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856449c2-c4a2-457f-864c-c3d92299225e | geann-scalable-graph-augmentations-for-multi | 2307.03595 | null | https://arxiv.org/abs/2307.03595v1 | https://arxiv.org/pdf/2307.03595v1.pdf | GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting | Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated models typically rely on a large number of time series examples with substantial history. A rapidly growing topic of ... | ['Michael W. Mahoney', 'Ronak Metha', 'Vincent Quenneville-Belair', 'Shankar Ramasubramanian', 'Malcolm Wolff', 'Sitan Yang'] | 2023-07-07 | null | null | null | null | ['time-series-forecasting'] | ['time-series'] | [ 5.01257554e-03 8.81417021e-02 -4.28305209e-01 -7.17948794e-01
-5.28639615e-01 -7.08169341e-01 5.57435751e-01 3.62094283e-01
1.66351765e-01 3.52085471e-01 4.64092731e-01 -7.15582132e-01
-6.89015025e-03 -9.85453069e-01 -1.02033317e+00 -2.20886707e-01
-6.55963659e-01 5.32942832e-01 -2.67660528e-01 -7.46484637... | [6.883018970489502, 2.8020589351654053] |
624f5507-d902-4f56-8e74-5ad67f08cae1 | revisiting-modality-imbalance-in-multimodal | 2302.12589 | null | https://arxiv.org/abs/2302.12589v2 | https://arxiv.org/pdf/2302.12589v2.pdf | Revisiting Modality Imbalance In Multimodal Pedestrian Detection | Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions. However, in most cases, the training distribution largely emphasizes t... | ['Ciarán Eising', 'Martin Glavin', 'Edward Jones', 'Ujjwal Bhattacharya', 'Jonathan Horgan', 'Ganesh Sistu', 'Sudip Das', 'Arindam Das'] | 2023-02-24 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 1.33530766e-01 -1.03231989e-01 -1.28468364e-01 -3.95600438e-01
-6.07945144e-01 -2.97153115e-01 6.70750082e-01 1.90076292e-01
-5.36973596e-01 8.19358647e-01 9.25546214e-02 -1.17708027e-01
-2.46188357e-01 -7.29512811e-01 -6.23377264e-01 -1.22520733e+00
4.37338740e-01 -1.49859069e-02 3.13771725e-01 -2.84476221... | [9.789265632629395, -1.295159101486206] |
92ea3e1e-0256-4659-a28b-e72470ca3f27 | a-bi-directional-transformer-for-musical | 1907.02698 | null | https://arxiv.org/abs/1907.02698v1 | https://arxiv.org/pdf/1907.02698v1.pdf | A Bi-directional Transformer for Musical Chord Recognition | Chord recognition is an important task since chords are highly abstract and descriptive features of music. For effective chord recognition, it is essential to utilize relevant context in audio sequence. While various machine learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNN... | ['Dokyun Kim', 'Sungwook Jeon', 'Kyoyun Choi', 'Jonghun Park', 'Jonggwon Park'] | 2019-07-05 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 2.48042539e-01 -1.18120492e-01 2.23824024e-01 4.78346609e-02
-4.67253953e-01 -6.70229435e-01 4.05101359e-01 2.31212884e-01
-5.46011984e-01 4.28971529e-01 4.49480772e-01 -8.20026174e-02
-3.49973291e-01 -7.31410682e-01 -3.90452236e-01 -5.32759309e-01
-1.76918924e-01 1.82038322e-01 3.77122670e-01 -5.60162306... | [15.803908348083496, 5.2602925300598145] |
f754b81f-623a-447d-b8c6-4e945688dd8d | analysis-of-evolutionary-program-synthesis | 2101.03172 | null | https://arxiv.org/abs/2101.03172v1 | https://arxiv.org/pdf/2101.03172v1.pdf | Analysis of Evolutionary Program Synthesis for Card Games | In this report, we inspect the application of an evolutionary approach to the game of Rack'O, which is a card game revolving around the notion of decision making. We first apply the evolutionary technique for obtaining a set of rules over many generations and then compare them with a script written by a human player. A... | ['Cassidy Pirlot', 'Rohan Saha'] | 2021-01-08 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 3.22389692e-01 -1.74362451e-01 7.52222314e-02 -2.56565511e-01
4.18720454e-01 -8.52276862e-01 5.42422950e-01 -1.64115295e-01
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-4.22552794e-01 -1.02141666e+00 -3.26894850e-01 -4.51204181e-01
-1.54247984e-01 4.35315222e-01 6.26201868e-01 -1.06967008... | [3.456991195678711, 1.5637187957763672] |
d601a86b-a6d9-4572-a79e-7130246e550d | fakemix-augmentation-improves-transparent | 2103.13279 | null | https://arxiv.org/abs/2103.13279v2 | https://arxiv.org/pdf/2103.13279v2.pdf | FakeMix Augmentation Improves Transparent Object Detection | Detecting transparent objects in natural scenes is challenging due to the low contrast in texture, brightness and colors. Recent deep-learning-based works reveal that it is effective to leverage boundaries for transparent object detection (TOD). However, these methods usually encounter boundary-related imbalance proble... | ['Jian Tuo', 'Xiangui Luo', 'Kai Zhao', 'Qibin Hou', 'Enze Xie', 'Zhengqiang Zhang', 'Yang Cao'] | 2021-03-24 | null | null | null | null | ['transparent-objects', 'transparent-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.24294358e-01 -2.30513752e-01 1.24496356e-01 -2.33272150e-01
-4.84405458e-01 -4.64436650e-01 1.23324558e-01 -4.96715903e-01
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3.76249135e-01 -5.90395808e-01 -8.51291060e-01 -9.18778300e-01
2.18629777e-01 4.17345650e-02 8.20576787e-01 -1.54975966... | [10.252519607543945, -0.8492931127548218] |
d6e24615-5736-49e7-8d17-0a0ff139555c | learning-asymmetric-embedding-for-attributed | 2202.06307 | null | https://arxiv.org/abs/2202.06307v1 | https://arxiv.org/pdf/2202.06307v1.pdf | Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network | Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clustering. The objective of network embedding is to represent network nodes in a low-dimensional vector space while retaining as much informat... | ['Xinghuo Yu', 'Mahdi Jalili', 'Ahmad Asgharian Rezaei', 'Hossein Ghorbanzadeh', 'Mohammadreza Radmanesh'] | 2022-02-13 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.59612671e-01 3.05768639e-01 -2.02423528e-01 -3.14758509e-01
3.69457185e-01 -1.86529383e-01 6.78283155e-01 4.12214220e-01
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-5.93605816e-01 -1.20847952e+00 -8.03301111e-02 -7.83714890e-01
-5.85869431e-01 4.25334662e-01 2.23158881e-01 -1.10384174... | [7.22449254989624, 6.2042694091796875] |
49b2f3fc-08c5-45d9-b280-d3270e3f1f53 | computational-discovery-of-new-2d-materials | 2012.09314 | null | https://arxiv.org/abs/2012.09314v1 | https://arxiv.org/pdf/2012.09314v1.pdf | Computational discovery of new 2D materials using deep learning generative models | Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to... | ['Jianjun Hu', 'Yong Zhao', 'Edirisuriya M. Dilanga Siriwardane', 'Yuqi Song'] | 2020-12-16 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.18740517e-01 -8.90585184e-02 -3.12703520e-01 -2.24938586e-01
-5.29433370e-01 -3.86475086e-01 7.20674157e-01 1.16450498e-02
2.26006195e-01 1.40445352e+00 3.25240880e-01 -4.79221851e-01
1.45407915e-01 -1.08082533e+00 -7.86773503e-01 -1.07686961e+00
-4.92671877e-02 7.51578867e-01 1.32512853e-01 -1.76983684... | [5.143200397491455, 5.403586387634277] |
0727e262-de8c-4e4a-a675-028e8e13eb52 | survey-on-the-attention-based-rnn-model-and | 1601.06823 | null | http://arxiv.org/abs/1601.06823v1 | http://arxiv.org/pdf/1601.06823v1.pdf | Survey on the attention based RNN model and its applications in computer vision | The recurrent neural networks (RNN) can be used to solve the sequence to
sequence problem, where both the input and the output have sequential
structures. Usually there are some implicit relations between the structures.
However, it is hard for the common RNN model to fully explore the relations
between the sequences. ... | ['Feng Wang', 'David M. J. Tax'] | 2016-01-25 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 3.24777186e-01 -6.72901943e-02 -1.37436137e-01 -2.08195075e-01
2.72632539e-01 -2.54415244e-01 2.66897529e-01 -2.94453084e-01
-3.90880257e-01 5.15212834e-01 5.03124356e-01 -4.32523131e-01
-1.91720068e-01 -7.25410223e-01 -1.71733737e-01 -6.96128368e-01
1.41269222e-01 3.07777882e-01 1.26198247e-01 -6.32254958... | [11.03646183013916, 6.974472999572754] |
efa35633-10ad-4fd6-9fb7-6d90d4e3291d | self-learning-symmetric-multi-view | 2305.07307 | null | https://arxiv.org/abs/2305.07307v2 | https://arxiv.org/pdf/2305.07307v2.pdf | Self-Learning Symmetric Multi-view Probabilistic Clustering | Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing vi... | ['Jieping Ye', 'Chen Shen', 'Yaowu Chen', 'Rongxin Jiang', 'Junlong Liu', 'Junjie Liu'] | 2023-05-12 | null | null | null | null | ['incomplete-multi-view-clustering', 'self-learning'] | ['computer-vision', 'natural-language-processing'] | [-1.86710045e-01 -1.84905365e-01 -2.65491903e-01 -3.23361695e-01
-9.73190188e-01 -5.99746704e-01 2.79048562e-01 2.52151549e-01
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-2.58129984e-01 -5.99095583e-01 -6.81770921e-01 -9.00447011e-01
1.98418975e-01 7.83365905e-01 6.66390955e-01 2.35419229... | [8.25517749786377, 4.625959396362305] |
f87fee91-d137-480c-a879-54a946d4c783 | dycsc-modeling-the-evolutionary-process-of | 2210.12690 | null | https://arxiv.org/abs/2210.12690v2 | https://arxiv.org/pdf/2210.12690v2.pdf | DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure | Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent sp... | ['Zhan Bu', 'Shanfan Zhang'] | 2022-10-23 | null | null | null | null | ['dynamic-link-prediction', 'network-embedding'] | ['graphs', 'methodology'] | [-2.60649294e-01 -1.19757734e-01 -3.90886813e-01 9.91214663e-02
5.14933765e-01 -6.32293701e-01 8.77812445e-01 4.74919192e-02
5.06142639e-02 3.57934684e-01 3.49220008e-01 -3.30704898e-01
-8.58805180e-01 -8.03496897e-01 -1.46624193e-01 -7.36539066e-01
-7.45968878e-01 2.54946738e-01 4.60063964e-01 -2.90044576... | [7.199100017547607, 6.0484795570373535] |
18523426-79fd-4d13-858c-53d9eb328df5 | adapting-descriptions-of-people-to-the-point | null | null | https://aclanthology.org/W18-6540 | https://aclanthology.org/W18-6540.pdf | Adapting Descriptions of People to the Point of View of a Moving Observer | This paper addresses the task of generating descriptions of people for an observer that is moving within a scene. As the observer moves, the descriptions of the people around him also change. A referring expression generation algorithm adapted to this task needs to continuously monitor the changes in the field of view ... | ['Daniel Ruiz', "Pablo Gerv{\\'a}s", "Raquel Herv{\\'a}s", "Gonzalo M{\\'e}ndez", 'Ricardo de la Rosa'] | 2018-11-01 | null | null | null | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [ 9.71709266e-02 1.97249223e-02 4.82656568e-01 -3.92311960e-01
-7.82319531e-02 -5.97204447e-01 9.08055902e-01 4.31760967e-01
-5.30920923e-01 5.85419714e-01 2.43849456e-01 2.63965040e-01
9.17978305e-03 -8.88017714e-01 -6.92942813e-02 -2.99243271e-01
7.39918351e-02 8.50829303e-01 5.74141979e-01 -4.94062781... | [5.093184947967529, 0.49617475271224976] |
582c4af0-de76-4ae2-8143-c92df3fe179d | bridging-active-exploration-and-uncertainty | 2305.12240 | null | https://arxiv.org/abs/2305.12240v2 | https://arxiv.org/pdf/2305.12240v2.pdf | Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics | In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computational capabilities, this approach is becoming increasingly important for solving challenging contro... | ['Seongil Hong', 'Beomsu Kim', 'Junwon Seo', 'Jungwi Mun', 'Taekyung Kim'] | 2023-05-20 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-3.15153636e-02 4.04342949e-01 -6.08749211e-01 5.84497489e-02
-6.57764494e-01 -2.93547034e-01 6.31804705e-01 -5.97303025e-02
-5.61262667e-01 1.16550243e+00 -2.49550074e-01 -1.92136630e-01
-5.79346299e-01 -7.28967905e-01 -7.26070881e-01 -1.20225275e+00
-4.29954678e-01 6.05857849e-01 1.75998032e-01 -2.64976978... | [4.701535224914551, 2.0122108459472656] |
d6722d93-4636-4827-85d6-42d606730398 | adversarial-contrastive-estimation | 1805.03642 | null | http://arxiv.org/abs/1805.03642v3 | http://arxiv.org/pdf/1805.03642v3.pdf | Adversarial Contrastive Estimation | Learning by contrasting positive and negative samples is a general strategy
adopted by many methods. Noise contrastive estimation (NCE) for word embeddings
and translating embeddings for knowledge graphs are examples in NLP employing
this approach. In this work, we view contrastive learning as an abstraction of
all suc... | ['Yanshuai Cao', 'Avishek Joey Bose', 'Huan Ling'] | 2018-05-09 | adversarial-contrastive-estimation-1 | https://aclanthology.org/P18-1094 | https://aclanthology.org/P18-1094.pdf | acl-2018-7 | ['learning-word-embeddings'] | ['methodology'] | [-9.99851823e-02 3.34603548e-01 -4.10817355e-01 2.35677939e-02
-7.43069410e-01 -7.44338989e-01 9.11332011e-01 2.35910892e-01
-8.20724666e-01 6.58185780e-01 1.35515809e-01 -1.18108846e-01
-5.29236607e-02 -1.11735535e+00 -5.88129818e-01 -7.18713999e-01
-1.77397981e-01 7.09031820e-01 4.23997611e-01 -2.22642168... | [10.382803916931152, 8.499197959899902] |
31771b06-3613-4a44-9149-f86634ddda86 | fchd-a-fast-and-accurate-head-detector | 1809.08766 | null | https://arxiv.org/abs/1809.08766v3 | https://arxiv.org/pdf/1809.08766v3.pdf | FCHD: Fast and accurate head detection in crowded scenes | In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model. Our proposed architecture is a single fully convolutional network which is responsible for both bounding box prediction and classification. This makes our model lightweight with low inference time and memory ... | ['Aditya Vora', 'Vinay Chilaka'] | 2018-09-24 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-5.87918699e-01 2.10854754e-01 7.49382675e-02 -5.51172674e-01
