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CSANet: High Speed Channel Spatial Attention Network for Mobile ISP

CSANet: High Speed Channel Spatial Attention Network for Mob...

引用

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

作者： Hsyu, Ming-Chun Liu, Chih-Wei Chen, Chao-Hung Chen, Chao-Wei Tsai, Wen-Chia Ind Technol Res Inst Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781665448994

The Image Signal Processor (ISP) is a customized device to restore RGB images from the pixel signals of CMOS image sensor. In order to realize this function, a series of processing units are leveraged to tackle different artifacts, such as color shifts, signal noise, moire effects, and so on, that are introduced from the photo-capturing devices. However, tuning each processing unit is highly complicated and requires a lot of experience and effort from image experts. In this paper, a novel network architecture, CSANet, with emphases on inference speed and high PSNR is proposed for end-to-end learned ISP task. The proposed CSANet applies a double attention module employing both channel and spatial attentions. Particularly, its spatial attention is simplified to a light-weighted dilated depth-wise convolution and still performs as well as others. As proof of performance, CSANet won 2nd place in the Mobile AI 2021 Learned Smartphone ISP Challenge with 1st place PSNR score.

关键词： Performance evaluation Runtime Pipelines Network architecture Service-oriented architecture pattern recognition Image restoration

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A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera

A Lightweight Spatiotemporal Network for Online Eye Tracking...

引用

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

作者： Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Olivier Coenen Brainchip Inc. Laguna Hills CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three ways: 1) deliberately targets a simple architecture and set of operations (convolutions, ReLU activations) 2) can be configured to perform online inference efficiently via buffering of layer outputs 3) can achieve more than 90% activation sparsity through regularization during training, enabling very significant efficiency gains on event-based processors. In addition, we propose a general affine augmentation strategy acting directly on the events, which alleviates the problem of dataset scarcity for event-based systems. We apply our model on the AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10 accuracy on the Kaggle private testset.

关键词： Training Program processors Gaze tracking Streaming media Spatiotemporal phenomena pattern recognition Convolutional neural networks

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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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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 ...

引用

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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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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Exploring Text-to-Motion Generation with Human Preference

Exploring Text-to-Motion Generation with Human Preference

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

作者： Jenny Sheng Matthieu Lin Andrew Zhao Kevin Pruvost Yu-Hui Wen Yangguang Li Gao Huang Yong-Jin Liu Tsinghua University Beijing Jiaotong University Shanghai AI Lab

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents an exploration of preference learning in text-to-motion generation. We find that current improvements in text-to-motion generation still rely on datasets requiring expert labelers with motion capture systems. Instead, learning from human preference data does not require motion capture systems; a labeler with no expertise simply compares two generated motions. This is particularly efficient because evaluating the model’s output is easier than gathering the motion that performs a desired task (e.g. backflip). To pioneer the exploration of this paradigm, we annotate 3,528 preference pairs generated by MotionGPT, marking the first effort to investigate various algorithms for learning from preference data. In particular, our exploration highlights important design choices when using preference data. Additionally, our experimental results show that preference learning has the potential to greatly improve current text-to-motion generative models. Our code and dataset are publicly available at https://***/THU-LYJ-Lab/InstructMotion to further facilitate research in this area.

关键词： computer vision Codes conferences Computational modeling Motion capture pattern recognition

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Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Real-Time Quantized Image Super-Resolution on Mobile NPUs, M...

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

作者： Ignatov, Andrey Timofte, Radu Denna, Maurizio Younes, Abdel Lek, Andrew Ayazoglu, Mustafa Liu, Jie Du, Zongcai Guo, Jiaming Zhou, Xueyi Jia, Hao Yan, Youliang Zhang, Zexin Chen, Yixin Peng, Yunbo Lin, Yue Zhang, Xindong Zeng, Hui Zeng, Kun Li, Peirong Liu, Zhihuang Xue, Shiqi Wang, Shengpeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Synapt Europe Lausanne Switzerland Synapt HQ San Jose CA USA AI Witchlabs Zurich Switzerland Aselsan Corp Ankara Turkey Nanjing Univ Nanjing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Netease Games AI Lab Beijing Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Minjiang Univ Fuzhou Peoples R China

ISBN: (纸本)9781665448994

Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Smart TV Superresolution Neural networks Smart homes Real-time systems

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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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Adversarial Identity Injection for Semantic Face Image Synthesis

Adversarial Identity Injection for Semantic Face Image Synth...

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

作者： Giuseppe Tarollo Tomaso Fontanini Claudio Ferrari Guido Borghi Andrea Prati Department of Engineering and Architecture University of Parma Parma Italy Department of Computer Science and Engineering University of Bologna Cesena Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Nowadays, deep learning models have reached incredible performance in the task of image generation. Plenty of literature works address the task of face generation and editing, with human and automatic systems that struggle to distinguish what’s real from generated. Whereas most systems reached excellent visual generation quality, they still face difficulties in preserving the identity of the starting input subject. Among all the explored techniques, Semantic Image Synthesis (SIS) methods, whose goal is to generate an image conditioned on a semantic segmentation mask, are the most promising, even though preserving the perceived identity of the input subject is not their main concern. Therefore, in this paper, we investigate the problem of identity preservation in face image generation and present an SIS architecture that exploits a cross-attention mechanism to merge identity, style, and semantic features to generate faces whose identities are as similar as possible to the input ones. Experimental results reveal that the proposed method is not only suitable for preserving the identity but is also effective in the face recognition adversarial attack, i.e. hiding a second identity in the generated faces.

关键词： Deep learning Visualization computer vision Image synthesis Face recognition Semantic segmentation conferences

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