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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

共 4655 条记录，以下是1321-1330 订阅

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Symmetric Parallax Attention for Stereo Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resoluti...

引用

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

作者： Wang, Yingqian Ying, Xinyi Wang, Longguang Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Zunyi Guizhou Peoples R China

ISBN: (纸本)9781665448994

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method.

关键词： Training computer vision conferences Superresolution Estimation Bidirectional control Robustness

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OTST: A Two-Phase Framework for Joint Denoising and Remosaicing in RGBW CFA

OTST: A Two-Phase Framework for Joint Denoising and Remosaic...

引用

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

作者： Fan, Zhihao Wu, Xun Meng, Fanqing Wu, Yaqi Zhang, Feng Tetras.AI University of Shanghai for Science and Technology China Tsinghua University China Tongji University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798350302493

RGBW, a newly emerged type of Color Filter Array (CFA), possesses strong low-light photography capabilities. RGBW CFA shows significant application value when low-light sensitivity is critical, such as in security cameras and smartphones. However, the majority of commercial image signal processors (ISP) are primarily designed for Bayer CFA, research pertaining to RGBW CFA is very rare. To address above limitations, in this study, we propose a two-phase framework named OTST for the RGBW Joint Denoising and Remosaicing (RGBW-JRD) task. For the denoising stage, we propose Omni-dimensional Dynamic Convolution based Half-Shuffle Transformer (ODC-HST) which can fully utilize image's long-range dependencies to dynamically remove the noise. For the remosaicing stage, we propose a Spatial Compressive Transformer (SCT) to efficiently capture both local and global dependencies across spatial and channel dimensions. Experimental results demonstrate that our two-phase RGBW-JRD framework outperforms existing RGBW denoising and remosaicing solutions across a wide range of noise levels. In addition, the proposed approach ranks the 2nd place in MIPI 2023 RGBW Joint Remosaic and Denoise competition. © 2023 ieee.

关键词： computer vision

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Learning Tracking Representations from Single Point Annotations

Learning Tracking Representations from Single Point Annotati...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qiangqiang Wu Antoni B. Chan Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing deep trackers are typically trained with large-scale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5 × faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise.

关键词： Training Visualization Target tracking Costs Correlation Annotations Noise

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Sample-free white-box out-of-distribution detection for deep learning

Sample-free white-box out-of-distribution detection for deep...

引用

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

作者： Begon, Jean-Michel Geurts, Pierre Univ Liege Liege Belgium

ISBN: (纸本)9781665448994

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in-distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.

关键词： Deep learning computer vision Filtering Computational modeling conferences computer architecture Data models

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Scattering Prompt Tuning: A Fine-tuned Foundation Model for SAR Object recognition

Scattering Prompt Tuning: A Fine-tuned Foundation Model for ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Weilong Guo Shengyang Liv Jian Yang Key Laboratory of Space Utilization Chinese Academy of Sciences Technology and the Engineering Center for Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic Aperture Radar (SAR) serves as a vital tool in various earth observation applications, providing robust imaging under challenging weather conditions. While the fine-tuned foundation models excel in many downstream tasks, they struggle with SAR object recognition because of SAR's unique imaging and scattering characteristics. In this study, we propose a novel approach named Scattering Prompt Tuning (SPT) based vision foundation model. It uses SAR image scattering information as a prompt and integrates learnable parameters into the pre-trained model's input space to help learn SAR's unique information. We also employ a lightweight Residual AdapterMLP for fine-tuning, design a Sequential Feature Aggregation (SFA) to selectively fuse features from different transformer blocks effectively, and develop a Dynamic Distributional Contrast loss (DCLoss) to maintain the proper distance between different objects in feature space. Additionally, a four-stage training strategy, incorporating semi-supervised learning, is deployed to enhance SAR object recognition performance further Our approach reaches a Top-1 accuracy of 37.9% and an AUROC of 0.83 on the final dataset, winning the first place in the SAR Classification track of PBVS 2024 Multi-modal Aerial View Object Classification Challenge, which is better than the latest advanced fine-tuned foundation models.

关键词： Training Scattering Imaging Semisupervised learning Radar imaging Radar polarimetry pattern recognition

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Point-Supervised Semantic Segmentation of Natural Scenes via Hyperspectral Imaging

Point-Supervised Semantic Segmentation of Natural Scenes via...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tianqi Ren Qiu Shen Ying Fu Shaodi You Nanjing Univerisity Beijing Institute of Technology University of Amsterdam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Natural scene semantic segmentation is an important task in computer vision. While training accurate models for semantic segmentation relies heavily on detailed and accurate pixel-level annotations, which are hard and time-consuming to be collected especially for complicated natural scenes. Weakly-supervised methods can reduce labeling cost greatly at the expense of significant performance degradation. In this paper, we explore the possibility of introducing hyperspectral imaging to improve the performance of weakly-supervised semantic segmentation. We take two challenging hyperspectral datasets of outdoor natural scenes as example, and randomly label dozens of points with semantic categories to conduct a point-supervised semantic segmentation benchmark. Then, a spectral and spatial fusion method is proposed to generate detailed pixel-level annotations, which are used to supervise the semantic segmentation models. With multiple experiments we find that hyperspectral information can be greatly helpful to point-supervised semantic segmentation as it is more distinctive than RGB. As a result, our proposed method with only point-supervision can achieve approximate performance of the fully-supervised method in many cases. 1

