147e-01 -8.36656511e-01 -1.31212842e+00 -6.88680410e-01
2.68846214e-01 6.01237565e-02 -1.06275722e-01 -1.66312754... | [8.215133666992188, -3.59460186958313] |
d6e00d47-9854-4d07-90f7-c0132557284a | endnet-sparse-autoencoder-network-for | 1708.01894 | null | http://arxiv.org/abs/1708.01894v4 | http://arxiv.org/pdf/1708.01894v4.pdf | EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing | Data acquired from multi-channel sensors is a highly valuable asset to
interpret the environment for a variety of remote sensing applications.
However, low spatial resolution is a critical limitation for previous sensors
and the constituent materials of a scene can be mixed in different fractions
due to their spatial i... | ['Gozde Bozdagi Akar', 'Savas Ozkan', 'Berk Kaya'] | 2017-08-06 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.91149992e-01 -7.37830162e-01 1.92152545e-01 -2.66156346e-01
-2.89682359e-01 -3.08840930e-01 3.74737263e-01 -9.06997360e-03
-5.22103131e-01 7.98609376e-01 -1.48547180e-02 -1.29039556e-01
-4.47983682e-01 -8.37836742e-01 -6.49414539e-01 -1.21735060e+00
2.38866284e-02 -1.54705480e-01 -8.77849758e-02 -1.78294957... | [10.142683982849121, -2.035994291305542] |
d77fccae-50d1-4af4-b92a-49e99171d6ce | co-evolving-graph-reasoning-network-for | 2306.04340 | null | https://arxiv.org/abs/2306.04340v1 | https://arxiv.org/pdf/2306.04340v1.pdf | Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction | Emotion-Cause Pair Extraction (ECPE) aims to extract all emotion clauses and their corresponding cause clauses from a document. Existing approaches tackle this task through multi-task learning (MTL) framework in which the two subtasks provide indicative clues for ECPE. However, the previous MTL framework considers only... | ['Ivor W. Tsang', 'Bowen Xing'] | 2023-06-07 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [-1.38716185e-02 3.85649592e-01 -3.17146689e-01 -3.49436611e-01
-8.44248533e-01 -3.09212476e-01 6.17023230e-01 -1.11366279e-01
3.87170091e-02 5.72353005e-01 5.14160991e-01 -1.53516874e-01
-2.23201007e-01 -6.06838822e-01 -7.18200684e-01 -3.95835310e-01
-1.51251614e-01 4.03314471e-01 3.72278631e-01 -4.27557170... | [12.581441879272461, 6.2674713134765625] |
43eb10b0-7df4-4d6c-8de0-37b245ac04d5 | intelligent-video-editing-incorporating | 2110.08580 | null | https://arxiv.org/abs/2110.08580v1 | https://arxiv.org/pdf/2110.08580v1.pdf | Intelligent Video Editing: Incorporating Modern Talking Face Generation Algorithms in a Video Editor | This paper proposes a video editor based on OpenShot with several state-of-the-art facial video editing algorithms as added functionalities. Our editor provides an easy-to-use interface to apply modern lip-syncing algorithms interactively. Apart from lip-syncing, the editor also uses audio and facial re-enactment to ge... | ['C. V. Jawahar', 'Vinay P. Namboodiri', 'Rudrabha Mukhopadhyay', 'Faizan Farooq Khan', 'Anchit Gupta'] | 2021-10-16 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 1.90273792e-01 4.48027283e-01 1.76477388e-01 -1.64627805e-01
-7.62401819e-01 -5.56270003e-01 7.12190032e-01 -2.76364893e-01
-1.69625044e-01 6.45295143e-01 3.63659084e-01 4.88525778e-02
2.64771223e-01 -3.51719409e-01 -6.33150041e-01 -2.63833702e-01
3.21037769e-01 -4.00468633e-02 2.55416989e-01 -2.73083568... | [13.2421875, -0.4506858289241791] |
73bcb3f7-c7cc-44c2-8e25-3b6684a6e5d6 | designing-and-evaluating-speech-emotion | 2304.00860 | null | https://arxiv.org/abs/2304.00860v1 | https://arxiv.org/pdf/2304.00860v1.pdf | Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP | There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test mode... | ['Shrikanth Narayanan', 'Theodoros Giannakopoulos', 'Athanasios Katsamanis', 'Nikolaos Antoniou'] | 2023-04-03 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.38378918e-01 -2.56111145e-01 -9.63389352e-02 -3.50932389e-01
-8.08377862e-01 -6.07487321e-01 5.84641933e-01 -1.41935110e-01
-6.14051998e-01 4.52131331e-01 4.19001669e-01 -1.03084426e-02
-9.56425071e-02 1.87555149e-01 -3.45531285e-01 -2.46991187e-01
-2.67778426e-01 -1.20688997e-01 1.06557339e-01 -2.85411328... | [13.328653335571289, 5.559164047241211] |
bf466837-c7d1-4a5a-b7a9-83e5bcf8c516 | perturb-predict-paraphrase-semi-supervised | null | null | https://www.ijcai.org/proceedings/2021/105 | https://www.ijcai.org/proceedings/2021/0105.pdf | Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning | Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Stude... | ['Maneesh Singh', 'Deepak Mittal', 'Preethi Jyothi', 'Pranay Reddy Samala', 'Arjit Jain'] | 2021-08-19 | null | null | null | ijcai-2021-8 | ['semi-supervised-learning-for-image-captioning', 'image-augmentation'] | ['computer-vision', 'computer-vision'] | [ 1.01178312e+00 4.30200279e-01 -4.29615438e-01 -4.68189836e-01
-1.17334175e+00 -9.62576568e-01 8.80520344e-01 3.08944464e-01
-6.35359108e-01 8.24654222e-01 5.64944185e-02 -5.36201715e-01
4.14726853e-01 -3.45381647e-01 -1.26368749e+00 -6.37681544e-01
3.85852993e-01 6.19383156e-01 5.90544343e-02 -1.78364187... | [10.954536437988281, 0.6306782960891724] |
4ed9a996-06a0-4887-a783-34526184b94d | learning-typographic-style | 1603.04000 | null | http://arxiv.org/abs/1603.04000v1 | http://arxiv.org/pdf/1603.04000v1.pdf | Learning Typographic Style | Typography is a ubiquitous art form that affects our understanding,
perception, and trust in what we read. Thousands of different font-faces have
been created with enormous variations in the characters. In this paper, we
learn the style of a font by analyzing a small subset of only four letters.
From these four letters... | ['Shumeet Baluja'] | 2016-03-13 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 1.97066367e-01 -2.92534649e-01 1.13430262e-01 -3.68169338e-01
2.92783901e-02 -1.11224079e+00 5.91126800e-01 -1.44531325e-01
-1.69089988e-01 8.45541418e-01 2.14845687e-01 -3.38637561e-01
1.00785188e-01 -7.68911064e-01 -9.17915642e-01 -3.97723913e-01
6.22826278e-01 4.53858942e-01 1.85150683e-01 -4.19237733... | [11.702119827270508, -0.11591404676437378] |
6403ac25-c4a5-445f-9bc2-4ad57c61fd93 | self-attention-between-datapoints-going | 2106.02584 | null | https://arxiv.org/abs/2106.02584v2 | https://arxiv.org/pdf/2106.02584v2.pdf | Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning | We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoin... | ['Yarin Gal', 'Tom Rainforth', 'Aidan N. Gomez', 'Clare Lyle', 'Neil Band', 'Jannik Kossen'] | 2021-06-04 | null | http://proceedings.neurips.cc/paper/2021/hash/f1507aba9fc82ffa7cc7373c58f8a613-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/f1507aba9fc82ffa7cc7373c58f8a613-Paper.pdf | neurips-2021-12 | ['3d-part-segmentation'] | ['computer-vision'] | [-2.70672202e-01 4.63633120e-01 -5.20128965e-01 -9.43902552e-01
-6.87030733e-01 -5.99923968e-01 6.20853484e-01 3.29383820e-01
-4.30927008e-01 4.83169556e-01 2.82671511e-01 -5.01495540e-01
-2.46090636e-01 -9.18129444e-01 -1.34611726e+00 -2.10989550e-01
-4.93854806e-02 1.29777682e+00 4.39954847e-02 -9.23142508... | [9.609108924865723, 7.069088935852051] |
db86d175-eed7-4500-9d99-7d63994bf250 | robot-motion-planning-as-video-prediction-a | 2208.11287 | null | https://arxiv.org/abs/2208.11287v1 | https://arxiv.org/pdf/2208.11287v1.pdf | Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner | Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parallelism. Despite the current development in this direction, the efficient capture and processing of important sequential and spatial informati... | ['Bo Yuan', 'Saman Zonouz', 'Jingjin Yu', 'Lingyi Huang', 'Miao Yin', 'Xiao Zang'] | 2022-08-24 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.55304420e-01 -1.23943590e-01 -4.53833908e-01 3.95314917e-02
-4.41187859e-01 -2.97751933e-01 4.83113468e-01 -9.72704738e-02
-8.13069642e-01 5.80305755e-01 8.35453570e-02 -3.61244380e-01
-3.71223003e-01 -8.93640161e-01 -5.95345497e-01 -5.33599377e-01
-4.01383966e-01 7.11395919e-01 5.62110960e-01 -1.76712275... | [4.784753799438477, 0.9893090128898621] |
0c1faeac-f992-4d61-8db5-4b474b7d4949 | joint-architecture-and-knowledge-distillation | 1912.07806 | null | https://arxiv.org/abs/1912.07806v3 | https://arxiv.org/pdf/1912.07806v3.pdf | Joint Architecture and Knowledge Distillation in CNN for Chinese Text Recognition | The technique of distillation helps transform cumbersome neural network into compact network so that the model can be deployed on alternative hardware devices. The main advantages of distillation based approaches include simple training process, supported by most off-the-shelf deep learning softwares and no special req... | ['Zi-Rui Wang', 'Jun Du'] | 2019-12-17 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 5.15772998e-02 1.92324072e-01 7.23632611e-03 -3.70453447e-01
1.80701584e-01 -2.86345184e-01 2.11487651e-01 -2.65496224e-01
-9.86118734e-01 7.33834684e-01 -4.98854101e-01 -6.98945165e-01
-1.17146738e-01 -8.67644668e-01 -8.58444095e-01 -7.44644523e-01
1.72123119e-01 8.91079679e-02 3.95202935e-01 -5.72921708... | [8.582704544067383, 2.977672815322876] |
