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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是91-100 订阅

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MoreFusion: Multi-object Reasoning for 6D Pose Estimation from Volumetric Fusion

MoreFusion: Multi-object Reasoning for 6D Pose Estimation fr...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wada, Kentaro Sucar, Edgar James, Stephen Lenton, Daniel Davison, Andrew J. Imperial Coll London Dyson Robot Lab London England

ISBN: (纸本)9781728171685

Robots and other smart devices need efficient object-based scene representations from their on-board vision systems to reason about contact, physics and occlusion. Recognized precise object models will play an important role alongside non-parametric reconstructions of unrecognized structures. We present a system which can estimate the accurate poses of multiple known objects in contact and occlusion from real-time, embodied multi-view vision. Our approach makes 3D object pose proposals from single RGB-D views, accumulates pose estimates and non-parametric occupancy information from multiple views as the camera moves, and performs joint optimization to estimate consistent, non-intersecting poses for multiple objects in contact. We verify the accuracy and robustness of our approach experimentally on 2 object datasets: YCB-Video, and our own challenging Cluttered YCB-Video. We demonstrate a real-time robotics application where a robot arm precisely and orderly disassembles complicated piles of objects, using only on-board RGB-D vision.

关键词： computer vision

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Intelligent Scene Caching to Improve Accuracy for Energy-Constrained Embedded vision

Intelligent Scene Caching to Improve Accuracy for Energy-Con...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Simpson, Benjamin Lubana, Ekdeep Liu, Yuchen Dick, Robert Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781728193601

We describe an efficient method of improving the performance of vision algorithms operating on video streams by reducing the amount of data captured and transferred from image sensors to analysis servers in a data-aware manner. The key concept is to combine guided, highly heterogeneous sampling with an intelligent Scene Cache. This enables the system to adapt to spatial and temporal patterns in the scene, thus reducing redundant data capture and processing. A software prototype of our framework running on a general-purpose embedded processor enables superior object detection accuracy (by 56%) at similar energy consumption (slight improvement of 4%) compared to an H.264 hardware accelerator.

关键词： Image enhancement

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Crossing cuts polygonal puzzles: Models and Solvers

Crossing cuts polygonal puzzles: Models and Solvers

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Harel, Peleg Ben-Shahar, Ohad

ISBN: (纸本)9781665445092

Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered fragments, is fundamental to numerous applications, and yet most of the literature has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape with an arbitrary number of straight cuts. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process that is guided by the pictorial content of the pieces. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to indicate that they are solvable completely automatically.

关键词： Measurement computer vision Shape Computational modeling pattern recognition

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A Low-cost & Real-time Motion Capture System

A Low-cost & Real-time Motion Capture System

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chatzitofis, Anargyros Albanis, Georgios Zioulis, Nikolaos Thermos, Spyridon Codewheel Larnaka Larnaca Cyprus Univ Thessaly Dept Informat & Telecommun Volos Greece

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications.

关键词： computer vision Costs Noise reduction Virtual reality Motion capture Sensor systems Real-time systems

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Multi-Task Learning Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computer vision and pattern recognition (cvpr), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieee cvpr 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.

关键词： Measurement computer vision Databases conferences Estimation Benchmark testing Multitasking

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Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images

Deep Adversarial Decomposition: A Unified Framework for Sepa...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zou, Zhengxia Lei, Sen Shi, Tianyang Shi, Zhenwei Ye, Jieping Univ Michigan Ann Arbor MI 48109 USA Beihang Univ Beijing Peoples R China NetEase Fuxi AI Lab Hangzhou Peoples R China Didi Chuxing Beijing Peoples R China

ISBN: (纸本)9781728171685

Separating individual image layers from a single mixed image has long been an important but challenging task. We propose a unified framework named "deep adversarial decomposition" for single superimposed image separation. Our method deals with both linear and non-linear mixtures under an adversarial training paradigm. Considering the layer separating ambiguity that given a single mixed input, there could be an infinite number of possible solutions, we introduce a "Separation-Critic" - a discriminative network which is trained to identify whether the output layers are well-separated and thus further improves the layer separation. We also introduce a "crossroad l(1)" loss function, which computes the distance between the unordered outputs and their references in a crossover manner so that the training can be well-instructed with pixel-wise supervision. Experimental results suggest that our method significantly outperforms other popular image separation frameworks. Without specific tuning, our method achieves the state of the art results on multiple computer vision tasks, including the image deraining, photo reflection removal, and image shadow removal.

