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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22908 条记录，以下是4331-4340 订阅

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SAM-PM: Enhancing Video Camouflaged Object Detection using Spatio-Temporal Attention

SAM-PM: Enhancing Video Camouflaged Object Detection using S...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Muhammad Nawfal Meeran Gokul Adethya T Bhanu Pratyush Mantha National Institute of Technology Tiruchirappalli

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the domain of large foundation models, the Segment Anything Model (SAM) has gained notable recognition for its exceptional performance in image segmentation. However, tackling the video camouflage object detection (VCOD) task presents a unique challenge. Camouflaged objects typically blend into the background, making them difficult to distinguish in still images. Additionally, ensuring temporal consistency in this context is a challenging problem. As a result, SAM encounters limitations and falls short when applied to the VCOD task. To overcome these challenges, we propose a new method called the SAM Propagation Module (SAM-PM). Our propagation module enforces temporal consistency within SAM by employing spatio-temporal cross-attention mechanisms. Moreover, we exclusively train the propagation module while keeping the SAM network weights frozen, allowing us to integrate task-specific insights with the vast knowledge accumulated by the large model. Our method effectively incorporates temporal consistency and domain-specific expertise into the segmentation network with an addition of less than 1% of SAM’s parameters. Extensive experimentation reveals a substantial performance improvement in the VCOD benchmark when compared to the most recent state-of-the-art techniques. Code and pre-trained weights are open-sourced at https://***/SpiderNitt/SAM-PM

关键词： Training Knowledge engineering Image segmentation computer vision Image recognition Codes conferences

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Towards High Fidelity Face Relighting with Realistic Shadows

Towards High Fidelity Face Relighting with Realistic Shadows

引用

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

作者： Hou, Andrew Zhang, Ze Sarkis, Michel Bi, Ning Tong, Yiying Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9781665445092

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance.

关键词： Training computer vision Face recognition Lighting Estimation Mouth Data models

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Mikolaj Cieslak Umabharathi Govindarajan Alejandro Garcia Anuradha Chandrashekar Torsten Hädrich Aleksander Mendoza-Drosik Dominik L. Michels Sören Pirk Chia-Chun Fu Wojciech Pałubicki GreenMatterAI Blue River Technology King Abdullah University of Science and Technology Saudi Arabia and Technical University of Darmstadt Germany Christian-Albrecht University of Kiel Germany Adam Mickiewicz University in Poznań Poland

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model’s effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

关键词： Training Adaptation models Semantic segmentation Crops Training data Soil Data augmentation

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ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR: A General Framework to Accelerate Super-Resolution ...

引用

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

作者： Kong, Xiangtao Zhao, Hengyuan Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline - ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses - Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks.

关键词： Training computer vision Superresolution Pipelines Containers Image restoration pattern recognition

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MixSyn: Compositional Image Synthesis with Fuzzy Masks and Style Fusion

MixSyn: Compositional Image Synthesis with Fuzzy Masks and S...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： İlke Demir Umur Aybars Çiftçi Intel Labs Binghamton University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images created by generative models increase in quality and expressiveness as newer models utilize larger datasets and novel architectures. Although this photorealism is a benefit from a creative standpoint, expressiveness is still limited by the training data. Most of these approaches are built on the transfer between source and target pairs, or they generate completely new samples based on an ideal distribution, still resembling the closest real sample while missing less frequent or non-existent compositions. We propose MixSyn (read as " mixin’ ") to learn novel fuzzy compositions from multiple sources and to create novel images as a mix of image regions corresponding to the compositions. MixSyn not only combines uncorrelated regions from multiple source masks into a coherent semantic composition, but also generates mask-aware high quality reconstructions of non-existing images. We compare MixSyn to state-of-the-art single-source sequential generation and collage generation approaches in terms of quality, diversity, realism, and expressive power; comparing region-wise reconstruction and similarity scores. We also showcase interactive synthesis, mix & match, design space exploration, and edit propagation tasks, with no mask dependency.