7ac4e8b8-6f3c-4f9a-9cca-ca55c1f009fa | multi-class-zero-shot-learning-for-artistic | 2010.13850 | null | https://arxiv.org/abs/2010.13850v1 | https://arxiv.org/pdf/2010.13850v1.pdf | Multi-Class Zero-Shot Learning for Artistic Material Recognition | Zero-Shot Learning (ZSL) is an extreme form of transfer learning, where no labelled examples of the data to be classified are provided during the training stage. Instead, ZSL uses additional information learned about the domain, and relies upon transfer learning algorithms to infer knowledge about the missing instances... | ['Tom Bock', 'Andreea Cucu', 'Alexander W Olson'] | 2020-10-26 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 3.58203322e-01 1.90133765e-01 -1.34119824e-01 -5.07256389e-01
-8.79852772e-01 -5.83138347e-01 8.65390539e-01 1.59148127e-02
-4.66444969e-01 7.80286610e-01 2.39367113e-01 1.18920490e-01
-3.45533907e-01 -1.05927885e+00 -9.20702755e-01 -3.82315308e-01
-2.52621695e-02 1.07986784e+00 2.57031530e-01 -1.31641343... | [9.98342227935791, 2.990692138671875] |
c4087a30-683f-4621-bc64-f0441bc520a7 | unsupervised-learning-for-intrinsic-image | 1911.09930 | null | https://arxiv.org/abs/1911.09930v2 | https://arxiv.org/pdf/1911.09930v2.pdf | Unsupervised Learning for Intrinsic Image Decomposition from a Single Image | Intrinsic image decomposition, which is an essential task in computer vision, aims to infer the reflectance and shading of the scene. It is challenging since it needs to separate one image into two components. To tackle this, conventional methods introduce various priors to constrain the solution, yet with limited perf... | ['ShaoDi You', 'Yunfei Liu', 'Feng Lu', 'Yu Li'] | 2019-11-22 | unsupervised-learning-for-intrinsic-image-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Unsupervised_Learning_for_Intrinsic_Image_Decomposition_From_a_Single_Image_CVPR_2020_paper.pdf | cvpr-2020-6 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 8.69005620e-01 -1.09188393e-01 1.76651940e-01 -2.86676794e-01
-5.11862457e-01 -3.17513704e-01 5.26927590e-01 -2.23559067e-01
-2.03193471e-01 6.70931995e-01 -2.28889603e-02 -1.15904910e-02
-5.94912618e-02 -6.62533700e-01 -3.91696751e-01 -1.15833557e+00
6.46440089e-01 1.51619911e-01 3.16604264e-02 5.51349595... | [10.00757122039795, -2.7585465908050537] |
c4b1aa51-05bb-4e57-af94-127be4992462 | enabling-factorized-piano-music-modeling-and | 1810.12247 | null | http://arxiv.org/abs/1810.12247v5 | http://arxiv.org/pdf/1810.12247v5.pdf | Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset | Generating musical audio directly with neural networks is notoriously
difficult because it requires coherently modeling structure at many different
timescales. Fortunately, most music is also highly structured and can be
represented as discrete note events played on musical instruments. Herein, we
show that by using no... | ['Cheng-Zhi Anna Huang', 'Adam Roberts', 'Sander Dieleman', 'Jesse Engel', 'Douglas Eck', 'Ian Simon', 'Erich Elsen', 'Curtis Hawthorne', 'Andriy Stasyuk'] | 2018-10-29 | enabling-factorized-piano-music-modeling-and-1 | https://openreview.net/forum?id=r1lYRjC9F7 | https://openreview.net/pdf?id=r1lYRjC9F7 | iclr-2019-5 | ['music-modeling', 'piano-music-modeling'] | ['music', 'music'] | [ 4.47343171e-01 7.12233782e-03 1.47376493e-01 -8.47291499e-02
-9.68671560e-01 -1.31162202e+00 3.94721210e-01 -1.48726150e-01
1.38426825e-01 4.79140431e-01 5.72203398e-01 1.85956419e-01
-4.39201206e-01 -5.05263448e-01 -7.06983805e-01 -3.13357592e-01
-4.46727097e-01 5.47045469e-01 -2.72571683e-01 -3.26703578... | [15.95609188079834, 5.500547409057617] |
ce8cb608-b6d9-46f7-b84c-c0b4913b1432 | movie-plot-analysis-via-turning-point | 1908.10328 | null | https://arxiv.org/abs/1908.10328v2 | https://arxiv.org/pdf/1908.10328v2.pdf | Movie Plot Analysis via Turning Point Identification | According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of tur... | ['Frank Keller', 'Pinelopi Papalampidi', 'Mirella Lapata'] | 2019-08-27 | movie-plot-analysis-via-turning-point-1 | https://aclanthology.org/D19-1180 | https://aclanthology.org/D19-1180.pdf | ijcnlp-2019-11 | ['turning-point-identification'] | ['natural-language-processing'] | [ 4.12390858e-01 6.82472512e-02 -4.01612788e-01 -5.22434831e-01
-8.62063289e-01 -1.32952321e+00 1.01758361e+00 6.89710915e-01
3.32287923e-02 2.38150924e-01 1.20047402e+00 -2.98975140e-01
1.31550118e-01 -7.63461292e-01 -7.64701426e-01 3.00362229e-01
1.68292865e-01 3.26368481e-01 2.63064593e-01 -4.96731013... | [12.405367851257324, 9.441787719726562] |
94ead6d8-c006-419d-8582-2cef2fa8b088 | storm-a-diffusion-based-stochastic | 2212.11851 | null | https://arxiv.org/abs/2212.11851v1 | https://arxiv.org/pdf/2212.11851v1.pdf | StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation | Diffusion models have shown a great ability at bridging the performance gap between predictive and generative approaches for speech enhancement. We have shown that they may even outperform their predictive counterparts for non-additive corruption types or when they are evaluated on mismatched conditions. However, diffu... | ['Timo Gerkmann', 'Simon Welker', 'Julius Richter', 'Jean-Marie Lemercier'] | 2022-12-22 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.90225407e-01 1.96713969e-01 2.29414597e-01 1.85735136e-01
-1.00246322e+00 -4.41657186e-01 5.35904050e-01 -3.45486253e-02
-1.88895434e-01 6.55218780e-01 3.48578542e-01 -1.84379280e-01
-9.44150537e-02 -6.36435807e-01 -4.60338205e-01 -1.03359032e+00
6.25605136e-02 -2.87895426e-02 1.36893466e-01 -2.51095623... | [15.171443939208984, 5.937561511993408] |
e990a128-b862-46c7-9410-d0af8ace7c9f | similarity-based-android-malware-detection | 1908.05759 | null | https://arxiv.org/abs/1908.05759v2 | https://arxiv.org/pdf/1908.05759v2.pdf | Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features | In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm i... | ['Mauro Conti', 'Zahra Pooranian', 'Rahim Taheri', 'Meysam Ghahramani', 'Mohammad Shojafar', 'Reza Javidan'] | 2019-08-13 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.85670227e-01 -5.36583006e-01 -5.62011957e-01 -8.11854098e-03
-1.85001254e-01 -6.25500381e-01 7.99269021e-01 3.43147755e-01
-2.52467364e-01 5.28773785e-01 -2.13598281e-01 -4.89209145e-01
-3.10850739e-01 -8.66319299e-01 -1.73344478e-01 -3.89577478e-01
-4.54082400e-01 -1.47949919e-01 6.43387556e-01 -1.16513327... | [14.415809631347656, 9.673895835876465] |
c272578b-b73f-47a4-adfe-6383648e84b2 | cn-lbp-complex-networks-based-local-binary | 2105.06652 | null | https://arxiv.org/abs/2105.06652v3 | https://arxiv.org/pdf/2105.06652v3.pdf | CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification | To overcome the limitations of original local binary patterns (LBP), this article proposes a new texture descriptor aided by complex networks (CN) and LBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as directed graphs over different bands with the help of pixel distance, intensity, and gradient ... | ['Zhengrui Huang'] | 2021-05-14 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.04324886e-01 -5.86432993e-01 -3.32855552e-01 -2.04743832e-01
-2.44384676e-01 5.30603761e-03 2.64266551e-01 7.03992918e-02
7.19141588e-02 6.17636323e-01 -2.53181085e-02 1.04798339e-01
-7.03043938e-01 -1.17367637e+00 -1.29545376e-01 -1.29061592e+00
-4.83494490e-01 -1.28163800e-01 4.19125736e-01 -1.77527685... | [10.418937683105469, -0.3842658996582031] |
f9d2750e-350a-4609-90bd-961d9573ad21 | multimodal-learning-with-channel-mixing-and | 2209.12244 | null | https://arxiv.org/abs/2209.12244v1 | https://arxiv.org/pdf/2209.12244v1.pdf | Multimodal Learning with Channel-Mixing and Masked Autoencoder on Facial Action Unit Detection | Recent studies utilizing multi-modal data aimed at building a robust model for facial Action Unit (AU) detection. However, due to the heterogeneity of multi-modal data, multi-modal representation learning becomes one of the main challenges. On one hand, it is difficult to extract the relevant features from multi-modali... | ['Lijun Yin', 'Xiaotian Li', 'Taoyue Wang', 'Huiyuan Yang', 'Xiang Zhang'] | 2022-09-25 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.75982869e-01 -5.90641424e-02 -2.61884302e-01 -2.31848910e-01
-1.34511948e+00 -1.83650061e-01 8.49366605e-01 -4.70759720e-01
-3.61922085e-01 5.47044218e-01 4.52145338e-01 1.31157815e-01
3.06949914e-01 -4.72247481e-01 -7.32662797e-01 -8.95183206e-01
2.17320517e-01 -1.03210092e-01 3.06598246e-02 -1.73838511... | [13.537264823913574, 1.7722654342651367] |
54eaffe2-2b3f-468f-b7b3-dfb4daf86804 | scene-text-detection-for-augmented-reality | 2101.01054 | null | https://arxiv.org/abs/2101.01054v1 | https://arxiv.org/pdf/2101.01054v1.pdf | Scene Text Detection for Augmented Reality -- Character Bigram Approach to reduce False Positive Rate | Natural scene text detection is an important aspect of scene understanding and could be a useful tool in building engaging augmented reality applications. In this work, we address the problem of false positives in text spotting. We propose improving the performace of sliding window text spotters by looking for characte... | ['Bharadwaj Amrutur', 'Sagar Gubbi'] | 2020-12-26 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 9.53398645e-01 -2.02253878e-01 -6.48738369e-02 -2.63160944e-01