-6.69703662e-01 -2.94623584e-01 2.53453374e-01 8.55032131e-02
-7.14795768e-01 4.52885419e-01 1.93105340e-01 -6.45223558e-02
4.68436420e-01 -7.04488575e-01 -7.58654237e-01 -5.23831427e-01
-2.95609504e-01 1.58696085e-01 5.18487692e-01 1.85177028... | [13.576828956604004, 0.3143201172351837] |
a87bfbb4-e957-47b7-bfb1-ed6fad99c94b | fsganv2-improved-subject-agnostic-face | 2202.12972 | null | https://arxiv.org/abs/2202.12972v1 | https://arxiv.org/pdf/2202.12972v1.pdf | FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment | We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces. We derive a novel iterative deep learning--based approach for face reenactment which adjusts signific... | ['Tal Hassner', 'Yosi Keller', 'Yuval Nirkin'] | 2022-02-25 | null | null | null | null | ['face-reenactment', 'facial-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.75982428e-01 2.95343429e-01 3.84090066e-01 -5.65226674e-01
-7.38802493e-01 -6.65022075e-01 5.83520889e-01 -5.72827160e-01
-2.12915719e-01 7.38219917e-01 -2.24823225e-02 1.23549551e-01
2.42509589e-01 -4.77087617e-01 -9.62082982e-01 -5.73093057e-01
1.09824747e-01 3.96859944e-01 -2.16607880e-02 -2.32263073... | [12.750121116638184, -0.1631173938512802] |
68834f2f-f0e5-416c-93ba-ccbdb339d35c | one-pass-incomplete-multi-view-clustering | 1903.00637 | null | http://arxiv.org/abs/1903.00637v1 | http://arxiv.org/pdf/1903.00637v1.pdf | One-Pass Incomplete Multi-view Clustering | Real data are often with multiple modalities or from multiple heterogeneous
sources, thus forming so-called multi-view data, which receives more and more
attentions in machine learning. Multi-view clustering (MVC) becomes its
important paradigm. In real-world applications, some views often suffer from
instances missing... | ['Songcan Chen', 'Menglei Hu'] | 2019-03-02 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-1.10206731e-01 -6.05133533e-01 -1.93502620e-01 -1.61501259e-01
-9.26826596e-01 -5.39898753e-01 3.81952733e-01 -1.36067988e-02
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-3.06866527e-01 -4.59373772e-01 -2.43899256e-01 -8.27463150e-01
2.46133909e-01 5.65659404e-01 1.28783777e-01 2.49086782... | [8.270694732666016, 4.635233402252197] |
0c5fac6b-b2ae-4e3e-ab63-fa054797804a | semantic-image-segmentation-with-deep-1 | 2210.13296 | null | https://arxiv.org/abs/2210.13296v1 | https://arxiv.org/pdf/2210.13296v1.pdf | Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping | Plant phenotyping refers to a quantitative description of the plants properties, however in image-based phenotyping analysis, our focus is primarily on the plants anatomical, ontogenetical and physiological properties.This technique reinforced by the success of Deep Learning in the field of image based analysis is appl... | ['Nestoras C. Tsirliganis', 'George Ioannakis', 'Alexandra D. Solomou', 'Chairi Kiourt', 'Petros N. Tamvakis'] | 2022-10-24 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.68479276e-01 -1.12715609e-01 -1.08798377e-01 -1.26799671e-02
-8.64375606e-02 -1.12980056e+00 8.70088488e-02 8.37332249e-01
1.85498714e-01 3.57901841e-01 -6.52326107e-01 -5.50521612e-01
-4.45374817e-01 -1.14715481e+00 -1.99707106e-01 -9.49840784e-01
-2.20529199e-01 5.06463885e-01 4.37405407e-02 -2.41421863... | [9.155440330505371, -1.5614023208618164] |
1a50111e-f70f-47a3-9faf-6824f11806dd | learning-deep-neural-network-representations | 1708.06850 | null | http://arxiv.org/abs/1708.06850v2 | http://arxiv.org/pdf/1708.06850v2.pdf | Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems | The Koopman operator has recently garnered much attention for its value in
dynamical systems analysis and data-driven model discovery. However, its
application has been hindered by the computational complexity of extended
dynamic mode decomposition; this requires a combinatorially large basis set to
adequately describe... | ['Soumya Kundu', 'Enoch Yeung', 'Nathan Hodas'] | 2017-08-22 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.23554617e-01 -1.72946095e-01 7.52477422e-02 2.76847035e-01
-1.77289903e-01 -7.75996089e-01 7.39843011e-01 2.07296833e-01
-3.64282459e-01 8.48993599e-01 -2.33103812e-01 -2.57507056e-01
-5.38447917e-01 -5.57963371e-01 -3.16206247e-01 -1.11650777e+00
-6.22108161e-01 9.41623628e-01 1.51356116e-01 -5.94916105... | [6.565204620361328, 3.58657169342041] |
4e177ef1-5178-4943-91dc-6f152358bf0f | multimodal-emotion-cause-pair-extraction-in | 2110.08020 | null | https://arxiv.org/abs/2110.08020v1 | https://arxiv.org/pdf/2110.08020v1.pdf | Multimodal Emotion-Cause Pair Extraction in Conversations | Emotion cause analysis has received considerable attention in recent years. Previous studies primarily focused on emotion cause extraction from texts in news articles or microblogs. It is also interesting to discover emotions and their causes in conversations. As conversation in its natural form is multimodal, a large ... | ['Jianfei Yu', 'Zhaoyu Li', 'Rui Xia', 'Zixiang Ding', 'Fanfan Wang'] | 2021-10-15 | null | null | null | null | ['multimodal-emotion-recognition', 'emotion-cause-pair-extraction', 'emotion-cause-extraction', 'multimodal-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.06511343e-01 1.15338378e-01 1.23718590e-01 -7.06947684e-01
-1.06596160e+00 -7.14480340e-01 8.92835736e-01 1.59652844e-01
1.28711313e-01 8.51832330e-01 1.01377952e+00 2.84814149e-01
-1.43449128e-01 -1.78466797e-01 -2.30816141e-01 -6.08500540e-01
-1.80227503e-01 -3.50168301e-03 -5.50928175e-01 -3.32462430... | [13.079998016357422, 5.650579452514648] |
f1e5f167-d626-4f6d-89aa-14fa97e5ed94 | compositional-scalable-object-slam | 2011.02658 | null | https://arxiv.org/abs/2011.02658v1 | https://arxiv.org/pdf/2011.02658v1.pdf | Compositional Scalable Object SLAM | We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional scalable object mapping formulation... | ['Michael Kaess', 'Wei Dong', 'Akash Sharma'] | 2020-11-05 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [ 1.63926393e-01 -2.48811215e-01 1.18082963e-01 -5.16284525e-01
-9.19283807e-01 -7.38317966e-01 7.29139090e-01 1.83279589e-01
-3.44556153e-01 5.98056793e-01 -3.85261467e-03 -2.12604314e-01
-6.48303926e-02 -7.63379633e-01 -1.03842151e+00 -1.83730841e-01
-2.19411001e-01 7.74614096e-01 5.29731452e-01 -9.02139172... | [7.4131317138671875, -2.257479429244995] |
4b517bb2-85e2-4edb-973f-010c8976b993 | low-discrepancy-points-via-energetic | 2111.10722 | null | https://arxiv.org/abs/2111.10722v2 | https://arxiv.org/pdf/2111.10722v2.pdf | A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel | We propose a novel deterministic sampling method to approximate a target distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy (MMD). By employing the general \emph{energetic variational inference} framework (Wang et al., 2021), we convert the problem of minimizing MMD t... | ['Chun Liu', 'Lulu Kang', 'Yiwei Wang', 'Yindong Chen'] | 2021-11-21 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-5.46016097e-02 -1.27220415e-02 -6.80602193e-02 8.06205440e-04
-7.58006215e-01 -1.47279426e-01 5.05237222e-01 -3.75271916e-01
-5.45278609e-01 1.23564029e+00 -2.74959058e-01 -2.37343013e-01
-7.05702826e-02 -9.81483340e-01 -7.14216590e-01 -1.12822962e+00
5.44292629e-01 4.99912471e-01 2.43414015e-01 2.27060914... | [6.945245265960693, 3.9291462898254395] |
03894c0e-2d5c-4f49-a9ba-c989a3bbbd5c | strong-transcenter-improved-multi-object | 2210.13570 | null | https://arxiv.org/abs/2210.13570v1 | https://arxiv.org/pdf/2210.13570v1.pdf | Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations | Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. A few attempts have been made to apply this method to the task of Multiple Object Tracking (MOT), among those the state-of-the-art was TransCenter... | ['Ben-Zion Bobrovsky', 'Roy Orfaig', 'Amit Galor'] | 2022-10-24 | null | null | null | null | ['multiple-object-tracking-with-transformer'] | ['computer-vision'] | [-1.53025359e-01 -4.12877858e-01 9.45987180e-02 1.17522873e-01
-6.90818727e-01 -6.80548072e-01 8.68438840e-01 -8.28576609e-02
-4.21037853e-01 4.91371274e-01 -1.41904965e-01 4.44836356e-02
-2.14041010e-01 -4.48117286e-01 -7.03396380e-01 -8.00771236e-01
-2.79559493e-01 8.37217331e-01 1.02474201e+00 -1.78826034... | [6.3498759269714355, -2.048076629638672] |
067c44a9-50bd-4c2b-9a58-5a1a5beea990 | multibodysync-multi-body-segmentation-and | 2101.06605 | null | https://arxiv.org/abs/2101.06605v3 | https://arxiv.org/pdf/2101.06605v3.pdf | MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization | We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency acro... | ['Leonidas Guibas', 'Shi-Min Hu', 'Federica Arrigoni', 'Minhyuk Sung', 'Tolga Birdal', 'He Wang', 'Jiahui Huang'] | 2021-01-17 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Huang_MultiBodySync_Multi-Body_Segmentation_and_Motion_Estimation_via_3D_Scan_Synchronization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['motion-segmentation'] | ['computer-vision'] | [-5.56237735e-02 -1.23788372e-01 4.77311090e-02 -2.51091242e-01
-1.02861452e+00 -9.85151827e-01 4.21644449e-01 -3.17996979e-01
-2.22480759e-01 2.52517057e-03 -1.23320937e-01 1.80017784e-01
-1.75430179e-01 -3.59426022e-01 -9.94657576e-01 -5.22426009e-01
1.38015732e-01 1.27952683e+00 5.10296345e-01 -8.95402208... | [8.076740264892578, -2.5034186840057373] |
bd90f8ce-520c-42e8-9c64-3ab6af7a1cf5 | bc-vad-a-robust-bone-conduction-voice | 2212.02996 | null | https://arxiv.org/abs/2212.02996v1 | https://arxiv.org/pdf/2212.02996v1.pdf | BC-VAD: A Robust Bone Conduction Voice Activity Detection | Voice Activity Detection (VAD) is a fundamental module in many audio applications. Recent state-of-the-art VAD systems are often based on neural networks, but they require a computational budget that usually exceeds the capabilities of a small battery-operated device when preserving the performance of larger models. In... | ['Milos Cernak', 'Damien Ronssin', "Niccolo' Polvani"] | 2022-12-06 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 9.49356258e-02 -2.34751999e-01 2.33800426e-01 1.57539278e-01
-1.09835601e+00 -2.63561279e-01 2.20510155e-01 -3.01774949e-01
-3.80498022e-01 3.58971268e-01 2.39545748e-01 -4.07451272e-01
1.60127595e-01 -1.85301885e-01 -6.45062864e-01 -9.11791921e-01
3.15660425e-02 5.89434393e-02 5.45498908e-01 3.65061462... | [14.834747314453125, 5.8069844245910645] |
db0d4160-ff23-4e81-a696-053f91b0e6c0 | cost-effective-task-offloading-scheduling-for | 2306.14588 | null | https://arxiv.org/abs/2306.14588v1 | https://arxiv.org/pdf/2306.14588v1.pdf | Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing | In this paper, we aim to address the challenge of hybrid mobile edge-quantum computing (MEQC) for sustainable task offloading scheduling in mobile networks. We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for... | ['Dusit Niyato', 'Han Yu', 'Minrui Xu', 'Yue Xiao', 'Yulan Gao', 'Ziqiang Ye'] | 2023-06-26 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 1.71637595e-01 -2.05280602e-01 -5.20174861e-01 9.65175033e-02
-6.37484074e-01 -3.17598224e-01 -2.89948046e-01 -5.09260774e-01
-4.03402716e-01 9.89689469e-01 -2.22106948e-01 -7.12625861e-01
-4.53122377e-01 -6.79377854e-01 -4.73431915e-01 -9.04457331e-01
-1.71860531e-01 4.22965407e-01 -2.94031590e-01 -2.39771828... | [5.869668960571289, 1.742750644683838] |
039985d3-0806-4ad7-b729-72470f5fb778 | pillarnext-rethinking-network-designs-for-3d | 2305.04925 | null | https://arxiv.org/abs/2305.04925v1 | https://arxiv.org/pdf/2305.04925v1.pdf | PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds | In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object detection research mostly focuses on designing dedicated local point aggregators for fine-grained geometrical modeling. In this paper, we revisit the local point aggregators from the perspective of allocating computational resourc... | ['Xiaodong Yang', 'Chenxu Luo', 'Jinyu Li'] | 2023-05-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_PillarNeXt_Rethinking_Network_Designs_for_3D_Object_Detection_in_LiDAR_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_PillarNeXt_Rethinking_Network_Designs_for_3D_Object_Detection_in_LiDAR_CVPR_2023_paper.pdf | cvpr-2023-1 | ['2d-object-detection'] | ['computer-vision'] | [-2.17226014e-01 -3.07327569e-01 4.10420373e-02 -1.98142916e-01
-6.06335342e-01 -6.30882502e-01 6.19651794e-01 2.38638669e-02
-2.89981037e-01 1.62584215e-01 -3.23021770e-01 -4.12815630e-01
-6.38833269e-02 -1.00098300e+00 -1.05658567e+00 -3.73209208e-01
-3.06357205e-01 6.04785323e-01 6.77810550e-01 -2.15874732... | [7.688499927520752, -2.9190080165863037] |
61474bd1-f630-46b2-940b-6066c7cfc205 | reward-balancing-for-statistical-spoken | 1707.06299 | null | http://arxiv.org/abs/1707.06299v1 | http://arxiv.org/pdf/1707.06299v1.pdf | Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning | Reinforcement learning is widely used for dialogue policy optimization where
the reward function often consists of more than one component, e.g., the
dialogue success and the dialogue length. In this work, we propose a structured