关键词： Photorealism computer vision Image synthesis conferences Semantics Training data computer architecture

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nnMobileNet: Rethinking CNN for Retinopathy Research

nnMobileNet: Rethinking CNN for Retinopathy Research

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Wenhui Zhu Peijie Qiu Xiwen Chen Xin Li Natasha Lepore Oana M. Dumitrascu Yalin Wang School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis School of Computing Clemson University Department of Radiology Children’s Hospital Los Angeles CIBORG Lab Department of Neurology Mayo Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the trajectory of RD model development. The leading-edge performance of ViT-based models in RD can be largely credited to their scalability—their ability to improve as more parameters are added. As a result, ViT-based models tend to outshine traditional CNNs in RD applications, albeit at the cost of increased data and computational demands. ViTs also differ from CNNs in their approach to processing images, working with patches rather than local regions, which can complicate the precise localization of small, variably presented lesions in RD. In our study, we revisited and updated the architecture of a CNN model, specifically MobileNet, to enhance its utility in RD diagnostics. We found that an optimized MobileNet, through selective modifications, can surpass ViT-based models in various RD benchmarks, including diabetic retinopathy grading, detection of multiple fundus diseases, and classification of diabetic macular edema. The code is available at https://***/Retinal-Research/NN-MOBILENET

关键词： Location awareness computer vision Computational modeling Transformers Retina Trajectory pattern recognition

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Luca Ostrogovich Roberto Del Prete Giuseppe Tomasicchio Nicolas Longépé Alfredo Renga Department of Industrial Engineering University of Naples Federico II Telespazio SRL Φ-Lab ESA ESRIN

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems—akin to those on-board spacecraft—were conducted. These tests are pivotal in demonstrating the system’s operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Space vehicles Accuracy Navigation Space missions Moon Software algorithms Switches

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Few-shot Open-set recognition by Transformation Consistency

Few-shot Open-set Recognition by Transformation Consistency

引用

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

作者： Jeong, Minki Choi, Seokeon Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed-set classification, existing OSR and FSL methods show poor performances in solving FSOSR problems. The previous FSOSR method follows the pseudo-unseen class sample-based methods, which collect pseudo-unseen samples from the other dataset or synthesize samples to model unseen class representations. However, this approach is heavily dependent on the composition of the pseudo samples. In this paper, we propose a novel unknown class sample detector, named SnaTCHer, that does not require pseudo-unseen samples. Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set. The modified set is composed by replacing a query feature and its predicted class prototype. SnaTCHer rejects samples with large differences to the transformed prototypes. Our method alters the unseen class distribution estimation problem to a relative feature transformation problem, independent of pseudo-unseen class samples. We investigate our SnaTCHer with various prototype transformation methods and observe that our method consistently improves unseen class sample detection performance without closed-set classification reduction.

关键词： Learning systems computer vision Adaptation models Prototypes Estimation Detectors pattern recognition

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Rugby Scene Classification Enhanced by vision Language Model

Rugby Scene Classification Enhanced by Vision Language Model

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Naoki Nonaka Ryo Fujihira Toshiki Koshiba Akira Maeda Jun Seita Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters Hakata Knee & Sports Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study investigates the integration of vision language models (VLM) to enhance the classification of situations within rugby match broadcasts. The importance of accurately identifying situations in sports videos is emphasized for understanding game dynamics and facilitating downstream tasks like performance evaluation and injury prevention. Utilizing a dataset comprising 18, 000 labeled images extracted at 0.2-second intervals from 100 minutes of rugby match broadcasts, scene classification tasks including contact plays (scrums, mauls, rucks, tackles, lineouts), rucks, tackles, lineouts, and multiclass classification were performed. The study aims to validate the utility of VLM outputs in improving classification performance compared to using solely image data. Experimental results demonstrate substantial performance improvements across all tasks with the incorporation of VLM outputs. Our analysis of prompts suggests that, when provided with appropriate contextual information through natural language, VLMs can effectively capture the context of a given image. The findings of our study indicate that leveraging VLMs in the domain of sports analysis holds promise for developing image processing models capable of incorpolating the tacit knowledge encoded within language models, as well as information conveyed through natural language descriptions.

关键词： Performance evaluation Scene classification Analytical models Computational modeling Prevention and mitigation Natural languages pattern recognition

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Contrastive Clothing and Pose Generation for Cloth-Changing Person Re-Identification

Contrastive Clothing and Pose Generation for Cloth-Changing ...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Vuong D. Nguyen Pranav Mantini Shishir K. Shah Dept. of Computer Science Quantitative Imaging Lab University of Houston

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cloth-Changing Person Re-Identification (CCRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing compounded with changes in pose, viewpoint, etc. In this work, we propose CCPG: Contrastive Clothing and Pose Generation framework for CCRe-ID. Beyond appearance, CCPG captures cloth-invariant body shape information using a Relational Graph Attention Network. Training a robust CCRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current CCRe-ID datasets. To address this, we propose a GAN-based model for clothing and pose transfer across identities to augment images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on CCRe-ID datasets demonstrate the effectiveness of our CCPG framework. Code will be available here.

关键词： Training computer vision Shape conferences Computational modeling Clothing Data models

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