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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"

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Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images

Training Rare Object Detection in Satellite Imagery with Syn...

引用

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

作者： Martinson, Eric Furlong, Bridget Gillies, Andy Soar Technol 3600 Green StSte 600 Ann Arbor MI 48105 USA

ISBN: (纸本)9781665448994

When creating a new labeled dataset, human analysts or data reductionists must review and annotate large numbers of images. This process is time consuming and a barrier to the deployment of new computer vision solutions, particularly for rarely occurring objects. To reduce the number of images requiring human attention, we evaluate the utility of images created from 3D models refined with a generative adversarial network to select confidence thresholds that significantly reduce false alarms rates. The resulting approach has been demonstrated to cut the number of images needing to be reviewed by 50% while preserving a 95% recall rate, with only 6 labeled examples of the target.

关键词： Training Solid modeling computer vision Analytical models Three-dimensional displays Satellites Generative adversarial networks

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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

引用

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

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

引用

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

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision

Open-world Instance Segmentation: Top-down Learning with Bot...

引用

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

作者： Tarun Kalluri Weiyao Wang Heng Wang Manmohan Chandraker Lorenzo Torresani Du Tran UC San Diego Meta AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Top-down instance segmentation architectures excel with predefined closed-world taxonomies but exhibit biases and performance degradation in open-world scenarios. In this work, we introduce bottom-Up and top-Down Open-world Segmentation (UDOS), a novel approach that combines classical bottom-up segmentation methods within a top-down learning framework. UDOS leverages a top-down network trained with weak supervision derived from class-agnostic bottom-up segmentation to predict object parts. These part-masks undergo affinity-based grouping and refinement to generate precise instance-level segmentations. UDOS balances the efficiency of top-down architectures with the capacity to handle unseen categories through bottom-up supervision. We validate UDOS on challenging datasets (MS-COCO, LVIS, ADE20k, UVO, and OpenImages), achieving superior performance over state-of-the-art methods in cross-category and cross-dataset transfer tasks. Our code and models will be publicly available.

关键词： Instance segmentation Degradation computer vision Codes conferences Computational modeling Taxonomy

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Exploring the Zero-Shot Capabilities of vision-Language Models for Improving Gaze Following

Exploring the Zero-Shot Capabilities of Vision-Language Mode...

引用

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

作者： Anshul Gupta Pierre Vuillecard Arya Farkhondeh Jean-Marc Odobez Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne Switzerland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Contextual cues related to a person’s pose and interactions with objects and other people in the scene can provide valuable information for gaze following. While existing methods have focused on dedicated cue extraction methods, in this work we investigate the zero-shot capabilities of vision-Language Models (VLMs) for extracting a wide array of contextual cues to improve gaze following performance. We first evaluate various VLMs, prompting strategies, and in-context learning (ICL) techniques for zero-shot cue recognition performance. We then use these insights to extract contextual cues for gaze following, and investigate their impact when incorporated into a state of the art model for the task. Our analysis indicates that BLIP-2 is the overall top performing VLM and that ICL can improve performance. We also observe that VLMs are sensitive to the choice of the text prompt although ensembling over multiple text prompts can provide more robust performance. Additionally, we discover that using the entire image along with an ellipse drawn around the target person is the most effective strategy for visual prompting. For gaze following, incorporating the extracted cues results in better generalization performance, especially when considering a larger set of cues, highlighting the potential of this approach.

关键词： Visualization computer vision conferences Computational modeling Predictive models pattern recognition Context modeling

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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

AIS 2024 Challenge on Video Quality Assessment of User-Gener...

