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Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy

Self-Supervised Learning with Generative Adversarial Network...

引用

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

作者： Bashir Kazimi Karina Ruzaeva Stefan Sandfeld IAS-9 Forschungszentrum Jülich GmbH Jülich Germany RWTH Aachen University Aachen Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise & background removal, and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy, allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst, being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important.

关键词： Training computer vision Superresolution Noise reduction Noise Self-supervised learning Predictive models

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Listen Then See: Video Alignment with Speaker Attention

Listen Then See: Video Alignment with Speaker Attention

引用

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

作者： Aviral Agrawal Carlos Mateo Samudio Lezcano Iqui Balam Heredia-Marin Prabhdeep Singh Sethi Carnegie Mellon University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video-based Question Answering (Video QA) is a challenging task and becomes even more intricate when addressing Socially Intelligent Question Answering (SIQA). SIQA requires context understanding, temporal reasoning, and the integration of multimodal information, but in addition, it requires processing nuanced human behavior. Furthermore, the complexities involved are exacerbated by the dominance of the primary modality (text) over the others. Thus, there is a need to help the task’s secondary modalities to work in tandem with the primary modality. In this work, we introduce a cross-modal alignment and subsequent representation fusion approach that achieves state-of-the-art results (82.06% accuracy) on the Social IQ 2.0 dataset for SIQA. Our approach exhibits an improved ability to leverage the video modality by using the audio modality as a bridge with the language modality. This leads to enhanced performance by reducing the prevalent issue of language overfitting and resultant video modality bypassing encountered by current existing techniques. Our code and models are publicly available at [1].

关键词： Bridges Visualization computer vision Codes Accuracy conferences Question answering (information retrieval)

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Efficient Exploration of Image Classifier Failures with Bayesian Optimization and Text-to-Image Models

Efficient Exploration of Image Classifier Failures with Baye...

引用

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

作者： Adrien Le Coz Houssem Ouertatani Stéphane Herbin Faouzi Adjed IRT SystemX Palaiseau France DTIS ONERA Université Paris Saclay Palaiseau France INRIA Lille France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image classifiers should be used with caution in the real world. Performance evaluated on a validation set may not reflect performance in the real world. In particular, classifiers may perform well for conditions that are frequently encountered during training, but poorly for other infrequent conditions. In this study, we hypothesize that recent advances in text-to-image generative models make them valuable for benchmarking computer vision models such as image classifiers: they can generate images conditioned by textual prompts that cause classifier failures, allowing failure conditions to be described with textual attributes. However, their generation cost becomes an issue when a large number of synthetic images need to be generated, which is the case when many different attribute combinations need to be tested. We propose an image classifier benchmarking method as an iterative process that alternates image generation, classifier evaluation, and attribute selection. This method efficiently explores the attributes that ultimately lead to poor behavior detection.

关键词： Training computer vision Costs Image synthesis Computational modeling Text to image Benchmark testing

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Temporal surface frame anomalies for deepfake video detection

Temporal surface frame anomalies for deepfake video detectio...

引用

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

作者： Andrea Ciamarra Roberto Caldelli Alberto Del Bimbo University of Florence Florence Italy Universitas Mercatorum Rome Italy CNIT Florence Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Looking at a video sequence where a foreground person is represented is not as time ago anymore. Deepfakes have revolutionized our way to watch at such contents and nowadays we are more often used to wonder if what we are seeing is real or is just a mystification. In this context of generalized disinformation, the need for reliable solutions to help common users, and not only, to make an assessment on this kind of video sequences is strongly upcoming. In this paper, a novel approach which leverages on temporal surface frame anomalies in order to reveal deepfake videos is introduced. The method searches for possible discrepancies, induced by deepfake manipulation, in the surfaces belonging to the captured scene and in their evolution along the temporal axis. These features are used as input of a pipeline based on deep neural networks to perform a binary assessment on the video itself. Experimental results witness that such a methodology can achieve significant performance in terms of detection accuracy.

关键词： Deepfakes Accuracy Instruments Video sequences Pipelines Watches pattern recognition

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Lost in Translation: Lip-Sync Deepfake Detection from Audio-Video Mismatch

Lost in Translation: Lip-Sync Deepfake Detection from Audio-...

引用

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

作者： Matyas Bohacek Hany Farid Stanford University Stanford CA USA University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Highly realistic voice cloning combined with AI-powered video manipulation allows for the creation of compelling lip-sync deepfakes where anyone can be made to say things they never did. The resulting fakes are being used to entertain, but also for everything from election related disinformation to small- and large-scale fraud. Lip-sync deepfakes can be particularly difficult to detect because only the mouth and jaw of the person talking is modified. We describe a robust and general-purpose technique to detect these fakes. This technique begins by independently translating the audio (using audio-to-text transcription) and video (using automated lip-reading). We then show that the resulting transcriptions are significantly mismatched for lip-sync deepfakes as compared to authentic videos. The robustness of this technique is evaluated against a controlled dataset of our creation and in-the-wild fakes, all of varying length and resolution.

