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ee2114e9-539a-41ff-8504-3d0e32c0df58 | a-fixed-viewpoint-approach-for-dense | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Han_A_Fixed_Viewpoint_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Han_A_Fixed_Viewpoint_2015_CVPR_paper.pdf | A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects | This paper addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this paper, we develop a fixed viewpoint approach for dense surface reconstruction of transparent objects based on refraction of light. We introduce a simple setup that allows us alter the incident light paths before light rays enter the object, and develop a method for recovering the object surface based on reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the shape of the object nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a complex shape and structure, with unknown and even inhomogeneous refractive index. Experimental results on both synthetic and real data are presented which demonstrate the feasibility and accuracy of our proposed approach. | ['Kwan-Yee K. Wong', 'Miaomiao Liu', 'Kai Han'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['transparent-objects'] | ['computer-vision'] | [ 6.38616681e-01 5.33009432e-02 7.61883616e-01 -2.40428805e-01
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c0e21506-5e9c-43a0-b4ef-acfcad2b09e1 | optimal-order-simple-regret-for-gaussian | 2108.09262 | null | https://arxiv.org/abs/2108.09262v1 | https://arxiv.org/pdf/2108.09262v1.pdf | Optimal Order Simple Regret for Gaussian Process Bandits | Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$. The problem can be cast as a Gaussian Process (GP) bandit where $f$ lives in a reproducing kernel Hilbert space (RKHS). The state of the art analysis of several learning algorithms shows a significant gap between the lower and upper bounds on the simple regret performance. When $N$ is the number of exploration trials and $\gamma_N$ is the maximal information gain, we prove an $\tilde{\mathcal{O}}(\sqrt{\gamma_N/N})$ bound on the simple regret performance of a pure exploration algorithm that is significantly tighter than the existing bounds. We show that this bound is order optimal up to logarithmic factors for the cases where a lower bound on regret is known. To establish these results, we prove novel and sharp confidence intervals for GP models applicable to RKHS elements which may be of broader interest. | ['Da-Shan Shiu', 'Alberto Bernacchia', 'Sepehr Jalali', 'Nacime Bouziani', 'Sattar Vakili'] | 2021-08-20 | null | http://proceedings.neurips.cc/paper/2021/hash/b1300291698eadedb559786c809cc592-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b1300291698eadedb559786c809cc592-Paper.pdf | neurips-2021-12 | ['art-analysis'] | ['computer-vision'] | [ 2.86267251e-01 6.16840541e-01 -8.84248987e-02 -2.54846126e-01
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6acc9f40-1093-4330-a8b4-e0b91fa4040d | frustum-voxnet-for-3d-object-detection-from | 1910.05483 | null | https://arxiv.org/abs/1910.05483v3 | https://arxiv.org/pdf/1910.05483v3.pdf | Frustum VoxNet for 3D object detection from RGB-D or Depth images | Recently, there have been a plethora of classification and detection systems from RGB as well as 3D images. In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system first detects objects in 2D (either RGB or pseudo-RGB constructed from depth). The next step is to detect 3D objects within the 3D frustums these 2D detections define. This is achieved by voxelizing parts of the frustums (since frustums can be really large), instead of using the whole frustums as done in earlier work. The main novelty of our system has to do with determining which parts (3D proposals) of the frustums to voxelize, thus allowing us to provide high resolution representations around the objects of interest. It also allows our system to have reduced memory requirements. These 3D proposals are fed to an efficient ResNet-based 3D Fully Convolutional Network (FCN). Our 3D detection system is fast and can be integrated into a robotics platform. With respect to systems that do not perform voxelization (such as PointNet), our methods can operate without the requirement of subsampling of the datasets. We have also introduced a pipelining approach that further improves the efficiency of our system. Results on SUN RGB-D dataset show that our system, which is based on a small network, can process 20 frames per second with comparable detection results to the state-of-the-art, achieving a 2 times speedup. | ['Ioannis Stamos', 'Xiaoke Shen'] | 2019-10-12 | null | null | null | null | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [-7.00875446e-02 6.95876926e-02 4.52462643e-01 -4.71524149e-03
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603cd483-92c3-4228-ade6-4fe3d9eb12a1 | do-pedestrians-pay-attention-eye-contact | 2112.04212 | null | https://arxiv.org/abs/2112.04212v1 | https://arxiv.org/pdf/2112.04212v1.pdf | Do Pedestrians Pay Attention? Eye Contact Detection in the Wild | In urban or crowded environments, humans rely on eye contact for fast and efficient communication with nearby people. Autonomous agents also need to detect eye contact to interact with pedestrians and safely navigate around them. In this paper, we focus on eye contact detection in the wild, i.e., real-world scenarios for autonomous vehicles with no control over the environment or the distance of pedestrians. We introduce a model that leverages semantic keypoints to detect eye contact and show that this high-level representation (i) achieves state-of-the-art results on the publicly-available dataset JAAD, and (ii) conveys better generalization properties than leveraging raw images in an end-to-end network. To study domain adaptation, we create LOOK: a large-scale dataset for eye contact detection in the wild, which focuses on diverse and unconstrained scenarios for real-world generalization. The source code and the LOOK dataset are publicly shared towards an open science mission. | ['Alexandre Alahi', 'Taylor Mordan', 'Romain Caristan', 'Lorenzo Bertoni', 'Younes Belkada'] | 2021-12-08 | null | null | null | null | ['contact-detection'] | ['robots'] | [-3.36633623e-01 -3.74293700e-02 1.99723169e-01 -5.40596485e-01
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ea102e7c-3eb3-43ec-9e64-3b1a83feec96 | lorral-facial-action-unit-detection-based-on | 2009.10892 | null | https://arxiv.org/abs/2009.10892v3 | https://arxiv.org/pdf/2009.10892v3.pdf | HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning | The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual \textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7\% and 61.8\% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task. | ['Zhongling Liu', 'and Kentaro Murase', 'Rujie Liu', 'Ziqiang Shi', 'Xiaoyu Mi', 'Liu Liu'] | 2020-09-23 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.55053192e-01 2.06307188e-01 1.34383917e-01 -4.94651347e-01
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9e7b2f6f-d21a-4953-9d1b-8543a2bd1c7e | sciwing-a-software-toolkit-for-scientific-1 | null | null | https://aclanthology.org/2020.sdp-1.13 | https://aclanthology.org/2020.sdp-1.13.pdf | SciWING– A Software Toolkit for Scientific Document Processing | We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo. | ['Min-Yen Kan', 'Abhinav Ramesh Kashyap'] | null | null | null | null | emnlp-sdp-2020-11 | ['citation-intent-classification'] | ['natural-language-processing'] | [-2.28576303e-01 -1.19031049e-01 -2.34474987e-01 -6.68024659e-01
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e79f9cd6-5ac8-49f5-89e9-2b97514240d3 | pointtrack-for-effective-online-multi-object | 2007.01549 | null | https://arxiv.org/abs/2007.01549v1 | https://arxiv.org/pdf/2007.01549v1.pdf | PointTrack++ for Effective Online Multi-Object Tracking and Segmentation | Multiple-object tracking and segmentation (MOTS) is a novel computer vision task that aims to jointly perform multiple object tracking (MOT) and instance segmentation. In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework. To begin with, PointTrack adopts an efficient one-stage framework for instance segmentation, and learns instance embeddings by converting compact image representations to un-ordered 2D point cloud. Compared with PointTrack, our proposed PointTrack++ offers three major improvements. Firstly, in the instance segmentation stage, we adopt a semantic segmentation decoder trained with focal loss to improve the instance selection quality. Secondly, to further boost the segmentation performance, we propose a data augmentation strategy by copy-and-paste instances into training images. Finally, we introduce a better training strategy in the instance association stage to improve the distinguishability of learned instance embeddings. The resulting framework achieves the state-of-the-art performance on the 5th BMTT MOTChallenge. | ['Wei zhang', 'Errui Ding', 'Xiangbo Su', 'Wei Yang', 'Shilei Wen', 'Zhenbo Xu', 'Hongwu Zhang', 'Yuchen Yuan', 'Xiao Tan', 'Liusheng Huang'] | 2020-07-03 | null | null | null | null | ['online-multi-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.60273534e-01 4.21502665e-02 -2.58367479e-01 -3.38950753e-01
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c9763ba9-1e1d-473d-846f-b90da8410345 | an-extensible-framework-for-verification-of | null | null | https://aclanthology.org/E17-3010 | https://aclanthology.org/E17-3010.pdf | An Extensible Framework for Verification of Numerical Claims | In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge. We focused on simple numerical claims such as {``}population of Germany in 2015 was 80 million{''} which comprised a quarter of the test instances in the challenge, achieving 68{\%} accuracy. Our system extends previous work on semantic parsing and claim identification to handle temporal expressions and knowledge bases consisting of multiple tables, while relying solely on automatically generated training data. We demonstrate the extensible nature of our system by evaluating it on relations used in previous work. We make our system publicly available so that it can be used and extended by the community. | ['James Thorne', 'Andreas Vlachos'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['rumour-detection'] | ['natural-language-processing'] | [ 6.71206862e-02 6.48336232e-01 -5.40009379e-01 -1.39306709e-01
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cde20b25-8f0d-4c4d-9967-2907d7c24805 | geo-referencing-place-from-everyday-natural | 1710.03346 | null | http://arxiv.org/abs/1710.03346v1 | http://arxiv.org/pdf/1710.03346v1.pdf | Geo-referencing Place from Everyday Natural Language Descriptions | Natural language place descriptions in everyday communication provide a rich
source of spatial knowledge about places. An important step to utilize such
knowledge in information systems is geo-referencing all the places referred to
in these descriptions. Current techniques for geo-referencing places from text
documents are using place name recognition and disambiguation; however, place
descriptions often contain place references that are not known by gazetteers,
or that are expressed in other, more flexible ways. Hence, the approach for
geo-referencing presented in this paper starts from a place graph that contains
the place references as well as spatial relationships extracted from place
descriptions. Spatial relationships are used to constrain the locations of
places and allow the later best-matching process for geo-referencing. The novel
geo-referencing process results in higher precision and recall compared to
state-of-art toponym resolution approaches on several tested place description
datasets. | ['Hao Chen', 'Stephan Winter', 'Maria Vasardani'] | 2017-10-09 | null | null | null | null | ['toponym-resolution'] | ['natural-language-processing'] | [-1.51740566e-01 -1.37595132e-01 -1.18488565e-01 -3.77641082e-01
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b0aaeca3-86fb-4b97-82bd-90438b01a883 | deep-learning-of-fmri-big-data-a-novel | 1502.00093 | null | http://arxiv.org/abs/1502.00093v1 | http://arxiv.org/pdf/1502.00093v1.pdf | Deep learning of fMRI big data: a novel approach to subject-transfer decoding | As a technology to read brain states from measurable brain activities, brain
decoding are widely applied in industries and medical sciences. In spite of
high demands in these applications for a universal decoder that can be applied
to all individuals simultaneously, large variation in brain activities across
individuals has limited the scope of many studies to the development of
individual-specific decoders. In this study, we used deep neural network (DNN),
a nonlinear hierarchical model, to construct a subject-transfer decoder. Our
decoder is the first successful DNN-based subject-transfer decoder. When
applied to a large-scale functional magnetic resonance imaging (fMRI) database,
our DNN-based decoder achieved higher decoding accuracy than other baseline
methods, including support vector machine (SVM). In order to analyze the
knowledge acquired by this decoder, we applied principal sensitivity analysis
(PSA) to the decoder and visualized the discriminative features that are common
to all subjects in the dataset. Our PSA successfully visualized the
subject-independent features contributing to the subject-transferability of the
trained decoder. | ['Sotetsu Koyamada', 'Shin Ishii', 'Yumi Shikauchi', 'Masanori Koyama', 'Ken Nakae'] | 2015-01-31 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 5.17273009e-01 -1.43801486e-02 7.39245415e-02 -3.54362100e-01
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f0502fe4-e83c-4a53-ab95-4527d5126f50 | ranking-and-sampling-in-open-domain-question | null | null | https://aclanthology.org/D19-1245 | https://aclanthology.org/D19-1245.pdf | Ranking and Sampling in Open-Domain Question Answering | Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs. Existing approaches always follow the distantly supervised setup where some of the paragraphs are wrong-labeled (noisy), and mainly utilize the paragraph-question relevance to denoise. However, the paragraph-paragraph relevance, which may aggregate the evidence among relevant paragraphs, can also be utilized to discover more useful paragraphs. Moreover, current approaches mainly focus on the positive paragraphs which are known to contain the answer during training. This will affect the generalization ability of the model and make it be disturbed by the similar but irrelevant (distracting) paragraphs during testing. In this paper, we first introduce a ranking model leveraging the paragraph-question and the paragraph-paragraph relevance to compute a confidence score for each paragraph. Furthermore, based on the scores, we design a modified weighted sampling strategy for training to mitigate the influence of the noisy and distracting paragraphs. Experiments on three public datasets (Quasar-T, SearchQA and TriviaQA) show that our model advances the state of the art. | ['Weiping Wang', 'Dan Meng', 'Zheng Lin', 'Yanfu Xu', 'Rui Liu', 'Yuanxin Liu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['triviaqa'] | ['miscellaneous'] | [-2.47714520e-01 3.21355999e-01 1.24036092e-02 -4.61816102e-01
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1c0f5b78-a3f8-4b7b-a1b4-1c1fa4b0a417 | task-specific-scene-structure-representations | 2301.00555 | null | https://arxiv.org/abs/2301.00555v1 | https://arxiv.org/pdf/2301.00555v1.pdf | Task-specific Scene Structure Representations | Understanding the informative structures of scenes is essential for low-level vision tasks. Unfortunately, it is difficult to obtain a concrete visual definition of the informative structures because influences of visual features are task-specific. In this paper, we propose a single general neural network architecture for extracting task-specific structure guidance for scenes. To do this, we first analyze traditional spectral clustering methods, which computes a set of eigenvectors to model a segmented graph forming small compact structures on image domains. We then unfold the traditional graph-partitioning problem into a learnable network, named \textit{Scene Structure Guidance Network (SSGNet)}, to represent the task-specific informative structures. The SSGNet yields a set of coefficients of eigenvectors that produces explicit feature representations of image structures. In addition, our SSGNet is light-weight ($\sim$ 55K parameters), and can be used as a plug-and-play module for off-the-shelf architectures. We optimize the SSGNet without any supervision by proposing two novel training losses that enforce task-specific scene structure generation during training. Our main contribution is to show that such a simple network can achieve state-of-the-art results for several low-level vision applications including joint upsampling and image denoising. We also demonstrate that our SSGNet generalizes well on unseen datasets, compared to existing methods which use structural embedding frameworks. Our source codes are available at https://github.com/jsshin98/SSGNet. | ['Hae-Gon Jeon', 'Seunghyun Shin', 'Jisu Shin'] | 2023-01-02 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 4.79727924e-01 9.26802680e-02 1.14057124e-01 -4.76527154e-01
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22a4f14e-5358-4e7d-a4a2-45d553133947 | mltr-multi-label-classification-with | 2106.06195 | null | https://arxiv.org/abs/2106.06195v1 | https://arxiv.org/pdf/2106.06195v1.pdf | MlTr: Multi-label Classification with Transformer | The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects with high conditional probability are still the main bottlenecks of previous convolutional neural network(CNN) based models, limited by convolutional kernels' representational capacity. Recent vision transformer networks utilize the self-attention mechanism to extract the feature of pixel granularity, which expresses richer local semantic information, while is insufficient for mining global spatial dependence. In this paper, we point out the three crucial problems that CNN-based methods encounter and explore the possibility of conducting specific transformer modules to settle them. We put forward a Multi-label Transformer architecture(MlTr) constructed with windows partitioning, in-window pixel attention, cross-window attention, particularly improving the performance of multi-label image classification tasks. The proposed MlTr shows state-of-the-art results on various prevalent multi-label datasets such as MS-COCO, Pascal-VOC, and NUS-WIDE with 88.5%, 95.8%, and 65.5% respectively. The code will be available soon at https://github.com/starmemda/MlTr/ | ['Honglin Liu', 'Nian Shi', 'Zhongyuan Wang', 'Dong Shen', 'Fan Yang', 'Xiangyu Wu', 'Hezheng Lin', 'Xing Cheng'] | 2021-06-11 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.05265844e-01 -2.60208428e-01 -3.22160065e-01 -3.96809310e-01
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d1794029-9180-423b-b095-246237840821 | il-net-using-expert-knowledge-to-guide-the | 1809.05127 | null | http://arxiv.org/abs/1809.05127v1 | http://arxiv.org/pdf/1809.05127v1.pdf | IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks | Deep neural networks (DNN) excel at extracting patterns. Through
representation learning and automated feature engineering on large datasets,
such models have been highly successful in computer vision and natural language
applications. Designing optimal network architectures from a principled or
rational approach however has been less than successful, with the best
successful approaches utilizing an additional machine learning algorithm to
tune the network hyperparameters. However, in many technical fields, there
exist established domain knowledge and understanding about the subject matter.