-7.03786731e-01 -2.40616053e-01 7.76682019e-01 1.39547884e-01
-6.56509042e-01 4.17927444e-01 3.84684652e-01 -4.83164787e-01
5.48353612e-01 -8.97905409e-01 -9.31728840e-01 -2.20082089e-01
2.86540329e-01 1.14970520e-01 6.25147283e-01 -1.47390664... | [11.948110580444336, 2.2691969871520996] |
9f4ee903-fa53-4651-983c-e3a52893ab55 | audio-inpainting-with-generative-adversarial | 2003.07704 | null | https://arxiv.org/abs/2003.07704v1 | https://arxiv.org/pdf/2003.07704v1.pdf | Audio inpainting with generative adversarial network | We study the ability of Wasserstein Generative Adversarial Network (WGAN) to generate missing audio content which is, in context, (statistically similar) to the sound and the neighboring borders. We deal with the challenge of audio inpainting long range gaps (500 ms) using WGAN models. We improved the quality of the in... | ['A. Eltelt', 'P. P. Ebner'] | 2020-03-13 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 1.85335517e-01 2.81051755e-01 5.78287363e-01 2.32845053e-01
-1.21075857e+00 -7.00642884e-01 2.60435581e-01 -3.19316655e-01
-2.10251167e-01 1.05397952e+00 3.78334314e-01 2.59508222e-01
-1.79960072e-01 -6.98884547e-01 -9.87362087e-01 -8.97299647e-01
-2.91799903e-01 2.70128936e-01 1.64522171e-01 -4.59695131... | [15.566678047180176, 5.954543590545654] |
cc20ca01-bebd-4359-9c33-4a6a4ad289ba | deep-learning-ensembles-for-skin-lesion | 1808.08480 | null | http://arxiv.org/abs/1808.08480v1 | http://arxiv.org/pdf/1808.08480v1.pdf | Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018 | This extended abstract describes the participation of RECOD Titans in parts 1
to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma
Detection" (MICCAI 2018). Although our team has a long experience with melanoma
classification and moderate experience with lesion segmentation, the ISIC
Challenge 2018 wa... | ['Michel Fornaciali', 'Vinícius Ribeiro', 'Fábio Perez', 'Alceu Bissoto', 'Sandra Avila', 'Eduardo Valle'] | 2018-08-25 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.44639289e-01 1.91829711e-01 -1.29452303e-01 1.01495601e-01
-1.10800314e+00 -6.65309429e-01 8.68857026e-01 4.30120409e-01
-6.78539217e-01 7.49264956e-01 -3.57301980e-02 -4.04934019e-01
-8.27730671e-02 -4.18256760e-01 -1.68453142e-01 -8.69935274e-01
1.03963591e-01 3.31540197e-01 2.18762070e-01 -1.37977794... | [15.721688270568848, -3.000516891479492] |
fe0f41a1-515a-4cbc-b688-fc71945cbac9 | bennettnlp-at-semeval-2021-task-5-toxic-spans | null | null | https://aclanthology.org/2021.semeval-1.128 | https://aclanthology.org/2021.semeval-1.128.pdf | BennettNLP at SemEval-2021 Task 5: Toxic Spans Detection using Stacked Embedding Powered Toxic Entity Recognizer | With the rapid growth in technology, social media activity has seen a boom across all age groups. It is humanly impossible to check all the tweets, comments and status manually whether they follow proper community guidelines. A lot of toxicity is regularly posted on these social media platforms. This research aims to f... | ['Vipul Mishra', 'Ambuje Gupta', 'Harsh Kataria'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [-4.32187766e-01 1.13356583e-01 1.37761841e-02 -3.04441065e-01
-3.42960387e-01 -2.96568573e-01 6.18410647e-01 8.53882492e-01
-4.66516763e-01 6.65167391e-01 5.79813421e-01 -1.02509923e-01
-2.43902504e-01 -8.43615294e-01 -6.69987947e-02 -2.17143759e-01
-1.12293065e-01 1.45126000e-01 1.52119786e-01 -3.28065395... | [8.861722946166992, 10.513983726501465] |
08402206-2acf-4f80-aedc-3067175dea43 | blind-image-quality-assessment-using-a-deep | 1907.02665 | null | https://arxiv.org/abs/1907.02665v1 | https://arxiv.org/pdf/1907.02665v1.pdf | Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network | We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortio... | ['Zhou Wang', 'Kede Ma', 'Weixia Zhang', 'Jia Yan', 'Dexiang Deng'] | 2019-07-05 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-6.56451061e-02 -5.17356217e-01 -8.60711746e-03 -7.66171455e-01
-1.56402767e+00 -4.97867078e-01 2.63162971e-01 -3.53512287e-01
-3.53327960e-01 3.77144068e-01 5.10204136e-01 -2.78025389e-01
9.93929058e-02 -4.94511485e-01 -7.19191194e-01 -4.81201351e-01
-1.61033735e-01 -1.18671864e-01 -7.46146217e-02 -2.58536786... | [11.88398551940918, -1.8052458763122559] |
12620c92-a521-4fc7-a25d-b174f40d4913 | bertective-language-models-and-contextual | null | null | https://aclanthology.org/2021.eacl-main.232 | https://aclanthology.org/2021.eacl-main.232.pdf | BERTective: Language Models and Contextual Information for Deception Detection | Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts{'} preceding context is not considered. This is a severe limitation, as any com... | ['Dirk Hovy', 'Massimo Poesio', 'Federico Bianchi', 'Tommaso Fornaciari'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['deception-detection'] | ['miscellaneous'] | [ 1.54235987e-02 7.38192052e-02 -1.94121525e-02 -6.10429704e-01
-8.77230704e-01 -9.16666985e-01 8.38968694e-01 2.75398999e-01
-4.29674327e-01 7.46159911e-01 6.25665069e-01 -4.71301913e-01
1.66206256e-01 -4.40104008e-01 -4.45884019e-01 -4.66780573e-01
3.49806666e-01 1.43410072e-01 -1.84400864e-02 -5.20448446... | [8.190781593322754, 10.409466743469238] |
c91a4d84-317b-4e0b-872c-44dda50e2c16 | tleague-a-framework-for-competitive-self-play | 2011.12895 | null | https://arxiv.org/abs/2011.12895v2 | https://arxiv.org/pdf/2011.12895v2.pdf | TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning | Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL) has shown phenomenal breakthroughs recently. Strong AIs are achieved for several benchmarks, including Dota 2, Glory of Kings, Quake III, StarCraft II, to name a few. Despite the success, the MARL training is extremely data thirsty, requiring t... | ['Zhengyou Zhang', 'Meng Fang', 'Jiawei Xu', 'Shuxing Li', 'Xinghai Sun', 'Lei Han', 'Jiechao Xiong', 'Peng Sun'] | 2020-11-25 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-5.47151685e-01 -4.82410401e-01 -2.12527990e-01 9.64496806e-02
-8.18278015e-01 -7.11922228e-01 6.37444615e-01 3.76671664e-02
-6.32147908e-01 1.06464231e+00 -4.69698459e-01 -3.64202559e-01
-1.55882731e-01 -7.12532938e-01 -8.04851770e-01 -1.09354019e+00
-4.13974613e-01 9.79420960e-01 4.71496284e-01 -4.29222137... | [3.83294939994812, 1.6925885677337646] |
888cff46-ced8-4f87-994b-9d1149f11687 | bayesian-optimisation-for-mixed-variable | 2202.04832 | null | https://arxiv.org/abs/2202.04832v2 | https://arxiv.org/pdf/2202.04832v2.pdf | Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals | Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not designed tohandle such mixed-variable search spaces. Recent approaches to this problem cast the selection of the categorical variables as a bandi... | ['Benjamin Ward Muir', 'David Alexander', 'Iadine Chades', 'Amir Dezfouli', 'Yan Zuo'] | 2022-02-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 3.36216152e-01 1.06755085e-01 -7.25643277e-01 -5.11391044e-01
-1.24427629e+00 -6.57430351e-01 9.49120164e-01 2.08728433e-01
-6.83853745e-01 1.09322727e+00 1.64451793e-01 -6.37436569e-01
-8.72506857e-01 -5.60552418e-01 -3.86655241e-01 -1.08856511e+00
-4.15983275e-02 9.07961428e-01 -3.90225112e-01 4.65995222... | [6.320610523223877, 3.8592095375061035] |
4da1b5b8-f900-425b-aa05-d8fe9b11372b | scale-adaptive-blind-deblurring | null | null | http://papers.nips.cc/paper/5566-scale-adaptive-blind-deblurring | http://papers.nips.cc/paper/5566-scale-adaptive-blind-deblurring.pdf | Scale Adaptive Blind Deblurring | The presence of noise and small scale structures usually leads to large kernel estimation errors in blind image deblurring empirically, if not a total failure. We present a scale space perspective on blind deblurring algorithms, and introduce a cascaded scale space formulation for blind deblurring. This new formulation... | ['Jianchao Yang', 'Haichao Zhang'] | 2014-12-01 | null | null | null | neurips-2014-12 | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.98111232e-02 -6.49349988e-01 1.31817177e-01 -1.18435258e-02
-4.33274388e-01 -6.53171241e-01 6.28007352e-01 -3.12453300e-01
-2.89700598e-01 6.89262629e-01 7.61199594e-01 -1.00588379e-02
-4.22576964e-01 -1.20189078e-01 -2.95727551e-01 -7.91100383e-01
6.05357401e-02 -1.86248735e-01 3.64250034e-01 6.14379160... | [11.630448341369629, -2.75104022026062] |