关键词： Deepfakes computer vision Voting conferences Mouth Cloning Robustness

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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...

引用

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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Cluster Self-Refinement for Enhanced Online Multi-Camera People Tracking

Cluster Self-Refinement for Enhanced Online Multi-Camera Peo...

引用

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

作者： Jeongho Kim Wooksu Shin Hancheol Park Donghyuk Choi Nota Inc. Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, there has been a significant amount of research on Multi-Camera People Tracking (MCPT). MCPT presents more challenges compared to Multi-Object Single Camera Tracking, leading many existing studies to address them using offline methods. However, offline methods can only analyze pre-recorded videos, which presents less practical application in real industries compared to online methods. Therefore, we aimed to focus on resolving major problems that arise when using the online approach. Specifically, to address problems that could critically affect the performance of the online MCPT, such as storing inaccurate or low-quality appearance features and situations where a person is assigned multiple IDs, we proposed a Cluster Self-Refinement module. We achieved a third-place at the 2024 AI City Challenge Track 1 with a HOTA score of 60.9261%, and our code is available at https://***/nota-github/AIC2024_Track1_Nota.

关键词： Industries computer vision Accuracy conferences Urban areas Refining Pose estimation

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Test-time Specialization of Dynamic Neural Networks

Test-time Specialization of Dynamic Neural Networks

引用

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

作者： Sam Leroux Dewant Katare Aaron Yi Ding Pieter Simoens Department of Information Technology IDLab Ghent University - Imec Belgium Department of Engineering Systems and Services Delft University of Technology The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, there has been a notable increase in the size of commonly used image classification models. This growth has empowered models to recognize thousands of diverse object types. However, their computational demands pose significant challenges, especially when deploying them on resource-constrained edge devices. In many use cases where a model is deployed on an edge device, only a small subset of the classes will ever be observed by a given model instance. Our proposed test-time specialization of dynamic neural networks allows these models to become faster at recognizing the classes that are observed frequently, while maintaining the ability to recognize all other classes, albeit slightly less efficient. We benchmark our approach on a real-world edge device, obtaining significant speedups compared to the baseline model without test-time adaptation.

关键词： Adaptation models computer vision Computational modeling Image edge detection conferences Neural networks Benchmark testing

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TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos

TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking i...

引用

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

作者： Atom Scott Ikuma Uchida Ning Ding Rikuhei Umemoto Rory Bunker Ren Kobayashi Takeshi Koyama Masaki Onishi Yoshinari Kameda Keisuke Fujii Nagoya University AIST University of Tsukuba Tokai University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, largely reliant on object detection and appearance, often fail to track targets in such complex scenarios accurately. This limitation is further exacerbated by the lack of comprehensive and diverse datasets covering the full view of sports pitches. Addressing these issues, we introduce TeamTrack, a pioneering benchmark dataset specifically designed for MOT in sports. TeamTrack is an extensive collection of full-pitch video data from various sports, including soccer, basketball, and handball. Furthermore, we perform a comprehensive analysis and benchmarking effort to underscore TeamTrack’s utility and potential impact. Our work signifies a crucial step forward, promising to elevate the precision and effectiveness of MOT in complex, dynamic settings such as team sports. The dataset, project code and competition is released at: https://***/TeamTrack/.

关键词： Measurement computer vision Target tracking Computational modeling Object detection Games Benchmark testing

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DiffSeg: Towards Detecting Diffusion-Based Inpainting Attacks Using Multi-Feature Segmentation

DiffSeg: Towards Detecting Diffusion-Based Inpainting Attack...

引用

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

作者： Raphael Antonius Frick Martin Steinebach Fraunhofer SIT — ATHENE Center Darmstadt Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advancements made in deep learning over the past years, creating convincing media manipulations has become easy and accessible than ever before. In particular, diffusion models such as Stable-Diffusion allow users to synthesize realistic images based on a given text input. Apart from synthesizing entirely new images, diffusion models can also be used to make edits to images using inpainting. To combat the spread of disinformation and illegal content created with diffusion-based inpainting, this paper presents a new detection method based on multi-feature segmentation. Apart from information derived from the raw pixel values, noise, and frequency information are also exploited to detect and localize regions that have been subject to editing. Evaluation results strongly suggest that the proposed method can achieve high mIoU and AUC scores, outperforming state-of-the-art methods, even for syntheses generated by unseen diffusion models, or highly compressed images.

关键词： Deep learning Image segmentation computer vision Image coding conferences Noise Diffusion models

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