In this work, we develop a novel furcated neural network architecture that
utilizes domain knowledge as high-level design principles of the network. We
demonstrate proof-of-concept by developing IL-Net, a furcated network for
predicting the properties of ionic liquids, which is a class of complex
multi-chemicals entities. Compared to existing state-of-the-art approaches, we
show that furcated networks can improve model accuracy by approximately 20-35%,
without using additional labeled data. Lastly, we distill two key design
principles for furcated networks that can be adapted to other domains. | ['Wesley Beckner', 'Jim Pfaendtner', 'Khushmeen Sakloth', 'Garrett B. Goh'] | 2018-09-13 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.35942358e-01 1.88272223e-01 -4.75366414e-01 -4.27134007e-01
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53309515-3c4c-4e77-81f1-c3bfb7e7b068 | why-does-zero-shot-cross-lingual-generation | 2305.17325 | null | https://arxiv.org/abs/2305.17325v1 | https://arxiv.org/pdf/2305.17325v1.pdf | Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution | Zero-shot cross-lingual transfer is when a multilingual model is trained to perform a task in one language and then is applied to another language. Although the zero-shot cross-lingual transfer approach has achieved success in various classification tasks, its performance on natural language generation tasks falls short in quality and sometimes outputs an incorrect language. In our study, we show that the fine-tuning process learns language invariant representations, which is beneficial for classification tasks but harmful for generation tasks. Motivated by this, we propose a simple method to regularize the model from learning language invariant representations and a method to select model checkpoints without a development set in the target language, both resulting in better generation quality. Experiments on three semantically diverse generation tasks show that our method reduces the accidental translation problem by 68% and improves the ROUGE-L score by 1.5 on average. | ['Kenton Murray', 'Tianjian Li'] | 2023-05-27 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.17740834e-01 1.58103719e-01 -4.03972030e-01 -4.16172981e-01
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b659e9ea-c2d7-40bf-afed-baadf1a0a587 | differentiable-dictionary-search-integrating | 2211.15524 | null | https://arxiv.org/abs/2211.15524v1 | https://arxiv.org/pdf/2211.15524v1.pdf | Differentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation | This paper describes several improvements to a new method for signal decomposition that we recently formulated under the name of Differentiable Dictionary Search (DDS). The fundamental idea of DDS is to exploit a class of powerful deep invertible density estimators called normalizing flows, to model the dictionary in a linear decomposition method such as NMF, effectively creating a bijection between the space of dictionary elements and the associated probability space, allowing a differentiable search through the dictionary space, guided by the estimated densities. As the initial formulation was a proof of concept with some practical limitations, we will present several steps towards making it scalable, hoping to improve both the computational complexity of the method and its signal decomposition capabilities. As a testbed for experimental evaluation, we choose the task of frame-level piano transcription, where the signal is to be decomposed into sources whose activity is attributed to individual piano notes. To highlight the impact of improved non-linear modelling of sources, we compare variants of our method to a linear overcomplete NMF baseline. Experimental results will show that even in the absence of additional constraints, our models produce increasingly sparse and precise decompositions, according to two pertinent evaluation measures. | ['Gerhard Widmer', 'Rainer Kelz', 'Lukáš Samuel Marták'] | 2022-11-28 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 3.20901066e-01 1.45743310e-01 -8.42821971e-02 7.69132450e-02
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d8571362-1ac4-400c-bb33-18acf1d7f354 | icurb-imitation-learning-based-detection-of | 2103.17118 | null | https://arxiv.org/abs/2103.17118v1 | https://arxiv.org/pdf/2103.17118v1.pdf | iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving | Detection of road curbs is an essential capability for autonomous driving. It can be used for autonomous vehicles to determine drivable areas on roads. Usually, road curbs are detected on-line using vehicle-mounted sensors, such as video cameras and 3-D Lidars. However, on-line detection using video cameras may suffer from challenging illumination conditions, and Lidar-based approaches may be difficult to detect far-away road curbs due to the sparsity issue of point clouds. In recent years, aerial images are becoming more and more worldwide available. We find that the visual appearances between road areas and off-road areas are usually different in aerial images, so we propose a novel solution to detect road curbs off-line using aerial images. The input to our method is an aerial image, and the output is directly a graph (i.e., vertices and edges) representing road curbs. To this end, we formulate the problem as an imitation learning problem, and design a novel network and an innovative training strategy to train an agent to iteratively find the road-curb graph. The experimental results on a public dataset confirm the effectiveness and superiority of our method. This work is accompanied with a demonstration video and a supplementary document at https://tonyxuqaq.github.io/iCurb/. | ['Ming Liu', 'Yuxiang Sun', 'Zhenhua Xu'] | 2021-03-31 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.30646208e-01 -2.73148477e-01 -2.20706448e-01 -2.86674291e-01
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1b8a2d1a-eb9f-47f8-81fe-183c84a89bed | an-iterative-clustering-algorithm-for-the | 2112.10467 | null | https://arxiv.org/abs/2112.10467v2 | https://arxiv.org/pdf/2112.10467v2.pdf | An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees | Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used to incorporate it optimally in clustering algorithms are relatively less understood. We propose a new iterative algorithm to cluster networks with side information for nodes (in the form of covariates) and show that our algorithm is optimal under the Contextual Symmetric Stochastic Block Model. Our algorithm can be applied to general Contextual Stochastic Block Models and avoids hyperparameter tuning in contrast to previously proposed methods. We confirm our theoretical results on synthetic data experiments where our algorithm significantly outperforms other methods, and show that it can also be applied to signed graphs. Finally we demonstrate the practical interest of our method on real data. | ['Christophe Biernacki', 'Hemant Tyagi', 'Guillaume Braun'] | 2021-12-20 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.02777728e-01 1.66048288e-01 -5.17855763e-01 -3.24014336e-01
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5bc87e05-c4cf-49c2-af9b-5200e2e6d118 | pos-bert-point-cloud-one-stage-bert-pre | 2204.00989 | null | https://arxiv.org/abs/2204.00989v1 | https://arxiv.org/pdf/2204.00989v1.pdf | POS-BERT: Point Cloud One-Stage BERT Pre-Training | Recently, the pre-training paradigm combining Transformer and masked language modeling has achieved tremendous success in NLP, images, and point clouds, such as BERT. However, directly extending BERT from NLP to point clouds requires training a fixed discrete Variational AutoEncoder (dVAE) before pre-training, which results in a complex two-stage method called Point-BERT. Inspired by BERT and MoCo, we propose POS-BERT, a one-stage BERT pre-training method for point clouds. Specifically, we use the mask patch modeling (MPM) task to perform point cloud pre-training, which aims to recover masked patches information under the supervision of the corresponding tokenizer output. Unlike Point-BERT, its tokenizer is extra-trained and frozen. We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process. Further, in order to learn high-level semantic representation, we combine contrastive learning to maximize the class token consistency between different transformation point clouds. Extensive experiments have demonstrated that POS-BERT can extract high-quality pre-training features and promote downstream tasks to improve performance. Using the pre-training model without any fine-tuning to extract features and train linear SVM on ModelNet40, POS-BERT achieves the state-of-the-art classification accuracy, which exceeds Point-BERT by 3.5\%. In addition, our approach has significantly improved many downstream tasks, such as fine-tuned classification, few-shot classification, part segmentation. The code and trained-models will be available at: \url{https://github.com/fukexue/POS-BERT}. | ['Manning Wang', 'Yu Qiao', 'Renrui Zhang', 'Shaolei Liu', 'Peng Gao', 'Kexue Fu'] | 2022-04-03 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [-6.68343678e-02 2.85795659e-01 -2.26036340e-01 -3.65530908e-01
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2870185a-ac0a-4a99-9014-65bc1bd8eaa1 | theme-driven-keyphrase-extraction-from-social | 2301.11508 | null | https://arxiv.org/abs/2301.11508v2 | https://arxiv.org/pdf/2301.11508v2.pdf | Theme-driven Keyphrase Extraction to Analyze Social Media Discourse | Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to identify salient concepts in social media discourse without being constrained by predefined entity classes. This paper introduces a theme-driven keyphrase extraction framework tailored for social media, a pioneering approach designed to capture clinically relevant keyphrases from user-generated health texts. Themes are defined as broad categories determined by the objectives of the extraction task. We formulate this novel task of theme-driven keyphrase extraction and demonstrate its potential for efficiently mining social media text for the use case of treatment for opioid use disorder. This paper leverages qualitative and quantitative analysis to demonstrate the feasibility of extracting actionable insights from social media data and efficiently extracting keyphrases using minimally supervised NLP models. Our contributions include the development of a novel data collection and curation framework for theme-driven keyphrase extraction and the creation of MOUD-Keyphrase, the first dataset of its kind comprising human-annotated keyphrases from a Reddit community. We also identify the scope of minimally supervised NLP models to extract keyphrases from social media data efficiently. Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task. | ['Sarah Preum', 'Joseph Gatto', 'Madhusudan Basak', 'Omar Sharif', 'William Romano'] | 2023-01-27 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 4.31278944e-01 7.56185412e-01 -8.53595614e-01 2.14070320e-01
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b28ddc1d-daad-4164-9c94-68207182022c | sf2se3-clustering-scene-flow-into-se-3 | 2209.08532 | null | https://arxiv.org/abs/2209.08532v2 | https://arxiv.org/pdf/2209.08532v2.pdf | SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection | We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms. SF2SE3 then iteratively (1) samples pixel sets to compute SE(3)-motion proposals, and (2) selects the best SE(3)-motion proposal with respect to a maximum coverage formulation. Finally, objects are formed by assigning pixels uniquely to the selected SE(3)-motions based on consistency with the input scene flow and spatial proximity. The main novelties are a more informed strategy for the sampling of motion proposals and a maximum coverage formulation for the proposal selection. We conduct evaluations on multiple datasets regarding application of SF2SE3 for scene flow estimation, object segmentation and visual odometry. SF2SE3 performs on par with the state of the art for scene flow estimation and is more accurate for segmentation and odometry. | ['Thomas Brox', 'Philipp Schröppel', 'Leonhard Sommer'] | 2022-09-18 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.91964114e-01 -2.09740117e-01 -2.01216519e-01 -1.34699866e-01
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bc90fa8f-ab04-45ea-89d0-ec4127f25ca5 | mmnet-multi-collaboration-and-multi | 2307.02733 | null | https://arxiv.org/abs/2307.02733v1 | https://arxiv.org/pdf/2307.02733v1.pdf | MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection | Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images. In response, various deepfake detection methods have been proposed to assess image authenticity. Sequential deepfake detection, which is an extension of deepfake detection, aims to identify forged facial regions with the correct sequence for recovery. Nonetheless, due to the different combinations of spatial and sequential manipulations, forged face images exhibit substantial discrepancies that severely impact detection performance. Additionally, the recovery of forged images requires knowledge of the manipulation model to implement inverse transformations, which is difficult to ascertain as relevant techniques are often concealed by attackers. To address these issues, we propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images and achieve recovery without requiring knowledge of the corresponding manipulation method. Furthermore, existing evaluation metrics only consider detection accuracy at a single inferring step, without accounting for the matching degree with ground-truth under continuous multiple steps. To overcome this limitation, we propose a novel evaluation metric called Complete Sequence Matching (CSM), which considers the detection accuracy at multiple inferring steps, reflecting the ability to detect integrally forged sequences. Extensive experiments on several typical datasets demonstrate that MMNet achieves state-of-the-art detection performance and independent recovery performance. | ['Xinbo Gao', 'Nannan Wang', 'Lin Yuan', 'Jie Li', 'Decheng Liu', 'Ruiyang Xia'] | 2023-07-06 | null | null | null | null | ['deepfake-detection', 'face-swapping'] | ['computer-vision', 'computer-vision'] | [ 5.06956220e-01 -3.87208581e-01 -2.21878644e-02 -7.30438530e-02
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e79588af-4852-4b4b-aece-bbdc47a464e5 | whose-hands-are-these-hand-detection-and-hand | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Narasimhaswamy_Whose_Hands_Are_These_Hand_Detection_and_Hand-Body_Association_in_CVPR_2022_paper.pdf | Whose Hands Are These? Hand Detection and Hand-Body Association in the Wild | We study a new problem of detecting hands and finding the location of the corresponding person for each detected hand. This task is helpful for many downstream tasks such as hand tracking and hand contact estimation. Associating hands with people is challenging in unconstrained conditions since multiple people can be present in the scene with varying overlaps and occlusions. We propose a novel end-to-end trainable convolutional network that can jointly detect hands and the body location for the corresponding person. Our method first detects a set of hands and bodies and uses a novel Hand-Body Association Network to predict association scores between them. We use these association scores to find the body location for each detected hand. We also introduce a new challenging dataset called BodyHands containing unconstrained images with hand and their corresponding body locations annotations. We conduct extensive experiments on BodyHands and another public dataset to show the effectiveness of our method. Finally, we demonstrate the benefits of hand-body association in two critical applications: hand tracking and hand contact estimation. Our experiments show that hand tracking and hand contact estimation methods can be improved significantly by reasoning about the hand-body association. | ['Minh Hoai', 'Mingzhen Huang', 'Thanh Nguyen', 'Supreeth Narasimhaswamy'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['hand-detection'] | ['computer-vision'] | [-4.32817310e-01 -3.48305672e-01 -2.06833869e-01 -2.83861578e-01