c014c1f0-5879-47c6-a422-e2cb0c86a372 | parameter-free-geometric-document-layout | null | null | https://ieeexplore.ieee.org/abstract/document/969115 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=969115 | Parameter-free Geometric Document Layout Analysis | Automatic transformation of paper documents into electronic documents requires geometric document layout analysis at the first stage. However, variations in character font sizes, text line spacing, and document layout structures have made it difficult to design a general-purpose document layout analysis algorithm for m... | ['and Dae-Seok Ryu', 'IEEE', 'Senior Member', 'Seong-Whan Lee'] | 2001-11-01 | null | null | null | ieee-transactions-on-pattern-analysis-and-19 | ['document-layout-analysis', 'texture-classification'] | ['computer-vision', 'computer-vision'] | [ 4.07639205e-01 -5.41656733e-01 1.33053318e-01 -7.67211914e-02
-1.73119217e-01 -7.70288765e-01 4.09138411e-01 4.75075841e-01
-2.99890880e-02 5.14122963e-01 -1.21054016e-01 -5.86897373e-01
-4.61415827e-01 -7.38674283e-01 -1.88013941e-01 -4.70919758e-01
1.85379490e-01 2.06159934e-01 5.51269352e-01 1.31934896... | [11.851146697998047, 2.58912992477417] |
b527ea7a-22a1-45fb-9501-4c4eb0a85b1c | optimized-deep-encoder-decoder-methods-for | 2008.06266 | null | https://arxiv.org/abs/2008.06266v2 | https://arxiv.org/pdf/2008.06266v2.pdf | Optimized Deep Encoder-Decoder Methods for Crack Segmentation | Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-... | ['Jacob König', 'Mark Jenkins', 'Peter Barrie', 'Mike Mannion', 'Gordon Morison'] | 2020-08-14 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 5.19636393e-01 5.55348545e-02 4.14258718e-01 -2.94536531e-01
-1.08369899e+00 -2.62256265e-01 4.54822481e-01 4.57569808e-01
-6.97629273e-01 4.67559665e-01 -2.20177874e-01 -7.17365444e-02
2.44559512e-01 -8.70148242e-01 -8.54139268e-01 -7.06581652e-01
1.26321822e-01 6.51241899e-01 6.77568078e-01 -2.28655726... | [7.486310005187988, 1.5333507061004639] |
bdce5d0e-eea2-4b93-bcf7-1e9cc44ab427 | explainable-systematic-analysis-for-synthetic | 2101.03134 | null | https://arxiv.org/abs/2101.03134v3 | https://arxiv.org/pdf/2101.03134v3.pdf | Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery | In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data. We examine the sensi... | ['Alina Zare', 'James Keller', 'Jeff Dale', 'Joshua Peeples', 'Sarah Walker'] | 2021-01-06 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.01822466e-03 2.06843480e-01 2.72426307e-02 -6.11483634e-01
-6.16802394e-01 -6.37657464e-01 5.19192874e-01 1.69668332e-01
-1.40898243e-01 5.38780510e-01 6.88648641e-01 -6.65381014e-01
-9.48064983e-01 -8.22182775e-01 -7.41966724e-01 -7.85045862e-01
-2.25899577e-01 2.52836913e-01 -1.44282803e-01 -4.81620729... | [6.804311752319336, 2.8775060176849365] |
decc5c6e-75d4-4990-93ed-585cfceeeddd | natural-language-processing-for-policymaking | 2302.03490 | null | https://arxiv.org/abs/2302.03490v1 | https://arxiv.org/pdf/2302.03490v1.pdf | Natural Language Processing for Policymaking | Language is the medium for many political activities, from campaigns to news reports. Natural language processing (NLP) uses computational tools to parse text into key information that is needed for policymaking. In this chapter, we introduce common methods of NLP, including text classification, topic modeling, event e... | ['Rada Mihalcea', 'Zhijing Jin'] | 2023-02-07 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.80420995e-01 4.82500643e-01 -1.16625929e+00 -2.85746515e-01
-1.05190575e+00 -8.03886771e-01 1.07529747e+00 9.91862178e-01
-6.71416402e-01 9.92172480e-01 1.42070413e+00 -1.54495800e+00
-1.32321283e-01 -6.97668016e-01 -3.91723543e-01 -3.88196111e-01
3.43166083e-01 3.00558716e-01 -2.33780339e-01 1.44185960... | [9.002686500549316, 9.863202095031738] |
24860f01-b50f-457d-9656-3fa82856904b | more-complete-resultset-retrieval-from-large | null | null | https://dl.acm.org/doi/10.1145/3360901.3364436#d2419191e1 | https://svn.aksw.org/papers/2019/KCAP2019_WIMUQ/public.pdf | More Complete Resultset Retrieval from Large Heterogeneous RDF Sources | Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or direc... | ['Andre Valdestilhas', 'Tommaso Soru', 'Muhammad Saleem'] | 2019-11-12 | null | null | null | acm-10th-international-conference-on | ['rdf-dataset-discovery'] | ['knowledge-base'] | [-9.53253925e-01 6.51888624e-02 -2.92612016e-01 -6.95241690e-01
-7.90776432e-01 -7.72978127e-01 5.91730356e-01 8.60913038e-01
-2.58441150e-01 1.15852046e+00 5.33343017e-01 6.29009083e-02
-6.98972702e-01 -1.93261814e+00 -6.93011165e-01 -3.87832080e-03
-2.24722356e-01 1.09033263e+00 1.02990675e+00 -6.90508723... | [9.186179161071777, 7.872093200683594] |
124a5550-45b4-4f07-89e7-9ae5a914417b | deep-unfolding-as-iterative-regularization | 2211.13452 | null | https://arxiv.org/abs/2211.13452v1 | https://arxiv.org/pdf/2211.13452v1.pdf | Deep unfolding as iterative regularization for imaging inverse problems | Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems. Unlike general end-to-end DNNs, unfolding methods have better interpretability and performance. However, to our knowledge, their accurac... | ['Dong Liang', 'Jing Cheng', 'Qingyong Zhu', 'Zhuo-Xu Cui'] | 2022-11-24 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 1.30521521e-01 5.77236593e-01 1.44741267e-01 -4.28657025e-01
-5.60815036e-01 -4.34110790e-01 6.73353225e-02 -4.78099287e-01
-4.29569304e-01 8.18783700e-01 1.07983097e-01 -2.51352489e-01
-3.28784078e-01 -4.27732140e-01 -1.27346170e+00 -8.43292236e-01
4.51853573e-02 5.23762941e-01 -3.70200127e-01 -1.73019171... | [11.90147590637207, -2.429399251937866] |
ffdb8d58-abee-49f9-a973-1f52565679e4 | multi-label-logo-recognition-and-retrieval | 2205.05419 | null | https://arxiv.org/abs/2205.05419v2 | https://arxiv.org/pdf/2205.05419v2.pdf | Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features | Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have seve... | ['Antonio Pertusa', 'Antonio Javier Gallego', 'Marisa Bernabeu'] | 2022-05-11 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [-2.00490151e-02 -2.48089254e-01 -4.73144323e-01 -2.20889524e-01
-5.35172880e-01 -9.59984004e-01 6.09471798e-01 7.17404485e-01
-2.46384487e-01 4.29458767e-01 -3.35459918e-01 -1.14167534e-01
-6.49646699e-01 -8.35475206e-01 -4.49578941e-01 -6.07109427e-01
9.43282545e-02 1.09937954e+00 1.31908506e-01 -5.21320514... | [10.243022918701172, -0.10115228593349457] |
cdc90c82-8435-46f4-bffa-0662c4cdd7ea | learning-word-embeddings-for-hyponymy-with | 1710.02437 | null | http://arxiv.org/abs/1710.02437v1 | http://arxiv.org/pdf/1710.02437v1.pdf | Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics | Lexical entailment, such as hyponymy, is a fundamental issue in the semantics
of natural language. This paper proposes distributional semantic models which
efficiently learn word embeddings for entailment, using a recently-proposed
framework for modelling entailment in a vector-space. These models postulate a
latent ve... | ['James Henderson'] | 2017-10-06 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 1.32869557e-01 2.75259078e-01 -7.06041276e-01 -6.40130758e-01
4.47675325e-02 -4.02378231e-01 9.46475625e-01 5.84561706e-01
-9.33027983e-01 3.13839912e-01 1.06613159e+00 -6.28106475e-01
-4.86333743e-02 -8.74786854e-01 -3.71531844e-01 -3.70472133e-01
9.56319943e-02 6.78014636e-01 -8.33369195e-02 -4.48821157... | [10.398630142211914, 8.80810546875] |
15d422c1-53f7-4624-b2c9-647b851072cd | pose-mum-reinforcing-key-points-relationship | 2203.07837 | null | https://arxiv.org/abs/2203.07837v1 | https://arxiv.org/pdf/2203.07837v1.pdf | Pose-MUM : Reinforcing Key Points Relationship for Semi-Supervised Human Pose Estimation | A well-designed strong-weak augmentation strategy and the stable teacher to generate reliable pseudo labels are essential in the teacher-student framework of semi-supervised learning (SSL). Considering these in mind, to suit the semi-supervised human pose estimation (SSHPE) task, we propose a novel approach referred to... | ['Jin Young Choi', 'Nojun Kwak', 'Jongkeun Na', 'Jaeseung Lim', 'Hwijun Lee', 'Jongmok Kim'] | 2022-03-15 | null | null | null | null | ['semi-supervised-human-pose-estimation'] | ['computer-vision'] | [ 9.27794352e-02 3.46477598e-01 -5.97553253e-02 -4.74671066e-01
-8.96109521e-01 -1.65470824e-01 7.30658472e-01 -1.23208284e-01
-7.00187743e-01 6.11067951e-01 2.20605239e-01 1.90532357e-01
5.86942807e-02 -3.30084413e-01 -9.93846059e-01 -8.18421721e-01
2.51499772e-01 7.19981194e-01 4.03002799e-01 -2.98582613... | [7.195310592651367, -0.751413106918335] |
2a8e1115-09a3-4ce6-a53c-0f9bd27fd968 | end-to-end-abnormality-detection-in-medical | null | null | https://openreview.net/forum?id=rk1FQA0pW | https://openreview.net/pdf?id=rk1FQA0pW | End-to-End Abnormality Detection in Medical Imaging | Deep neural networks (DNN) have shown promising performance in computer vision. In medical imaging, encouraging results have been achieved with deep learning for applications such as segmentation, lesion detection and classification. Nearly all of the deep learning based image analysis methods work on reconstructed ima... | ['Kyungsang Kim', 'Dufan Wu', 'Bin Dong', 'Quanzheng Li'] | 2018-01-01 | null | null | null | iclr-2018-1 | ['lung-nodule-detection'] | ['medical'] | [ 4.05114889e-01 4.86121833e-01 4.72418070e-02 -3.87797982e-01