method for finding a good balance between these components by searching for the
optimal re... | ['Milica Gašić', 'Tsung-Hsien Wen', 'Pei-Hao Su', 'Lina Rojas-Barahona', 'Nikola Mrkšić', 'Iñigo Casanueva', 'Paweł Budzianowski', 'Stefan Ultes', 'Steve Young'] | 2017-07-19 | reward-balancing-for-statistical-spoken-1 | https://aclanthology.org/W17-5509 | https://aclanthology.org/W17-5509.pdf | ws-2017-8 | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-8.58626664e-02 1.00586936e-01 -4.58260626e-01 -3.49069744e-01
-8.26779246e-01 -6.13913655e-01 5.92632651e-01 2.86143392e-01
-8.48635733e-01 1.10249484e+00 3.17229658e-01 -2.84774899e-01
-1.07083451e-02 -6.71192944e-01 -5.14963940e-02 -5.39077759e-01
1.47051975e-01 4.99130636e-01 3.23329955e-01 -5.57804167... | [13.06534194946289, 8.02713680267334] |
0bb4cea1-4755-4585-af3a-ab640f65c8f8 | kalman-filter-is-all-you-need-optimization | null | null | https://openreview.net/forum?id=cMBKc-0OTY5 | https://openreview.net/pdf?id=cMBKc-0OTY5 | Kalman Filter Is All You Need: Optimization Works When Noise Estimation Fails | Determining the noise parameters of a Kalman Filter (KF) has been studied for decades. A huge body of research focuses on the task of noise estimation under various conditions, since precise noise estimation is considered equivalent to minimization of the filtering errors. However, we show that even a small violation o... | ['Netanel Yannay', 'Shie Mannor', 'Ido Greenberg'] | 2021-09-29 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 2.10056156e-01 1.46799490e-01 2.41585061e-01 1.04153547e-02
-4.81383115e-01 -7.29032516e-01 4.82234776e-01 -1.24960104e-02
-5.90424359e-01 8.26746702e-01 -1.25618803e-03 -5.19128025e-01
-6.16850495e-01 -3.60553175e-01 -7.02211082e-01 -1.08331716e+00
1.64486632e-01 4.01604436e-02 1.74541533e-01 -2.97786862... | [6.6008405685424805, 3.559725761413574] |
3d36cdd3-cbdc-49ba-9291-3f4c81816658 | context-aware-cnns-for-person-head-detection | 1511.07917 | null | http://arxiv.org/abs/1511.07917v1 | http://arxiv.org/pdf/1511.07917v1.pdf | Context-aware CNNs for person head detection | Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. St... | ['Tuan-Hung Vu', 'Ivan Laptev', 'Anton Osokin'] | 2015-11-24 | context-aware-cnns-for-person-head-detection-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Vu_Context-Aware_CNNs_for_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Vu_Context-Aware_CNNs_for_ICCV_2015_paper.pdf | iccv-2015-12 | ['head-detection'] | ['computer-vision'] | [-1.57979921e-01 -9.79808643e-02 1.75911993e-01 -7.87095428e-01
-6.09879673e-01 -4.04896677e-01 5.52200377e-01 -2.61497736e-01
-7.74806440e-01 4.48567718e-01 3.77295583e-01 3.93472672e-01
3.75306338e-01 -3.56027186e-01 -7.61665463e-01 -4.83117133e-01
-1.92548335e-02 5.26004314e-01 3.31183076e-01 1.02562971... | [13.788528442382812, 0.4282793700695038] |
94f5c5a6-cf30-469f-96e3-0a1a014182f3 | megane-morphable-eyeglass-and-avatar-network | 2302.04868 | null | https://arxiv.org/abs/2302.04868v1 | https://arxiv.org/pdf/2302.04868v1.pdf | MEGANE: Morphable Eyeglass and Avatar Network | Eyeglasses play an important role in the perception of identity. Authentic virtual representations of faces can benefit greatly from their inclusion. However, modeling the geometric and appearance interactions of glasses and the face of virtual representations of humans is challenging. Glasses and faces affect each oth... | ['Jason Saragih', 'Hongdong Li', 'Stephen Lombardi', 'Tomas Simon', 'Shunsuke Saito', 'Junxuan Li'] | 2023-02-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_MEGANE_Morphable_Eyeglass_and_Avatar_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_MEGANE_Morphable_Eyeglass_and_Avatar_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['inverse-rendering'] | ['computer-vision'] | [ 1.47916645e-01 2.90755540e-01 4.13264692e-01 -1.69462636e-01
-4.83892970e-02 -6.21298313e-01 5.01029909e-01 -4.09007728e-01
3.81866664e-01 5.15628040e-01 1.64691154e-02 2.06852645e-01
5.41462779e-01 -9.75931704e-01 -9.31995749e-01 -3.60790640e-01
3.67118955e-01 4.09381479e-01 4.03053552e-01 -3.07337165... | [12.738277435302734, -0.33872440457344055] |
54b751e9-d015-44eb-add1-44e9b9c66d4a | joint-visual-grounding-and-tracking-with | 2303.12027 | null | https://arxiv.org/abs/2303.12027v1 | https://arxiv.org/pdf/2303.12027v1.pdf | Joint Visual Grounding and Tracking with Natural Language Specification | Tracking by natural language specification aims to locate the referred target in a sequence based on the natural language description. Existing algorithms solve this issue in two steps, visual grounding and tracking, and accordingly deploy the separated grounding model and tracking model to implement these two steps, r... | ['Zhenyu He', 'Kaige Mao', 'Zikun Zhou', 'Li Zhou'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Joint_Visual_Grounding_and_Tracking_With_Natural_Language_Specification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Joint_Visual_Grounding_and_Tracking_With_Natural_Language_Specification_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-grounding', 'visual-tracking'] | ['computer-vision', 'computer-vision'] | [-4.69489932e-01 -3.96336526e-01 -5.32665253e-01 -1.80308685e-01
-6.51019573e-01 -7.49070168e-01 6.67002916e-01 -1.38818398e-01
-1.94483444e-01 3.47589672e-01 -1.61820520e-02 -1.60960346e-01
2.80446321e-01 -4.52871829e-01 -5.58595657e-01 -5.98376632e-01
1.84004530e-01 2.53946513e-01 9.39933002e-01 -6.71417266... | [6.329048156738281, -2.091935634613037] |
f3ad7678-d966-424e-8f78-a533be1de737 | dense-teacher-dense-pseudo-labels-for-semi | 2207.02541 | null | https://arxiv.org/abs/2207.02541v2 | https://arxiv.org/pdf/2207.02541v2.pdf | Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection | To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes with the dense prediction as a united and straightforward form of pseudo-label. Compared to... | ['Jian Sun', 'Haiyan Yu', 'Zeming Li', 'Weixin Mao', 'Songtao Liu', 'Zheng Ge', 'HongYu Zhou'] | 2022-07-06 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.76490508e-02 2.49303475e-01 -2.74636060e-01 -4.94920105e-01
-7.66081631e-01 -3.77254963e-01 6.03537917e-01 1.84463896e-02
-4.37050909e-01 5.01793325e-01 9.08320770e-02 2.80965455e-02
1.91492289e-01 -4.13781404e-01 -4.94181484e-01 -9.06371117e-01
3.12358528e-01 4.27134484e-01 1.01810026e+00 2.32920751... | [9.171998023986816, 1.2698352336883545] |
1489d1fd-b3d9-404e-8078-3e2e440e46c6 | deep-attractor-network-for-single-microphone | 1611.08930 | null | http://arxiv.org/abs/1611.08930v2 | http://arxiv.org/pdf/1611.08930v2.pdf | Deep attractor network for single-microphone speaker separation | Despite the overwhelming success of deep learning in various speech
processing tasks, the problem of separating simultaneous speakers in a mixture
remains challenging. Two major difficulties in such systems are the arbitrary
source permutation and unknown number of sources in the mixture. We propose a
novel deep learni... | ['Zhuo Chen', 'Nima Mesgarani', 'Yi Luo'] | 2016-11-27 | null | null | null | null | ['speaker-separation'] | ['speech'] | [-1.73231423e-01 -3.00412983e-01 2.29789868e-01 -6.09435551e-02
-9.08535361e-01 -5.34304917e-01 4.08155978e-01 6.02580085e-02
-4.55666155e-01 2.18029499e-01 3.26154262e-01 -2.27395490e-01
-5.88230550e-01 -3.24012756e-01 -5.00440598e-01 -1.10741770e+00
-2.45523825e-01 3.96404505e-01 1.02200463e-01 -4.48595453... | [14.970771789550781, 5.8420023918151855] |
13f0af40-878a-440f-9e1e-a0491af13fbd | tempo-lexical-context-driven-word-embedding | null | null | https://aclanthology.org/N18-1026 | https://aclanthology.org/N18-1026.pdf | Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction | Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in th... | ['Debasis Ganguly', 'Procheta Sen', 'Gareth Jones'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['session-search'] | ['natural-language-processing'] | [ 6.69429421e-01 -3.05383921e-01 -5.81549585e-01 -3.26670527e-01
-1.03696394e+00 -9.31221187e-01 1.04171503e+00 4.41830188e-01
-7.57984400e-01 2.38967448e-01 4.39671785e-01 -7.23566189e-02
-4.25555259e-01 -4.29292709e-01 -1.95825025e-01 -1.79258406e-01
-3.00045639e-01 2.17585117e-01 4.62454140e-01 9.36286747... | [11.567388534545898, 7.609540939331055] |
0df6b838-6f39-44d2-b1ce-a4bf554c59fd | the-whole-and-the-parts-the-mdl-principle-and | 2112.06853 | null | https://arxiv.org/abs/2112.06853v1 | https://arxiv.org/pdf/2112.06853v1.pdf | The whole and the parts: the MDL principle and the a-contrario framework | This work explores the connections between the Minimum Description Length (MDL) principle as developed by Rissanen, and the a-contrario framework for structure detection proposed by Desolneux, Moisan and Morel. The MDL principle focuses on the best interpretation for the whole data while the a-contrario approach concen... | ['Gregory Randall', 'Ignacio Ramírez Paulino', 'Rafael Grompone von Gioi'] | 2021-12-13 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [ 2.13505790e-01 2.41083235e-01 -5.87864816e-02 -2.32822075e-02
-3.13062102e-01 -7.95609355e-01 6.20798826e-01 6.28091097e-01
-2.90678628e-02 4.71312374e-01 -3.36496741e-01 -3.62251431e-01
-5.93600094e-01 -8.79363000e-01 -4.03891623e-01 -7.65089571e-01
-1.20728657e-01 4.65728194e-01 6.75821304e-01 -3.45769912... | [7.371376037597656, 4.13771390914917] |
7508b96f-052d-4687-a68d-3292d6622971 | t-yolo-tiny-vehicle-detection-based-on-yolo | null | null | https://ieeexplore.ieee.org/document/9658533 | https://www.researchgate.net/publication/357258399_T-YOLO_Tiny_vehicle_detection_based_on_YOLO_and_multi-scale_convolutional_neural_networks/fulltext/61c3c877c48a3d26b74a7594/T-YOLO-Tiny-vehicle-detection-based-on-YOLO-and-multi-scale-convolutional-neural-networks.pdf?origin=publicationDetail&_sg%5B0%5D=cnnzAJDqU61aGdw... | T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks | To solve real-life problems for different smart city applications, using deep Neural Network, such as parking occupancy detection, requires fine-tuning of these networks. For large parking, it is desirable to use a cenital-plane camera located at a high distance that allows the monitoring of the entire parking space or... | ['Domenec Puig', 'Miguel Ángel García', 'Hatem RashwanHatem Rashwan', 'Daniel Padilla Carrasco'] | 2021-12-01 | null | null | null | ieee-access-2021-12 | ['parking-space-occupancy'] | ['computer-vision'] | [-5.83178878e-01 -3.06337863e-01 5.00340015e-03 -1.20712087e-01
-6.99796677e-01 -1.53188154e-01 2.40844026e-01 -2.56399959e-01
-8.07617724e-01 5.14043987e-01 -5.24738073e-01 -3.20670694e-01
3.34022552e-01 -9.92724359e-01 -8.64650309e-01 -7.42611766e-01
1.75517648e-01 5.28379261e-01 7.08487153e-01 -1.48532033... | [8.284208297729492, -0.8639853000640869] |
27454017-e1a2-4856-bd32-af2ce18182d3 | pirc-net-using-proposal-indexing | 1812.03213 | null | http://arxiv.org/abs/1812.03213v1 | http://arxiv.org/pdf/1812.03213v1.pdf | PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding | Phrase Grounding aims to detect and localize objects in images that are
referred to and are queried by natural language phrases. Phrase grounding finds
applications in tasks such as Visual Dialog, Visual Search and Image-text
co-reference resolution. In this paper, we present a framework that leverages
information such... | ['Rama Kovvuri', 'Ram Nevatia'] | 2018-12-07 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.16581523e-01 3.55563045e-01 -6.82748616e-01 -2.20606461e-01
-1.15275633e+00 -8.46729040e-01 9.42265391e-01 4.52487022e-01
-4.52667594e-01 4.59765911e-01 5.03258586e-01 -2.19185635e-01
-6.40174150e-02 -4.84854788e-01 -8.97512019e-01 -3.88982654e-01
3.34349275e-02 4.54999954e-01 7.73105204e-01 -6.56713247... | [10.477545738220215, 1.469075083732605] |
5f6e5296-0a7d-4877-be37-86246b8d7183 | improving-solar-flare-prediction-by-time | 2206.07197 | null | https://arxiv.org/abs/2206.07197v1 | https://arxiv.org/pdf/2206.07197v1.pdf | Improving Solar Flare Prediction by Time Series Outlier Detection | Solar flares not only pose risks to outer space technologies and astronauts' well being, but also cause disruptions on earth to our hight-tech, interconnected infrastructure our lives highly depend on. While a number of machine-learning methods have been proposed to improve flare prediction, none of them, to the best o... | ['Rafal A. Angryk', 'Azim Ahmadzadeh', 'Md Reazul Islam', 'Junzhi Wen'] | 2022-06-14 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [-3.96649353e-02 -2.51512289e-01 5.74521944e-02 -9.09883678e-02
-5.25075555e-01 -5.98183930e-01 6.31029189e-01 -5.20543754e-02
1.45786628e-01 1.10049880e+00 6.99711293e-02 -1.58705279e-01
-6.55377269e-01 -7.08580732e-01 -6.87833726e-01 -6.99554920e-01
-1.22754611e-01 2.83883423e-01 2.58242577e-01 -3.03869545... | [6.7673659324646, 2.8233373165130615] |
295821b5-6ed5-45d1-affd-bc4550abda19 | expression-empowered-residen-network-for | 1806.04957 | null | http://arxiv.org/abs/1806.04957v1 | http://arxiv.org/pdf/1806.04957v1.pdf | Expression Empowered ResiDen Network for Facial Action Unit Detection | The paper explores the topic of Facial Action Unit (FAU) detection in the
wild. In particular, we are interested in answering the following questions:
(1) how useful are residual connections across dense blocks for face analysis?