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6d384604-1fde-487b-8d45-36d466b98adb | an-internal-cluster-validity-index-based-on | 2009.01328 | null | https://arxiv.org/abs/2009.01328v2 | https://arxiv.org/pdf/2009.01328v2.pdf | An Internal Cluster Validity Index Using a Distance-based Separability Measure | To evaluate clustering results is a significant part of cluster analysis. There are no true class labels for clustering in typical unsupervised learning. Thus, a number of internal evaluations, which use predicted labels and data, have been created. They are also named internal cluster validity indices (CVIs). Without true labels, to design an effective CVI is not simple because it is similar to create a clustering method. And, to have more CVIs is crucial because there is no universal CVI that can be used to measure all datasets, and no specific method for selecting a proper CVI for clusters without true labels. Therefore, to apply more CVIs to evaluate clustering results is necessary. In this paper, we propose a novel CVI - called Distance-based Separability Index (DSI), based on a data separability measure. We applied the DSI and eight other internal CVIs including early studies from Dunn (1974) to most recent studies CVDD (2019) as comparison. We used an external CVI as ground truth for clustering results of five clustering algorithms on 12 real and 97 synthetic datasets. Results show DSI is an effective, unique, and competitive CVI to other compared CVIs. In addition, we summarized the general process to evaluate CVIs and created a new method - rank difference - to compare the results of CVIs. | ['Shuyue Guan', 'Murray Loew'] | 2020-09-02 | null | null | null | null | ['clustering-algorithms-evaluation'] | ['methodology'] | [-3.33827198e-01 -2.71116495e-01 -4.56529967e-02 -5.49688756e-01
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ca1ff7e3-d4b1-41c9-9b01-9fce8ddb95fa | partially-view-aligned-representation | null | null | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-MvCLNwith-supp.pdf | http://pengxi.me/wp-content/uploads/2021/03/2021CVPR-MvCLNwith-supp.pdf | Partially View-aligned Representation Learning with Noise-robust Contrastive Loss | In real-world applications, it is common that only a portion of data is aligned across views due to spatial, temporal, or spatiotemporal asynchronism, thus leading to socalled Partially View-aligned Problem (PVP). To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss. In brief, for each sample from one view, our method aims to identify its within-category counterparts from other views, and thus the cross-view correspondence could be established. As the contrastive learning needs data pairs as input, we construct positive pairs using the known correspondences and negative pairs using random sampling. To alleviate or even eliminate the influence of the false negatives caused by random sampling, we propose a noise-robust contrastive loss which could adaptively prevent the false negatives from dominating the network optimization. Different from the Hungarian algorithm and its variants, our solution to PVP aims to achieve category- instead of instance-level alignment. Thanks to the higher accessibility and scalability of the category-level alignment, it is more desirable for the tasks such as clustering and classification. In addition, to the best of our knowledge, this could be the first successful attempt of enabling contrastive learning robust to noisy labels. Extensive experiments show the promising performance of our method comparing with 10 state-of-the-art multi-view approaches in the clustering and classification tasks. | ['Xi Peng', 'Peng Hu', 'Zitao Liu', 'Zhenyu Huang', 'Yunfan Li', 'Mouxing Yang'] | 2021-03-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Partially_View-Aligned_Representation_Learning_With_Noise-Robust_Contrastive_Loss_CVPR_2021_paper.pdf | cvpr-2021-1 | ['partially-view-aligned-multi-view-learning'] | ['computer-vision'] | [-1.06071219e-01 -2.03819051e-01 -1.39976263e-01 -4.27613556e-01
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f39034aa-bd9a-43f0-961d-6a99f06fd37a | dvcflow-modeling-information-flow-towards | 2111.10146 | null | https://arxiv.org/abs/2111.10146v1 | https://arxiv.org/pdf/2111.10146v1.pdf | DVCFlow: Modeling Information Flow Towards Human-like Video Captioning | Dense video captioning (DVC) aims to generate multi-sentence descriptions to elucidate the multiple events in the video, which is challenging and demands visual consistency, discoursal coherence, and linguistic diversity. Existing methods mainly generate captions from individual video segments, lacking adaptation to the global visual context and progressive alignment between the fast-evolved visual content and textual descriptions, which results in redundant and spliced descriptions. In this paper, we introduce the concept of information flow to model the progressive information changing across video sequence and captions. By designing a Cross-modal Information Flow Alignment mechanism, the visual and textual information flows are captured and aligned, which endows the captioning process with richer context and dynamics on event/topic evolution. Based on the Cross-modal Information Flow Alignment module, we further put forward DVCFlow framework, which consists of a Global-local Visual Encoder to capture both global features and local features for each video segment, and a pre-trained Caption Generator to produce captions. Extensive experiments on the popular ActivityNet Captions and YouCookII datasets demonstrate that our method significantly outperforms competitive baselines, and generates more human-like text according to subject and objective tests. | ['Qi Tian', 'Qingming Huang', 'Shuhui Wang', 'Zhengcong Fei', 'Xu Yan'] | 2021-11-19 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 2.01830864e-01 -1.62178069e-01 -3.52370054e-01 -2.84981310e-01
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c592d290-96aa-4bbf-885d-86e996ea7284 | embedding-logical-queries-on-knowledge-graphs | 1806.01445 | null | https://arxiv.org/abs/1806.01445v4 | https://arxiv.org/pdf/1806.01445v4.pdf | Embedding Logical Queries on Knowledge Graphs | Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that {\em might} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum. | ['Payal Bajaj', 'Marinka Zitnik', 'William L. Hamilton', 'Jure Leskovec', 'Dan Jurafsky'] | 2018-06-05 | embedding-logical-queries-on-knowledge-graphs-1 | http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs | http://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs.pdf | neurips-2018-12 | ['complex-query-answering'] | ['knowledge-base'] | [ 9.00814161e-02 6.88183784e-01 -2.90560246e-01 -2.11970091e-01
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f565a826-9da6-4e29-aa79-96de696aa73c | disp-r-cnn-stereo-3d-object-detection-via | 2004.03572 | null | https://arxiv.org/abs/2004.03572v1 | https://arxiv.org/pdf/2004.03572v1.pdf | Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation | In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images. Many recent works solve this problem by first recovering a point cloud with disparity estimation and then apply a 3D detector. The disparity map is computed for the entire image, which is costly and fails to leverage category-specific prior. In contrast, we design an instance disparity estimation network (iDispNet) that predicts disparity only for pixels on objects of interest and learns a category-specific shape prior for more accurate disparity estimation. To address the challenge from scarcity of disparity annotation in training, we propose to use a statistical shape model to generate dense disparity pseudo-ground-truth without the need of LiDAR point clouds, which makes our system more widely applicable. Experiments on the KITTI dataset show that, even when LiDAR ground-truth is not available at training time, Disp R-CNN achieves competitive performance and outperforms previous state-of-the-art methods by 20% in terms of average precision. | ['Siyu Zhang', 'Hujun Bao', 'Xiaowei Zhou', 'Qinhong Jiang', 'Jiaming Sun', 'Yiming Xie', 'Linghao Chen'] | 2020-04-07 | disp-r-cnn-stereo-3d-object-detection-via-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Disp_R-CNN_Stereo_3D_Object_Detection_via_Shape_Prior_Guided_CVPR_2020_paper.pdf | cvpr-2020-6 | ['vehicle-pose-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 1.80611819e-01 -1.82367899e-02 -5.18103875e-02 -4.22035038e-01
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4f58c37e-6246-4adc-965a-4aa313ffad66 | autonomous-overtaking-in-gran-turismo-sport | 2103.14666 | null | https://arxiv.org/abs/2103.14666v2 | https://arxiv.org/pdf/2103.14666v2.pdf | Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning | Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics, and cannot leverage the large volume of data generated by simulation or real-world driving. To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem. We evaluate our approach in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks. By leveraging curriculum learning, our approach leads to faster convergence as well as increased performance compared to vanilla reinforcement learning. As a result, the trained controller outperforms the built-in model-based game AI and achieves comparable overtaking performance with an experienced human driver. | ['Davide Scaramuzza', 'Peter Duerr', 'Elia Kaufmann', 'HaoChih Lin', 'Yunlong Song'] | 2021-03-26 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-3.21219623e-01 2.18084961e-01 -3.95846575e-01 7.74549171e-02
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213d9c04-aee8-4726-96ad-35aa9cc21901 | der-dynamically-expandable-representation-for | 2103.16788 | null | https://arxiv.org/abs/2103.16788v1 | https://arxiv.org/pdf/2103.16788v1.pdf | DER: Dynamically Expandable Representation for Class Incremental Learning | We address the problem of class incremental learning, which is a core step towards achieving adaptive vision intelligence. In particular, we consider the task setting of incremental learning with limited memory and aim to achieve better stability-plasticity trade-off. To this end, we propose a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling. Specifically, at each incremental step, we freeze the previously learned representation and augment it with additional feature dimensions from a new learnable feature extractor. This enables us to integrate new visual concepts with retaining learned knowledge. We dynamically expand the representation according to the complexity of novel concepts by introducing a channel-level mask-based pruning strategy. Moreover, we introduce an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts. We conduct extensive experiments on the three class incremental learning benchmarks and our method consistently outperforms other methods with a large margin. | ['Xuming He', 'Jiangwei Xie', 'Shipeng Yan'] | 2021-03-31 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yan_DER_Dynamically_Expandable_Representation_for_Class_Incremental_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['novel-concepts'] | ['reasoning'] | [ 4.23037231e-01 -5.36075272e-02 -3.36861193e-01 -2.42844880e-01
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8aec11a0-731f-44e2-9fc7-fa64729517d3 | indl-a-new-datasets-and-benchmark-for-in | 2305.17716 | null | https://arxiv.org/abs/2305.17716v4 | https://arxiv.org/pdf/2305.17716v4.pdf | InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion | This paper introduces a novel approach to evaluating deep learning models' capacity for in-diagram logic interpretation. Leveraging the intriguing realm of visual illusions, we establish a unique dataset, InDL, designed to rigorously test and benchmark these models. Deep learning has witnessed remarkable progress in domains such as computer vision and natural language processing. However, models often stumble in tasks requiring logical reasoning due to their inherent 'black box' characteristics, which obscure the decision-making process. Our work presents a new lens to understand these models better by focusing on their handling of visual illusions -- a complex interplay of perception and logic. We utilize six classic geometric optical illusions to create a comparative framework between human and machine visual perception. This methodology offers a quantifiable measure to rank models, elucidating potential weaknesses and providing actionable insights for model improvements. Our experimental results affirm the efficacy of our benchmarking strategy, demonstrating its ability to effectively rank models based on their logic interpretation ability. As part of our commitment to reproducible research, the source code and datasets will be made publicly available at https://github.com/rabbit-magic-wh/InDL | ['Xuchen Liu', 'Zhekai Duan', 'Ze Cao', 'Wenyu Wang', 'Haobo Yang'] | 2023-05-28 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 6.68266490e-02 3.81335244e-02 -9.44040418e-02 -4.34572935e-01
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d650784c-02d8-4b29-94c8-525ed1ae59c3 | content-based-image-retrieval-and-the | 2011.06490 | null | https://arxiv.org/abs/2011.06490v1 | https://arxiv.org/pdf/2011.06490v1.pdf | Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era | Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image. This scenario is called instance or object retrieval and requires matching fine-grained visual patterns between images. Semantics, however, do not play a crucial role. This brings rise to the question: Do the recent advances in instance retrieval transfer to more generic image retrieval scenarios? To answer this question, we first provide a brief overview of the most relevant milestones of instance retrieval. We then apply them to a semantic image retrieval task and find that they perform inferior to much less sophisticated and more generic methods in a setting that requires image understanding. Following this, we review existing approaches to closing this so-called semantic gap by integrating prior world knowledge. We conclude that the key problem for the further advancement of semantic image retrieval lies in the lack of a standardized task definition and an appropriate benchmark dataset. | ['Joachim Denzler', 'Björn Barz'] | 2020-11-12 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 5.38731098e-01 -2.50424147e-01 -3.44501138e-01 -3.15135866e-01
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4c77a4f0-ba52-4b15-9ed9-ca478be0c5a2 | joint-learning-of-self-representation-and | 1905.04432 | null | https://arxiv.org/abs/1905.04432v1 | https://arxiv.org/pdf/1905.04432v1.pdf | Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering | Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based methods achieve promotion in multi-view clustering, their utility is limited by the separate learning manner in which affinity matrix construction and cluster indicator estimation are isolated. In this paper, we propose to jointly learn the self-representation, continue and discrete cluster indicators in an unified model. Our model can explore the subspace structure of each view and fusion them to facilitate clustering simultaneously. Experimental results on two benchmark datasets demonstrate that our method outperforms other existing competitive multi-view clustering methods. | ['Xiao-Yuan Jing', 'Zuoyong Li', 'Songsong Wu', 'Yan Yan', 'Hao Tang', 'Zhiqiang Lu', 'Songhao Zhu'] | 2019-05-11 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-5.23162603e-01 -5.93846619e-01 -5.03228784e-01 -1.65510669e-01
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f49dfa52-908b-49be-a60e-9b46d0549a40 | meta-pu-an-arbitrary-scale-upsampling-network-1 | null | null | https://ieeexplore.ieee.org/document/9351772/ | https://ieeexplore.ieee.org/document/9351772/ | Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud | Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this work, we propose a novel method called ``Meta-PU" to firstly support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model. In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only. | ['Shuquan Ye; Dongdong Chen; Songfang Han; Ziyu Wan; Jing Liao'] | 2021-02-09 | null | null | null | null | ['point-cloud-super-resolution'] | ['computer-vision'] | [-2.06310555e-01 -1.94190353e-01 -1.19488537e-01 -1.24329589e-01
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1d58611a-1e50-478f-9462-e4e881123767 | a-cross-document-coreference-dataset-for | null | null | https://aclanthology.org/2022.lrec-1.393 | https://aclanthology.org/2022.lrec-1.393.pdf | A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports | This paper proposes a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient’s radiology reports. Our annotated corpus contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies. There are a total of 2292 mention chains. We describe the annotation process in detail, highlighting the complexities involved in creating a sizable and realistic dataset for radiology CDCR. We apply two baseline methods–string matching and transformer language models (BERT)–to identify cross-report coreferences. Our results indicate the requirement of further model development targeting better understanding of domain language and context to address this challenging and unexplored task. This dataset can serve as a resource to develop more advanced natural language processing CDCR methods in the future. This is one of the first attempts focusing on CDCR in the clinical domain and holds potential in benefiting physicians and clinical research through long-term tracking of radiology findings. | ['Kirk Roberts', 'Sunitha Mogalla', 'Atieh Pajouhi', 'Hio Cheng Lam', 'Surabhi Datta'] | null | null | null | null | lrec-2022-6 | ['cross-document-coreference-resolution', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.21496856e-01 5.47496259e-01 -5.28821588e-01 -2.83022881e-01