-6.91347122e-01 -2.36822248e-01 2.25623146e-01 -1.25754327e-01
-7.41974831e-01 3.37094665e-01 -2.47517489e-02 -5.15663087e-01
-1.15695909e-01 -7.49056578e-01 -6.11526430e-01 -7.97629833e-01
4.44171391e-02 7.72178590e-01 3.81244510e-01 2.16211528... | [15.289369583129883, -2.142106056213379] |
c53ed74e-4bf6-4e46-88b9-2350a9140fe0 | generative-poisoning-using-random | 2211.01086 | null | https://arxiv.org/abs/2211.01086v1 | https://arxiv.org/pdf/2211.01086v1.pdf | Generative Poisoning Using Random Discriminators | We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator. The key novelty of ShortcutGen is the use of a randomly-initialized discriminator, which provides spurious shortcuts needed for generating poisons. Different from recent, iterat... | ['Martha Larson', 'Zhengyu Zhao', 'Zhuoran Liu', 'Alex Kolmus', 'Dirren van Vlijmen'] | 2022-11-02 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 3.36193711e-01 3.00880075e-01 -3.07119638e-01 1.66833639e-01
-1.02094591e+00 -1.13327014e+00 9.63034213e-01 1.54824376e-01
-4.32713002e-01 1.05981255e+00 6.66464791e-02 -4.58741099e-01
3.45667720e-01 -8.06107581e-01 -8.45538914e-01 -8.06145668e-01
1.37892300e-02 6.97467625e-01 1.20672897e-01 -2.34367520... | [5.868206024169922, 7.660614967346191] |
b621a0b2-e3ed-49e7-9ef1-ed9aa1c9988e | cgan-based-high-dimensional-imu-sensor-data | 2302.07998 | null | https://arxiv.org/abs/2302.07998v1 | https://arxiv.org/pdf/2302.07998v1.pdf | cGAN-Based High Dimensional IMU Sensor Data Generation for Therapeutic Activities | Human activity recognition is a core technology for applications such as rehabilitation, ambient health monitoring, and human-computer interactions. Wearable devices, particularly IMU sensors, can help us collect rich features of human movements that can be leveraged in activity recognition. Developing a robust classif... | ['Saeed Behzadipour', 'Alireza Taheri', 'Ali Ghadami', 'Mohammad Mohammadzadeh'] | 2023-02-16 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.79361594e-01 -1.21560104e-01 -7.22069591e-02 -3.87687311e-02
-4.71767485e-01 -2.73824662e-01 4.75735098e-01 -4.81608175e-02
-2.27872849e-01 8.88865709e-01 4.59189206e-01 2.21062109e-01
-6.39917105e-02 -6.74388945e-01 -7.12906420e-01 -8.86856377e-01
-1.46677226e-01 -2.14985490e-01 -1.79124087e-01 -8.76152441... | [7.348008155822754, 0.5259703397750854] |
ac2e46fa-68ed-4bac-8ae3-efd46798d996 | explainable-artificial-intelligence-in | 2203.16073 | null | https://arxiv.org/abs/2203.16073v4 | https://arxiv.org/pdf/2203.16073v4.pdf | Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models | Although a recent shift has been made in the field of predictive process monitoring to use models from the explainable artificial intelligence field, the evaluation still occurs mainly through performance-based metrics, thus not accounting for the actionability and implications of the explanations. In this paper, we de... | ['Johannes De Smedt', 'Alexander Stevens'] | 2022-03-30 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.40620667e-01 7.87795603e-01 -2.31630221e-01 -5.94639838e-01
-3.97110470e-02 -3.94168288e-01 1.07447958e+00 9.22267854e-01
3.82339776e-01 4.75090414e-01 4.59438622e-01 -7.40656853e-01
-1.04329014e+00 -8.56350183e-01 -3.15522671e-01 -3.19376111e-01
-8.75147507e-02 6.86240673e-01 -3.33576918e-01 3.88094068... | [8.637079238891602, 5.902775287628174] |
3f83c4e4-2306-4474-b105-b229b9751fb8 | temporally-consistent-online-depth-estimation-1 | 2304.07435 | null | https://arxiv.org/abs/2304.07435v2 | https://arxiv.org/pdf/2304.07435v2.pdf | Temporally Consistent Online Depth Estimation Using Point-Based Fusion | Depth estimation is an important step in many computer vision problems such as 3D reconstruction, novel view synthesis, and computational photography. Most existing work focuses on depth estimation from single frames. When applied to videos, the result lacks temporal consistency, showing flickering and swimming artifac... | ['Lei Xiao', 'Douglas Lanman', 'Eric Penner', 'Numair Khan'] | 2023-04-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Khan_Temporally_Consistent_Online_Depth_Estimation_Using_Point-Based_Fusion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Khan_Temporally_Consistent_Online_Depth_Estimation_Using_Point-Based_Fusion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 2.82655358e-01 -2.80418217e-01 1.90475732e-01 -1.27481133e-01
-5.47294021e-01 -4.81866777e-01 4.91040975e-01 -4.73436080e-02
-4.81675237e-01 7.45712042e-01 -9.65948217e-03 2.51559615e-01
1.16754584e-01 -5.71488142e-01 -7.06823230e-01 -7.28384912e-01
-1.89038496e-02 3.13698351e-01 8.41898263e-01 6.06541857... | [8.785134315490723, -2.2335731983184814] |
6b7058ff-8250-4d39-867f-d54792770199 | performance-analysis-of-a-foreground | 2105.12311 | null | https://arxiv.org/abs/2105.12311v1 | https://arxiv.org/pdf/2105.12311v1.pdf | Performance Analysis of a Foreground Segmentation Neural Network Model | In recent years the interest in segmentation has been growing, being used in a wide range of applications such as fraud detection, anomaly detection in public health and intrusion detection. We present an ablation study of FgSegNet_v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling Module and (iii) Decod... | ['Bruno Faria', 'André Leite Ferreira', 'António Ramires Fernandes', 'Joel Tomás Morais'] | 2021-05-26 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 1.60159826e-01 -5.80584034e-02 1.75685346e-01 -2.90909022e-01
-3.48082632e-01 -2.61833370e-01 8.78163993e-01 4.58986014e-01
-1.00039959e+00 7.96545267e-01 8.54740441e-02 -3.44952315e-01
-7.11462200e-02 -8.61287951e-01 -3.60736340e-01 -4.08960640e-01
-1.75460652e-01 4.53154981e-01 9.53855634e-01 -1.99083626... | [8.900558471679688, -0.712554395198822] |
56dce76d-1d0a-46ce-8295-54db71620ffe | neural-adaptation-layers-for-cross-domain | 1810.06368 | null | http://arxiv.org/abs/1810.06368v1 | http://arxiv.org/pdf/1810.06368v1.pdf | Neural Adaptation Layers for Cross-domain Named Entity Recognition | Recent research efforts have shown that neural architectures can be effective
in conventional information extraction tasks such as named entity recognition,
yielding state-of-the-art results on standard newswire datasets. However,
despite significant resources required for training such models, the
performance of a mod... | ['Bill Yuchen Lin', 'Wei Lu'] | 2018-10-15 | neural-adaptation-layers-for-cross-domain-1 | https://aclanthology.org/D18-1226 | https://aclanthology.org/D18-1226.pdf | emnlp-2018-10 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [-4.72755544e-02 3.36244971e-01 -4.15159464e-01 -6.23284101e-01
-8.54590952e-01 -6.42230809e-01 7.78048575e-01 2.63539493e-01
-1.09455609e+00 9.69778359e-01 3.19030643e-01 -2.55452722e-01
1.50189579e-01 -9.19302464e-01 -8.61935198e-01 1.69837791e-02
3.58965583e-02 6.11782193e-01 3.59471470e-01 -2.88064808... | [9.676016807556152, 9.42609691619873] |
0351e7a8-d3c6-4db1-b0fd-6cdd04a6cd11 | interactive-matching-network-for-multi-turn | 1901.01824 | null | https://arxiv.org/abs/1901.01824v2 | https://arxiv.org/pdf/1901.01824v2.pdf | Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots | In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding se... | ['Zhen-Hua Ling', 'Jia-Chen Gu', 'Quan Liu'] | 2019-01-07 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 2.72737890e-01 -8.02197456e-02 -5.02923489e-01 -6.31457984e-01
-1.19048512e+00 -2.11456373e-01 5.04513264e-01 5.50180115e-02
-4.92125601e-01 4.90501791e-01 9.28171873e-01 -1.98496222e-01
1.18568957e-01 -6.85942411e-01 -3.62669408e-01 -1.31327301e-01
2.53769726e-01 5.64958811e-01 1.68627307e-01 -8.10228765... | [12.440510749816895, 7.8762078285217285] |
7fc40c2e-a2b7-4328-b292-f1e0e19734eb | handwriting-styles-benchmarks-and-evaluation | 1809.00862 | null | http://arxiv.org/abs/1809.00862v1 | http://arxiv.org/pdf/1809.00862v1.pdf | Handwriting styles: benchmarks and evaluation metrics | Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality o... | ['Gerard Bailly', 'Damien Pellier', 'Omar Mohammed'] | 2018-09-04 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [ 3.25124115e-01 -1.22133419e-01 -6.48486987e-02 -3.96320999e-01
-4.70890969e-01 -8.12139094e-01 9.46712077e-01 -2.37838164e-01
-1.98284090e-01 1.07980001e+00 4.11412686e-01 1.91174764e-02
-1.99192435e-01 -7.11478889e-01 -5.34320652e-01 -7.73732662e-01
1.11456640e-01 8.31351995e-01 1.37371466e-01 -4.22552615... | [11.800114631652832, 2.2799978256225586] |
eeefbac7-5a09-4856-a3a8-a3a36fbdafab | on-guiding-video-object-segmentation | 1904.11256 | null | http://arxiv.org/abs/1904.11256v1 | http://arxiv.org/pdf/1904.11256v1.pdf | On guiding video object segmentation | This paper presents a novel approach for segmenting moving objects in
unconstrained environments using guided convolutional neural networks. This
guiding process relies on foreground masks from independent algorithms (i.e.
