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

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A case for using rotation invariant features in state of the art feature matchers

A case for using rotation invariant features in state of the...

引用

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

作者： Bokman, Georg Kahl, Fredrik Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781665487399

The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable CNN which is equivariant to translations and image rotations. It is experimentally shown that this boost is obtained without reducing performance on ordinary illumination and viewpoint matching sequences.

关键词： computer vision conferences Lighting pattern matching

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The Unreasonable Effectiveness of CLIP Features for Image Captioning: An Experimental Analysis

The Unreasonable Effectiveness of CLIP Features for Image Ca...

引用

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

作者： Barraco, Manuele Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating textual descriptions from visual inputs is a fundamental step towards machine intelligence, as it entails modeling the connections between the visual and textual modalities. For years, image captioning models have relied on pre-trained visual encoders and object detectors, trained on relatively small sets of data. Recently, it has been observed that large-scale multi-modal approaches like CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, provide a strong zero-shot capability on various vision tasks. In this paper, we study the advantage brought by CLIP in image captioning, employing it as a visual encoder. Through extensive experiments, we show how CLIP can significantly out-perform widely-used visual encoders and quantify its role under different architectures, variants, and evaluation protocols, ranging from classical captioning performance to zero-shot transfer.

关键词： Visualization computer vision Protocols conferences Detectors Distance measurement Data models

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KBody: Balanced monocular whole-body estimation

KBody: Balanced monocular whole-body estimation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zioulis, Nikolaos O'Brien, James F. Klothed Technologies Inc. United States Uc Berkeley United States

ISBN: (纸本)9798350302493

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Compared to other approaches, it introduces virtual joints to identify higher quality correspondences and disentangles the optimization between the pose and shape parameters to achieve a more balanced result in terms of pose and shape capturing capacity, as well as pixel alignment. © 2023 ieee.

关键词： computer vision

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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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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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

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhou, Yutong Shimada, Nobutaka Ritsumeikan University Shiga Japan

ISBN: (纸本)9798350302493

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image. © 2023 ieee.

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Joint Forecasting of Panoptic Segmentations with Difference Attention

Joint Forecasting of Panoptic Segmentations with Difference ...

引用

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

作者： Graber, Colin Jazra, Cyril Luo, Wenjie Gui, Liangyan Schwing, Alexander Univ Illinois Urbana IL 61801 USA Waymo Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665487399

Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other;second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on 'difference attention.' It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics.

关键词： Measurement computer vision conferences Predictive models Transformers pattern recognition Forecasting

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Privacy Leakage of Adversarial Training Models in Federated Learning Systems

Privacy Leakage of Adversarial Training Models in Federated ...

引用

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

作者： Zhang, Jingyang Chen, Yiran Li, Hai Duke Univ Dept Elect & Comp Engn Durham NC 27706 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks. In this work, we further reveal this unsettling property of AT by designing a novel privacy attack that is practically applicable to the privacy-sensitive Federated Learning (FL) systems. Using our method, the attacker can exploit AT models in the FL system to accurately reconstruct users' private training images even when the training batch size is large. Code is available at https://***/zjysteven/PrivayAttack_AT_FL.

关键词： Training Deep learning Privacy computer vision conferences Neural networks Collaborative work

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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data

Variational Autoencoders for Generating Hyperspectral Imagin...

引用

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

作者： Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar concentrations. Previous work in this area has used a minimal number of honey types, as sample preparation and data capture is a time-consuming process. This paper develops a new approach using variational autoencoders (VAEs) for generating adulterated honey data for unseen honey types. The results show that the binary adulteration detector can achieve on average 81.3% accuracy on unseen honey types by adding the generated data to the existing training data. Without including the generated data while training, the classifier can only achieve 44% on unseen honey types.

关键词： Training computer vision conferences Training data Machine learning Detectors pattern recognition

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