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0a76939a-6ad4-4d41-af58-83773b7adb0c | debate-dynamics-for-human-comprehensible-fact | 2001.03436 | null | https://arxiv.org/abs/2001.03436v1 | https://arxiv.org/pdf/2001.03436v1.pdf | Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs | We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness. | ['Jorge Andres Quintero Serna', 'Yunpu Ma', 'Mitchell Joblin', 'Martin Ringsquandl', 'Marcel Hildebrandt', 'Volker Tresp'] | 2020-01-09 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 1.50776520e-01 8.04868996e-01 -4.99415249e-01 -3.59983742e-01
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d7cf2a9a-f5eb-412e-8538-ca0aaef980f0 | improving-the-classification-of-rare-chords | 2012.07055 | null | https://arxiv.org/abs/2012.07055v2 | https://arxiv.org/pdf/2012.07055v2.pdf | Improving the Classification of Rare Chords with Unlabeled Data | In this work, we explore techniques to improve performance for rare classes in the task of Automatic Chord Recognition (ACR). We first explored the use of the focal loss in the context of ACR, which was originally proposed to improve the classification of hard samples. In parallel, we adapted a self-learning technique originally designed for image recognition to the musical domain. Our experiments show that both approaches individually (and their combination) improve the recognition of rare chords, but using only self-learning with noise addition yields the best results. | ['Claudio R. Jung', 'Rodrigo Schramm', 'Marcelo Bortolozzo'] | 2020-12-13 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 5.23615599e-01 -5.51436320e-02 2.04298988e-01 -5.14719561e-02
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03005733-dd48-415a-b5b8-ce62eda2f806 | learning-with-contrastive-examples-for-data | null | null | https://aclanthology.org/2020.coling-main.213 | https://aclanthology.org/2020.coling-main.213.pdf | Learning with Contrastive Examples for Data-to-Text Generation | Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., {``}Nikkei gains{''} is generated when {``}Nikkei drops{''} is expected. We investigate models trained on contrastive examples i.e., incorrect sentences or terms, in addition to correct ones to reduce such errors. We first create rules to produce contrastive examples from correct ones by replacing frequent crucial terms such as {``}gain{''} or {``}drop{''}. We then use learning methods with several losses that exploit contrastive examples. Experiments on the market comment generation task show that 1) exploiting contrastive examples improves the capability of generating sentences with better lexical choice, without degrading the fluency, 2) the choice of the loss function is an important factor because the performances on different metrics depend on the types of loss functions, and 3) the use of the examples produced by some specific rules further improves performance. Human evaluation also supports the effectiveness of using contrastive examples. | ['Yusuke Miyao', 'Hiroya Takamura', 'Ichiro Kobayashi', 'Keiichi Goshima', 'Hiroshi Noji', 'Kasumi Aoki', 'Tatsuya Ishigaki', 'Yui Uehara'] | 2020-12-01 | null | null | null | coling-2020-8 | ['comment-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.82807225e-01 3.27496767e-01 -8.85596313e-03 -5.29345751e-01
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a429be23-7e68-48ff-9a99-c4fd04f59a56 | unlimited-sampling-radar-a-real-time-end-to | 2306.17684 | null | https://arxiv.org/abs/2306.17684v1 | https://arxiv.org/pdf/2306.17684v1.pdf | Unlimited Sampling Radar: a Real-Time End-to-End Demonstrator | In this paper, the trade-off between the quantization noise and the dynamic range of ADCs used to acquire radar signals is revisited using the Unlimited Sensing Framework (USF) in a practical setting. Trade-offs between saturation and resolution arise in many applications, like radar, where sensors acquire signals which exhibit a high degree of variability in amplitude. To solve this issue, we propose the use of the co-design approach of the USF which acquires folded version of the signal of interest and leverages its structure to reconstruct it after its acquisition. We demonstrate that this method outperforms other standard acquisition methods for Doppler radars. Taking our theory all the way to practice, we develop a prototype USF-enabled Doppler Radar and show the clear benefits of our method. In each experiment, we show that using the USF increases sensitivity compared to a classic acquisition approach. | ['Ayush Bhandari', 'Bhavani Shankar MRR', 'Thomas Feuillen'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 6.82309389e-01 -1.73385575e-01 -1.68391839e-02 -2.59687454e-01
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ce02d0bc-50f8-4e8e-933f-01bb4fd6a02d | scaling-evidence-based-instructional-design | 2306.01006 | null | https://arxiv.org/abs/2306.01006v2 | https://arxiv.org/pdf/2306.01006v2.pdf | Scaling Evidence-based Instructional Design Expertise through Large Language Models | This paper presents a comprehensive exploration of leveraging Large Language Models (LLMs), specifically GPT-4, in the field of instructional design. With a focus on scaling evidence-based instructional design expertise, our research aims to bridge the gap between theoretical educational studies and practical implementation. We discuss the benefits and limitations of AI-driven content generation, emphasizing the necessity of human oversight in ensuring the quality of educational materials. This work is elucidated through two detailed case studies where we applied GPT-4 in creating complex higher-order assessments and active learning components for different courses. From our experiences, we provide best practices for effectively using LLMs in instructional design tasks, such as utilizing templates, fine-tuning, handling unexpected output, implementing LLM chains, citing references, evaluating output, creating rubrics, grading, and generating distractors. We also share our vision of a future recommendation system, where a customized GPT-4 extracts instructional design principles from educational studies and creates personalized, evidence-supported strategies for users' unique educational contexts. Our research contributes to understanding and optimally harnessing the potential of AI-driven language models in enhancing educational outcomes. | ['Gautam Yadav'] | 2023-05-31 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [-2.28083543e-02 1.93134710e-01 -1.14138432e-01 -6.61800578e-02
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581c8c8a-76e4-4fde-8eb8-ed5dfb4536f9 | coarse-grained-decomposition-and-fine-grained | 2101.05988 | null | https://arxiv.org/abs/2101.05988v1 | https://arxiv.org/pdf/2101.05988v1.pdf | Coarse-grained decomposition and fine-grained interaction for multi-hop question answering | Recent advances regarding question answering and reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text, requiring only single-hop reasoning. However, in actual scenarios, lots of complex queries require multi-hop reasoning. The key to the Question Answering task is semantic feature interaction between documents and questions, which is widely processed by Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the surface semantics of words in complex questions and fails to capture implied semantic feature of intermediate answers. As a result, Bi-DAF partially ignores part of the contexts related to the question and cannot extract the most important parts of multiple documents. In this paper we propose a new model architecture for multi-hop question answering, by applying two completion strategies: (1) Coarse-Grain complex question Decomposition (CGDe) strategy are introduced to decompose complex question into simple ones under the condition of without any additional annotations (2) Fine-Grained Interaction (FGIn) strategy are introduced to better represent each word in the document and extract more comprehensive and accurate sentences related to the inference path. The above two strategies are combined and tested on the SQuAD and HotpotQA datasets, and the experimental results show that our method outperforms state-of-the-art baselines. | ['Yun Liu', 'Xing Cao'] | 2021-01-15 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 4.01388146e-02 1.60298586e-01 2.90103674e-01 -5.03212154e-01
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248cd04e-f706-4426-8425-80604347e231 | clothfit-cloth-human-attribute-guided-virtual | 2306.13908 | null | https://arxiv.org/abs/2306.13908v1 | https://arxiv.org/pdf/2306.13908v1.pdf | ClothFit: Cloth-Human-Attribute Guided Virtual Try-On Network Using 3D Simulated Dataset | Online clothing shopping has become increasingly popular, but the high rate of returns due to size and fit issues has remained a major challenge. To address this problem, virtual try-on systems have been developed to provide customers with a more realistic and personalized way to try on clothing. In this paper, we propose a novel virtual try-on method called ClothFit, which can predict the draping shape of a garment on a target body based on the actual size of the garment and human attributes. Unlike existing try-on models, ClothFit considers the actual body proportions of the person and available cloth sizes for clothing virtualization, making it more appropriate for current online apparel outlets. The proposed method utilizes a U-Net-based network architecture that incorporates cloth and human attributes to guide the realistic virtual try-on synthesis. Specifically, we extract features from a cloth image using an auto-encoder and combine them with features from the user's height, weight, and cloth size. The features are concatenated with the features from the U-Net encoder, and the U-Net decoder synthesizes the final virtual try-on image. Our experimental results demonstrate that ClothFit can significantly improve the existing state-of-the-art methods in terms of photo-realistic virtual try-on results. | ['Paul Lukowicz', 'Sungho Suh', 'Kundan Sai Prabhu Thota', 'Lala Shakti Swarup Ray', 'Yunmin Cho'] | 2023-06-24 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [-1.11267015e-01 -2.33425684e-02 -2.22738236e-01 -7.61437893e-01
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b37e7e4d-80d9-4e9a-bc8d-25bf6909cb57 | strass-a-light-and-effective-method-for | 1907.07323 | null | https://arxiv.org/abs/1907.07323v1 | https://arxiv.org/pdf/1907.07323v1.pdf | STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings | This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model learns a transformation of the document embedding to minimize the similarity between the extractive summary and the ground truth summary. As the transformation is only composed of a dense layer, the training can be done on CPU, therefore, inexpensive. Moreover, inference time is short and linear according to the number of sentences. As a second contribution, we introduce the French CASS dataset, composed of judgments from the French Court of cassation and their corresponding summaries. On this dataset, our results show that our method performs similarly to the state of the art extractive methods with effective training and inferring time. | ['Cécile Pereira', 'Léo Bouscarrat', 'Thomas Peel', 'Antoine Bonnefoy'] | 2019-07-16 | strass-a-light-and-effective-method-for-1 | https://aclanthology.org/P19-2034 | https://aclanthology.org/P19-2034.pdf | acl-2019-7 | ['document-embedding', 'extractive-document-summarization'] | ['methodology', 'natural-language-processing'] | [ 4.64630902e-01 6.38322175e-01 -2.80277252e-01 -1.86937854e-01
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2aa32576-2e4b-4196-ad2f-8404a5ff3b32 | predictive-business-process-monitoring-with | 1612.02130 | null | http://arxiv.org/abs/1612.02130v2 | http://arxiv.org/pdf/1612.02130v2.pdf | Predictive Business Process Monitoring with LSTM Neural Networks | Predictive business process monitoring methods exploit logs of completed
cases of a process in order to make predictions about running cases thereof.
Existing methods in this space are tailor-made for specific prediction tasks.
Moreover, their relative accuracy is highly sensitive to the dataset at hand,
thus requiring users to engage in trial-and-error and tuning when applying them
in a specific setting. This paper investigates Long Short-Term Memory (LSTM)
neural networks as an approach to build consistently accurate models for a wide
range of predictive process monitoring tasks. First, we show that LSTMs
outperform existing techniques to predict the next event of a running case and
its timestamp. Next, we show how to use models for predicting the next task in
order to predict the full continuation of a running case. Finally, we apply the
same approach to predict the remaining time, and show that this approach
outperforms existing tailor-made methods. | ['Niek Tax', 'Marcello La Rosa', 'Ilya Verenich', 'Marlon Dumas'] | 2016-12-07 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 3.08961421e-01 -2.82936729e-02 -4.17684019e-01 -3.50645542e-01
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85555573-563b-4e49-9a40-6cd730c5f06a | optimal-transport-vs-fisher-rao-distance | 1604.08634 | null | http://arxiv.org/abs/1604.08634v2 | http://arxiv.org/pdf/1604.08634v2.pdf | Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series | We present a methodology for clustering N objects which are described by
multivariate time series, i.e. several sequences of real-valued random
variables. This clustering methodology leverages copulas which are
distributions encoding the dependence structure between several random
variables. To take fully into account the dependence information while
clustering, we need a distance between copulas. In this work, we compare
renowned distances between distributions: the Fisher-Rao geodesic distance,
related divergences and optimal transport, and discuss their advantages and
disadvantages. Applications of such methodology can be found in the clustering
of financial assets. A tutorial, experiments and implementation for
reproducible research can be found at www.datagrapple.com/Tech. | ['Sébastien Andler', 'Philippe Donnat', 'Gautier Marti', 'Frank Nielsen'] | 2016-04-28 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-6.02714479e-01 -5.95334888e-01 -2.50073359e-03 -4.78326887e-01
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e6a9a828-7d72-45b3-a709-f162e75b80a5 | deep-sequence-learning-for-accurate | 2012.00553 | null | https://arxiv.org/abs/2012.00553v1 | https://arxiv.org/pdf/2012.00553v1.pdf | Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device | Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estimating gestational age (GA). Doppler time series were collected from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed model for GA estimation is based on sequence learning by forming a temporally dependent model using a convolutional long-short-term memory network. Time-frequency features are extracted from Doppler signals and regularized before feeding to the network. The overall mean absolute GA error with respect to the last menstrual period was found to be 0.71 month, which outperforms all previous works. | ['Gari D. Clifford', 'Reza Sameni', 'Nasim Katebi'] | 2020-11-24 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.65490612e-01 -3.74509469e-02 1.42820001e-01 -4.97834593e-01
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ce3e32f1-6fe0-45b9-90f9-89ec983932a2 | textgraphs-16-natural-language-premise | null | null | https://aclanthology.org/2022.textgraphs-1.15 | https://aclanthology.org/2022.textgraphs-1.15.pdf | TextGraphs-16 Natural Language Premise Selection Task: Zero-Shot Premise Selection with Prompting Generative Language Models | Automated theorem proving can benefit a lot from methods employed in natural language processing, knowledge graphs and information retrieval: this non-trivial task combines formal languages understanding, reasoning, similarity search. We tackle this task by enhancing semantic similarity ranking with prompt engineering, which has become a new paradigm in natural language understanding. None of our approaches requires additional training. Despite encouraging results reported by prompt engineering approaches for a range of NLP tasks, for the premise selection task vanilla re-ranking by prompting GPT-3 doesn’t outperform semantic similarity ranking with SBERT, but merging of the both rankings shows better results. | ['Robert Wardenga', 'Roman Teucher', 'Liubov Kovriguina'] | null | null | null | null | coling-textgraphs-2022-10 | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.21371442e-01 6.81645334e-01 -2.02115729e-01 -5.56345358e-02