(2) how useful is the information from a network trained for categorical Facial
Expression... | ['Abhinav Dhall', 'Shreyank Jyoti'] | 2018-06-13 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.78740984e-02 1.40277758e-01 4.04408909e-02 -5.76360703e-01
-3.54885042e-01 2.23005321e-02 3.36567461e-01 -6.18435919e-01
-2.91932702e-01 5.56508541e-01 2.11924121e-01 4.61163014e-01
2.13719279e-01 -4.77615863e-01 -5.39477766e-01 -8.74604464e-01
-4.08172399e-01 -2.62395591e-01 -2.41308734e-01 -6.36476696... | [13.599461555480957, 1.7990878820419312] |
908ee594-631f-4781-a76d-a4da0e8cd1de | coreference-resolution-through-a-seq2seq | 2211.12142 | null | https://arxiv.org/abs/2211.12142v1 | https://arxiv.org/pdf/2211.12142v1.pdf | Coreference Resolution through a seq2seq Transition-Based System | Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition sys... | ['Michael Collins', 'Chris Alberti', 'Bernd Bohnet'] | 2022-11-22 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-8.05407390e-02 3.22874784e-01 -7.28403211e-01 -2.26851389e-01
-1.55363691e+00 -8.12772512e-01 9.75141048e-01 1.22918017e-01
-8.05044413e-01 1.16398478e+00 6.71242237e-01 -1.07851923e-01
-2.16099858e-01 -3.33442122e-01 -6.79294527e-01 -3.00528467e-01
-1.28014341e-01 1.16771841e+00 3.91884446e-01 -7.19393194... | [9.245495796203613, 9.589848518371582] |
8842ecd6-ea26-4047-9af5-acf9e9e59ca7 | learning-to-search-for-and-detect-objects-in | 2304.05741 | null | https://arxiv.org/abs/2304.05741v1 | https://arxiv.org/pdf/2304.05741v1.pdf | Learning to search for and detect objects in foveal images using deep learning | The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view's periphery. However, the majority of existing object localization methods rely on images acquired by image se... | ['Plinio Moreno', 'Beatriz Paula'] | 2023-04-12 | null | null | null | null | ['object-localization'] | ['computer-vision'] | [ 3.73206526e-01 -6.01031482e-02 3.74519788e-02 1.24844117e-02
-1.78526789e-01 -6.29421949e-01 4.24146503e-01 3.11552912e-01
-8.34238946e-01 5.86185992e-01 -7.96447173e-02 1.58384088e-02
-8.34290087e-02 -6.78320289e-01 -6.86045527e-01 -8.48082125e-01
1.47789970e-01 -8.82352144e-02 6.62966728e-01 1.65365726... | [10.084147453308105, 1.6663780212402344] |
7b407a3c-2dca-473d-8d2e-521df2641d76 | summary-oriented-vision-modeling-for | 2212.07672 | null | https://arxiv.org/abs/2212.07672v2 | https://arxiv.org/pdf/2212.07672v2.pdf | Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization | Multimodal abstractive summarization (MAS) aims to produce a concise summary given the multimodal data (text and vision). Existing studies mainly focus on how to effectively use the visual features from the perspective of an article, having achieved impressive success on the high-resource English dataset. However, less... | ['Jie zhou', 'Yufeng Chen', 'Jiaan Wang', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2022-12-15 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 2.02923536e-01 -3.27905059e-01 -2.02166542e-01 -2.39917547e-01
-1.16055477e+00 -2.94683814e-01 8.27916086e-01 2.61717290e-01
-5.07498026e-01 7.21540749e-01 6.86246037e-01 8.86990223e-03
2.09463820e-01 -3.85863215e-01 -6.24818027e-01 -5.32080710e-01
4.29849833e-01 -7.62043670e-02 -1.21908098e-01 -1.61579587... | [10.684871673583984, 0.7446709871292114] |
debc8b98-67a9-4ee7-ab40-b90ee18e7c7b | dynamic-inter-treatment-information-sharing | 2305.15984 | null | https://arxiv.org/abs/2305.15984v1 | https://arxiv.org/pdf/2305.15984v1.pdf | Dynamic Inter-treatment Information Sharing for Heterogeneous Treatment Effects Estimation | Existing heterogeneous treatment effects learners, also known as conditional average treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment information sharing, and data have to be split among potential outcome functions to train CATE learners which can lead to biased estimates with ... | ['David A. Clifton', 'Ghadeer Ghosheh', 'Soheila Molaei', 'Jiandong Zhou', 'Vinod Kumar Chauhan'] | 2023-05-25 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 6.05369397e-02 2.53968209e-01 -1.05697227e+00 -6.47335052e-01
-8.25754285e-01 -2.01980144e-01 4.79906440e-01 -3.87678631e-02
-6.73057318e-01 1.16316676e+00 6.43099606e-01 -4.47782189e-01
-6.69764936e-01 -7.86641061e-01 -9.34199989e-01 -7.07484543e-01
-2.05711812e-01 5.27683318e-01 -1.32408708e-01 9.52342078... | [8.0557222366333, 5.417015075683594] |
7e486e9d-0636-4616-9b6a-988b82c03367 | inspire-creativity-with-oriba-transform | 2306.09776 | null | https://arxiv.org/abs/2306.09776v1 | https://arxiv.org/pdf/2306.09776v1.pdf | Inspire creativity with ORIBA: Transform Artists' Original Characters into Chatbots through Large Language Model | This research delves into the intersection of illustration art and artificial intelligence (AI), focusing on how illustrators engage with AI agents that embody their original characters (OCs). We introduce 'ORIBA', a customizable AI chatbot that enables illustrators to converse with their OCs. This approach allows arti... | ['Ze Gao', 'Xingyu Li', 'Yuqian Sun'] | 2023-06-16 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [ 1.25861377e-01 5.64328790e-01 8.98174047e-02 2.02870056e-01
2.56750733e-01 -1.08659899e+00 1.07299650e+00 -4.81432170e-01
6.53411523e-02 4.92057860e-01 5.58734894e-01 -1.12047538e-01
7.47325793e-02 -4.49604362e-01 -1.77230537e-01 -1.91006005e-01
4.90708768e-01 7.20534563e-01 -4.53860283e-01 -2.53344417... | [9.403361320495605, 6.347986221313477] |
f3a4aaf9-9140-48e3-8ab6-20db805584e5 | complex-deep-learning-models-for-denoising-of | 1908.10417 | null | https://arxiv.org/abs/1908.10417v3 | https://arxiv.org/pdf/1908.10417v3.pdf | Complex Deep Learning Models for Denoising of Human Heart ECG signals | Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper pres... | ['Corneliu Arsene'] | 2019-08-27 | null | null | null | null | ['ecg-denoising', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.67000544e-01 -1.64729096e-02 7.35352635e-01 -1.98332429e-01
-4.78515267e-01 -2.22188234e-01 1.96258634e-01 1.03867851e-01
-8.84327888e-01 1.00777435e+00 -1.51125461e-01 -2.15780318e-01
-3.93942863e-01 -7.46845722e-01 -4.24603611e-01 -8.56206417e-01
-3.66395175e-01 3.62851471e-01 -8.55496079e-02 -2.74497241... | [14.22307014465332, 3.2845358848571777] |
168edc11-f3e6-4507-b02b-935327d99b7f | knowledge-distillation-for-efficient-audio | 2306.09947 | null | https://arxiv.org/abs/2306.09947v1 | https://arxiv.org/pdf/2306.09947v1.pdf | Knowledge Distillation for Efficient Audio-Visual Video Captioning | Automatically describing audio-visual content with texts, namely video captioning, has received significant attention due to its potential applications across diverse fields. Deep neural networks are the dominant methods, offering state-of-the-art performance. However, these methods are often undeployable in low-power ... | ['Wenwu Wang', 'Volkan Kılıç', 'Xubo Liu', 'Özkan Çaylı'] | 2023-06-16 | null | null | null | null | ['audio-visual-video-captioning', 'video-captioning'] | ['computer-vision', 'computer-vision'] | [ 4.06746894e-01 1.68639228e-01 -1.62929282e-01 -3.78272980e-01
-1.29318011e+00 -4.38491791e-01 3.70866537e-01 6.91639110e-02
-4.02146608e-01 7.16778576e-01 2.37734616e-01 -1.28247380e-01
2.55667180e-01 -3.47246975e-01 -9.09434080e-01 -5.73397338e-01
3.16233188e-01 1.54509470e-01 1.73280165e-01 2.39589810... | [15.15964126586914, 4.816662311553955] |
005608d4-1b4e-47fb-b77a-f725b9aa885e | trustworthy-multi-phase-liver-tumor | 2305.05344 | null | https://arxiv.org/abs/2305.05344v2 | https://arxiv.org/pdf/2305.05344v2.pdf | Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty | Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically. However, the performances of existing multi-phase liver tumor segmentation (MPLiTS)-based meth... | ['Xinde Li', 'Shenghong Ju', 'Wenbo Xiao', 'Yuancheng Wang', 'Quchen Zou', 'Ying Cui', 'Tianyi Xia', 'Chuanfei Hu'] | 2023-05-09 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-3.91512126e-01 -1.45587578e-01 -1.91100240e-01 -8.02230984e-02
-1.11694646e+00 -1.16833806e-01 2.66418427e-01 2.46521860e-01
1.14406668e-01 7.64107943e-01 1.63042128e-01 -2.69472629e-01
-6.13139212e-01 -3.71977746e-01 -1.33004501e-01 -1.54721260e+00
4.09093350e-02 7.20865786e-01 2.85723716e-01 3.51775259... | [14.32898998260498, -2.6747381687164307] |
38c40391-68dd-42c2-a097-e418194f5326 | logically-at-the-factify-2022-multimodal-fact | 2112.09253 | null | https://arxiv.org/abs/2112.09253v2 | https://arxiv.org/pdf/2112.09253v2.pdf | Logically at Factify 2022: Multimodal Fact Verification | This paper describes our participant system for the multi-modal fact verification (Factify) challenge at AAAI 2022. Despite the recent advance in text based verification techniques and large pre-trained multimodal models cross vision and language, very limited work has been done in applying multimodal techniques to aut... | ['Anil Bandhakavi', 'David Kiskovski', 'Stylianos Oikonomou', 'Hella-Franziska Hoffmann', 'Jie Gao'] | 2021-12-16 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 1.97850034e-01 2.24070236e-01 -6.43661171e-02 -2.84167498e-01
-1.31394279e+00 -8.31076682e-01 1.12732494e+00 3.19165975e-01
-3.91127974e-01 4.99896646e-01 5.22562683e-01 -4.68425572e-01
1.59794793e-01 -9.20218080e-02 -9.76428926e-01 -1.93812594e-01
2.47322679e-01 4.22536880e-01 4.71266499e-03 -1.94813475... | [8.272506713867188, 10.332731246948242] |
0deadd79-2060-4d39-9abb-d0601b636ded | interpretable-gait-recognition-by-granger | 2206.06714 | null | https://arxiv.org/abs/2206.06714v3 | https://arxiv.org/pdf/2206.06714v3.pdf | Interpretable Gait Recognition by Granger Causality | Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We propose an interpretable feature representation of gait sequences by the graphical Granger causal inference. Gait sequence of a person in the s... | ['Claudia Plant', 'Petr Sojka', 'Katerina Hlavackova-Schindler', 'Michal Balazia'] | 2022-06-14 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.88129544e-01 -1.97404861e-01 -1.88575804e-01 -1.21567830e-01
-1.94522321e-01 -2.97882766e-01 7.09766746e-01 -3.41377384e-03
-2.27522179e-01 7.83195555e-01 4.97572184e-01 -1.88195437e-01
-7.51717448e-01 -5.43938100e-01 -2.91142285e-01 -8.27736497e-01
-8.24581921e-01 3.91704649e-01 1.73505366e-01 -2.82352298... | [14.207072257995605, 1.4591526985168457] |
de5227f3-dc76-478f-bc5e-c18367c5660e | amee-a-robust-framework-for-explanation | 2306.05501 | null | https://arxiv.org/abs/2306.05501v1 | https://arxiv.org/pdf/2306.05501v1.pdf | AMEE: A Robust Framework for Explanation Evaluation in Time Series Classification | This paper aims to provide a framework to quantitatively evaluate and rank explanation methods for the time series classification task, which deals with a prevalent data type in critical domains such as healthcare and finance. The recent surge of research interest in explanation methods for time series classification h... | ['Georgiana Ifrim', 'Thach Le Nguyen', 'Thu Trang Nguyen'] | 2023-06-08 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.89834309e-01 3.69970016e-02 -2.57468075e-01 -3.35818380e-01
-4.60865170e-01 -6.67128444e-01 6.94459856e-01 6.80282891e-01
6.23734184e-02 4.76074308e-01 3.40849042e-01 -3.91559094e-01
-4.88527834e-01 -3.65099162e-01 -4.72758234e-01 -6.71349764e-01
-2.23269895e-01 1.58319965e-01 -7.86035322e-03 -3.63567561... | [7.363716125488281, 3.3044629096984863] |
8374b77e-72c4-41c4-85da-be557aca3234 | beyond-arabic-software-for-perso-arabic | 2301.11406 | null | https://arxiv.org/abs/2301.11406v1 | https://arxiv.org/pdf/2301.11406v1.pdf | Beyond Arabic: Software for Perso-Arabic Script Manipulation | This paper presents an open-source software library that provides a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script. The operations include various levels of script normalization, including visual invariance-p... | ['Richard Sproat', 'Brian Roark', 'Raiomond Doctor', 'Cibu Johny', 'Alexander Gutkin'] | 2023-01-26 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.48195818e-01 -2.31142730e-01 9.73326787e-02 -2.99731612e-01
2.79414430e-02 -1.31653333e+00 9.87259090e-01 -1.60054088e-01
-4.35688645e-01 4.26912099e-01 1.34699449e-01 -7.39107847e-01
1.64170668e-01 -8.23454738e-01 -6.25322759e-02 -4.68432277e-01
2.96417087e-01 5.54610848e-01 5.17325282e-01 -9.38498020... | [10.486077308654785, 10.455249786376953] |
