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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Clas...

引用

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

作者： Conde, Marcos, V Turgutlu, Kerem Univ Valladolid Valladolid Spain Adobe Mountain View CA USA

ISBN: (纸本)9781665448994

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. In this work, we use CLIP (Contrastive Language-Image Pre-Training) [12] for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels. Model's zero-shot capability allows predicting the most relevant natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset [20], which we consider the largest annotated artwork dataset. Our code and models will be available at https://***/KeremTurgutlu/clip_art

关键词： Training computer vision Art Image recognition Databases Natural languages Neural networks

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Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Surv...

引用

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

作者： Marcos V. Conde Florin-Alexandru Vasluianu Radu Timofte Jianxing Zhang Jia Li Fan Wang Xiaopeng Li Zikun Liu Hyunhee Park Sejun Song Changho Kim Zhijuan Huang Hongyuan Yu Cheng Wan Wending Xiang Jiamin Lin Hang Zhong Qiaosong Zhang Yue Sun Xuanwu Yin Kunlong Zuo Senyan Xu Siyuan Jiang Zhijing Sun Jiaying Zhu Liangyan Li Ke Chen Yunzhe Li Yimo Ning Guanhua Zhao Jun Chen Jinyang Yu Kele Xu Qisheng Xu Yong Dou CAIDAS & IFI Computer Vision Lab. University of Würzburg Samsung Research China - Beijing (SRC-B) Samsung MX(Mobile eXperience) Business Xiaomi Inc. Georgia Institute of Technology University of Science and Technology of China McMaster University Computer Dept. National University of Defense Technology Changsha China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 sub-missions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

关键词： Surveys Degradation computer vision Reviews conferences Superresolution Pipelines Noise pattern recognition

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer

FedProK: Trustworthy Federated Class-Incremental Learning vi...

引用

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

作者： Xin Gao Xin Yang Hao Yu Yan Kang Tianrui Li Southwestern University of Finance and Economics Webank Southwest Jiaotong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing methods do not consider the trustworthiness of FCIL, i.e., improving continual utility, privacy, and efficiency simultaneously, which is greatly influenced by catastrophic forgetting and data heterogeneity among clients. To address this issue, we propose FedProK (Federated Prototypical Feature Knowledge Transfer), leveraging prototypical feature as a novel representation of knowledge to perform spatial-temporal knowledge transfer. Specifically, FedProK consists of two components: (1) feature translation procedure on the client side by temporal knowledge transfer from the learned classes and (2) prototypical knowledge fusion on the server side by spatial knowledge transfer among clients. Extensive experiments conducted in both synchronous and asynchronous settings demonstrate that our FedProK outperforms the other state-of-the-art methods in three perspectives of trustworthiness, validating its effectiveness in selectively transferring spatial-temporal knowledge.

关键词： Privacy Data privacy computer vision Federated learning conferences Prototypes Benchmark testing

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CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

CE-PeopleSeg: Real-time people segmentation with 10% CPU usa...

引用

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

作者： Jiang, Ziyu He, Zhenhua Huang, Xueqin Yang, Zibin Tan, Pearl Texas A&M Univ College Stn TX 77843 USA EEO Washington DC USA

ISBN: (纸本)9781665448994

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users' privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://***/geekJZY/***.

关键词： Visualization Privacy Government Education Graphics processing units Switches Streaming media

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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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ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer

ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fou...

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

作者： Wei Dong Han Zhou Yuqiong Tian Jingke Sun Xiaohong Liu Guangtao Zhai Jun Chen McMaster University Shanghai Jiao Tong University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://***/movingforward100/Shadow_R.

关键词： Representation learning Image quality Image segmentation computer vision Image color analysis conferences Lighting

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Content-aware Input Scaling and Deep Learning Computation Offloading for Low-Latency Embedded vision

Content-aware Input Scaling and Deep Learning Computation Of...

引用

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

作者： Omkar Prabhune Tianen Chen Younghyun Kim Purdue University University of Wisconsin-Madison

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication *** this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a content-aware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： Deep learning Training Radio frequency Visualization Accuracy Machine vision Face recognition

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Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models

Investigating the Effectiveness of Cross-Attention to Unlock...

引用

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

作者： Saman Motamed Wouter Van Gansbeke Luc Van Gool INSAIT Sofia University Bulgaria ETH Zurich KU Leuven

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content. In particular, manipulating the cross-attention layers of Text-to-Image (T2I) diffusion models has shown great promise in controlling the shape and location of objects in the scene. Transferring image-editing techniques to the video domain, however, is extremely challenging as object motion and temporal consistency are difficult to capture accurately. In this work, we take a first look at the role of cross-attention in Text-to-Video (T2V) diffusion models for zero-shot video editing. While one-shot models have shown potential in controlling motion and camera movement, we demonstrate zero-shot control over object shape, position and movement in T2V models. We show that despite the limitations of current T2V models, cross-attention guidance can be a promising approach for editing videos. Code: https://***/sam-motamed/***

关键词： computer vision Codes Shape Computational modeling conferences Text to image Diffusion models

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Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks

Faster Than Lies: Real-time Deepfake Detection using Binary ...

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

作者： Romeo Lanzino Federico Fontana Anxhelo Diko Marco Raoul Marini Luigi Cinque Department of Computer Science Sapienza University of Rome Rome Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method’s state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20× reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection.

关键词： Deepfakes Accuracy Fast Fourier transforms Computational modeling Neural networks Media Real-time systems

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