关键词： computer vision

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Gold Seeker: Information Gain from Policy Distributions for Goal-oriented vision-and-Langauge Reasoning

Gold Seeker: Information Gain from Policy Distributions for ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Abbasnejad, Ehsan Abbasnejad, Iman Wu, Qi Shi, Javen van den Hengel, Anton Australian Inst Machine Learning Adelaide SA Australia Univ Adelaide Adelaide SA Australia Fugro Australia Marine Perth WA Australia

ISBN: (纸本)9781728171685

As computer vision moves from passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases the method actively selects actions that will best reduce its internal uncertainty, and outperforms its competitors in achieving the goal of the challenge.

关键词： Semantics

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Generative-discriminative Feature Representations for Open-set recognition

Generative-discriminative Feature Representations for Open-s...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Perera, Pramuditha Morariu, Vlad, I Jain, Rajiv Manjunatha, Varun Wigington, Curtis Ordonez, Vicente Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Res San Francisco CA USA Univ Virginia Charlottesville VA USA

ISBN: (纸本)9781728171685

We address the problem of open-set recognition, where the goal is to determine if a given sample belongs to one of the classes used for training a model (known classes). The main challenge in open-set recognition is to disentangle open-set samples that produce high class activations from known-set samples. We propose two techniques to force class activations of open-set samples to be low. First, we train a generative model for all known classes and then augment the input with the representation obtained from the generative model to learn a classifier. This network learns to associate high classification probabilities both when the image content is from the correct class as well as when the input and the reconstructed image are consistent with each other. Second, we use self-supervision to force the network to learn more informative features when assigning class scores to improve separation of classes from each other and from open-set samples. We evaluate the performance of the proposed method with recent open-set recognition works across three datasets, where we obtain state-of-the-art results.

关键词： Chemical activation

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Mixture Dense Regression for Object Detection and Human Pose Estimation

Mixture Dense Regression for Object Detection and Human Pose...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Varamesh, Ali Tuytelaars, Tinne Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Mixture models are well-established learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into relatively homogeneous subsets in a data-driven manner. Not only ill-defined but also well-defined complex problems should benefit from them. To this end, we devise a framework for spatial regression using mixture density networks. We realize the framework for object detection and human pose estimation. For both tasks, a mixture model yields higher accuracy and divides the input space into interpretable modes. For object detection, mixture components focus on object scale, with the distribution of components closely following that of ground truth the object scale. This practically alleviates the need for multiscale testing, providing a superior speed-accuracy trade-off. For human pose estimation, a mixture model divides the data based on viewpoint and uncertainty - namely, front and back views, with back view imposing higher uncertainty. We conduct experiments on the MS COCO dataset and do not face any mode collapse.

关键词： Pose estimation Object detection Mixture models Task analysis Predictive models Two dimensional displays computer vision

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TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning

TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Pro...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Pengcheng Wang, Zihao Ji, Zhilong Liu, Xiao Yang, Songfan Wu, Zhongqin TAL Educ Grp Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately. The co-occurrence of the expression features and the head pose features are explored. We observe that different AUs converge at various speed. By choosing the optimal checkpoint for each AU, the recognition results are improved. We are able to obtain a final score of 0.746 in validation set and 0.7306 in the test set of the challenge.

关键词： Gold Training Noise measurement Face recognition Face Data models Optimization

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