293ec8d6-ae9e-4a80-93c0-fa63af8ec7af | on-certified-generalization-in-structured | 2306.09112 | null | https://arxiv.org/abs/2306.09112v1 | https://arxiv.org/pdf/2306.09112v1.pdf | On Certified Generalization in Structured Prediction | In structured prediction, target objects have rich internal structure which does not factorize into independent components and violates common i.i.d. assumptions. This challenge becomes apparent through the exponentially large output space in applications such as image segmentation or scene graph generation. We present... | ['Christoph Schnörr', 'Bastian Boll'] | 2023-06-15 | null | null | null | null | ['scene-graph-generation', 'structured-prediction', 'generalization-bounds'] | ['computer-vision', 'methodology', 'methodology'] | [ 8.25539529e-01 6.93222940e-01 -1.27185941e-01 -5.47556698e-01
-9.75273848e-01 -8.43045592e-01 5.45057476e-01 -1.23376124e-01
-1.94419637e-01 5.95414162e-01 1.83142483e-01 -2.35389188e-01
-2.48780370e-01 -5.37568510e-01 -9.68963921e-01 -1.05597365e+00
-7.55381435e-02 8.83024454e-01 1.21132448e-01 4.09288466... | [7.676203727722168, 4.004696369171143] |
3cfda1d9-d6bf-4006-9137-7c9572ad2803 | a-framework-for-csi-based-indoor-localization | 2205.08068 | null | https://arxiv.org/abs/2205.08068v1 | https://arxiv.org/pdf/2205.08068v1.pdf | A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks | Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization with signal fingerprints. However, CSI signal patterns can be complicated in the large and highly dynam... | ['Sudeep Pasricha', 'Liping Wang'] | 2022-05-17 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-8.03701673e-03 -6.22171640e-01 3.11104000e-01 -6.61129296e-01
-8.96748483e-01 -4.12342340e-01 7.03028813e-02 -1.55797794e-01
-2.95841485e-01 1.07535338e+00 2.62143523e-01 -6.03632808e-01
-4.09320980e-01 -8.97014618e-01 -9.23596025e-01 -7.00264513e-01
-4.41555113e-01 -1.25999562e-02 -6.38787672e-02 2.03711400... | [6.405378341674805, 0.8989027142524719] |
68fed536-2bd3-4670-8805-e964fe7b85e0 | sambert-improve-aspect-sentiment-triplet | null | null | https://openreview.net/forum?id=Z9vIuaFlIXx | https://openreview.net/pdf?id=Z9vIuaFlIXx | SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT | Aspect Sentiment Triplet Extraction (ASTE) performs fine-grained sentiment analysis in a unified way through extracting sentiment triplets comprised of aspect terms, opinion spans, and their sentiment relations in sentences. The previous works show the adoption of BERT, which simply leverages its last layer output as t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-7.30220526e-02 -7.31985718e-02 -7.61641562e-02 -5.87546468e-01
-6.49104357e-01 -6.91315770e-01 4.25712615e-01 5.26503101e-02
-2.38463238e-01 3.37506205e-01 3.68450254e-01 -1.30319744e-01
1.09798744e-01 -8.02434385e-01 -5.12881458e-01 -7.47184694e-01
5.59670568e-01 1.83324918e-01 1.19525634e-01 -4.74363416... | [11.475707054138184, 6.592503547668457] |
51f47584-920b-4cee-ba50-b900fe9ad636 | scallop-a-language-for-neurosymbolic | 2304.04812 | null | https://arxiv.org/abs/2304.04812v1 | https://arxiv.org/pdf/2304.04812v1.pdf | Scallop: A Language for Neurosymbolic Programming | We present Scallop, a language which combines the benefits of deep learning and logical reasoning. Scallop enables users to write a wide range of neurosymbolic applications and train them in a data- and compute-efficient manner. It achieves these goals through three key features: 1) a flexible symbolic representation t... | ['Mayur Naik', 'Jiani Huang', 'Ziyang Li'] | 2023-04-10 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-4.40212965e-01 2.01000243e-01 -5.01723886e-01 -3.03622931e-01
-1.20499596e-01 -6.81834698e-01 8.49294722e-01 4.54393983e-01
-7.15590715e-02 6.64916873e-01 8.43149498e-02 -8.09384763e-01
-4.45376575e-01 -1.15757358e+00 -9.27938819e-01 -2.04570033e-02
-5.06428123e-01 8.34549546e-01 5.54008782e-01 -3.57068926... | [9.097552299499512, 7.161159992218018] |
2ee67c1c-87d6-4c34-9858-e46f4b09fe14 | billion-user-customer-lifetime-value | 2208.13358 | null | https://arxiv.org/abs/2208.13358v1 | https://arxiv.org/pdf/2208.13358v1.pdf | Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou | Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business. It is widely used in a variety of business scenarios to make operational decisions when acquiring new customers. Modeling LTV is a challenging problem, due to its complex and mutable data distribution. Existing appr... | ['Yang song', 'Xiao Fang', 'Naijun Yang', 'Guangcui Shao', 'Kunpeng Li'] | 2022-08-29 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-3.01205486e-01 -3.45510066e-01 -7.33543694e-01 -8.39406490e-01
-5.88193774e-01 -3.03687990e-01 4.36083883e-01 6.20736554e-02
-4.18293625e-02 7.82981932e-01 -2.74512947e-01 -3.92996073e-01
-5.89751542e-01 -1.01846337e+00 -5.40885746e-01 -9.41737115e-01
7.80731589e-02 1.14425302e+00 -2.96003342e-01 -3.47811803... | [9.72372055053711, 5.252973556518555] |
b5b022f7-4cc5-4387-b79d-db9d33176708 | a-proposal-of-automatic-error-correction-in | 2112.01846 | null | https://arxiv.org/abs/2112.01846v1 | https://arxiv.org/pdf/2112.01846v1.pdf | A Proposal of Automatic Error Correction in Text | The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both pr... | ['Carlos R. Jaimez-González', 'Wulfrano A. Luna-Ramírez'] | 2021-09-24 | null | null | null | null | ['word-similarity', 'text-categorization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.07564628e-01 -1.92357705e-03 1.46448195e-01 -1.74081624e-01
-5.50105274e-01 -4.99788791e-01 5.39212167e-01 9.03734744e-01
-9.00376976e-01 6.72561765e-01 1.36522830e-01 -8.84471059e-01
-2.39339635e-01 -8.47149551e-01 -1.58226550e-01 -2.21970931e-01
5.06217897e-01 8.29849184e-01 4.76972014e-01 -4.04270977... | [10.812272071838379, 10.520987510681152] |
5d28d779-ce98-497f-abb2-df5e6cc10014 | training-free-object-counting-with-prompts | 2307.00038 | null | https://arxiv.org/abs/2307.00038v1 | https://arxiv.org/pdf/2307.00038v1.pdf | Training-free Object Counting with Prompts | This paper tackles the problem of object counting in images. Existing approaches rely on extensive training data with point annotations for each object, making data collection labor-intensive and time-consuming. To overcome this, we propose a training-free object counter that treats the counting task as a segmentation ... | ['Mengmi Zhang', 'Ying Sun', 'Zenglin Shi'] | 2023-06-30 | null | null | null | null | ['zero-shot-segmentation', 'object-counting'] | ['computer-vision', 'computer-vision'] | [ 4.51677561e-01 -1.95766971e-01 -9.04011875e-02 -4.57364410e-01
-8.43482137e-01 -5.06431103e-01 6.21412456e-01 2.38285527e-01
-8.10490012e-01 5.49823642e-01 -3.63109350e-01 -2.65492409e-01
2.92259783e-01 -8.42999756e-01 -5.58133543e-01 -4.65218872e-01
5.15615821e-01 6.02293909e-01 7.51953840e-01 3.59478682... | [9.141847610473633, 0.4487902820110321] |
0048fd10-e125-4879-923e-f31873399ba2 | demn-distilled-exposition-enhanced-matching | 1901.02252 | null | http://arxiv.org/abs/1901.02252v1 | http://arxiv.org/pdf/1901.02252v1.pdf | DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension | This paper proposes a Distilled-Exposition Enhanced Matching Network (DEMN)
for story-cloze test, which is still a challenging task in story comprehension.
We divide a complete story into three narrative segments: an
\textit{exposition}, a \textit{climax}, and an \textit{ending}. The model
consists of three modules: in... | ['Shan Jiang', 'Haiou Zhang', 'Dong Yu', 'Chunhua Liu'] | 2019-01-08 | null | https://aclanthology.org/Y18-1045 | https://aclanthology.org/Y18-1045.pdf | paclic-2018-12 | ['cloze-test'] | ['natural-language-processing'] | [ 2.19909638e-01 8.31519142e-02 -4.22063656e-02 -2.45731652e-01
-9.02928710e-01 -5.23787856e-01 8.76564264e-01 2.32997507e-01
-3.20992798e-01 4.98198032e-01 8.43096495e-01 -1.50394499e-01
-2.87924036e-02 -8.81104529e-01 -5.07573128e-01 -4.50305551e-01
1.71525165e-01 4.62953508e-01 3.62652034e-01 -5.19653976... | [11.360431671142578, 8.842357635498047] |
959d71c3-9939-4c0d-9af5-abcf41e2598e | a-tale-of-a-probe-and-a-parser | 2005.01641 | null | https://arxiv.org/abs/2005.01641v2 | https://arxiv.org/pdf/2005.01641v2.pdf | A Tale of a Probe and a Parser | Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training "probes" - supervised models designed to extract linguistic structure from another model's output. One such probe is the structural probe (Hewitt and Manning, 2019), ... | ['Josef Valvoda', 'Rowan Hall Maudslay', 'Adina Williams', 'Ryan Cotterell', 'Tiago Pimentel'] | 2020-05-04 | a-tale-of-a-probe-and-a-parser-1 | https://aclanthology.org/2020.acl-main.659 | https://aclanthology.org/2020.acl-main.659.pdf | acl-2020-6 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 2.72150099e-01 4.76143926e-01 -2.47011185e-01 -5.93713403e-01
-6.50102317e-01 -1.02609229e+00 7.50972569e-01 2.47198209e-01
-6.95926726e-01 6.86900258e-01 6.37617230e-01 -7.81335354e-01
-1.24534622e-01 -6.22775555e-01 -3.63459527e-01 -6.28137589e-01
2.35311352e-02 1.07695773e-01 1.54943630e-01 -2.39339903... | [10.517135620117188, 9.481322288513184] |
1cb1bd23-8969-450f-bd52-8596bfe43727 | semi-supervised-classification-with-graph | 1609.02907 | null | http://arxiv.org/abs/1609.02907v4 | http://arxiv.org/pdf/1609.02907v4.pdf | Semi-Supervised Classification with Graph Convolutional Networks | We present a scalable approach for semi-supervised learning on
graph-structured data that is based on an efficient variant of convolutional
neural networks which operate directly on graphs. We motivate the choice of our
convolutional architecture via a localized first-order approximation of
spectral graph convolutions.... | ['Max Welling', 'Thomas N. Kipf'] | 2016-09-09 | null | null | null | null | ['node-classification-on-non-homophilic', 'graph-regression'] | ['graphs', 'graphs'] | [-9.09897238e-02 6.89871609e-01 -4.10701245e-01 -3.73881310e-01
-1.27521604e-01 -5.33955216e-01 6.78766906e-01 5.79975486e-01
-6.08203560e-02 3.59659165e-01 2.57143080e-01 -7.32616842e-01
-2.57839829e-01 -1.31869984e+00 -9.86867547e-01 -5.11790253e-03
-1.02426302e+00 4.46664572e-01 1.21716321e-01 -1.81297019... | [6.916823387145996, 6.2176313400268555] |
b3ce9c43-d03d-4401-b761-aa8d02e74e61 | learning-from-label-relationships-in-human | 2207.05577 | null | https://arxiv.org/abs/2207.05577v2 | https://arxiv.org/pdf/2207.05577v2.pdf | Learning from Label Relationships in Human Affect | Human affect and mental state estimation in an automated manner, face a number of difficulties, including learning from labels with poor or no temporal resolution, learning from few datasets with little data (often due to confidentiality constraints) and, (very) long, in-the-wild videos. For these reasons, deep learnin... | ['Ioannis Patras', 'Niki Maria Foteinopoulou'] | 2022-07-12 | null | null | null | null | ['continuous-affect-estimation'] | ['computer-vision'] | [ 2.52716333e-01 2.31601045e-01 -1.61393762e-01 -5.84102035e-01
-1.03202927e+00 -3.01285833e-01 6.48632407e-01 2.84502417e-01
-6.91672444e-01 7.49097645e-01 3.48844528e-01 6.52063012e-01
-3.65478337e-01 -9.98872593e-02 -1.35336801e-01 -8.91121328e-01
-1.58801958e-01 4.51712519e-01 -3.32407802e-01 5.28095327... | [13.598389625549316, 1.9669139385223389] |
2d653c66-656a-47f6-9282-18c9bc330ca9 | calibrated-one-class-classification-for | 2207.12201 | null | https://arxiv.org/abs/2207.12201v1 | https://arxiv.org/pdf/2207.12201v1.pdf | Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection | Unsupervised time series anomaly detection is instrumental in monitoring and alarming potential faults of target systems in various domains. Current state-of-the-art time series anomaly detectors mainly focus on devising advanced neural network structures and new reconstruction/prediction learning objectives to learn d... | ['Guansong Pang', 'Yongjun Wang', 'Qing Liao', 'Songlei Jian', 'Yijie Wang', 'Hongzuo Xu'] | 2022-07-25 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 1.90140590e-01 -8.22066516e-02 -8.01955238e-02 -4.10107940e-01
-6.66089296e-01 -2.80591339e-01 4.03705508e-01 3.39729339e-01
3.91243733e-02 3.92499924e-01 -2.39976615e-01 -6.33026242e-01
-3.08792014e-02 -7.28073657e-01 -7.40412593e-01 -9.01857734e-01
-3.02998155e-01 4.10589695e-01 8.84535015e-02 -1.28272578... | [7.572902679443359, 2.3890087604522705] |
75894d3c-9f54-4b73-acd2-553623668400 | zero-shot-sketch-image-hashing | 1803.02284 | null | http://arxiv.org/abs/1803.02284v1 | http://arxiv.org/pdf/1803.02284v1.pdf | Zero-Shot Sketch-Image Hashing | Recent studies show that large-scale sketch-based image retrieval (SBIR) can
be efficiently tackled by cross-modal binary representation learning methods,
where Hamming distance matching significantly speeds up the process of