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e0fa9190-71ea-43d4-be71-612643e75f0f | robust-learning-based-incipient-slip | 2307.04011 | null | https://arxiv.org/abs/2307.04011v1 | https://arxiv.org/pdf/2307.04011v1.pdf | Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping | The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: https://sites.google.com/view/incipient-slip-detection. | ['Stephen J. Redmond', 'David Cordova Bulens', 'Robert Burke', 'Pablo Martinez Ulloa', 'Qiang Wang'] | 2023-07-08 | null | null | null | null | ['data-augmentation'] | ['methodology'] | [ 3.10547531e-01 -1.53958634e-01 -2.36321449e-01 1.13711454e-01
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8469b7cf-d3d6-44a9-a7fd-272edfdb7c6c | casein-cascading-explicit-and-implicit | 2307.00020 | null | https://arxiv.org/abs/2307.00020v1 | https://arxiv.org/pdf/2307.00020v1.pdf | CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation | Existing fine-grained intensity regulation methods rely on explicit control through predicted emotion probabilities. However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning. Especially when we attempt to mix multiple emotion intensities for specific phonemes, resulting in markedly reduced controllability and naturalness of the synthesis. To address this issue, we propose the CAScaded Explicit and Implicit coNtrol framework (CASEIN), which leverages accurate disentanglement of emotion manifolds from the reference speech to learn the implicit representation at a lower semantic level. This representation bridges the semantical gap between explicit probabilities and the synthesis model, reducing bias in learning. In experiments, our CASEIN surpasses existing methods in both controllability and naturalness. Notably, we are the first to achieve fine-grained control over the mixed intensity of multiple emotions. | ['Haiqing Chen', 'Wei Zhou', 'Zhongzhou Zhao', 'Xiongwei Wang', 'Yuhao Cui'] | 2023-06-27 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 4.04241800e-01 3.98286670e-01 -4.19932812e-01 -4.23039079e-01
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5bcb263c-29ae-4430-a041-15dcf71c39b2 | steganogan-high-capacity-image-steganography | 1901.03892 | null | http://arxiv.org/abs/1901.03892v2 | http://arxiv.org/pdf/1901.03892v2.pdf | SteganoGAN: High Capacity Image Steganography with GANs | Image steganography is a procedure for hiding messages inside pictures. While
other techniques such as cryptography aim to prevent adversaries from reading
the secret message, steganography aims to hide the presence of the message
itself. In this paper, we propose a novel technique for hiding arbitrary binary
data in images using generative adversarial networks which allow us to optimize
the perceptual quality of the images produced by our model. We show that our
approach achieves state-of-the-art payloads of 4.4 bits per pixel, evades
detection by steganalysis tools, and is effective on images from multiple
datasets. To enable fair comparisons, we have released an open source library
that is available online at https://github.com/DAI-Lab/SteganoGAN. | ['Kalyan Veeramachaneni', 'Alfredo Cuesta-Infante', 'Kevin Alex Zhang', 'Lei Xu'] | 2019-01-12 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 0.89813614 0.3657292 0.24577469 0.11848558 -0.5895426 -0.7304073
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cf4cc925-0ba0-43b2-9aac-c0a5e55b0e60 | alternative-semantic-representations-for-zero | 1706.09317 | null | http://arxiv.org/abs/1706.09317v1 | http://arxiv.org/pdf/1706.09317v1.pdf | Alternative Semantic Representations for Zero-Shot Human Action Recognition | A proper semantic representation for encoding side information is key to the
success of zero-shot learning. In this paper, we explore two alternative
semantic representations especially for zero-shot human action recognition:
textual descriptions of human actions and deep features extracted from still
images relevant to human actions. Such side information are accessible on Web
with little cost, which paves a new way in gaining side information for
large-scale zero-shot human action recognition. We investigate different
encoding methods to generate semantic representations for human actions from
such side information. Based on our zero-shot visual recognition method, we
conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic
representations . The results suggest that our proposed text- and image-based
semantic representations outperform traditional attributes and word vectors
considerably for zero-shot human action recognition. In particular, the
image-based semantic representations yield the favourable performance even
though the representation is extracted from a small number of images per class. | ['Ke Chen', 'Qian Wang'] | 2017-06-28 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.33875728e-01 1.17512770e-01 -4.62706923e-01 -3.46940219e-01
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b05eec2b-83f6-4b88-b25b-2aa499801d9e | context-aware-saliency-detection-for-image | 1910.08071 | null | https://arxiv.org/abs/1910.08071v1 | https://arxiv.org/pdf/1910.08071v1.pdf | Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks | Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual information and low-level features such as texture remain as intact as possible to the human visual system, while the output image may have different dimensions. Thus, simple methods such as scaling and cropping are not adequate for this purpose. In recent years, researchers have tried to improve the existing retargeting methods and introduce new ones. However, a specific method cannot be utilized to retarget all types of images. In other words, different images require different retargeting methods. Image retargeting has a close relationship to image saliency detection, which is relatively a new image processing task. Earlier saliency detection methods were based on local and global but low-level image information. These methods are called bottom-up methods. On the other hand, newer approaches are top-down and mixed methods that consider the high level and semantic information of the image too. In this paper, we introduce the proposed methods in both saliency detection and retargeting. For the saliency detection, the use of image context and semantic segmentation are examined, and a novel mixed bottom-up, and top-down saliency detection method is introduced. After saliency detection, a modified version of an existing retargeting method is utilized for retargeting the images. The results suggest that the proposed image retargeting pipeline has excellent performance compared to other tested methods. Also, the subjective evaluations on the Pascal dataset can be used as a retargeting quality assessment dataset for further research. | ['Shadrokh Samavi', 'Nader Karimi', 'Mahdi Ahmadi'] | 2019-10-17 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 5.09422541e-01 -1.43369168e-01 -1.13072589e-01 -3.52052301e-01
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7ce8997c-3e5c-4410-adba-9f0823db6b55 | argument-mining-a-survey | null | null | https://aclanthology.org/J19-4006 | https://aclanthology.org/J19-4006.pdf | Argument Mining: A Survey | Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general. | ['Chris Reed', 'John Lawrence'] | 2019-12-01 | null | null | null | cl-2019-12 | ['public-relations'] | ['miscellaneous'] | [ 1.37835607e-01 9.65901852e-01 -8.48492920e-01 -6.26623690e-01
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9f9545e8-be68-447d-b4d6-bb847f4f4192 | a-survey-on-deep-neural-network-partition | 2304.10020 | null | https://arxiv.org/abs/2304.10020v1 | https://arxiv.org/pdf/2304.10020v1.pdf | A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices | Deep neural network (DNN) partition is a research problem that involves splitting a DNN into multiple parts and offloading them to specific locations. Because of the recent advancement in multi-access edge computing and edge intelligence, DNN partition has been considered as a powerful tool for improving DNN inference performance when the computing resources of edge and end devices are limited and the remote transmission of data from these devices to clouds is costly. This paper provides a comprehensive survey on the recent advances and challenges in DNN partition approaches over the cloud, edge, and end devices based on a detailed literature collection. We review how DNN partition works in various application scenarios, and provide a unified mathematical model of the DNN partition problem. We developed a five-dimensional classification framework for DNN partition approaches, consisting of deployment locations, partition granularity, partition constraints, optimization objectives, and optimization algorithms. Each existing DNN partition approache can be perfectly defined in this framework by instantiating each dimension into specific values. In addition, we suggest a set of metrics for comparing and evaluating the DNN partition approaches. Based on this, we identify and discuss research challenges that have not yet been investigated or fully addressed. We hope that this work helps DNN partition researchers by highlighting significant future research directions in this domain. | ['Zhongjie Wang', 'Tonghua Su', 'Xiang He', 'Di Xu'] | 2023-04-20 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-2.06129864e-01 -2.87587047e-01 -5.88780701e-01 -2.10211441e-01
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ba702fad-9d96-4d4d-ba87-2643b24120ca | red-reinforced-encoder-decoder-networks-for | 1707.04818 | null | http://arxiv.org/abs/1707.04818v1 | http://arxiv.org/pdf/1707.04818v1.pdf | RED: Reinforced Encoder-Decoder Networks for Action Anticipation | Action anticipation aims to detect an action before it happens. Many real
world applications in robotics and surveillance are related to this predictive
capability. Current methods address this problem by first anticipating visual
representations of future frames and then categorizing the anticipated
representations to actions. However, anticipation is based on a single past
frame's representation, which ignores the history trend. Besides, it can only
anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED)
network for action anticipation. RED takes multiple history representations as
input and learns to anticipate a sequence of future representations. One
salient aspect of RED is that a reinforcement module is adopted to provide
sequence-level supervision; the reward function is designed to encourage the
system to make correct predictions as early as possible. We test RED on
TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation
and achieve state-of-the-art performance on all datasets. | ['Jiyang Gao', 'Ram Nevatia', 'Zhenheng Yang'] | 2017-07-16 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.76354682e-01 7.04579830e-01 -4.93342489e-01 -6.26780093e-01
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f31e9956-e52a-47d5-9131-2b3c2e240cf2 | learning-to-generate-product-reviews-from | null | null | https://aclanthology.org/E17-1059 | https://aclanthology.org/E17-1059.pdf | Learning to Generate Product Reviews from Attributes | Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model. | ['Li Dong', 'Furu Wei', 'Shaohan Huang', 'Ming Zhou', 'Mirella Lapata', 'Ke Xu'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['review-generation'] | ['natural-language-processing'] | [ 4.50436562e-01 2.98710823e-01 -4.14761841e-01 -1.03517497e+00
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86a25ae5-783a-4d55-904d-69d99c70da7b | graph-neural-networks-for-temperature | 2206.11776 | null | https://arxiv.org/abs/2206.11776v1 | https://arxiv.org/pdf/2206.11776v1.pdf | Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids | Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MCMs), transformers, and graph neural networks (GNNs) have shown high accuracy in predicting ACs of binary mixtures, superior to well-established models, e.g., COSMO-RS and UNIFAC. GNNs are particularly promising here as they learn a molecular graph-to-property relationship without pretraining, typically required for transformers, and are, unlike MCMs, applicable to molecules not included in training. For ILs, however, GNN applications are currently missing. Herein, we present a GNN to predict temperature-dependent infinite dilution ACs of solutes in ILs. We train the GNN on a database including more than 40,000 AC values and compare it to a state-of-the-art MCM. The GNN and MCM achieve similar high prediction performance, with the GNN additionally enabling high-quality predictions for ACs of solutions that contain ILs and solutes not considered during training. | ['Alexander Mitsos', 'Manuel Dahmen', 'Artur M. Schweidtmann', 'Karim Ben Hicham', 'Jan G. Rittig'] | 2022-06-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.81837732e-01 -4.23709214e-01 -3.33076417e-02 -8.59355256e-02
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0ab80aca-97d0-42e3-9063-e7d0219644da | neural-additive-models-for-location-scale-and | 2301.11862 | null | https://arxiv.org/abs/2301.11862v1 | https://arxiv.org/pdf/2301.11862v1.pdf | Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean | Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This lack of interpretability has led to increased research on inherently interpretable neural networks in recent years. Models such as Neural Additive Models (NAMs) achieve visual interpretability through the combination of classical statistical methods with DNNs. However, these approaches only concentrate on mean response predictions, leaving out other properties of the response distribution of the underlying data. We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models. | ['Benjamin Säfken', 'Thomas Kneib', 'René-Marcel Kruse', 'Anton Thielmann'] | 2023-01-27 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.28456900e-01 2.61753559e-01 -1.16544232e-01 -6.69467986e-01
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a4be9cc2-ba39-4a93-8403-6ef880811ab7 | unique-unsupervised-image-quality-estimation | 1810.06631 | null | http://arxiv.org/abs/1810.06631v2 | http://arxiv.org/pdf/1810.06631v2.pdf | UNIQUE: Unsupervised Image Quality Estimation | In this paper, we estimate perceived image quality using sparse
representations obtained from generic image databases through an unsupervised
learning approach. A color space transformation, a mean subtraction, and a
whitening operation are used to enhance descriptiveness of images by reducing
spatial redundancy; a linear decoder is used to obtain sparse representations;
and a thresholding stage is used to formulate suppression mechanisms in a
visual system. A linear decoder is trained with 7 GB worth of data, which
corresponds to 100,000 8x8 image patches randomly obtained from nearly 1,000
images in the ImageNet 2013 database. A patch-wise training approach is
preferred to maintain local information. The proposed quality estimator UNIQUE
is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases
and compared with thirteen quality estimators. Experimental results show that
UNIQUE is generally a top performing quality estimator in terms of accuracy,
consistency, linearity, and monotonic behavior. | ['M. Prabhushankar', 'D. Temel', 'G. AlRegib'] | 2018-10-15 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 4.10872400e-01 -4.00756598e-01 -1.61705077e-01 -3.27342629e-01
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cbec830d-fbca-41db-9695-67868065d605 | integrating-multi-type-aberrations-from-dna | 2212.05064 | null | https://arxiv.org/abs/2212.05064v1 | https://arxiv.org/pdf/2212.05064v1.pdf | Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery | Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. One the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity respectively. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication. | ['Wen Shi', 'Qian Wang', 'Yang Liu', 'Zhen Deng', 'Jianing Xi'] | 2022-12-09 | null | null | null | null | ['type'] | ['speech'] | [ 2.15707302e-01 -4.16891485e-01 -5.98775327e-01 -3.46164316e-01
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f953d91e-65bd-49a4-a972-24966c90e6bb | divide-and-conquer-a-deep-casa-approach-to | 1904.11148 | null | http://arxiv.org/abs/1904.11148v1 | http://arxiv.org/pdf/1904.11148v1.pdf | Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation | We address talker-independent monaural speaker separation from the
perspectives of deep learning and computational auditory scene analysis (CASA).
