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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是31-40 订阅

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Persistent-Transient Duality in Human Behavior Modeling

Persistent-Transient Duality in Human Behavior Modeling

引用

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

作者： Hung Tran Vuong Le Venkatesh, Svetha Truyen Tran Deakin Univ Appl AI Inst Geelong Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose to model the persistent-transient duality in human behavior using a parent-child multi-channel neural network, which features a parent persistent channel that manages the global dynamics and children transient channels that are initiated and terminated on-demand to handle detailed interactive actions. The short-lived transient sessions are managed by a proposed Transient Switch. The neural framework is trained to discover the structure of the duality automatically. Our model shows superior performances in human-object interaction motion prediction.

关键词： computer vision Computational modeling conferences Dynamics Neural networks Switches Predictive models

来源：评论

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Attention! Stay Focus!

Attention! Stay Focus!

引用

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

作者： Vo, Tu BridgeAI Inc Seoul South Korea

ISBN: (纸本)9781665448994

We develop a deep convolutional neural networks (CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images. Specifically, we develop a double attention network which consists of attentional encoders, triple locals and global local modules to effectively extract useful information from each image in the dual-pixels and select the useful information from each image and synthesize the final output image. We demonstrate the effectiveness of the proposed deblurring algorithm in terms of both qualitative and quantitative aspects by evaluating on the test set in the NTIRE 2021 Defocus Deblurring using Dual-pixel Images Challenge [1] [4].

关键词： Deep learning computer vision conferences computer architecture Feature extraction Cameras Decoding

来源：评论

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Bridging the Gap Between Automated and Human Facial Emotion Perception

Bridging the Gap Between Automated and Human Facial Emotion ...

引用

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

作者： Stratton, Derek Hand, Emily Univ Nevada Reno Reno NV 89557 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the complex relationship between emotions and facial expressions is important for both psychologists and computer scientists. A large body of research in psychology investigates facial expressions, emotions, and how emotions are perceived from facial expressions. As computer scientists look to incorporate this research into automatic emotion perception systems, it is important to understand the nature and limitations of human emotion perception. These principles of emotion science affect the way datasets are created, methods are implemented, and results are interpreted in automated emotion perception. This paper aims to distill and align prior work in automated and human facial emotion perception to facilitate future discussions and research at the intersection of the two disciplines.

关键词： Emotion recognition computer vision Uncertainty conferences Sociology Psychology Natural language processing

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Training Strategies for vision Transformers for Object Detection

Training Strategies for Vision Transformers for Object Detec...

引用

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

作者： Singh, Apoorv Motional Boston MA 02210 USA

ISBN: (纸本)9798350302493

vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy, and not so much on the inference-time budget. For a safety critical system like autonomous driving, real-time inference at the on-board compute is an absolute necessity. This keeps our object detection algorithm under a very tight run-time budget. In this paper, we evaluated a variety of strategies to optimize on the inference-time of vision transformers based object detection methods keeping a close-watch on any performance variations. Our chosen metric for these strategies is accuracy-runtime joint optimization. Moreover, for actual inference-time analysis we profile our strategies with float32 and float16 precision with TensorRT module. This is the most common format used by the industry for deployment of their Machine Learning networks on the edge devices. We showed that our strategies are able to improve inference-time by 63% at the cost of performance drop of mere 3% for our problem-statement defined in Sec. 3. These strategies brings down vision Transformers detectors [3, 15, 18, 19, 36] inference-time even less than traditional single-image based CNN detectors like FCOS [17, 25, 33]. We recommend practitioners use these techniques to deploy Transformers based hefty multi-view networks on a budge-constrained robotic platform.

关键词： Autonomous vehicles

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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

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

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

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Importance is in your attention: agent importance prediction for autonomous driving

Importance is in your attention: agent importance prediction...

引用

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

作者： Hazard, Christopher Bhagat, Akshay Buddharaju, Balarama Raju Liu, Zhongtao Shao, Yunming Lu, Lu Omari, Sammy Cui, Henggang Motional Boston MA 02210 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agents. In this paper, we show that the attention information from such models can also be used to measure the importance of each agent with respect to the ego vehicle's future planned trajectory. Our experiment results on the nuPlans dataset show that our method can effectively find and rank surrounding agents by their impact on the ego's plan.

关键词： computer vision conferences Predictive models Trajectory pattern recognition Task analysis Autonomous vehicles

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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

引用

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

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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Live Demonstration: Incremental Motion Estimation for Event-based Cameras by Dispersion Minimisation

Live Demonstration: Incremental Motion Estimation for Event-...

引用

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

作者： Nunes, Urbano Miguel Demiris, Yiannis Imperial Coll London Personal Robot Lab London England

ISBN: (纸本)9781665448994

Live demonstration setup. (Left) The setup consists of a DAVIS346B event camera connected to a standard consumer laptop and undergoes some motion. (Right) The motion estimates are plotted in red and, for rotation-like motions, the angular velocities provided by the camera IMU are also plotted in blue. This plot exemplifies an event camera undergoing large rotational motions (up to ~ 1000 deg/s) around the (a) x-axis, (b) y-axis and (c) z-axis. Overall, the incremental motion estimation method follows the IMU measurements. Optionally, the resultant global optical flow can also be shown, as well as the corresponding generated events by accumulating them onto the image plane (bottom left corner).

关键词： Portable computers Motion estimation conferences Cameras Minimization pattern recognition Motion measurement

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SCVRL: Shuffled Contrastive Video Representation Learning

SCVRL: Shuffled Contrastive Video Representation Learning

引用

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

作者： Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCVRL is capable of learning both semantic and motion patterns. For that, we reformulate the popular shuffling pretext task within a modern contrastive learning paradigm. We show that our transformer-based network has a natural capacity to learn motion in self-supervised settings and achieves strong performance, outperforming CVRL on four benchmarks.

关键词： Representation learning Visualization Semantics Self-supervised learning Benchmark testing Transformers pattern recognition

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

引用

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

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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