similarity search. Providing training and test data subjected to a fixed set of
pre-defined ca... | ['Yuming Shen', 'Li Liu', 'Fumin Shen', 'Ling Shao'] | 2018-03-06 | zero-shot-sketch-image-hashing-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Shen_Zero-Shot_Sketch-Image_Hashing_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_Zero-Shot_Sketch-Image_Hashing_CVPR_2018_paper.pdf | cvpr-2018-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-2.43283948e-03 -4.43516642e-01 -5.01759648e-01 -2.64794558e-01
-1.16480970e+00 -3.54373842e-01 7.56807327e-01 -7.32162893e-02
-3.23749095e-01 3.35639983e-01 -9.69337821e-02 3.14502977e-02
-4.90960687e-01 -1.00363684e+00 -6.76484108e-01 -6.64264977e-01
1.14272512e-01 5.59831262e-01 2.51948774e-01 -3.02575350... | [11.55553913116455, 0.7607250213623047] |
9ab57deb-1058-4880-8f97-f8fc70c8def3 | recolornerf-layer-decomposed-radiance-field | 2301.07958 | null | https://arxiv.org/abs/2301.07958v2 | https://arxiv.org/pdf/2301.07958v2.pdf | RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D Scenes | Radiance fields have gradually become a main representation of media. Although its appearance editing has been studied, how to achieve view-consistent recoloring in an efficient manner is still under explored. We present RecolorNeRF, a novel user-friendly color editing approach for the neural radiance fields. Our key i... | ['Qi Dou', 'Xiaoguang Han', 'Yuehao Wang', 'Bingchen Gong'] | 2023-01-19 | null | null | null | null | ['color-manipulation'] | ['computer-vision'] | [ 4.14851993e-01 -2.63607681e-01 2.79453188e-01 -4.29990351e-01
-3.38174999e-01 -6.51810527e-01 4.29777473e-01 -1.96545154e-01
-2.44377121e-01 4.70427901e-01 7.47246742e-02 -9.74904522e-02
2.58799672e-01 -8.33969951e-01 -9.16344166e-01 -7.39178360e-01
4.03943777e-01 -1.44236200e-02 7.47064352e-02 -2.43196458... | [11.410377502441406, -1.0391401052474976] |
7c5d02b6-57b0-4773-b059-21c2bcc93e7f | 4d-isip-4d-implicit-surface-interest-point | 1705.03634 | null | http://arxiv.org/abs/1705.03634v2 | http://arxiv.org/pdf/1705.03634v2.pdf | 4d isip: 4d implicit surface interest point detection | In this paper, we propose a new method to detect 4D spatiotemporal interest
points though an implicit surface, we refer to as the 4D-ISIP. We use a 3D
volume which has a truncated signed distance function(TSDF) for every voxel to
represent our 3D object model. The TSDF represents the distance between the
spatial points... | ['Changlin Xiao', 'Shirui Li', 'Alper Yilmaz', 'Hua Li'] | 2017-05-10 | null | null | null | null | ['interest-point-detection'] | ['computer-vision'] | [-2.35547125e-01 -4.37227875e-01 -7.92821869e-02 -1.21882074e-01
-1.91954821e-01 -2.30334446e-01 3.32921207e-01 -2.38490701e-01
-4.87022221e-01 2.74649829e-01 2.41914764e-02 3.00339282e-01
2.22925097e-01 -7.97980011e-01 -5.85815370e-01 -2.34675094e-01
-3.30119193e-01 2.97131956e-01 1.24923229e+00 -7.39679709... | [7.967260360717773, 0.1220424622297287] |
69c078de-fb71-44de-9029-9010ee07959a | stochastic-rising-bandits | 2212.03798 | null | https://arxiv.org/abs/2212.03798v1 | https://arxiv.org/pdf/2212.03798v1.pdf | Stochastic Rising Bandits | This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e., those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. arm). We study a particular case of the rested and restless bandits in which the arms' expected payoff is monotonically non-dec... | ['Marcello Restelli', 'Matteo Pirola', 'Francesco Trovò', 'Alberto Maria Metelli'] | 2022-12-07 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.57688099e-01 8.56489502e-03 -6.54689670e-01 -2.97124356e-01
-1.07262182e+00 -6.66201234e-01 9.58723798e-02 -2.97427773e-02
-5.41117966e-01 1.41125727e+00 -3.21631104e-01 -7.36527681e-01
-9.14380729e-01 -6.27460957e-01 -1.10665834e+00 -9.66592848e-01
-2.56758422e-01 8.33324373e-01 2.05754414e-02 9.95471925... | [4.537148475646973, 3.2817187309265137] |
ec94d39b-cea1-43dc-aa02-8afea571bbdb | composition-based-heterogeneous-graph-multi | null | null | https://aclanthology.org/2022.coling-1.594 | https://aclanthology.org/2022.coling-1.594.pdf | Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification | Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the ... | ['Yangyong Zhu', 'Yao Zhang', 'Hongrun Ren', 'Xiaosu Wang', 'Zhongchen Miao', 'Jian Gao', 'Yun Xiong', 'Hao Niu'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 8.96900892e-02 -1.05754361e-01 -2.05296740e-01 -5.36662400e-01
-2.49035671e-01 -4.04788703e-01 3.30669165e-01 2.65287519e-01
-7.66274938e-03 3.13427806e-01 4.88716751e-01 -4.09683406e-01
-6.23653270e-03 -1.16558790e+00 -5.55437565e-01 -4.19863492e-01
5.12851775e-01 1.06070764e-01 3.51101495e-02 -6.24684155... | [11.50041389465332, 6.602731704711914] |
3c16d982-0ba7-4f1a-a3bd-1a2d68811d60 | a-novel-attention-model-for-salient-structure | 2201.06174 | null | https://arxiv.org/abs/2201.06174v1 | https://arxiv.org/pdf/2201.06174v1.pdf | A novel attention model for salient structure detection in seismic volumes | A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimen... | ['Ghassan AlRegib', 'Haibin Di', 'Zhiling Long', 'Muhammad Amir Shafiq'] | 2022-01-17 | null | null | null | null | ['seismic-imaging', 'seismic-interpretation'] | ['miscellaneous', 'miscellaneous'] | [ 6.01489425e-01 7.98114110e-03 3.74184310e-01 4.16007861e-02
-5.87424278e-01 -2.72459269e-01 3.57715338e-01 3.68460000e-01
-3.87778014e-01 2.39061207e-01 5.78890085e-01 -2.56742239e-02
-2.66374588e-01 -4.58080471e-01 -4.68520612e-01 -8.93685937e-01
-3.72221440e-01 3.29409912e-03 1.12015760e+00 -3.92435431... | [9.676641464233398, -0.47169220447540283] |
2f03ef12-2da7-4c19-aa41-bd9b0bd63ad9 | adaptive-perception-transformer-for-temporal | 2208.11908 | null | https://arxiv.org/abs/2208.11908v2 | https://arxiv.org/pdf/2208.11908v2.pdf | Adaptive Perception Transformer for Temporal Action Localization | Temporal action localization aims to predict the boundary and category of each action instance in untrimmed long videos. Most of previous methods based on anchors or proposals neglect the global-local context interaction in entire video sequences. Besides, their multi-stage designs cannot generate action boundaries and... | ['Hongfa Wang', 'Weibo Gu', 'Tianjin Zhang', 'Yizheng Ouyang'] | 2022-08-25 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 4.75940883e-01 8.13455284e-02 -4.78734344e-01 -4.16310549e-01
-5.00226200e-01 -3.97692531e-01 6.48904502e-01 -4.27957922e-01
-3.25315148e-01 4.29595947e-01 7.72847176e-01 -9.40471217e-02
1.61198333e-01 -2.85885334e-01 -7.05014586e-01 -5.75842381e-01
-1.63315579e-01 -2.74035156e-01 7.24413991e-01 -5.11137843... | [8.5224027633667, 0.5764443278312683] |
24cd2f32-713e-4b0f-9115-e8f90f734f55 | spatiotemporal-multi-graph-convolution | null | null | http://202.119.24.249/cache/8/03/www-scf.usc.edu/59dc738bd683a198ef69fb39196799d7/aaai19_multi_graph_convolution.pdf | http://202.119.24.249/cache/8/03/www-scf.usc.edu/59dc738bd683a198ef69fb39196799d7/aaai19_multi_graph_convolution.pdf | Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand Forecasting | Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regi... | ['4', '4 Yan Liu 2', '1 Jieping Ye', '4 Qiang Yang', '3 Lingyu Zhang', '1', '∗2 Leye Wang', '∗1 Yaguang Li', 'Xu Geng'] | 2019-01-20 | null | null | null | conference-2019-1 | ['spatio-temporal-forecasting'] | ['time-series'] | [-5.61208487e-01 -5.34883201e-01 -5.41242242e-01 -6.00269854e-01
-4.67515379e-01 -3.49914104e-01 7.16605723e-01 6.18080907e-02
-2.94872038e-02 6.39141917e-01 6.45299017e-01 -6.07261479e-01
-2.16822088e-01 -1.00719523e+00 -7.16520786e-01 -5.39180696e-01
-5.09996474e-01 5.03457367e-01 3.56176525e-01 -6.57260060... | [6.462438106536865, 2.0686116218566895] |
d9d13fa6-0c8d-4fa9-a4e7-d8be597a5243 | exploring-the-robustness-of-large-language | 2306.14583 | null | https://arxiv.org/abs/2306.14583v1 | https://arxiv.org/pdf/2306.14583v1.pdf | Exploring the Robustness of Large Language Models for Solving Programming Problems | Using large language models (LLMs) for source code has recently gained attention. LLMs, such as Transformer-based models like Codex and ChatGPT, have been shown to be highly capable of solving a wide range of programming problems. However, the extent to which LLMs understand problem descriptions and generate programs a... | ['Jun Suzuki', 'Yusuke Oda', 'Yuki Nakamura', 'Makoto Morishita', 'Takumi Ito', 'Yutaka Watanobe', 'Atsushi Shirafuji'] | 2023-06-26 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 9.82575268e-02 -1.87705283e-03 -1.95817664e-01 -2.88268089e-01
-8.01789343e-01 -8.23568523e-01 3.92448485e-01 3.18909705e-01
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8.45673773e-03 -8.58937204e-01 -8.86003673e-01 -2.47112438e-01
-1.29384965e-01 2.21220359e-01 1.24396704e-01 -5.95296681... | [7.987740516662598, 7.703790664672852] |
7c8beda2-52b5-43c8-b0ad-3983e20d8c06 | pscc-net-progressive-spatio-channel | 2103.10596 | null | https://arxiv.org/abs/2103.10596v2 | https://arxiv.org/pdf/2103.10596v2.pdf | PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization | To defend against manipulation of image content, such as splicing, copy-move, and removal, we develop a Progressive Spatio-Channel Correlation Network (PSCC-Net) to detect and localize image manipulations. PSCC-Net processes the image in a two-path procedure: a top-down path that extracts local and global features and ... | ['Xiaoming Liu', 'Jun Chen', 'Yaojie Liu', 'Xiaohong Liu'] | 2021-03-19 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 4.73464072e-01 -7.00365901e-01 -3.41299325e-02 1.13702886e-01
-5.24204910e-01 -7.23547637e-01 3.14191490e-01 2.23601788e-01
-5.11731386e-01 -8.20341930e-02 2.25559995e-02 2.53669135e-02
2.47303173e-01 -7.09365845e-01 -7.96110332e-01 -5.22739947e-01
-4.67837960e-01 -6.24298573e-01 8.73088658e-01 -1.96637779... | [12.259861946105957, 0.9214255809783936] |
ae059c45-b0c7-4683-95fb-b7191f0b97ee | catfl-certificateless-authentication-based | 2302.00271 | null | https://arxiv.org/abs/2302.00271v1 | https://arxiv.org/pdf/2302.00271v1.pdf | CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications | Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leaving the devices. Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but... | ['Yi Li', 'YuanYuan Zhao', 'Gaolei Li'] | 2023-02-01 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.25363076e-01 1.41834974e-01 -2.15925947e-01 -3.34123224e-01
-6.66039824e-01 -8.62902403e-01 4.65425074e-01 -1.66433409e-01
-4.49085802e-01 7.67977357e-01 -6.29902035e-02 -3.53884131e-01
-7.37888785e-03 -9.77537811e-01 -5.90260267e-01 -8.69427383e-01
3.21528494e-01 1.91751122e-01 3.01893055e-01 -2.19834242... | [5.8123602867126465, 6.734630584716797] |
ee9e480c-d9b3-4168-b0cd-4bc9f36c9788 | scene-text-recognition-with-full | 2109.01034 | null | https://arxiv.org/abs/2109.01034v1 | https://arxiv.org/pdf/2109.01034v1.pdf | Scene Text recognition with Full Normalization | Scene text recognition has made significant progress in recent years and has become an important part of the work-flow. The widespread use of mobile devices opens up wide possibilities for using OCR technologies in everyday life. However, lack of training data for new research in this area remains relevant. In this art... | ['James Amelia', 'Robert Leer', 'Hessi Roma', 'Russell Elijah', 'Gerald Carl', 'Nathan Zachary'] | 2021-07-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.16525412e-01 -4.80519563e-01 -2.04649940e-01 -4.13919419e-01
-4.06200111e-01 -2.98165023e-01 8.40631366e-01 -1.79153867e-02
-4.49902415e-01 3.10464829e-01 1.39808431e-01 -2.68712699e-01
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4.97626215e-01 -8.28273520e-02 3.82357568e-01 -2.09830016... | [11.82872486114502, 2.5003867149353027] |
176da6f8-466f-4fad-8cef-4fdd1ccc7cc3 | modeling-caricature-expressions-by-3d | 2008.05714 | null | https://arxiv.org/abs/2008.05714v1 | https://arxiv.org/pdf/2008.05714v1.pdf | Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture | The problem of deforming an artist-drawn caricature according to a given normal face expression is of interest in applications such as social media, animation and entertainment. This paper presents a solution to the problem, with an emphasis on enhancing the ability to create desired expressions and meanwhile preserve ... | ['Juyong Zhang', 'Jianfei Cai', 'Keyu Chen', 'Jianmin Zheng'] | 2020-08-13 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.92981291e-01 2.55495131e-01 1.88885212e-01 -5.20249046e-02
-1.76503256e-01 -7.00841784e-01 6.28755271e-01 -6.01262510e-01