Specifically, we decompose the multi-speaker separation task into the stages of
simultaneous grouping and sequential grouping. Simultaneous grouping is first
performed in each time frame by separating the spectra of different speakers
with a permutation-invariantly trained neural network. In the second stage, the
frame-level separated spectra are sequentially grouped to different speakers by
a clustering network. The proposed deep CASA approach optimizes frame-level
separation and speaker tracking in turn, and produces excellent results for
both objectives. Experimental results on the benchmark WSJ0-2mix database show
that the new approach achieves the state-of-the-art results with a modest model
size. | ['Yuzhou Liu', 'DeLiang Wang'] | 2019-04-25 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 1.04276791e-01 -6.11452043e-01 5.40353477e-01 -2.99469203e-01
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bee7413f-b52b-4a89-a2ff-4d6595e8de11 | borm-bayesian-object-relation-model-for | 2108.00397 | null | https://arxiv.org/abs/2108.00397v1 | https://arxiv.org/pdf/2108.00397v1.pdf | BORM: Bayesian Object Relation Model for Indoor Scene Recognition | Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/hszhoushen/borm. | ['Yangsheng Xu', 'Tin Lun Lam', 'Zhenglong Sun', 'Xingchao Wang', 'Jun Cen', 'Liguang Zhou'] | 2021-08-01 | null | null | null | null | ['scene-parsing', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.84034604e-01 -1.27818346e-01 -1.50893837e-01 -9.56730545e-01
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563e71a6-bf52-4da7-9f05-639d2cf78816 | knowledge-detection-by-relevant-question-and | 2306.04938 | null | https://arxiv.org/abs/2306.04938v1 | https://arxiv.org/pdf/2306.04938v1.pdf | Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering | Visual question answering (VQA) is a Multidisciplinary research problem that pursued through practices of natural language processing and computer vision. Visual question answering automatically answers natural language questions according to the content of an image. Some testing questions require external knowledge to derive a solution. Such knowledge-based VQA uses various methods to retrieve features of image and text, and combine them to generate the answer. To generate knowledgebased answers either question dependent or image dependent knowledge retrieval methods are used. If knowledge about all the objects in the image is derived, then not all knowledge is relevant to the question. On other side only question related knowledge may lead to incorrect answers and over trained model that answers question that is irrelevant to image. Our proposed method takes image attributes and question features as input for knowledge derivation module and retrieves only question relevant knowledge about image objects which can provide accurate answers. | ['Dr. Hiteishi Diwanji', 'Param Ahir'] | 2023-06-08 | null | null | null | null | ['visual-question-answering-1'] | ['computer-vision'] | [ 2.45204985e-01 2.38905683e-01 2.40419716e-01 -3.26420873e-01
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53c50948-da68-4a82-aa89-b22a0b79c041 | neural-tensor-contractions-and-the-expressive | 2103.10293 | null | https://arxiv.org/abs/2103.10293v3 | https://arxiv.org/pdf/2103.10293v3.pdf | Neural tensor contractions and the expressive power of deep neural quantum states | We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks. The core of our results is the construction of neural-network layers that efficiently perform tensor contractions, and that use commonly adopted non-linear activation functions. The resulting deep networks feature a number of edges that closely matches the contraction complexity of the tensor networks to be approximated. In the context of many-body quantum states, this result establishes that neural-network states have strictly the same or higher expressive power than practically usable variational tensor networks. As an example, we show that all matrix product states can be efficiently written as neural-network states with a number of edges polynomial in the bond dimension and depth logarithmic in the system size. The opposite instead does not hold true, and our results imply that there exist quantum states that are not efficiently expressible in terms of matrix product states or PEPS, but that are instead efficiently expressible with neural network states. | ['Giuseppe Carleo', 'Amnon Shashua', 'Or Sharir'] | 2021-03-18 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-7.54576921e-02 5.29518545e-01 -1.17369846e-01 -1.28101021e-01
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03eab59d-de1d-4138-8135-9862bed600a8 | one-shot-face-swapping-on-megapixels | 2105.04932 | null | https://arxiv.org/abs/2105.04932v2 | https://arxiv.org/pdf/2105.04932v2.pdf | One Shot Face Swapping on Megapixels | Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc. Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and representative face swapping images to train DeepFake detection algorithms. This paper proposes the first Megapixel level method for one shot Face Swapping (or MegaFS for short). Firstly, MegaFS organizes face representation hierarchically by the proposed Hierarchical Representation Face Encoder (HieRFE) in an extended latent space to maintain more facial details, rather than compressed representation in previous face swapping methods. Secondly, a carefully designed Face Transfer Module (FTM) is proposed to transfer the identity from a source image to the target by a non-linear trajectory without explicit feature disentanglement. Finally, the swapped faces can be synthesized by StyleGAN2 with the benefits of its training stability and powerful generative capability. Each part of MegaFS can be trained separately so the requirement of our model for GPU memory can be satisfied for megapixel face swapping. In summary, complete face representation, stable training, and limited memory usage are the three novel contributions to the success of our method. Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain. The dataset is at this link. | ['Zhenan Sun', 'Chengzhong Xu', 'Jian Wang', 'Qi Li', 'Yuhao Zhu'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.pdf | cvpr-2021-1 | ['face-transfer'] | ['computer-vision'] | [ 3.55641693e-01 2.71230787e-01 -1.40608445e-01 -4.45676416e-01
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10a79d76-3d76-443a-a2e0-154df4c19189 | ot-filter-an-optimal-transport-filter-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_OT-Filter_An_Optimal_Transport_Filter_for_Learning_With_Noisy_Labels_CVPR_2023_paper.pdf | OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels | The success of deep learning is largely attributed to the training over clean data. However, data is often coupled with noisy labels in practice. Learning with noisy labels is challenging because the performance of the deep neural networks (DNN) drastically degenerates, due to confirmation bias caused by the network memorization over noisy labels. To alleviate that, a recent prominent direction is on sample selection, which retrieves clean data samples from noisy samples, so as to enhance the model's robustness and tolerance to noisy labels. In this paper, we revamp the sample selection from the perspective of optimal transport theory and propose a novel method, called the OT-Filter. The OT-Filter provides geometrically meaningful distances and preserves distribution patterns to measure the data discrepancy, thus alleviating the confirmation bias. Extensive experiments on benchmarks, such as Clothing1M and ANIMAL-10N, show that the performance of the OT- Filter outperforms its counterparts. Meanwhile, results on benchmarks with synthetic labels, such as CIFAR-10/100, show the superiority of the OT-Filter in handling data labels of high noise. | ['Xike Xie', 'Yilong Ren', 'Chuanwen Feng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['learning-with-noisy-labels', 'memorization', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-4.58820090e-02 -1.93769768e-01 1.75319180e-01 -6.67177260e-01
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2d988118-974b-4148-a409-8ec2045ab0b2 | lipreading-using-temporal-convolutional | 2001.08702 | null | https://arxiv.org/abs/2001.08702v1 | https://arxiv.org/pdf/2001.08702v1.pdf | Lipreading using Temporal Convolutional Networks | Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU) layers. In this work, we address the limitations of this model and we propose changes which further improve its performance. Firstly, the BGRU layers are replaced with Temporal Convolutional Networks (TCN). Secondly, we greatly simplify the training procedure, which allows us to train the model in one single stage. Thirdly, we show that the current state-of-the-art methodology produces models that do not generalize well to variations on the sequence length, and we addresses this issue by proposing a variable-length augmentation. We present results on the largest publicly-available datasets for isolated word recognition in English and Mandarin, LRW and LRW1000, respectively. Our proposed model results in an absolute improvement of 1.2% and 3.2%, respectively, in these datasets which is the new state-of-the-art performance. | ['Pingchuan Ma', 'Brais Martinez', 'Maja Pantic', 'Stavros Petridis'] | 2020-01-23 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 5.02631009e-01 -2.42962949e-02 -3.31880897e-01 -2.10750461e-01
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c1b8cd6a-0f48-4f3d-bde9-83a5654ba993 | fifo-learning-fog-invariant-features-for-1 | 2204.01587 | null | https://arxiv.org/abs/2204.01587v1 | https://arxiv.org/pdf/2204.01587v1.pdf | FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation | Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images with different fog conditions in neural style spaces of a segmentation model. In particular, since the neural style of an image is in general affected by other factors as well as fog, we introduce a fog-pass filter module that learns to extract a fog-relevant factor from the style. Optimizing the fog-pass filter and the segmentation model alternately gradually closes the style gap between different fog conditions and allows to learn fog-invariant features in consequence. Our method substantially outperforms previous work on three real foggy image datasets. Moreover, it improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes. | ['Suha Kwak', 'Taeyoung Son', 'Sohyun Lee'] | 2022-04-04 | fifo-learning-fog-invariant-features-for | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_FIFO_Learning_Fog-Invariant_Features_for_Foggy_Scene_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-segmentation'] | ['computer-vision'] | [ 1.80741325e-01 -4.16842163e-01 5.34709096e-01 -4.66939688e-01
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b6f45660-7edd-40cf-b0a1-3357c57677b0 | harnessing-the-power-of-large-language-models | 2305.15541 | null | https://arxiv.org/abs/2305.15541v1 | https://arxiv.org/pdf/2305.15541v1.pdf | Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation | Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for NL-FOL translation using LoRA on a single GPU. LogicLLaMA is capable of directly translating natural language into FOL rules, which outperforms GPT-3.5. LogicLLaMA is also equipped to correct FOL rules predicted by GPT-3.5, and can achieve similar performance as GPT-4 with a fraction of the cost. This correction ability was achieved by a novel supervised fine-tuning (SFT) + reinforcement learning with human feedback (RLHF) framework, which initially trains on synthetically perturbed NL-FOL pairs to encourage chain-of-thought reasoning and then fine-tunes with RLHF on GPT-3.5 outputs using a FOL verifier as the reward model. To train LogicLLaMA, we present MALLS (large language $\textbf{M}$odel gener$\textbf{A}$ted N$\textbf{L}$-FO$\textbf{L}$ pair$\textbf{S}$), a dataset of 34K high-quality and diverse sentence-level NL-FOL pairs collected from GPT-4. The dataset was created by implementing a pipeline that prompts GPT-4 for pairs, and dynamically adjusts the prompts to ensure the collection of pairs with rich and diverse contexts at different levels of complexity, and verifies the validity of the generated FOL rules. Codes, weights, and data are available at $\href{https://github.com/gblackout/LogicLLaMA}{{\small \text{https://github.com/gblackout/LogicLLaMA}}}$. | ['Faramarz Fekri', 'Ehsan Shareghi', 'Ali Payani', 'Siheng Xiong', 'Yuan Yang'] | 2023-05-24 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 5.25325052e-02 2.08665997e-01 -2.70692825e-01 -4.09105778e-01
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f0a920de-7df1-4ffb-ac84-4503d244e47b | interactive-video-corpus-moment-retrieval | 2302.09522 | null | https://arxiv.org/abs/2302.09522v1 | https://arxiv.org/pdf/2302.09522v1.pdf | Interactive Video Corpus Moment Retrieval using Reinforcement Learning | Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that maximizes the long-term reward for user comment. We conduct experiments for the challenging task of video corpus moment retrieval (VCMR) to localize moments from a large video corpus. The experimental results on TVR and DiDeMo datasets verify that our proposed work is effective in retrieving the moments that are hidden deep inside the ranked lists of CONQUER and HERO, which are the state-of-the-art auto-search engines for VCMR. | ['Chong-Wah Ngo', 'Zhixin Ma'] | 2023-02-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-2.76305974e-01 -4.76457477e-01 -5.29905379e-01 1.34582119e-02
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8eacd040-bfdb-403e-be60-9ed831056e37 | representation-and-correlation-enhanced | 2106.06960 | null | https://arxiv.org/abs/2106.06960v2 | https://arxiv.org/pdf/2106.06960v2.pdf | Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition | Attention-based encoder-decoder framework is widely used in the scene text recognition task. However, for the current state-of-the-art(SOTA) methods, there is room for improvement in terms of the efficient usage of local visual and global context information of the input text image, as well as the robust correlation between the scene processing module(encoder) and the text processing module(decoder). In this paper, we propose a Representation and Correlation Enhanced Encoder-Decoder Framework(RCEED) to address these deficiencies and break performance bottleneck. In the encoder module, local visual feature, global context feature, and position information are aligned and fused to generate a small-size comprehensive feature map. In the decoder module, two methods are utilized to enhance the correlation between scene and text feature space. 1) The decoder initialization is guided by the holistic feature and global glimpse vector exported from the encoder. 2) The feature enriched glimpse vector produced by the Multi-Head General Attention is used to assist the RNN iteration and the character prediction at each time step. Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's generalization towards changeable texts. Extensive experiments on the benchmarks demonstrate the advantageous performance of RCEED in scene text recognition tasks, especially the irregular ones. | ['Liang Wang', 'Jinjin Zhang', 'Wei Wang', 'Mengmeng Cui'] | 2021-06-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.89331573e-01 -5.48180938e-01 1.73038721e-01 -4.13454503e-01
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68ba9460-2262-44bc-9343-f0a445c033ad | gqfedwavg-optimization-based-quantized | 2306.07497 | null | https://arxiv.org/abs/2306.07497v1 | https://arxiv.org/pdf/2306.07497v1.pdf | GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems | The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers. Specifically, we first present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely GQFedWAvg. Specifically, GQFedWAvg applies the proposed quantization scheme to quantize wisely chosen model update-related vectors and adopts a generalized mini-batch stochastic gradient descent (SGD) method with the weighted average local model updates in global model aggregation. Besides, GQFedWAvg has several adjustable algorithm parameters to flexibly adapt to the computing and communication resources at the server and workers. We also analyze the convergence of GQFedWAvg. Next, we optimize the algorithm parameters of GQFedWAvg to minimize the convergence error under the time and energy constraints. We successfully tackle the challenging non-convex problem using general inner approximation (GIA) and multiple delicate tricks. Finally, we interpret GQFedWAvg's function principle and show its considerable gains over existing FL algorithms using numerical results. | ['Vincent Lau', 'Ying Cui', 'Yangchen Li'] | 2023-06-13 | null | null | null | null | ['quantization', 'edge-computing'] | ['methodology', 'time-series'] | [-4.39858824e-01 -4.08049256e-01 -2.19996929e-01 -1.55638173e-01
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83afac37-ce4a-4a03-8f48-1beffa43f819 | incorporating-structured-commonsense | 1811.00625 | null | http://arxiv.org/abs/1811.00625v1 | http://arxiv.org/pdf/1811.00625v1.pdf | Incorporating Structured Commonsense Knowledge in Story Completion | The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant. | ['Zhou Yu', 'Jianshu Chen', 'Jiaao Chen'] | 2018-11-01 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 2.18314648e-01 -2.11455643e-01 -4.89867151e-01 -4.72978115e-01
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75172067-7480-4b58-90ef-fd8b3a348b29 | blpnet-a-new-dnn-model-for-automatic-license | 2112.04752 | null | https://arxiv.org/abs/2112.04752v2 | https://arxiv.org/pdf/2112.04752v2.pdf | Modelling Lips-State Detection Using CNN for Non-Verbal Communications | Vision-based deep learning models can be promising for speech-and-hearing-impaired and secret communications. While such non-verbal communications are primarily investigated with hand-gestures and facial expressions, no research endeavour is tracked so far for the lips state (i.e., open/close)-based interpretation/translation system. In support of this development, this paper reports two new Convolutional Neural Network (CNN) models for lips state detection. Building upon two prominent lips landmark detectors, DLIB and MediaPipe, we simplify lips-state model with a set of six key landmarks, and use their distances for the lips state classification. Thereby, both the models are developed to count the opening and closing of lips and thus, they can classify a symbol with the total count. Varying frame-rates, lips-movements and face-angles are investigated to determine the effectiveness of the models. Our early experimental results demonstrate that the model with DLIB is relatively slower in terms of an average of 6 frames per second (FPS) and higher average detection accuracy of 95.25%. In contrast, the model with MediaPipe offers faster landmark detection capability with an average FPS of 20 and detection accuracy of 94.4%. Both models thus could effectively interpret the lips state for non-verbal semantics into a natural language. | ['Hossain Nyeem', 'Md. Akiful Haque Akif', 'Md. Saif Hassan Onim', 'Abtahi Ishmam', 'Mahmudul Hasan', 'Koushik Roy'] | 2021-12-09 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.34854212e-01 1.40512735e-01 -5.16081214e-01 -1.84087604e-01
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1c1a90c7-ba1f-494f-b9a9-f8a0810ea6e4 | a-case-study-in-bootstrapping-ontology-graphs | null | null | https://openreview.net/forum?id=nDe2D8DDXKR | https://openreview.net/pdf?id=nDe2D8DDXKR | A Case Study in Bootstrapping Ontology Graphs from Textbooks | Ontology graphs are graphs in which the nodes are generic classes and edges have labels that specify the relationships between the classes. In this paper, we address the question:to what extent can automated extraction and crowdsourcing techniques be combined to boostrap the creation of comprehensive and accurate ontology knowledge graphs? By adapting the state-of-the-art language model BERT to the task, and leveraging a novel relationship selection task, we show that even though it is difficult to achieve a high precision and recall, automated term extraction and crowd sourcing provide a way to bootstrap the ontology graph creation for further refinement and improvement through human effort. | ['Richard Baraniuk', 'Andrew C Waters', 'Debshila Basu Mallick', 'Han Lin Aung', 'Matthew Boggess', 'Vinay K. Chaudhri'] | 2021-06-22 | null | null | null | akbc-2021-10 | ['term-extraction'] | ['natural-language-processing'] | [ 1.29689351e-02 7.49826014e-01 -1.75801232e-01 -2.28017762e-01
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5ef29e87-13f6-405d-ab4d-5265937c2908 | real-time-mine-road-boundary-detection-and | null | null | https://www.semanticscholar.org/search?q=Real-Time%20Mine%20Road%20Boundary%20Detection%20and%20Tracking%20for%20Autonomous%20Truck&sort=relevance | https://www.semanticscholar.org/search?q=Real-Time%20Mine%20Road%20Boundary%20Detection%20and%20Tracking%20for%20Autonomous%20Truck&sort=relevance | Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck | Abstract: Road boundary detection is an important part of the perception of autonomous driving.