state-of-the-art algorithms) to implement an attention mechanism that
incorporates the spatial lo... | ["Noel E. O'Connor", 'José M. Martínez', 'Juan C. SanMiguel', 'Kevin McGuinness', 'Eric Arazo', 'Diego Ortego'] | 2019-04-25 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 9.03805375e-01 -2.98570007e-01 -1.57014355e-01 -4.53225702e-01
-4.58752245e-01 -7.46729076e-01 7.57834971e-01 -3.60492855e-01
-5.96262574e-01 6.09158099e-01 -9.24878847e-03 -2.23081842e-01
7.58844912e-02 -6.53537154e-01 -6.70480788e-01 -9.32973027e-01
-1.90876096e-01 6.58469945e-02 7.08136678e-01 1.96539849... | [9.163252830505371, -0.24157829582691193] |
e768bab4-e5c1-4e14-bf3f-9bbe4453415d | mask-textspotter-an-end-to-end-trainable | 1807.02242 | null | http://arxiv.org/abs/1807.02242v2 | http://arxiv.org/pdf/1807.02242v2.pdf | Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes | Recently, models based on deep neural networks have dominated the fields of
scene text detection and recognition. In this paper, we investigate the problem
of scene text spotting, which aims at simultaneous text detection and
recognition in natural images. An end-to-end trainable neural network model for
scene text spo... | ['Wenhao Wu', 'Pengyuan Lyu', 'Minghui Liao', 'Cong Yao', 'Xiang Bai'] | 2018-07-06 | null | null | null | eccv-2018-9 | ['text-spotting'] | ['computer-vision'] | [ 6.27645910e-01 -3.34323436e-01 3.01518023e-01 -4.45436001e-01
-6.12116754e-01 -3.44591647e-01 6.29471481e-01 -8.79039392e-02
-5.14478385e-01 8.66061524e-02 -5.17174825e-02 -1.67059451e-01
2.55233586e-01 -5.72769046e-01 -7.88626671e-01 -4.19718146e-01
8.68113220e-01 7.72997618e-01 2.52181590e-01 -3.98001671... | [12.031879425048828, 2.3055598735809326] |
d4b6044a-fbee-4ddd-9214-522b1ef59948 | beyond-simple-meta-learning-multi-purpose | 2201.05151 | null | https://arxiv.org/abs/2201.05151v2 | https://arxiv.org/pdf/2201.05151v2.pdf | Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning | Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examples. In previously proposed few-shot visual classifiers, it is assumed that the feature manifold, where classifier decisions are made, has u... | ['Frank Wood', 'Leonid Sigal', 'Jan-Willem van de Meent', 'Vaden Masrani', 'Raghav Goyal', 'Jarred Barber', 'Peyman Bateni'] | 2022-01-13 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.95251119e-01 2.13332623e-01 -4.28443849e-01 -4.35082376e-01
-1.04563463e+00 -2.77849823e-01 9.55557108e-01 8.38961601e-02
-5.89663625e-01 6.79358482e-01 -5.12574278e-02 1.18658431e-01
-3.29174489e-01 -6.01458073e-01 -6.61193073e-01 -1.06304836e+00
3.33240144e-02 4.33496088e-01 3.21426660e-01 -2.21961930... | [9.917192459106445, 2.812528610229492] |
f779404d-63d6-4d57-a6fa-54fa3919a74e | danish-fungi-2020-not-just-another-image | 2103.10107 | null | https://arxiv.org/abs/2103.10107v4 | https://arxiv.org/pdf/2103.10107v4.pdf | Danish Fungi 2020 -- Not Just Another Image Recognition Dataset | We introduce a novel fine-grained dataset and benchmark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, ... | ['Tobias Frøslev', 'Thomas Læssøe', 'Thomas S. Jeppesen', 'Jacob Heilmann-Clausen', 'Jiří Matas', 'Milan Šulc', 'Lukáš Picek'] | 2021-03-18 | null | null | null | null | ['fine-grained-image-recognition', 'classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-9.10779834e-03 -3.44690681e-01 -1.00513034e-01 -5.94575033e-02
-4.33591574e-01 -9.45605040e-01 9.60771143e-01 2.77814955e-01
-4.80096787e-01 7.48327315e-01 -8.22955072e-02 -2.22974628e-01
-2.21332878e-01 -9.31282401e-01 -7.61749744e-01 -7.72130132e-01
-1.91307604e-01 3.27041268e-01 2.80472428e-01 3.45769078... | [9.527166366577148, 2.144723415374756] |
6087374d-547d-4197-90bd-dfb18c1216e5 | deepremaster-temporal-source-reference | 2009.08692 | null | https://arxiv.org/abs/2009.08692v1 | https://arxiv.org/pdf/2009.08692v1.pdf | DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement | The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film medium to its original state. Additionally, due to the technical limitations of the time, most vintage film is either recorded in black and... | ['Edgar Simo-Serra', 'Satoshi Iizuka'] | 2020-09-18 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 3.59875262e-01 -4.57840204e-01 1.28633425e-01 8.61370713e-02
-6.40873194e-01 -6.92942560e-01 1.24343552e-01 -3.20650548e-01
-2.44438693e-01 4.68280911e-01 -1.10015951e-01 -2.00668141e-01
6.87734084e-03 -4.50984180e-01 -6.80256546e-01 -5.71938634e-01
6.01833798e-02 -2.13032097e-01 6.00880682e-01 -3.35448414... | [10.991805076599121, -1.2680683135986328] |
b365e2c3-d750-4c47-b6a5-148d19e614e3 | stylestegan-leak-free-style-transfer-based-on | 2307.00225 | null | https://arxiv.org/abs/2307.00225v1 | https://arxiv.org/pdf/2307.00225v1.pdf | StyleStegan: Leak-free Style Transfer Based on Feature Steganography | In modern social networks, existing style transfer methods suffer from a serious content leakage issue, which hampers the ability to achieve serial and reversible stylization, thereby hindering the further propagation of stylized images in social networks. To address this problem, we propose a leak-free style transfer ... | ['Xinpeng Zhang', 'Zhenxing Qian', 'Qichao Ying', 'Bingshan Liu', 'Xiujian Liang'] | 2023-07-01 | null | null | null | null | ['style-transfer', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 7.45456398e-01 -7.61284605e-02 -9.75777954e-02 8.01943764e-02
-1.28835320e-01 -6.92019522e-01 8.10410976e-01 -6.57544672e-01
-2.35556185e-01 8.86250794e-01 2.71394819e-01 -2.98989713e-01
6.49702787e-01 -9.44752872e-01 -8.82529438e-01 -5.84895492e-01
2.13826194e-01 -2.71490991e-01 1.65610269e-01 -3.92283916... | [11.647695541381836, -0.5964611172676086] |
df654152-f1fd-47b2-ab16-a96eee956cf3 | quantifying-the-lidar-sim-to-real-domain | 2303.01899 | null | https://arxiv.org/abs/2303.01899v1 | https://arxiv.org/pdf/2303.01899v1.pdf | Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level | LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. As real-world data collection and labeling are time-consuming and expensive, simulation-based synthetic data generation is a viable alternative. However, using simulated... | ['Markus Lienkamp', 'Esteban Rivera', 'Luca Scalerandi', 'Sebastian Huch'] | 2023-03-03 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.81299806e-01 -6.08306453e-02 3.58272135e-01 -5.20653725e-01
-4.82874781e-01 -4.68746483e-01 5.59822798e-01 3.93567055e-01
-8.26349318e-01 6.48570716e-01 -6.31537259e-01 -3.48102838e-01
1.50395244e-01 -1.06641769e+00 -1.14362204e+00 -3.91747653e-01
-1.83505014e-01 9.72469330e-01 7.86595762e-01 -1.23786308... | [7.948729515075684, -2.476039171218872] |
a28ce1c5-9ade-4fdc-8434-ca468e3ea29e | evolving-losses-for-unsupervised-video | 2002.12177 | null | https://arxiv.org/abs/2002.12177v1 | https://arxiv.org/pdf/2002.12177v1.pdf | Evolving Losses for Unsupervised Video Representation Learning | We present a new method to learn video representations from large-scale unlabeled video data. Ideally, this representation will be generic and transferable, directly usable for new tasks such as action recognition and zero or few-shot learning. We formulate unsupervised representation learning as a multi-modal, multi-t... | ['Michael S. Ryoo', 'Anelia Angelova', 'AJ Piergiovanni'] | 2020-02-26 | evolving-losses-for-unsupervised-video-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Piergiovanni_Evolving_Losses_for_Unsupervised_Video_Representation_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Piergiovanni_Evolving_Losses_for_Unsupervised_Video_Representation_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 6.52108669e-01 -8.74365345e-02 -5.90074301e-01 -4.63794470e-01
-9.56575155e-01 -4.46981758e-01 6.98455930e-01 6.81030527e-02
-5.43109059e-01 8.02262127e-01 2.72292793e-01 3.07288110e-01
-2.65167058e-01 -6.46233916e-01 -7.30644107e-01 -8.68187785e-01
2.30818465e-01 4.94522750e-01 2.61525095e-01 6.01844303... | [8.574716567993164, 0.7938348650932312] |