2.40543768e-01 5.46316087e-01 -1.97751373e-01 8.40600133e-02
9.03837197e-03 -1.05252922e+00 -7.09105730e-01 -7.98084915e-01
2.67239034e-01 2.82915235e-01 -3.35489251e-02 -4.62235630... | [12.443038940429688, -0.325054407119751] |
daa13aa7-5805-42a4-898f-70b7007f10d3 | lexical-characteristics-analysis-of-chinese | null | null | https://aclanthology.org/W15-3813 | https://aclanthology.org/W15-3813.pdf | Lexical Characteristics Analysis of Chinese Clinical Documents | null | ['Huilong Duan', 'Haomin Li', 'Meizhi Ju'] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.137484073638916, 3.750786304473877] |
b14a0632-eb86-4037-8061-9118670b56bb | amrs-assemble-learning-to-ensemble-with | 2306.10786 | null | https://arxiv.org/abs/2306.10786v1 | https://arxiv.org/pdf/2306.10786v1.pdf | AMRs Assemble! Learning to Ensemble with Autoregressive Models for AMR Parsing | In this paper, we examine the current state-of-the-art in AMR parsing, which relies on ensemble strategies by merging multiple graph predictions. Our analysis reveals that the present models often violate AMR structural constraints. To address this issue, we develop a validation method, and show how ensemble models can... | ['Roberto Navigli', 'Pere-Lluís Huguet Cabot', 'Abelardo Carlos Martínez Lorenzo'] | 2023-06-19 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 3.31391305e-01 5.23367643e-01 8.95521045e-02 -3.50530505e-01
-1.03841758e+00 -9.95281816e-01 2.48604968e-01 1.71342567e-01
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-6.88363612e-02 -8.96348536e-01 -5.70709348e-01 -3.48753691e-01
2.64938205e-01 4.16911155e-01 3.31862569e-01 -3.85149300... | [10.452759742736816, 9.558238983154297] |
72cdf4c2-fac3-4b50-97a2-fb9a1cc50bec | synthetic-target-domain-supervision-for-open | 2204.09248 | null | https://arxiv.org/abs/2204.09248v1 | https://arxiv.org/pdf/2204.09248v1.pdf | Synthetic Target Domain Supervision for Open Retrieval QA | Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on closed and specialized target domains such as COVID-19, and find that it lags behind standar... | ['Salim Roukos', 'Radu Florian', 'Vittorio Castelli', 'Avirup Sil', 'Rong Zhang', 'Md Arafat Sultan', 'Bhavani Iyer', 'Revanth Gangi Reddy'] | 2022-04-20 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-5.16857244e-02 4.55922037e-02 -1.14528090e-01 -1.63257960e-03
-1.86655056e+00 -8.28331828e-01 9.34694767e-01 3.32670199e-04
-5.85461020e-01 1.26232541e+00 5.57588935e-01 -4.49391395e-01
-3.58496100e-01 -6.65420353e-01 -8.33082259e-01 -1.60049096e-01
1.25147432e-01 1.29432523e+00 5.46213627e-01 -1.06662869... | [11.437810897827148, 7.845363140106201] |
2b05bb6a-68a5-4923-9868-c13955438e6c | context-aware-neural-model-for-temporal | null | null | https://aclanthology.org/P18-1049 | https://aclanthology.org/P18-1049.pdf | Context-Aware Neural Model for Temporal Information Extraction | We propose a context-aware neural network model for temporal information extraction. This model has a uniform architecture for event-event, event-timex and timex-timex pairs. A Global Context Layer (GCL), inspired by Neural Turing Machine (NTM), stores processed temporal relations in narrative order, and retrieves them... | ['Anna Rumshisky', 'Yuanliang Meng'] | 2018-07-01 | null | null | null | acl-2018-7 | ['temporal-information-extraction'] | ['natural-language-processing'] | [ 1.32047758e-01 4.98389512e-01 -4.28762168e-01 -4.02408957e-01
-5.52423477e-01 -5.89252055e-01 1.26601601e+00 4.81674790e-01
-8.42984796e-01 8.38758826e-01 7.60126173e-01 -5.63526630e-01
-3.50668520e-01 -1.08392715e+00 -6.33259594e-01 -7.81008527e-02
-6.65376246e-01 4.64750022e-01 3.52714747e-01 -2.47124344... | [9.094268798828125, 9.172510147094727] |
84cca294-aab2-4e5a-a835-e1678a354862 | aesthetic-text-logo-synthesis-via-content | 2204.02701 | null | https://arxiv.org/abs/2204.02701v1 | https://arxiv.org/pdf/2204.02701v1.pdf | Aesthetic Text Logo Synthesis via Content-aware Layout Inferring | Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this task which needs to take many factors (e.g., fonts, linguistics, topics, etc.) into consideration. In this p... | ['Zhouhui Lian', 'Hongwen Kang', 'Pengfei Xiong', 'Yexin Wang', 'Wenhan Luo', 'Guo Pu', 'Yizhi Wang'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Aesthetic_Text_Logo_Synthesis_via_Content-Aware_Layout_Inferring_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Aesthetic_Text_Logo_Synthesis_via_Content-Aware_Layout_Inferring_CVPR_2022_paper.pdf | cvpr-2022-1 | ['layout-design'] | ['computer-vision'] | [ 2.83056289e-01 -4.34952438e-01 2.65130401e-01 -3.11229140e-01
-4.28221896e-02 -6.07278645e-01 3.55677336e-01 -5.26182167e-02
1.52842514e-02 2.36150146e-01 3.62121135e-01 -4.26744014e-01
-2.36385651e-02 -6.69842362e-01 -6.76793039e-01 -4.81518000e-01
7.22506106e-01 2.60020971e-01 1.96192876e-01 -2.10067242... | [11.545196533203125, -0.30243536829948425] |
b418673b-b967-46f1-80b2-de76580d48ad | object-discovery-from-motion-guided-tokens | 2303.15555 | null | https://arxiv.org/abs/2303.15555v1 | https://arxiv.org/pdf/2303.15555v1.pdf | Object Discovery from Motion-Guided Tokens | Object discovery -- separating objects from the background without manual labels -- is a fundamental open challenge in computer vision. Previous methods struggle to go beyond clustering of low-level cues, whether handcrafted (e.g., color, texture) or learned (e.g., from auto-encoders). In this work, we augment the auto... | ['Martial Hebert', 'Adrien Gaidon', 'Yu-Xiong Wang', 'Pavel Tokmakov', 'Zhipeng Bao'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_Object_Discovery_From_Motion-Guided_Tokens_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_Object_Discovery_From_Motion-Guided_Tokens_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-discovery'] | ['computer-vision'] | [ 4.34173971e-01 1.33531585e-01 -2.34613940e-01 -3.10364723e-01
-7.57779598e-01 -8.17415595e-01 8.80072176e-01 1.86190233e-01
-4.08269614e-01 4.51216966e-01 2.46804893e-01 -2.52385825e-01
-1.65088788e-01 -5.83186507e-01 -8.67603123e-01 -6.52273536e-01
-2.07761183e-01 2.42824674e-01 4.14433360e-01 1.04258478... | [9.489168167114258, 0.5140320658683777] |
3543ce1e-07ee-4015-b911-4eac8baf4d2e | face-identification-by-means-of-a-neural-net | 2204.00305 | null | https://arxiv.org/abs/2204.00305v1 | https://arxiv.org/pdf/2204.00305v1.pdf | Face identification by means of a neural net classifier | This paper describes a novel face identification method that combines the eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in order to reduce the dimensionality of the input image, and a neural net classifier that performs the identification process. The method presented recognizes faces in the... | ['Marcos Faundez-Zanuy', 'Virginia Espinosa-Duro'] | 2022-04-01 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [-3.20095211e-01 -2.74602883e-02 1.21257193e-01 -5.44783533e-01
5.61296701e-01 -2.94835746e-01 5.60496986e-01 -7.79313445e-01
-4.50706631e-01 4.08027917e-01 -1.75645411e-01 -1.50085837e-01
3.12060546e-02 -5.45031250e-01 -1.13252020e-02 -6.48424625e-01
-2.97289509e-02 1.43418118e-01 -4.25850868e-01 -3.09941351... | [13.306941032409668, 0.9074081778526306] |
00200902-0bcb-4a1d-b8d7-d64c1e02bba9 | difformer-scalable-graph-transformers-induced | 2301.09474 | null | https://arxiv.org/abs/2301.09474v4 | https://arxiv.org/pdf/2301.09474v4.pdf | DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion | Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for uncovering the geometric structures for learning desired instance representations. To this end, we introduce an energy constrained diffusion m... | ['Junchi Yan', 'David Wipf', 'Yixuan He', 'Wentao Zhao', 'Chenxiao Yang', 'Qitian Wu'] | 2023-01-23 | null | null | null | null | ['image-text-classification'] | ['miscellaneous'] | [ 4.18852955e-01 4.64447200e-01 -4.63212401e-01 -4.56524521e-01
-4.26445633e-01 -6.67657435e-01 8.54188859e-01 2.09300086e-01
6.08078875e-02 7.34986663e-01 1.39276087e-01 -1.77617386e-01
-7.25854039e-01 -9.90030766e-01 -9.50388074e-01 -1.08845258e+00
-5.35015047e-01 8.15723777e-01 -1.36615830e-02 -2.10509494... | [6.961780071258545, 6.128242492675781] |
8af996a9-9c16-430f-9bd8-5bc1dc9d8990 | temporal-decoupling-graph-convolutional | null | null | https://ieeexplore.ieee.org/abstract/document/10113233/ | https://www.researchgate.net/profile/Mengyuan-Liu-2/publication/370452642_Temporal_Decoupling_Graph_Convolutional_Network_for_Skeleton-based_Gesture_Recognition/links/645bb28739c408339b3ace97/Temporal-Decoupling-Graph-Convolutional-Network-for-Skeleton-based-Gesture-Recognition.pdf | Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition | Skeleton-based gesture recognition methods have achieved high success using Graph Convolutional Network (GCN), which commonly uses an adjacency matrix to model the spatial topology of skeletons. However, previous methods use the same adjacency matrix for skeletons from different frames, which limits the flexibility of ... | ['Mengyuan Liu', 'Yuan Gao', 'Can Wang', 'Xinshun Wang', 'Jinfu Liu'] | 2023-05-01 | null | null | null | ieee-transactions-on-multimedia-2023-5 | ['hand-gesture-recognition', 'skeleton-based-action-recognition', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.84941709e-01 -6.32752180e-01 -2.03565091e-01 -2.78332591e-01
-3.11455250e-01 -2.90499538e-01 2.05012545e-01 -3.50542754e-01
-4.83051687e-01 2.39168316e-01 4.76898760e-01 6.93695247e-02
-5.37087396e-02 -8.76654804e-01 -4.92664665e-01 -5.81096768e-01
-3.87415111e-01 -3.71782528e-03 6.66088045e-01 -1.89205363... | [7.6502203941345215, 0.18366886675357819] |
98a97ae7-82b2-4bc8-ae21-c39a904f8359 | understanding-the-tradeoff-between-cost-and | null | null | https://aclanthology.org/2020.law-1.7 | https://aclanthology.org/2020.law-1.7.pdf | Understanding the Tradeoff between Cost and Quality of Expert Annotations for Keyphrase Extraction | Generating expert ground truth annotations of documents can be a very expensive process. However, such annotations are essential for training domain-specific keyphrase extraction models, especially when utilizing data-intensive deep learning models in unique domains such as real-estate. Therefore, it is critical to opt... | ['Ondrej Linda', 'Kai Liu', 'Saeid Balaneshin', 'Hung Chau'] | null | null | null | null | coling-law-2020-12 | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.15151040e-01 3.65123093e-01 -2.69275337e-01 -5.34645855e-01
-9.49372470e-01 -8.53497267e-01 6.95563078e-01 6.82654560e-01
-7.02493727e-01 9.51341987e-01 1.30327553e-01 -1.76152766e-01
-1.67421907e-01 -7.45024323e-01 -3.46744210e-01 -3.51104677e-01
5.00784934e-01 5.96038043e-01 -5.42682558e-02 -3.11981216... | [9.70946979522705, 4.828636646270752] |
ed3061f9-36da-4aaa-abb9-437848b99b8d | assumption-questioning-latent-copying-and | null | null | https://openreview.net/forum?id=r1lM_sA5Fm | https://openreview.net/pdf?id=r1lM_sA5Fm | Assumption Questioning: Latent Copying and Reward Exploitation in Question Generation | Question generation is an important task for improving our ability to process natural language data, with additional challenges over other sequence transformation tasks. Recent approaches use modifications to a Seq2Seq architecture inspired by advances in machine translation, but unlike translation the input and output... | ['Sebastian Riedel', 'Tom Hosking'] | 2018-09-27 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 6.13674045e-01 6.43306315e-01 -9.88387540e-02 -3.68393093e-01
-1.18256629e+00 -9.84311461e-01 9.67802584e-01 -2.31080614e-02
-6.40241206e-01 1.25143909e+00 6.16016805e-01 -3.86106461e-01
9.35653225e-02 -6.84409678e-01 -8.45727623e-01 -4.74738330e-01
3.72947991e-01 8.57703567e-01 -9.96587519e-03 -6.20240092... | [11.763516426086426, 9.047136306762695] |
73e25017-799e-4358-9618-d32ab73516ca | sparse-multi-modal-graph-transformer-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nakhli_Sparse_Multi-Modal_Graph_Transformer_With_Shared-Context_Processing_for_Representation_Learning_CVPR_2023_paper.pdf | Sparse Multi-Modal Graph Transformer With Shared-Context Processing for Representation Learning of Giga-Pixel Images | Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore explicit informa... | ['Ali Bashashati', 'Blake Gilks', 'Alexander Baras', 'Hossein Farahani', 'Haoyang Mi', 'Puria Azadi Moghadam', 'Ramin Nakhli'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['multiple-instance-learning'] | ['methodology'] | [ 3.85800838e-01 1.55616805e-01 1.39808301e-02 -5.40313050e-02
-1.32768512e+00 -4.86203462e-01 4.31109101e-01 6.35191083e-01