It is difficult to detect road boundaries of unstructured roads because there are no curbs. There are no
clear boundaries on mine roads to distinguish areas within the road boundary line and areas outside
the road boundary line. This paper proposes a real-time road boundary detection and tracking
method by a 3D-LIDAR sensor. The road boundary points are extracted from the detected elevated
point clouds above the ground point cloud according to the spatial distance characteristics and the
angular features. Road tracking is to predict and update the boundary point information in real-time,
in order to prevent false and missed detection. The experimental verification of mine road data shows
the accuracy and robustness of the proposed algorithm. | ['*', 'Yunfeng Ai1and Bin Tian3', '2', 'Xiaowei Lu1'] | 2020-01-01 | null | null | null | https-www-mdpi-com-journal-sensors-2020-1 | ['boundary-detection'] | ['computer-vision'] | [ 3.69209528e-01 -4.62411754e-02 9.03131962e-02 -4.71270144e-01
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cbdd4944-b20f-49da-84f3-12a39f79e7f5 | point-discriminative-learning-for | 2108.02104 | null | https://arxiv.org/abs/2108.02104v3 | https://arxiv.org/pdf/2108.02104v3.pdf | Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis | 3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficient learning methods. In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation. PointDisc imposes a novel point discrimination loss on the middle and global level features produced by the backbone network. This point discrimination loss enforces learned features to be consistent with points belonging to the corresponding local shape region and inconsistent with randomly sampled noisy points. We conduct extensive experiments on 3D object classification, 3D semantic and part segmentation, showing the benefits of PointDisc for data-efficient learning. Detailed analysis demonstrate that PointDisc learns unsupervised features that well capture local and global geometry. | ['Jie Lin', 'Chaitanya K. Joshi', 'Chuan-Sheng Foo', 'Guosheng Lin', 'Fayao Liu'] | 2021-08-04 | null | null | null | null | ['3d-object-classification', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.50055021e-01 -1.01382144e-01 -4.34847921e-01 -5.35649061e-01
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432a622a-7857-47fa-93d2-993f0e6dfc2b | few-shot-transfer-learning-for-device-free | 2201.12656 | null | https://arxiv.org/abs/2201.12656v1 | https://arxiv.org/pdf/2201.12656v1.pdf | Few-Shot Transfer Learning for Device-Free Fingerprinting Indoor Localization | Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of labeled data from the current environment and reuses a large amount of existing labeled data previously collected in other environments, thereby significantly reducing the data collection and labeling cost for localization in each new environment. The core method lies in graph neural network (GNN) based few-shot transfer learning and its modifications. Experimental results conducted on real-world environments show that the proposed system achieves comparable performance to a convolutional neural network (CNN) model, with 40 times fewer labeled data. | ['Ronald Y. Chang', 'Bing-Jia Chen'] | 2022-01-29 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 6.76984489e-02 -2.47010365e-01 -3.67969275e-01 -6.66891813e-01
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b4d2e13f-0de4-4f27-90be-aa9804df5f65 | distributed-scheduling-in-non-signalized | 2208.00141 | null | https://arxiv.org/abs/2208.00141v2 | https://arxiv.org/pdf/2208.00141v2.pdf | Distributed Scheduling at Non-Signalized Intersections with Mixed Cooperative and Non-Cooperative Vehicles | Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems. For fully non-cooperative systems, a minimax scheduling framework was established, while it is inefficient in mixed systems as the benefit of cooperation is not exploited. This letter focuses on the efficient scheduling in mixed systems and proposes a two-stage policy that makes full use of the cooperation relation. Specifically, a long-horizon self-organization policy is first developed to optimize the passing order of cooperative vehicles in a distributed manner, which is proved convergent when inbound roads are sufficiently long. Then a short-horizon trajectory planning policy is proposed to improve the efficiency when an ego-vehicle faces both cooperative and non-cooperative vehicles, and its safety and efficiency are theoretically validated. Furthermore, numerical simulations verify that the proposed policies can effectively reduce the scheduling cost and improve the throughput for cooperative vehicles. | ['Yuan Shen', 'Feihong Yang'] | 2022-07-30 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-5.73433280e-01 3.38801146e-01 -6.58365011e-01 -1.31437391e-01
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d382bae4-8270-4268-a167-a4b447bbb5cb | rethinking-atrous-convolution-for-semantic | 1706.05587 | null | http://arxiv.org/abs/1706.05587v3 | http://arxiv.org/pdf/1706.05587v3.pdf | Rethinking Atrous Convolution for Semantic Image Segmentation | In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scales, we design modules which employ atrous convolution in cascade
or in parallel to capture multi-scale context by adopting multiple atrous
rates. Furthermore, we propose to augment our previously proposed Atrous
Spatial Pyramid Pooling module, which probes convolutional features at multiple
scales, with image-level features encoding global context and further boost
performance. We also elaborate on implementation details and share our
experience on training our system. The proposed `DeepLabv3' system
significantly improves over our previous DeepLab versions without DenseCRF
post-processing and attains comparable performance with other state-of-art
models on the PASCAL VOC 2012 semantic image segmentation benchmark. | ['Liang-Chieh Chen', 'Florian Schroff', 'Hartwig Adam', 'George Papandreou'] | 2017-06-17 | null | null | null | null | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.66725951e-01 -4.39311936e-02 1.16567560e-01 -6.06207550e-01
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5056f42b-0819-4340-a6e1-44b952436a53 | human-level-reinforcement-learning-through | 2107.12544 | null | https://arxiv.org/abs/2107.12544v1 | https://arxiv.org/pdf/2107.12544v1.pdf | Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning | Reinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expertise at the world's oldest board games and many classic video games, but they require vast quantities of experience to learn successfully -- none of today's algorithms account for the human ability to learn so many different tasks, so quickly. Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories -- rich, abstract, causal models of physical objects, intentional agents, and their interactions -- to explore and model an environment, and plan effectively to achieve task goals. We instantiate the approach in a video game playing agent called EMPA (the Exploring, Modeling, and Planning Agent), which performs Bayesian inference to learn probabilistic generative models expressed as programs for a game-engine simulator, and runs internal simulations over these models to support efficient object-based, relational exploration and heuristic planning. EMPA closely matches human learning efficiency on a suite of 90 challenging Atari-style video games, learning new games in just minutes of game play and generalizing robustly to new game situations and new levels. The model also captures fine-grained structure in people's exploration trajectories and learning dynamics. Its design and behavior suggest a way forward for building more general human-like AI systems. | ['Joshua B. Tenenbaum', 'Samuel J. Gershman', 'Thomas Pouncy', 'Andres Campero', 'Nathan Foss', 'Jake Burga', 'Joao Loula', 'Pedro A. Tsividis'] | 2021-07-27 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.81685346e-01 3.82780164e-01 2.46620364e-02 1.67639241e-01
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1de0f373-fd80-4e62-ae4c-0b3922204cd9 | achieving-reliable-human-assessment-of-open-1 | 2203.05899 | null | https://arxiv.org/abs/2203.05899v1 | https://arxiv.org/pdf/2203.05899v1.pdf | Achieving Reliable Human Assessment of Open-Domain Dialogue Systems | Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed. Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable to yield sensible results. This is a serious problem since automatic metrics are not known to provide a good indication of what may or may not be a high-quality conversation. Answering the distress call of competitions that have emphasized the urgent need for better evaluation techniques in dialogue, we present the successful development of human evaluation that is highly reliable while still remaining feasible and low cost. Self-replication experiments reveal almost perfectly repeatable results with a correlation of $r=0.969$. Furthermore, due to the lack of appropriate methods of statistical significance testing, the likelihood of potential improvements to systems occurring due to chance is rarely taken into account in dialogue evaluation, and the evaluation we propose facilitates application of standard tests. Since we have developed a highly reliable evaluation method, new insights into system performance can be revealed. We therefore include a comparison of state-of-the-art models (i) with and without personas, to measure the contribution of personas to conversation quality, as well as (ii) prescribed versus freely chosen topics. Interestingly with respect to personas, results indicate that personas do not positively contribute to conversation quality as expected. | ['Qun Liu', 'Chenyang Lyu', 'Gareth J. F. Jones', 'Yvette Graham', 'Tianbo Ji'] | 2022-03-11 | null | https://aclanthology.org/2022.acl-long.445 | https://aclanthology.org/2022.acl-long.445.pdf | acl-2022-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-7.55537674e-02 2.87946135e-01 3.26565117e-01 -5.61136901e-01
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1006791e-8858-4264-b7ca-2cbd6f722ab0 | deep-multi-instance-networks-with-sparse-1 | 1705.08550 | null | http://arxiv.org/abs/1705.08550v1 | http://arxiv.org/pdf/1705.08550v1.pdf | Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification | Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods rely on regions of interest (ROIs) which
require great efforts to annotate. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
(MIL) for labeling a set of instances/patches, we propose end-to-end trained
deep multi-instance networks for mass classification based on whole mammogram
without the aforementioned ROIs. We explore three different schemes to
construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of
proposed networks compared to previous work using segmentation and detection
annotations. | ['Wentao Zhu', 'Yeeleng Scott Vang', 'Xiaohui Xie', 'Qi Lou'] | 2017-05-23 | null | null | null | null | ['whole-mammogram-classification'] | ['medical'] | [ 5.91484725e-01 7.41681635e-01 -3.77051502e-01 -8.88821423e-01
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1e5303a2-40b6-418f-9515-167aea895c44 | dynamic-conceptional-contrastive-learning-for | 2303.17393 | null | https://arxiv.org/abs/2303.17393v1 | https://arxiv.org/pdf/2303.17393v1.pdf | Dynamic Conceptional Contrastive Learning for Generalized Category Discovery | Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL) framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://github.com/TPCD/DCCL. | ['Nicu Sebe', 'Zhun Zhong', 'Nan Pu'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pu_Dynamic_Conceptional_Contrastive_Learning_for_Generalized_Category_Discovery_CVPR_2023_paper.pdf | cvpr-2023-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.90834885e-02 -3.01426761e-02 -2.22570866e-01 -4.00814116e-01
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829a0086-7905-4a15-b23c-0c4f63e755c5 | revisiting-transformer-for-point-cloud-based | 2303.11048 | null | https://arxiv.org/abs/2303.11048v2 | https://arxiv.org/pdf/2303.11048v2.pdf | Revisiting Transformer for Point Cloud-based 3D Scene Graph Generation | In this paper, we propose the semantic graph Transformer (SGT) for 3D scene graph generation. The task aims to parse a cloud point-based scene into a semantic structural graph, with the core challenge of modeling the complex global structure. Existing methods based on graph convolutional networks (GCNs) suffer from the over-smoothing dilemma and could only propagate information from limited neighboring nodes. In contrast, our SGT uses Transformer layers as the base building block to allow global information passing, with two types of proposed Transformer layers tailored for the 3D scene graph generation task. Specifically, we introduce the graph embedding layer to best utilize the global information in graph edges while maintaining comparable computation costs. Additionally, we propose the semantic injection layer to leverage categorical text labels and visual object knowledge. We benchmark our SGT on the established 3DSSG benchmark and achieve a 35.9% absolute improvement in relationship prediction's R@50 and an 80.4% boost on the subset with complex scenes over the state-of-the-art. Our analyses further show SGT's superiority in the long-tailed and zero-shot scenarios. We will release the code and model. | ['Huadong Ma', 'Zhengyuan Yang', 'Xia Li', 'Mengshi Qi', 'Changsheng Lv'] | 2023-03-20 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.31215522e-01 4.38548118e-01 -1.27896583e-02 -4.09555912e-01
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47689cef-2254-4b01-aa96-318cdc2ca2a9 | simplekt-a-simple-but-tough-to-beat-baseline | 2302.06881 | null | https://arxiv.org/abs/2302.06881v2 | https://arxiv.org/pdf/2302.06881v2.pdf | simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol \citep{liu2022pykt}, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 \citep{minn2018deep,yeung2018addressing}. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named \textsc{simpleKT}. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at \url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}. | ['Weiqi Luo', 'Shuyan Huang', 'Jiahao Chen', 'Qiongqiong Liu', 'Zitao Liu'] | 2023-02-14 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-2.74874538e-01 1.88198298e-01 -1.95575222e-01 -3.67652714e-01
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58e54f22-aafe-45f9-923b-455bcd89beb6 | combining-ocr-models-for-reading-early-modern | 2305.07131 | null | https://arxiv.org/abs/2305.07131v1 | https://arxiv.org/pdf/2305.07131v1.pdf | Combining OCR Models for Reading Early Modern Printed Books | In this paper, we investigate the usage of fine-grained font recognition on OCR for books printed from the 15th to the 18th century. We used a newly created dataset for OCR of early printed books for which fonts are labeled with bounding boxes. We know not only the font group used for each character, but the locations of font changes as well. In books of this period, we frequently find font group changes mid-line or even mid-word that indicate changes in language. We consider 8 different font groups present in our corpus and investigate 13 different subsets: the whole dataset and text lines with a single font, multiple fonts, Roman fonts, Gothic fonts, and each of the considered fonts, respectively. We show that OCR performance is strongly impacted by font style and that selecting fine-tuned models with font group recognition has a very positive impact on the results. Moreover, we developed a system using local font group recognition in order to combine the output of multiple font recognition models, and show that while slower, this approach performs better not only on text lines composed of multiple fonts but on the ones containing a single font only as well. | ['Vincent Christlein', 'Anguelos Nicolau', 'Florian Kordon', 'Tatjana Hass', 'Janina Molnar', 'Martin Mayr', 'Nikolaus Weichselbaumer', 'Janne van der Loop', 'Mathias Seuret'] | 2023-05-11 | null | null | null | null | ['optical-character-recognition', 'font-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.43150583e-01 -5.52179754e-01 2.82200307e-01 -3.00182968e-01
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1ee6bcb7-764f-4ef2-8ba4-bf7d470fcbce | generating-molecules-via-chemical-reactions | null | null | https://openreview.net/forum?id=BJlQEILY_N | https://openreview.net/pdf?id=BJlQEILY_N | Generating Molecules via Chemical Reactions | Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules. We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. Modeling the entire process of constructing a molecule during generation offers a number of advantages. First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions. Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes. Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product. | ['José Miguel Hernández-Lobato', 'Marwin H. S. Segler', 'Brooks Paige', 'Matt J. Kusner', 'John Bradshaw'] | 2019-03-27 | null | null | null | iclr-workshop-deepgenstruct-2019 | ['retrosynthesis'] | ['medical'] | [ 6.13499045e-01 3.26938510e-01 -2.25911677e-01 -9.69069973e-02
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444b4732-d17f-4403-82a2-468d3edc0a78 | a-structured-approach-to-predicting-image | 1704.01249 | null | http://arxiv.org/abs/1704.01249v1 | http://arxiv.org/pdf/1704.01249v1.pdf | A Structured Approach to Predicting Image Enhancement Parameters | Social networking on mobile devices has become a commonplace of everyday
life. In addition, photo capturing process has become trivial due to the
advances in mobile imaging. Hence people capture a lot of photos everyday and
they want them to be visually-attractive. This has given rise to automated,
one-touch enhancement tools. However, the inability of those tools to provide
personalized and content-adaptive enhancement has paved way for machine-learned
methods to do the same. The existing typical machine-learned methods
heuristically (e.g. kNN-search) predict the enhancement parameters for a new
image by relating the image to a set of similar training images. These
heuristic methods need constant interaction with the training images which
makes the parameter prediction sub-optimal and computationally expensive at
test time which is undesired. This paper presents a novel approach to
predicting the enhancement parameters given a new image using only its
features, without using any training images. We propose to model the
interaction between the image features and its corresponding enhancement
parameters using the matrix factorization (MF) principles. We also propose a