405aaee0-67db-451d-9dd2-12c891383856 | the-power-of-tiling-for-small-object | null | null | https://openaccess.thecvf.com/content_CVPRW_2019/papers/UAVision/Unel_The_Power_of_Tiling_for_Small_Object_Detection_CVPRW_2019_paper.pdf | https://openaccess.thecvf.com/content_CVPRW_2019/papers/UAVision/Unel_The_Power_of_Tiling_for_Small_Object_Detection_CVPRW_2019_paper.pdf | The Power of Tiling for Small Object Detection | Deep neural network based techniques are state-of-the-art for object detection and classification with the help ofthe development in computational power and memory ef-ficiency. Although these networks are adapted for mobileplatforms with sacrifice in accuracy; the resolution increasein visual sources makes the problem ... | ['F. Ozge UnelBurak OzkalayciCevahir Cigla'] | 2019-06-11 | null | null | null | computer-vision-and-pattern-recognition-2019-1 | ['small-object-detection'] | ['computer-vision'] | [ 1.61212951e-01 -1.89558923e-01 -2.11048368e-02 1.23364732e-01
-1.31901443e-01 -5.30856073e-01 6.20723069e-01 -2.10739851e-01
-7.31303155e-01 3.68476540e-01 -5.91995537e-01 -5.06962717e-01
-9.90201458e-02 -9.64984477e-01 -8.08673203e-01 -6.69547081e-01
-2.68292069e-01 1.38347656e-01 8.69146883e-01 -2.42240012... | [8.46604061126709, -0.9556877613067627] |
24bc625c-54d4-40cb-bac2-613b3ba33515 | ualberta-at-semeval-2023-task-1-context | 2306.14067 | null | https://arxiv.org/abs/2306.14067v1 | https://arxiv.org/pdf/2306.14067v1.pdf | UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation | We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the applicati... | ['Grzegorz Kondrak', 'Ning Shi', 'Talgat Omarov', 'Bradley Hauer', 'Michael Ogezi'] | 2023-06-24 | null | null | null | null | ['image-generation', 'word-sense-disambiguation'] | ['computer-vision', 'natural-language-processing'] | [ 1.82628468e-01 8.84616300e-02 -2.24092737e-01 -3.42565477e-01
-1.12725055e+00 -9.72332358e-01 1.01962245e+00 2.43055355e-02
-8.10732663e-01 8.80274117e-01 5.10658443e-01 -3.50070208e-01
3.44768554e-01 -4.48008031e-01 -8.45573604e-01 -2.55797148e-01
3.91880304e-01 7.51603723e-01 1.08129814e-01 -3.52051109... | [10.790388107299805, 1.5670826435089111] |
a8ba1f28-0815-4cf9-9474-c7c8396a748d | self-supervision-versus-synthetic-datasets | 2204.11493 | null | https://arxiv.org/abs/2204.11493v1 | https://arxiv.org/pdf/2204.11493v1.pdf | Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising? | Supervised training has led to state-of-the-art results in image and video denoising. However, its application to real data is limited since it requires large datasets of noisy-clean pairs that are difficult to obtain. For this reason, networks are often trained on realistic synthetic data. More recently, some self-sup... | ['Pablo Arias', 'Gabriele Facciolo', 'Aranud Barral', 'Valéry Dewil'] | 2022-04-25 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.66104454e-01 -9.05573368e-02 3.79178554e-01 -5.21574378e-01
-8.44482422e-01 -2.67045557e-01 6.13945425e-01 -4.62111682e-02
-6.75626099e-01 9.97935593e-01 1.26657009e-01 2.08414346e-01
-7.87728466e-03 -6.91171885e-01 -8.30793798e-01 -1.00176740e+00
9.67544410e-03 2.80086249e-01 1.07060425e-01 -2.79174924... | [11.478289604187012, -2.402952194213867] |
0ec83662-3c0a-4506-a117-f1a4aeddcceb | 1st-place-solution-for-youtubevos-challenge-1 | 2212.14679 | null | https://arxiv.org/abs/2212.14679v1 | https://arxiv.org/pdf/2212.14679v1.pdf | 1st Place Solution for YouTubeVOS Challenge 2022: Referring Video Object Segmentation | The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its su... | ['Jinfeng Bai', 'Zhilong Ji', 'Yuan Gao', 'Bo Chen', 'Zhiwei Hu'] | 2022-12-27 | null | null | null | null | ['referring-video-object-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.70554593e-01 -2.03353390e-01 -4.87370938e-01 -2.43029296e-01
-1.14423287e+00 -5.51233292e-01 3.59529912e-01 -5.02123475e-01
-2.71453679e-01 2.69337654e-01 1.26821488e-01 -7.83714354e-02
2.60448098e-01 -1.33683488e-01 -6.78529024e-01 -1.20332502e-01
3.26103568e-01 1.07108660e-01 5.12637258e-01 -1.49247617... | [9.513982772827148, 0.35508227348327637] |
afd85748-dba2-4c8f-895e-1f25394cf2cb | incremental-boosting-convolutional-neural | 1707.05395 | null | http://arxiv.org/abs/1707.05395v1 | http://arxiv.org/pdf/1707.05395v1.pdf | Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition | Recognizing facial action units (AUs) from spontaneous facial expressions is
still a challenging problem. Most recently, CNNs have shown promise on facial
AU recognition. However, the learned CNNs are often overfitted and do not
generalize well to unseen subjects due to limited AU-coded training images. We
proposed a n... | ['Zibo Meng', 'Yan Tong', 'Shizhong Han', 'Ahmed Shehab Khan'] | 2017-07-17 | incremental-boosting-convolutional-neural-1 | http://papers.nips.cc/paper/6258-incremental-boosting-convolutional-neural-network-for-facial-action-unit-recognition | http://papers.nips.cc/paper/6258-incremental-boosting-convolutional-neural-network-for-facial-action-unit-recognition.pdf | neurips-2016-12 | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.65138036e-01 6.51491284e-02 -2.95921922e-01 -8.82995009e-01
-6.54150128e-01 6.27602488e-02 2.82016009e-01 -6.06504977e-01
-3.41832042e-01 7.42384672e-01 3.82192321e-02 3.54683787e-01
5.33465683e-01 -5.68856359e-01 -8.69329691e-01 -9.22735572e-01
-1.66479781e-01 -4.38929461e-02 -4.03369553e-02 -4.19852197... | [13.601037979125977, 1.674950122833252] |
967ff50d-c5da-4029-a6a9-99ea80398a4e | recent-advancements-in-end-to-end-autonomous | 2307.04370 | null | https://arxiv.org/abs/2307.04370v1 | https://arxiv.org/pdf/2307.04370v1.pdf | Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey | End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems, such as their overwhelming complexity and propensity for error propagation. Autonomous driving transcends conventional traffic patterns by proactively recognizing critical events in advance, ensuring passengers' ... | ['Pravendra Singh', 'Pranav Singh Chib'] | 2023-07-10 | null | null | null | null | ['autonomous-driving'] | ['computer-vision'] | [-3.35537374e-01 6.52397722e-02 -4.38561380e-01 -5.66797554e-01
-7.00904369e-01 -6.83403313e-01 5.31058669e-01 -2.07933575e-01
-5.06187260e-01 5.57243288e-01 -1.04387350e-01 -5.55132747e-01
-2.02717304e-01 -6.60803139e-01 -7.85980999e-01 -6.36445343e-01
-2.56495386e-01 2.78082639e-01 2.53466189e-01 -7.99934864... | [5.694730281829834, 0.9634681940078735] |
33e92fad-a947-41e5-b138-b1e1b7f0b3e8 | feds-filtered-edit-distance-surrogate | 2103.04635 | null | https://arxiv.org/abs/2103.04635v2 | https://arxiv.org/pdf/2103.04635v2.pdf | FEDS -- Filtered Edit Distance Surrogate | This paper proposes a procedure to train a scene text recognition model using a robust learned surrogate of edit distance. The proposed method borrows from self-paced learning and filters out the training examples that are hard for the surrogate. The filtering is performed by judging the quality of the approximation, u... | ['Jiri Matas', 'Yash Patel'] | 2021-03-08 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.04140460e-01 -2.04604924e-01 1.04458451e-01 -8.02458286e-01
-8.01475763e-01 -4.76276964e-01 6.32248223e-01 3.89932752e-01
-7.23691523e-01 3.46835881e-01 -1.34170547e-01 -6.00473545e-02
-9.38162282e-02 -6.92815959e-01 -7.93889046e-01 -4.54389304e-01
5.09199873e-02 3.45233679e-01 1.05306515e-02 -1.46654099... | [11.868977546691895, 2.25020694732666] |
c02af192-ca17-4f40-99d3-a97ad14bcba6 | read-and-reap-the-rewards-learning-to-play | 2302.04449 | null | https://arxiv.org/abs/2302.04449v2 | https://arxiv.org/pdf/2302.04449v2.pdf | Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals | High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and wiki pages are among the most abundant data that could inform agents ... | ['Tom M. Mitchell', 'Yuanzhi Li', 'Amos Azaria', 'Paul Pu Liang', 'Yewen Fan', 'Yue Wu'] | 2023-02-09 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-3.49324420e-02 2.71838546e-01 -1.67777076e-01 -1.14981927e-01
-7.60277510e-01 -7.92394340e-01 5.72698295e-01 -1.69491872e-01
-8.33656609e-01 9.22221005e-01 2.05348328e-01 -2.03049362e-01
-1.90181166e-01 -5.51865697e-01 -6.00729406e-01 -4.50670481e-01
-3.52731571e-02 9.49806213e-01 5.20807683e-01 -5.65165639... | [4.036898612976074, 1.436249017715454] |
36506e60-b4c4-408d-9284-9f6945771452 | 3d-intracranial-aneurysm-classification-and | 2201.02198 | null | https://arxiv.org/abs/2201.02198v2 | https://arxiv.org/pdf/2201.02198v2.pdf | 3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning | Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. While most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud da... | ['Xiao Liu', 'Xuequan Lu', 'Di Shao'] | 2022-01-06 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [-2.18443781e-01 4.77207959e-01 -5.31107970e-02 -5.39716959e-01