-3.51598024e-01 4.97241616e-01 2.23431081e-01 -3.26918066e-01
-1.97624415e-01 -7.57054806e-01 -4.83937472e-01 -1.33689523e+00
-1.43208563e-01 5.93051076e-01 3.52423012e-01 -1.69908732... | [15.104144096374512, -3.0024819374084473] |
8220c514-9ba9-4d55-9f65-bdf62962a503 | learning-non-autoregressive-models-from-2 | 2205.14521 | null | https://arxiv.org/abs/2205.14521v1 | https://arxiv.org/pdf/2205.14521v1.pdf | Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization | Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as ... | ['Lili Mou', 'Chenyang Huang', 'Puyuan Liu'] | 2022-05-28 | learning-non-autoregressive-models-from-1 | https://aclanthology.org/2022.acl-long.545 | https://aclanthology.org/2022.acl-long.545.pdf | acl-2022-5 | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.03151584e-01 5.28100908e-01 -2.11784661e-01 -3.83664817e-01
-1.52928817e+00 -4.76695627e-01 5.56929469e-01 4.92457658e-01
-3.57110202e-01 8.06011617e-01 7.65323997e-01 -1.55585125e-01
2.99988419e-01 -7.95187235e-01 -9.58321810e-01 -4.34862375e-01
3.24050725e-01 8.00008178e-01 5.45618944e-02 -2.29464397... | [12.465516090393066, 9.431222915649414] |
f433ecb2-c4d5-4cf3-8f75-ae10da610b38 | comma-icon-multilingual-gender-biased-and | null | null | https://aclanthology.org/2021.icon-multigen.1 | https://aclanthology.org/2021.icon-multigen.1.pdf | ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Task at ICON-2021 | This paper presents the findings of the ICON-2021 shared task on Multilingual Gender Biased and Communal Language Identification, which aims to identify aggression, gender bias, and communal bias in data presented in four languages: Meitei, Bangla, Hindi and English. The participants were presented the option of approa... | ['Akanksha Bansal', 'Bornini Lahiri', 'Yogesh Dawer', 'Akash Bhagat', 'Laishram Niranjana Devi', 'Enakshi Nandi', 'Siddharth Singh', 'Shyam Ratan', 'Ritesh Kumar'] | null | null | null | null | icon-2021-12 | ['aggression-identification'] | ['natural-language-processing'] | [-7.03364611e-01 1.27247885e-01 -8.23785141e-02 -6.22272730e-01
-7.92198777e-01 -7.24235475e-01 8.57613981e-01 3.17354977e-01
-8.27203453e-01 9.84004796e-01 2.23047987e-01 -4.67238396e-01
-2.06857055e-01 -2.25059152e-01 6.39172364e-03 -5.58310449e-01
2.36361414e-01 1.07890010e+00 -3.82740125e-02 -5.54195762... | [9.093523025512695, 10.490554809570312] |
1665f9db-2867-4f05-8956-1756f03aaebb | performance-of-humans-in-iris-recognition-the | 1807.05245 | null | http://arxiv.org/abs/1807.05245v2 | http://arxiv.org/pdf/1807.05245v2.pdf | Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification | This paper advances the state of the art in human examination of iris images
by (1) assessing the impact of different iris conditions in identity
verification, and (2) introducing an annotation step that improves the accuracy
of people's decisions. In a first experimental session, 114 subjects were asked
to decide if p... | ['Kevin W. Bowyer', 'Adam Czajka', 'Mateusz Trokielewicz', 'Daniel Moreira', 'Patrick J. Flynn'] | 2018-07-13 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [ 3.90636384e-01 4.21821140e-02 1.59269303e-01 -4.37083036e-01
-4.58767772e-01 -8.16448092e-01 4.39694494e-01 1.71818241e-01
-5.08773923e-01 7.14374185e-01 8.45642935e-04 -4.84945536e-01
-2.01639950e-01 -2.41823226e-01 -3.74562413e-01 -7.49214172e-01
5.92670739e-02 4.99349594e-01 -3.97257134e-02 2.56447762... | [3.741464376449585, -3.6319007873535156] |
ff5600ff-f51e-4d74-9999-0761dd167dbe | strategic-resource-selection-with-homophilic | 2305.00843 | null | https://arxiv.org/abs/2305.00843v1 | https://arxiv.org/pdf/2305.00843v1.pdf | Strategic Resource Selection with Homophilic Agents | The strategic selection of resources by selfish agents is a classic research direction, with Resource Selection Games and Congestion Games as prominent examples. In these games, agents select available resources and their utility then depends on the number of agents using the same resources. This implies that there is ... | ['Alexander Skopalik', 'Pascal Lenzner', 'Simon Krogmann', 'Jonathan Gadea Harder'] | 2023-05-01 | null | null | null | null | ['type'] | ['speech'] | [-5.20226717e-01 2.86492944e-01 -5.21052063e-01 3.35220575e-01
4.62381728e-02 -8.89266670e-01 -1.02198660e-01 -1.06691934e-01
-1.02039862e+00 1.02640522e+00 -3.53741199e-02 -1.39210775e-01
-4.33330864e-01 -1.30596471e+00 -1.68836325e-01 -8.44781458e-01
-3.90967846e-01 6.97710454e-01 2.13984460e-01 -4.46816832... | [4.296154499053955, 2.9792773723602295] |
f087b6d3-8c7f-4e74-8a93-ac17fccaac48 | developmental-reinforcement-learning-of | 2007.07793 | null | https://arxiv.org/abs/2007.07793v1 | https://arxiv.org/pdf/2007.07793v1.pdf | Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors | In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robo... | ['Ali A. Minai', 'Rumit Kumar', 'Aditya M. Deshpande', 'Manish Kumar'] | 2020-07-15 | null | null | null | null | ['developmental-learning', 'drone-controller'] | ['robots', 'robots'] | [-1.87193006e-01 4.54084873e-01 9.70238000e-02 3.40979040e-01
2.96468318e-01 -8.70093346e-01 4.21309948e-01 -2.32628897e-01
-5.47424197e-01 1.17458820e+00 -5.95105827e-01 -3.66759330e-01
-5.45181632e-01 -6.63671315e-01 -9.35087204e-01 -9.83617961e-01
-1.62321359e-01 4.40990627e-01 3.05846274e-01 -9.06363666... | [4.804466247558594, 1.8025959730148315] |
7598a695-da87-45b3-9eb3-d6b0098f3057 | learning-utterance-level-representations | 2211.00523 | null | https://arxiv.org/abs/2211.00523v1 | https://arxiv.org/pdf/2211.00523v1.pdf | Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis | This paper proposes an Expressive Speech Synthesis model that utilizes token-level latent prosodic variables in order to capture and control utterance-level attributes, such as character acting voice and speaking style. Current works aim to explicitly factorize such fine-grained and utterance-level speech attributes in... | ['Pirros Tsiakoulis', 'Aimilios Chalamandaris', 'Spyros Raptis', 'Inchul Hwang', 'June Sig Sung', 'Georgia Maniati', 'Nikolaos Ellinas', 'Konstantinos Klapsas', 'Karolos Nikitaras'] | 2022-11-01 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [ 1.73644304e-01 4.88227367e-01 -3.59364599e-01 -5.93558311e-01
-9.98184860e-01 -5.83160460e-01 5.81077695e-01 4.26111892e-02
1.53520554e-02 6.42774045e-01 9.72796559e-01 1.21689767e-01
-2.74846703e-03 -6.25372529e-01 -3.03191721e-01 -7.33146608e-01
1.71792842e-02 4.66171861e-01 -2.54199624e-01 4.74272668... | [14.89428424835205, 6.637550354003906] |
b2a697ca-4751-4d74-931d-3bc94d6d331e | comformer-continual-learning-in-semantic-and | 2211.13999 | null | https://arxiv.org/abs/2211.13999v1 | https://arxiv.org/pdf/2211.13999v1.pdf | CoMFormer: Continual Learning in Semantic and Panoptic Segmentation | Continual learning for segmentation has recently seen increasing interest. However, all previous works focus on narrow semantic segmentation and disregard panoptic segmentation, an important task with real-world impacts. %a In this paper, we present the first continual learning model capable of operating on both semant... | ['Arthur Douillard', 'Matthieu Cord', 'Fabio Cermelli'] | 2022-11-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cermelli_CoMFormer_Continual_Learning_in_Semantic_and_Panoptic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cermelli_CoMFormer_Continual_Learning_in_Semantic_and_Panoptic_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', 'continual-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.83869940e-01 -6.55144155e-02 -1.32414877e-01 -5.48859775e-01
-6.97923124e-01 -8.90844762e-01 6.12008989e-01 1.44353688e-01
-6.75476372e-01 5.52030742e-01 -2.38218278e-01 -3.80682588e-01
-4.17695306e-02 -7.98883200e-01 -9.51101363e-01 -5.98820925e-01
1.34600699e-01 7.11185515e-01 7.72932112e-01 1.05128184... | [9.409417152404785, 1.880855679512024] |
bd62065c-97c9-49ee-8fdb-92a13c127313 | monocular-3d-object-detection-leveraging | 1904.01690 | null | http://arxiv.org/abs/1904.01690v1 | http://arxiv.org/pdf/1904.01690v1.pdf | Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction | We present MonoPSR, a monocular 3D object detection method that leverages
proposals and shape reconstruction. First, using the fundamental relations of a
pinhole camera model, detections from a mature 2D object detector are used to
generate a 3D proposal per object in a scene. The 3D location of these
proposals prove t... | ['Steven L. Waslander', 'Jason Ku', 'Alex D. Pon'] | 2019-04-02 | monocular-3d-object-detection-leveraging-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ku_Monocular_3D_Object_Detection_Leveraging_Accurate_Proposals_and_Shape_Reconstruction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ku_Monocular_3D_Object_Detection_Leveraging_Accurate_Proposals_and_Shape_Reconstruction_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-2.26994529e-01 -1.71195835e-01 -1.44203469e-01 -2.50486612e-01
-7.68140256e-01 -7.99369335e-01 6.22007370e-01 -9.01184883e-03
-6.33365154e-01 2.79495627e-01 -2.54920036e-01 -2.41844460e-01
5.76609850e-01 -3.25658113e-01 -1.03635192e+00 -4.59750533e-01
8.57195929e-02 5.48952222e-01 5.63622653e-01 2.44179308... | [7.716462135314941, -2.5901308059692383] |
395aa190-c9ac-405e-b4fe-944d54901484 | fine-grained-urban-flow-inference | 2002.02318 | null | https://arxiv.org/abs/2002.02318v1 | https://arxiv.org/pdf/2002.02318v1.pdf | Fine-Grained Urban Flow Inference | The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we present an approach for infer... | ['Yu Zheng', 'Kun Ouyang', 'Zekun Tong', 'Yuxuan Liang', 'Ye Liu', 'Sijie Ruan', 'David S. Rosenblum'] | 2020-02-05 | null | null | null | null | ['fine-grained-urban-flow-inference'] | ['miscellaneous'] | [-2.21312076e-01 -2.66533583e-01 -1.15721188e-01 -3.87175769e-01
-5.75100839e-01 -1.57790929e-01 7.79982328e-01 1.65597424e-01
-1.14728130e-01 1.11136448e+00 5.32388449e-01 -5.47923565e-01
-1.83893397e-01 -1.61610425e+00 -4.26772892e-01 -4.42938954e-01
-2.45301172e-01 3.09088707e-01 5.89827538e-01 -1.53494716... | [6.4544596672058105, 2.1297760009765625] |
77b14756-c71a-4e99-ba40-c5efac173f40 | google-usm-scaling-automatic-speech | 2303.01037 | null | https://arxiv.org/abs/2303.01037v2 | https://arxiv.org/pdf/2303.01037v2.pdf | Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages | We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smalle... | ['Yonghui Wu', 'Françoise Beaufays', 'Johan Schalkwyk', 'Chung-Cheng Chiu', 'Pedro Moreno', 'Tara Sainath', 'Bhuvana Ramabhadran', 'Trevor Strohman', 'Hagen Soltau', 'Ginger Perng', 'Jason Riesa', 'Parisa Haghani', 'Daniel S. Park', 'Rohit Prabhavalkar', 'Andrew Rosenberg', 'Ke Hu', 'Zhong Meng', 'Gary Wang', 'Vera Axe... | 2023-03-02 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.79064631e-01 3.76617283e-01 -4.76610690e-01 -5.18831313e-01
-1.78688896e+00 -7.38498867e-01 8.33047032e-01 -3.43225718e-01
-5.00765502e-01 6.52070165e-01 6.38389587e-01 -1.00101006e+00
6.55669212e-01 -1.62465423e-01 -9.14673567e-01 -1.71755895e-01
2.17652738e-01 9.33839381e-01 -1.21651471e-01 -3.02053064... | [14.391278266906738, 7.073164939880371] |
60305f8d-b5e5-4761-9ed8-265636e4563c | grammar-based-concept-alignment-for-domain | null | null | https://aclanthology.org/2021.cnl-1.2 | https://aclanthology.org/2021.cnl-1.2.pdf | Grammar-Based Concept Alignment for Domain-Specific Machine Translation | null | ['Aarne Ranta', 'Arianna Masciolini'] | null | null | null | null | cnl-2021-9 | ['concept-alignment'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.387082099914551, 3.806807041168213] |
37b54a5b-9515-4ac9-8fd2-8c7fb27881d9 | masked-autoencoders-in-3d-point-cloud | 2207.01545 | null | https://arxiv.org/abs/2207.01545v1 | https://arxiv.org/pdf/2207.01545v1.pdf | Masked Autoencoders in 3D Point Cloud Representation Learning | Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised learning based upon masking local surface patches for 3D point cloud data has been under-explored. In th... | ['Meili Wang', 'Richard Dazeley', 'Lizhi Zhao', 'Xuequan Lu', 'Jincen Jiang'] | 2022-07-04 | null | null | null | null | ['point-cloud-completion', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.38300568e-01 1.53984666e-01 -5.50760925e-02 -4.14559513e-01
-8.84086430e-01 -4.34965014e-01 3.79485190e-01 -2.71110028e-01
1.19827725e-01 2.46038586e-01 -1.76008388e-01 -9.07794312e-02
-5.83541244e-02 -1.02976263e+00 -1.27987659e+00 -8.36734772e-01
-1.23619653e-01 4.17285204e-01 2.82455757e-02 -1.15005402... | [8.097677230834961, -3.4093072414398193] |
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