引用

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

作者： Marcos V. Conde Saman Zadtootaghaj Nabajeet Barman Radu Timofte Chenlong He Qi Zheng Ruoxi Zhu Zhengzhong Tu Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Zicheng Zhang Haoning Wu Yingjie Zhou Chunyi Li Xiaohong Liu Weisi Lin Guangtao Zhai Wei Sun Yuqin Cao Yanwei Jiang Jun Jia Zhichao Zhang Zijian Chen Weixia Zhang Xiongkuo Min Steve Göring Zihao Qi Chen Feng CAIDAS & IFI Computer Vision Lab. University of Würzburg Sony Interactive Entertainment FTG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

关键词： Surveys Learning systems Video on demand Reviews User-generated content Quality assessment pattern recognition

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Event-based spacecraft landing using time-to-contact

Event-based spacecraft landing using time-to-contact

引用

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

作者： Sikorski, Olaf Izzo, Dario Meoni, Gabriele European Space Technol Ctr Adv Concepts Team Keplerlaan 1 NL-2201 AZ Noordwijk Netherlands

ISBN: (纸本)9781665448994

We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flow vectors (and therefore the time to contact) and use it in a simple control feedback scheme during simulated descents. The results obtained are very encouraging, albeit insufficient to meet the stringent safety constraints and modelling accuracy imposed upon space missions. We thus conclude by discussing future work aimed at addressing these limitations.

关键词： Space vehicles computer vision Space missions conferences Computational modeling Sensors Safety

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Burst Image Super-Resolution with Base Frame Selection

Burst Image Super-Resolution with Base Frame Selection

引用

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

作者： Sanghyun Kim Minjung Lee Woohyeok Kim Deunsol Jung Jaesung Rim Sunghyun Cho Minsu Cho Pohang University of Science and Technology (POSTECH) South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high resolution image using complementary information between multiple frames in the burst. In this work, we explore using burst shots with non-uniform exposures to confront real-world practical scenarios by introducing a new benchmark dataset, dubbed Non-uniformly Exposed Burst Image (NEBI), that includes the burst frames at varying exposure times to obtain a broader range of irradiance and motion characteristics within a scene. As burst shots with non-uniform exposures exhibit varying levels of degradation, fusing information of the burst shots into the first frame as a base frame may not result in optimal image quality. To address this limitation, we propose a Frame Selection Network (FSN) for non-uniform scenarios. This network seamlessly integrates into existing super-resolution methods in a plug-and-play manner with low computational cost. The comparative analysis reveals the effectiveness of the non-uniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.

关键词： Degradation Image quality computer vision conferences Superresolution Benchmark testing pattern recognition

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Active Transferability Estimation

Active Transferability Estimation

引用

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

作者： Tarun Ram Menta Surgan Jandial Akash Patil Saketh Bachu Vimal K. B Balaji Krishnamurthy Vineeth N. Balasubramanian Mausoom Sarkar Chirag Agarwal Adobe Systems Indian Institute of Technology Madras Indian Institute of Technology Hyderabad Harvard University Agyeya Foundation

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine-tuning. Inspired by active learning techniques, we propose ACT (ACtive Transferability), a new strategy to improve the performance of transferability estimation methods, by leveraging an informative subset of the target data. By leveraging the model’s internal and output representations, we introduce two techniques – class-agnostic and class-aware – to identify informative subsets and show that ACT can be applied to any existing transferability metric to improve their performance and reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that ACT metrics are consistently better or on par with the state-of-the-art transferability metrics.

关键词： Measurement computer vision Computational modeling conferences Transfer learning Estimation computer architecture

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Practical Region-level Attack against Segment Anything Models

Practical Region-level Attack against Segment Anything Model...

引用

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

作者： Yifan Shen Zhengyuan Li Gang Wang University of Illinois Urbana-Champaign

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Segment Anything Models (SAM) have made significant advancements in image segmentation, allowing users to segment target portions of an image with a single click (i.e., user prompt). Given its broad applications, the robustness of SAM against adversarial attacks is a critical concern. While recent works have explored adversarial attacks against a pre-defined prompt/click, their threat model is not yet realistic: (1) they often assume the user-click position is known to the attacker (point-based attack), and (2) they often operate under a white-box setting with limited transferability. In this paper, we propose a more practical region-level attack where attackers do not need to know the precise user prompt. The attack remains effective as the user clicks on any point on the target object in the image, hiding the object from SAM. Also, by adapting a spectrum transformation method, we make the attack more transferable under a black-box setting. Both control experiments and testing against real-world SAM services confirm its effectiveness.

关键词： Threat modeling Image segmentation computer vision conferences Computational modeling Closed box Robustness

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