-5.05984068e-01 -1.99598051e-03 5.04206836e-01 1.30064413e-01
-5.34671843e-01 2.12711498e-01 1.09619103e-01 -5.51986814e-01
-1.51572386e-02 -6.48864150e-01 -6.26964986e-01 -7.90883899e-01
-4.23453480e-01 1.02905846e+00 5.77919483e-01 1.03456445... | [14.468547821044922, -2.260490655899048] |
e69cea76-435c-4f0f-9c23-50b982b016e4 | deep-compositional-captioning-describing | 1511.05284 | null | http://arxiv.org/abs/1511.05284v2 | http://arxiv.org/pdf/1511.05284v2.pdf | Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data | While recent deep neural network models have achieved promising results on
the image captioning task, they rely largely on the availability of corpora
with paired image and sentence captions to describe objects in context. In this
work, we propose the Deep Compositional Captioner (DCC) to address the task of
generating... | ['Lisa Anne Hendricks', 'Kate Saenko', 'Raymond Mooney', 'Trevor Darrell', 'Subhashini Venugopalan', 'Marcus Rohrbach'] | 2015-11-17 | deep-compositional-captioning-describing-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Hendricks_Deep_Compositional_Captioning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Hendricks_Deep_Compositional_Captioning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['novel-concepts'] | ['reasoning'] | [ 5.18913686e-01 2.95515150e-01 2.64410049e-01 -5.95777214e-01
-9.65618908e-01 -5.06926477e-01 1.12250102e+00 9.71858948e-02
-3.22716177e-01 8.26088071e-01 5.78921795e-01 5.94720952e-02
3.71254951e-01 -5.56960642e-01 -1.42065322e+00 -1.90212294e-01
1.35009065e-01 6.15012288e-01 2.49988854e-01 -3.08928043... | [10.968169212341309, 1.0231789350509644] |
0c352f1a-2ee0-4948-8d42-8099c38c76e0 | modeling-diverse-chemical-reactions-for | 2208.05482 | null | https://arxiv.org/abs/2208.05482v1 | https://arxiv.org/pdf/2208.05482v1.pdf | Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables | Single-step retrosynthesis is the cornerstone of retrosynthesis planning, which is a crucial task for computer-aided drug discovery. The goal of single-step retrosynthesis is to identify the possible reactants that lead to the synthesis of the target product in one reaction. By representing organic molecules as canonic... | ['Feng Wu', 'Yunfei Liu', 'Jie Wang', 'Huarui He'] | 2022-08-10 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.28224027e-01 -1.43226549e-01 -4.49113101e-01 -1.73660778e-02
-6.85593247e-01 -1.13325763e+00 9.50309753e-01 1.00969598e-01
-2.68968850e-01 1.17876029e+00 3.67451698e-01 -5.27740240e-01
4.75072324e-01 -9.15749669e-01 -1.01422071e+00 -1.20624542e+00
6.04858279e-01 6.00844681e-01 -2.05412686e-01 -1.90759659... | [4.5003557205200195, 6.105567932128906] |
8cbc3ba7-5e65-4115-b0eb-83325406ffe1 | emrel-joint-representation-of-entities-and | null | null | https://openreview.net/forum?id=2csU2MGpRbN | https://openreview.net/pdf?id=2csU2MGpRbN | EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction | Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations and consequently rich interactions across the constituent entities and relations.
While existing works only explore cross-entity interactions, we propose to explicitly introduce relation representation, jointly r... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.37710330e-02 5.87038338e-01 -6.85333073e-01 -2.82637239e-01
-8.34806442e-01 -7.02464759e-01 6.71643376e-01 6.46376789e-01
-1.08679961e-02 1.07481468e+00 4.39854383e-01 -2.35170007e-01
-3.76167297e-01 -8.47387195e-01 -5.93494833e-01 -3.41420411e-04
-5.36191285e-01 5.80613375e-01 2.67496198e-01 -1.67277634... | [9.219468116760254, 8.580756187438965] |
90cfc217-0a98-4cc8-a08a-fce7ec25bfb1 | yolo-z-improving-small-object-detection-in | 2112.11798 | null | https://arxiv.org/abs/2112.11798v4 | https://arxiv.org/pdf/2112.11798v4.pdf | YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles | As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects (that is... | ['Andrew Bradley', 'Fabio Cuzzolin', 'Izzeddin Teeti', 'Aduen Benjumea'] | 2021-12-22 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.95554346e-01 3.06194555e-02 1.85333472e-02 -1.66888788e-01
-2.01024994e-01 -6.79589868e-01 3.91346931e-01 1.65119380e-01
-6.85192108e-01 3.37769091e-01 -3.65683645e-01 -3.66368204e-01
2.16381341e-01 -9.57936227e-01 -8.63268197e-01 -3.84657502e-01
9.17749628e-02 1.56143457e-01 1.01870334e+00 -2.44402498... | [8.218766212463379, -1.1341255903244019] |
9b1242c7-3b3a-4c5a-85de-6db4f63f8141 | news-driven-stock-prediction-with-attention | 2004.01878 | null | https://arxiv.org/abs/2004.01878v1 | https://arxiv.org/pdf/2004.01878v1.pdf | News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition | We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. ... | ['He-Yan Huang', 'Yue Zhang', 'Xiao Liu', 'Changsen Yuan'] | 2020-04-04 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-5.36578298e-01 -1.20498903e-01 -7.86851823e-01 -2.85191298e-01
-5.08048475e-01 -4.02964056e-01 1.06692684e+00 -9.50341821e-02
-2.02537686e-01 7.93000758e-01 1.05664706e+00 -1.74063519e-01
2.63797760e-01 -1.10710943e+00 -8.45741868e-01 -1.88808575e-01
-2.53565341e-01 3.02367985e-01 4.02102798e-01 -6.61596537... | [4.450096130371094, 4.25547456741333] |
d68c9fa1-2f0e-4ca4-b573-8ffa3761f92a | exploiting-unlabeled-data-for-neural | 1611.08987 | null | http://arxiv.org/abs/1611.08987v2 | http://arxiv.org/pdf/1611.08987v2.pdf | Exploiting Unlabeled Data for Neural Grammatical Error Detection | Identifying and correcting grammatical errors in the text written by
non-native writers has received increasing attention in recent years. Although
a number of annotated corpora have been established to facilitate data-driven
grammatical error detection and correction approaches, they are still limited
in terms of quan... | ['Yang Liu', 'Zhuoran Liu'] | 2016-11-28 | null | null | null | null | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 2.45581746e-01 -1.13609806e-01 -6.35989383e-02 -8.73130560e-01
-5.47002971e-01 -1.54362321e-01 6.91803843e-02 6.51127160e-01
-8.24881852e-01 9.37439442e-01 -9.14054140e-02 -4.42464411e-01
3.25736851e-01 -6.78655803e-01 -5.48045516e-01 -7.76936710e-02
3.64696920e-01 2.34805942e-01 6.80418536e-02 -1.78791419... | [10.995911598205566, 10.753350257873535] |
f975a332-2999-40c0-8373-2df47ea1e5b2 | context-aware-group-activity-recognition | null | null | https://lear.inrialpes.fr/people/alahari/papers/dasgupta20.pdf | https://lear.inrialpes.fr/people/alahari/papers/dasgupta20.pdf | Context Aware Group Activity Recognition | This paper addresses the task of group activity recognition in multi-person videos. Existing approaches decompose this task into feature learning and relational reasoning. Despite showing progress, these methods only rely on appearance features for people and overlook the available contextual information, which can pla... | ['Karteek Alahari', 'C. V. Jawahar', 'Avijit Dasgupta'] | 2021-01-01 | null | null | null | icpr-2021-1 | ['group-activity-recognition', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 3.18678141e-01 -2.25091517e-01 -2.88849354e-01 -5.28672814e-01
-3.78574610e-01 -3.66378278e-01 8.56099069e-01 4.87921268e-01
-2.59316087e-01 4.28585231e-01 7.30958521e-01 3.07511777e-01
-2.72574514e-01 -7.13146210e-01 -5.28133333e-01 -5.78960598e-01
6.33628219e-02 1.32812783e-01 1.30912319e-01 -2.34722078... | [8.193720817565918, 0.5783317685127258] |
74b80057-f347-4cc2-86fd-3eeb6ed1a9d4 | decomposing-the-generalization-gap-in | 2307.03659 | null | https://arxiv.org/abs/2307.03659v1 | https://arxiv.org/pdf/2307.03659v1.pdf | Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation | What makes generalization hard for imitation learning in visual robotic manipulation? This question is difficult to approach at face value, but the environment from the perspective of a robot can often be decomposed into enumerable factors of variation, such as the lighting conditions or the placement of the camera. Em... | ['Chelsea Finn', 'Ted Xiao', 'Lisa Lee', 'Annie Xie'] | 2023-07-07 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 1.0246364e-01 -2.5384340e-01 -1.6728611e-01 -8.2401820e-02
-4.8566356e-01 -9.5145363e-01 7.2100419e-01 -2.2023262e-01
-5.6264412e-01 6.7850679e-01 1.5601376e-01 -5.6732118e-01
-4.3317404e-01 -2.0380294e-01 -8.4483773e-01 -5.4481304e-01
-2.1528545e-01 3.3835900e-01 2.5423676e-01 -1.5801759e-01
3.3006826e-01... | [4.503927230834961, 1.0684868097305298] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.