关键词： Training Degradation computer vision Costs Accuracy Annotations Semantic segmentation

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Prompt Learning with One-Shot Setting based Feature Space Analysis in vision-and-Language Models

Prompt Learning with One-Shot Setting based Feature Space An...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuki Hirohashi Tsubasa Hirakawa Takayoshi Yamashita Hironobu Fujiyoshi OMRON Corp. Chubu University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

By using few-shot data and labels, prompt learning obtains optimal prompts that are capable of achieving high performance on downstream tasks. Existing prompt learning methods generate high-quality prompts that are suitable for downstream tasks but tend to perform poorly in scenarios where only very limited data (e.g., one-shot) is available. We address on this challenging one-shot scenario and propose a novel architecture for prompt learning, called Image-Text Feature Alignment Branch (ITFAB). ITFAB aligns text features closer to the centroids of image features and separates text features with different classes to resolve misalignment in the feature space, thereby facilitating the acquisition of high-quality prompts with very limited data. In one-shot setting, our method outperforms the existing CoOp and CoCoOp methods and in some cases even surpasses CoCoOp’s 16-shot performance. Testing on different datasets and domain, show that ITFAB almost matches CoCoOp’s effectiveness. It also works with current prompt learning methods like MapLe and PromptSRC, improving their performance in one-shot setting.

关键词： Learning systems computer vision Analytical models Adaptation models Image resolution Accuracy conferences

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ALPS: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS

ALPS: Adaptive Quantization of Deep Neural Networks with Gen...

引用

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

作者： Langroudi, Hamed F. Karia, Vedant Carmichael, Zachariah Zyarah, Abdullah Pandit, Tej Gustafson, John L. Kudithipudi, Dhireesha Univ Texas San Antonio Neuromorph AI Lab San Antonio TX 78249 USA Rochester Inst Technol Rochester NY 14623 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665448994

In this paper, a new adaptive quantization algorithm for generalized posit format is presented, to optimally represent the dynamic range and distribution of deep neural network parameters. Adaptation is achieved by minimizing the intra-layer posit quantization error with a compander. The efficacy of the proposed quantization algorithm is studied within a new low-precision framework, ALPS, on ResNet-50 and EfficientNet models for classification tasks. Results assert that the accuracy and energy dissipation of low-precision DNNs using generalized posits outperform other well-known numerical formats, including standard posits.

关键词： Deep learning Quantization (signal) Adaptive systems Upper bound Numerical analysis Heuristic algorithms pattern recognition

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Evaluating the Immediate Applicability of Pose Estimation for Sign Language recognition

Evaluating the Immediate Applicability of Pose Estimation fo...

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

作者： Moryossef, Amit Tsochantaridis, Ioannis Dinn, Joe Camgoez, Necati Cihan Bowden, Richard Jiang, Tao Rios, Annette Muller, Mathias Ebling, Sarah Bar Ilan Univ Ramat Gan Israel Google Mountain View CA 94043 USA Univ Surrey Guildford Surrey England Univ Zurich Zurich Switzerland

ISBN: (纸本)9781665448994

Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.

关键词： Analytical models Visualization computer vision Assistive technology Face recognition conferences Pose estimation

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Sharpness-Aware Optimization for Real-World Adversarial Attacks for Diverse Compute Platforms with Enhanced Transferability

Sharpness-Aware Optimization for Real-World Adversarial Atta...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Muchao Ye Xiang Xu Qin Zhang Jonathan Wu Amazon AI Labs Seattle WA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, deep neural networks (DNNs) have become integral to many real-world applications. A pressing concern in these deployments pertains to their vulnerability to adversarial attacks. In this work, we focus on the transferability of adversarial examples in a real-world deployment setting involving both a cloud model and an edge model. The cloud model is a black-box victim model, while the edge model is a surrogate model that is fully accessible to users. We investigated scenarios where attackers leverage information from the known surrogate model to generate adversarial examples to attack the unknown black-box victim model. Existing methods often optimize the adversarial example generation based on the steepest gradients estimated from the surrogate model, which do not generalize effectively to the victim model. To better gauge the for real-world adversarial risks in a cloud-edge deployment setting, we proposed an novel attack mechanism that enhanced transferability by incorporating a sharpness-aware objective into the optimization process. Our evaluation on image classification benchmarks demonstrates that our method significantly improves adversarial example’s transferability, even on the foundational computer vision models such as OFA-Large, showcasing its potential as a new standard in assessing attack transferability within a cloud-edge hybrid deployment scenario.

关键词： computer vision Computational modeling Perturbation methods Closed box Artificial neural networks Pressing pattern recognition

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