way to integrate the image features in the MF formulation. We show that our
approach outperforms heuristic approaches as well as recent approaches in MF
and structured prediction on synthetic as well as real-world data of image
enhancement. | ['Baoxin Li', 'Parag S. Chandakkar'] | 2017-04-05 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 5.38514972e-01 -6.57839850e-02 9.23225135e-02 -4.04601693e-01
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855e943c-ed2e-4964-bd6c-6e7731ba3c97 | energy-management-of-multi-mode-plug-in | 2303.09658 | null | https://arxiv.org/abs/2303.09658v1 | https://arxiv.org/pdf/2303.09658v1.pdf | Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning | The recently emerging multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization, and its energy management requires multiple-input and multiple-output (MIMO) control. At the present, the existing methods usually decouple the MIMO control into single-output (MISO) control and can only achieve its local optimal performance. To optimize the multi-mode vehicle globally, this paper studies a MIMO control method for energy management of the multi-mode PHEV based on multi-agent deep reinforcement learning (MADRL). By introducing a relevance ratio, a hand-shaking strategy is proposed to enable two learning agents to work collaboratively under the MADRL framework using the deep deterministic policy gradient (DDPG) algorithm. Unified settings for the DDPG agents are obtained through a sensitivity analysis of the influencing factors to the learning performance. The optimal working mode for the hand-shaking strategy is attained through a parametric study on the relevance ratio. The advantage of the proposed energy management method is demonstrated on a software-in-the-loop testing platform. The result of the study indiates that learning rate of the DDPG agents is the greatest factor in learning performance. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL method can save up to 4% energy compared to the single-agent method. | ['Quan Zhou', 'Hongming Xu', 'Xiaoli Yu', 'Zhi Li', 'Fanggang Zhang', 'Cetengfei Zhang', 'Min Hua'] | 2023-03-16 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.67811251e-01 1.59222677e-01 -3.38631868e-01 2.03308091e-01
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32d441ba-69ec-42d0-b0f4-1e7ca9c7b409 | deep-motion-prior-for-weakly-supervised | 2108.05607 | null | https://arxiv.org/abs/2108.05607v2 | https://arxiv.org/pdf/2108.05607v2.pdf | Deep Motion Prior for Weakly-Supervised Temporal Action Localization | Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize actions in untrimmed videos with only video-level labels. Currently, most state-of-the-art WSTAL methods follow a Multi-Instance Learning (MIL) pipeline: producing snippet-level predictions first and then aggregating to the video-level prediction. However, we argue that existing methods have overlooked two important drawbacks: 1) inadequate use of motion information and 2) the incompatibility of prevailing cross-entropy training loss. In this paper, we analyze that the motion cues behind the optical flow features are complementary informative. Inspired by this, we propose to build a context-dependent motion prior, termed as motionness. Specifically, a motion graph is introduced to model motionness based on the local motion carrier (e.g., optical flow). In addition, to highlight more informative video snippets, a motion-guided loss is proposed to modulate the network training conditioned on motionness scores. Extensive ablation studies confirm that motionness efficaciously models action-of-interest, and the motion-guided loss leads to more accurate results. Besides, our motion-guided loss is a plug-and-play loss function and is applicable with existing WSTAL methods. Without loss of generality, based on the standard MIL pipeline, our method achieves new state-of-the-art performance on three challenging benchmarks, including THUMOS'14, ActivityNet v1.2 and v1.3. | ['Yuexian Zou', 'Mike Zheng Shou', 'Long Chen', 'Can Zhang', 'Meng Cao'] | 2021-08-12 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.80234659e-01 -2.08434597e-01 -8.03278327e-01 -1.75944880e-01
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1940e604-bfe9-4464-9575-6ca5f7cf45d7 | intra-extra-source-exemplar-based-style | 2307.00648 | null | https://arxiv.org/abs/2307.00648v1 | https://arxiv.org/pdf/2307.00648v1.pdf | Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization | The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an exemplar-based style synthesis pipeline to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise encoder for StyleGAN2 inversion. The model learns to faithfully reconstruct the image, preserving its semantic layout through noise prediction. Using the proposed masked noise encoder to randomize style and content combinations in the training set, i.e., intra-source style augmentation (ISSA) effectively increases the diversity of training data and reduces spurious correlation. As a result, we achieve up to $12.4\%$ mIoU improvements on driving-scene semantic segmentation under different types of data shifts, i.e., changing geographic locations, adverse weather conditions, and day to night. ISSA is model-agnostic and straightforwardly applicable with CNNs and Transformers. It is also complementary to other domain generalization techniques, e.g., it improves the recent state-of-the-art solution RobustNet by $3\%$ mIoU in Cityscapes to Dark Z\"urich. In addition, we demonstrate the strong plug-n-play ability of the proposed style synthesis pipeline, which is readily usable for extra-source exemplars e.g., web-crawled images, without any retraining or fine-tuning. Moreover, we study a new use case to indicate neural network's generalization capability by building a stylized proxy validation set. This application has significant practical sense for selecting models to be deployed in the open-world environment. Our code is available at \url{https://github.com/boschresearch/ISSA}. | ['Anna Khoreva', 'Margret Keuper', 'Dan Zhang', 'Yumeng Li'] | 2023-07-02 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 1.51154682e-01 -3.78663652e-02 -1.38295041e-02 -4.62397188e-01
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28b676f1-3d44-4699-8e55-c9c68bf49a28 | a-novel-empirical-bayes-with-reversible-jump | 1808.05480 | null | http://arxiv.org/abs/1808.05480v1 | http://arxiv.org/pdf/1808.05480v1.pdf | A novel Empirical Bayes with Reversible Jump Markov Chain in User-Movie Recommendation system | In this article we select the unknown dimension of the feature by re-
versible jump MCMC inside a simulated annealing in bayesian set up of
collaborative filter. We implement the same in MovieLens small dataset. We also
tune the hyper parameter by using a modified empirical bayes. It can also be
used to guess an initial choice for hyper-parameters in grid search procedure
even for the datasets where MCMC oscillates around the true value or takes long
time to converge. | ['Himanshu Jhamb', 'Arabin Kumar Dey'] | 2018-08-15 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.16229343e-01 -2.81457633e-01 2.07172468e-01 -4.79482234e-01
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7bb5b1cb-c00a-4a1a-9ee8-a004e6296002 | provably-calibrated-regression-under | null | null | https://openreview.net/forum?id=bOcUqfdH3S8 | https://openreview.net/pdf?id=bOcUqfdH3S8 | Provably Calibrated Regression Under Distribution Drift | Accurate uncertainty quantification is a key building block of trustworthy machine learning systems. Uncertainty is typically represented by probability distributions over the possible outcomes, and these probabilities should be calibrated, \textit{e.g}. the 90\% credible interval should contain the true outcome 90\% of the times. In the online prediction setup, existing conformal methods can provably achieve calibration assuming no distribution shift; however, the assumption is difficult to verify, and unlikely to hold in many applications such as time series prediction. Inspired by control theory, we propose a prediction algorithm that guarantees calibration even under distribution shift, and achieves strong performance on metrics such as sharpness and proper scores. We compare our method with baselines on 19 time-series and regression datasets, and our method achieves approximately 2x reduction in calibration error, comparable sharpness, and improved downstream decision utility. | ['Stefano Ermon', 'Danny Tse', 'Yusuke Tashiro', 'Shengjia Zhao'] | 2021-09-29 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.48421720e-01 5.86131155e-01 -6.99031413e-01 -6.30016625e-01
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11acf9aa-0708-49eb-848e-a0f6142a97fb | an-iterative-co-saliency-framework-for-rgbd | 1711.01371 | null | http://arxiv.org/abs/1711.01371v1 | http://arxiv.org/pdf/1711.01371v1.pdf | An Iterative Co-Saliency Framework for RGBD Images | As a newly emerging and significant topic in computer vision community,
co-saliency detection aims at discovering the common salient objects in
multiple related images. The existing methods often generate the co-saliency
map through a direct forward pipeline which is based on the designed cues or
initialization, but lack the refinement-cycle scheme. Moreover, they mainly
focus on RGB image and ignore the depth information for RGBD images. In this
paper, we propose an iterative RGBD co-saliency framework, which utilizes the
existing single saliency maps as the initialization, and generates the final
RGBD cosaliency map by using a refinement-cycle model. Three schemes are
employed in the proposed RGBD co-saliency framework, which include the addition
scheme, deletion scheme, and iteration scheme. The addition scheme is used to
highlight the salient regions based on intra-image depth propagation and
saliency propagation, while the deletion scheme filters the saliency regions
and removes the non-common salient regions based on interimage constraint. The
iteration scheme is proposed to obtain more homogeneous and consistent
co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is
proposed in the addition scheme to introduce the depth information to enhance
identification of co-salient objects. The proposed method can effectively
exploit any existing 2D saliency model to work well in RGBD co-saliency
scenarios. The experiments on two RGBD cosaliency datasets demonstrate the
effectiveness of our proposed framework. | ['Xiaochun Cao', 'Huazhu Fu', 'Runmin Cong', 'Qingming Huang', 'Weisi Lin', 'Jianjun Lei', 'Chunping Hou'] | 2017-11-04 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 3.47334415e-01 -1.24449380e-01 -2.99218856e-02 -7.73520991e-02
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2adba1c7-e3df-40d2-ba3f-b78630b6b749 | eurnet-efficient-multi-range-relational | 2211.12941 | null | https://arxiv.org/abs/2211.12941v1 | https://arxiv.org/pdf/2211.12941v1.pdf | EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data | Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Previous works often use a unified solution like relative positional encoding. However, there exists different kinds of spatial relations, including short-range, medium-range and long-range relations, and modeling them separately can better capture the focus of different tasks on the multi-range relations (e.g., short-range relations can be important in instance segmentation, while long-range relations should be upweighted for semantic segmentation). In this work, we introduce the EurNet for Efficient multi-range relational modeling. EurNet constructs the multi-relational graph, where each type of edge corresponds to short-, medium- or long-range spatial interactions. In the constructed graph, EurNet adopts a novel modeling layer, called gated relational message passing (GRMP), to propagate multi-relational information across the data. GRMP captures multiple relations within the data with little extra computational cost. We study EurNets in two important domains for image and protein structure modeling. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation verify the gains of EurNet over the previous SoTA FocalNet. On the EC and GO protein function prediction benchmarks, EurNet consistently surpasses the previous SoTA GearNet. Our results demonstrate the strength of EurNets on modeling spatial multi-relational data from various domains. The implementations of EurNet for image modeling are available at https://github.com/hirl-team/EurNet-Image . The implementations for other applied domains/tasks will be released soon. | ['Yuandong Tian', 'Xinlei Chen', 'Jian Tang', 'Yi Xu', 'Yuanfan Guo', 'Minghao Xu'] | 2022-11-23 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.33752102e-02 1.03678403e-03 -5.21242559e-01 -6.72849178e-01
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c53dd138-3627-4b6b-8a81-94adff2afaa8 | uncertainty-aware-time-to-event-prediction | 2107.12250 | null | https://arxiv.org/abs/2107.12250v1 | https://arxiv.org/pdf/2107.12250v1.pdf | Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models | Recurrent neural network based solutions are increasingly being used in the analysis of longitudinal Electronic Health Record data. However, most works focus on prediction accuracy and neglect prediction uncertainty. We propose Deep Kernel Accelerated Failure Time models for the time-to-event prediction task, enabling uncertainty-awareness of the prediction by a pipeline of a recurrent neural network and a sparse Gaussian Process. Furthermore, a deep metric learning based pre-training step is adapted to enhance the proposed model. Our model shows better point estimate performance than recurrent neural network based baselines in experiments on two real-world datasets. More importantly, the predictive variance from our model can be used to quantify the uncertainty estimates of the time-to-event prediction: Our model delivers better performance when it is more confident in its prediction. Compared to related methods, such as Monte Carlo Dropout, our model offers better uncertainty estimates by leveraging an analytical solution and is more computationally efficient. | ['Volker Tresp', 'Peter A. Fasching', 'Yinchong Yang', 'Zhiliang Wu'] | 2021-07-26 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-1.47566095e-01 1.75336748e-01 -3.17768097e-01 -7.55860388e-01
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5f3ebd99-219f-4106-831d-76d2eb55a33d | knowledge-aware-attentional-neural-network | null | null | https://link.springer.com/article/10.1007/s00521-022-07689-1 | https://link.springer.com/content/pdf/10.1007/s00521-022-07689-1.pdf | Knowledge-aware attentional neural network for review-based movie recommendation with explanations | In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation
tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and
movies at the knowledge level. In most recommendation systems, review information is already widely utilized to uncover
the explicit preferences of users for items, especially for domains including movie recommendations, music recommendations,
and book recommendations, as reviews are full of knowledge entities relevant to the domain. When processing
review information, current methods usually use word embeddings to represent reviews for modeling users and items. As a
result, they may split the meaning of a phrase, and thereby induce erroneous predictions. Moreover, most methods capture
high-order interactions between users and items after obtaining latent low-dimensional representations, which means they
cannot discover understandable interactions or provide knowledge-level explanations. By incorporating knowledge graph
representation into movie recommendation tasks, the proposed KANN can not only capture the inner attention among user
(movie) reviews but also compute the outer attention values between users and movies before generating corresponding
latent vector representations. These characteristics enable the explicit preferences of users for movies to be learned and
understood. We test our model on two datasets (IMDb and Amazon) for the movie rating prediction task and the clickthrough
rate prediction task and show that it outperforms some of the existing state-of-the-art models and gains outstanding
prediction performances in cases with a very small amount of reviews. Furthermore, we demonstrate the high explainability
of the proposed KANN by visualizing the interaction between users and movies through a case study. Our results
and analyses highlight the relatively high effectiveness and reliability of KANN for movie recommendation tasks. | ['Jun Miyazaki', 'Yun Liu'] | 2022-09-04 | null | null | null | neural-computing-and-applications-2022-9 | ['movie-recommendation'] | ['miscellaneous'] | [-0.06284636 0.04006381 -0.7103017 -0.40405864 0.21371123 -0.44747725
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3713e153-6d6b-4cfb-83ba-4da78c865020 | keras-gpt-copilot-integrating-the-power-of | null | null | https://doi.org/10.5281/zenodo.7935183 | https://doi.org/10.5281/zenodo.7935183 | Keras GPT Copilot: Integrating the Power of Large Language Models in Deep Learning Model Development | Keras GPT Copilot is the first Python package designed to integrate an LLM copilot within the model development workflow, offering iterative feedback options for enhancing the performance of your Keras deep learning models. Utilizing the power of OpenAI's GPT models, Keras GPT Copilot can use any of the compatible models (GPT4 is recommended). However, the prompt-only mode allows for compatibility with other large language models. | ['Fabi Prezja'] | 2023-05-15 | null | null | null | zenodo-github-2023-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [-9.82215106e-01 -2.19160016e-03 -8.19542855e-02 -3.92484248e-01
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ba850d98-ca92-4534-956e-c2f3593a3d26 | urbanfm-inferring-fine-grained-urban-flows | 1902.05377 | null | http://arxiv.org/abs/1902.05377v1 | http://arxiv.org/pdf/1902.05377v1.pdf | UrbanFM: Inferring Fine-Grained Urban Flows | Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem. | ['Yu Zheng', 'Lin Jing', 'Yuxuan Liang', 'Ye Liu', 'Sijie Ruan', 'Kun Ouyang', 'David S. Rosenblum', 'Junbo Zhang'] | 2019-02-06 | null | null | null | null | ['fine-grained-urban-flow-inference'] | ['miscellaneous'] | [-2.90319800e-01 -3.87568235e-01 -1.97620749e-01 -4.41796213e-01
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6.36281073e-01 -8.68156433e-01 -3.71430874e-01 -1.97320491e-01] | [6.422889709472656, 2.0320215225219727] |
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