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Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

Neural Architecture Search of Deep Priors: Towards Continual...

引用

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

作者： Mundt, Martin Pliushch, Iuliia Ramesh, Visvanathan Goethe Univ Frankfurt Germany

ISBN: (纸本)9781665448994

In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.

关键词： Training computer vision conferences Neural networks computer architecture Interference pattern recognition

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GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsule...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Hengfei Oh, Jun O Jin Chang, Hyung Na, Jin Hee Tae, Minwoo Zhang, Zhongqun Choi, Sang-Il University of Birmingham United Kingdom Dankook University Korea Republic of VTouch Inc

ISBN: (纸本)9798350302493

Gaze estimation is the task of estimating eye gaze from facial features. People tend to infer gaze by considering different facial properties from the whole image and their relations. However, existing methods rarely consider these various properties. In this paper, we propose a novel GazeCaps framework that represents various facial properties as different capsules. The capsules respond sensitively to transforms of facial properties by vectorial expression, which is effective for gaze estimation in which many facial components are nonlinearly transformed according to the direction of the head in addition to the perspective. Furthermore, we propose a Self-Attention Routing (SAR) module which can dynamically allocate attention to different capsules that contain important information and can be optimized as a single process without iterations. Through rigorous experiments, we confirm that the proposed method achieves state-of-the-art performance on various benchmarks. We also detail the generalization performance of the proposed model through a cross-dataset evaluation. © 2023 ieee.

关键词： computer vision

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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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Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

Revisiting The Evaluation of Class Activation Mapping for Ex...

引用

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

作者： Poppi, Samuele Cornia, Marcella Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781665448994

As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.

关键词： Deep learning Visualization computer vision Protocols conferences Reproducibility of results pattern recognition

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Omni-Crack30k: A Benchmark for Crack Segmentation and the Reasonable Effectiveness of Transfer Learning

Omni-Crack30k: A Benchmark for Crack Segmentation and the Re...

引用

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

作者： Christian Benz Volker Rodehorst Bauhaus-Universität Weimar Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large benchmarking datasets, such as ImageNet, COCO, Cityscapes, or ScanNet, have enormously promoted research in computer vision. For the domain of crack segmentation, no such large and well-maintained benchmark exists. Crack segmentation is characterized by the decentralized creation of datasets, almost all of which have their specific right to existence. Each dataset covers a different aspect of the surprisingly complex landscape of materials, acquisition conditions, and appearances linked to crack segmentation. The OmniCrack30k dataset forms the first large-scale, systematic, and thorough approach to provide a sustainable basis for tracking methodical progress in the field of crack segmentation. It contains 30k samples from over 20 datasets summing up to 9 billion pixels in total. Featuring materials as diverse as asphalt, ceramic, concrete, masonry, and steel, it paves the road towards universal crack segmentation, a currently under-explored topic. Experiments indicate the effectiveness of transfer learning for crack segmentation: nnU-Net achieves a mean clIoU4px of 64% outperforming all other approaches by at least 10% points.

关键词： Training computer vision Systematics Semantic segmentation Roads Transfer learning computer architecture

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GESCAM : A Dataset and Method on Gaze Estimation for Classroom Attention Measurement

GESCAM : A Dataset and Method on Gaze Estimation for Classro...

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

作者： Athul M. Mathew Arshad Ali Khan Thariq Khalid Riad Souissi Elm Company Saudi Arabia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human gaze provides crucial insights into individual attention during social or educational interactions. Attention systems often rely on head and facial features to predict gaze direction, but reliable gaze target detection (GTD) requires rich contextual cues. These cues inform the system about an individual’s position within a scene and the surrounding objects they might be interacting with. Our paper proposes attention measurement using GTD in educational classrooms, leveraging a synthetic dataset called GESCAM (Gaze Estimation based Synthetic Classroom Attention Measurement). This dataset was meticulously generated using 3D modelling, animation, simulation, and rendering techniques comprising 60,000 images with 650,000 instances of individuals (students, teachers) engaged in various activities, including looking at blackboard, notebooks, mobile phones etc. Our novel network trained on GESCAM proficiently identifies gaze fixations within complex classroom scenes, offering insights into human attention in classrooms across diverse contexts.

关键词： Three-dimensional displays Head Estimation Object detection Rendering (computer graphics) Mobile handsets pattern recognition

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Contrastive Learning for Natural Language-Based Vehicle Retrieval

Contrastive Learning for Natural Language-Based Vehicle Retr...

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

作者： Tam Minh Nguyen Quang Huu Pham Linh Bao Doan Hoang Viet Trinh Viet-Anh Nguyen Viet-Hoang Phan Sun Asterisk Inc R&D Lab AI Res Team Tokyo Japan Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9781665448994

AI City Challenge 2021 Task 5: The Natural Language-Based Vehicle Tracking is a Natural Language-based Vehicle Retrieval task, which requires retrieving a single-camera track using a set of three natural language descriptions of the specific targets. In this paper, we present our methods to tackle the difficulties of the provided task. Experiments with our approaches on the competitive dataset from AICity Challenge 2021 show that our techniques achieve Mean Reciprocal Rank score of 0.1701 on the public test dataset and 0.1571 on the private test dataset.

关键词： computer vision Target tracking conferences Urban areas Natural languages computer architecture pattern recognition

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Improving Consistency in Cardiovascular Disease Risk Assessment: Cross-Camera Adaptation for Retinal Images

Improving Consistency in Cardiovascular Disease Risk Assessm...

引用

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

作者： Weiyi Zhang Danli Shi Mingguang He School of Optometry The Hong Kong Polytechnic University Research Centre for SHARP Vision (RCSV) The Hong Kong Polytechnic University Centre for Eye and Vision Research (CEVR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel cross-camera domain adaptation method to address the challenges associated with achieving consistency and adaptability in cardiovascular disease (CVD) risk assessment using retinal images captured by conventional and portable cameras. The proposed method leverages an enhanced ordinal CVD risk classification approach to predict CVD risk levels, effectively capturing the ordinal relationship and implicit information embedded within retinal images. Additionally, a plug-and-play risk consistency loss is incorporated into the image translation model to ensure alignment in risk assessment between different image domains. Experimental evaluations on diverse datasets demonstrate the effectiveness and superiority of the proposed method in achieving consistent CVD risk assessment across various camera models. The results highlight the potential of the proposed approach to enhance early detection and intervention of CVD, utilizing the convenience and cost-effectiveness of portable retinal imaging technology. Overall, this research contributes to the field of computer-aided medical imaging by providing a robust and adaptable solution for CVD risk assessment, ultimately benefiting patients and healthcare providers in their efforts to combat CVD.

关键词： Adaptation models Computational modeling conferences Medical services Retina Cameras pattern recognition

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Enhancing Targeted Attack Transferability via Diversified Weight Pruning

Enhancing Targeted Attack Transferability via Diversified We...

引用

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

作者： Hung-Jui Wang Yu-Yu Wu Shang-Tse Chen National Taiwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Malicious attackers generate adversarial instances by introducing imperceptible perturbations into data. Even in the black-box setting where model details are concealed, attackers still exploit networks with cross-model transferability. Despite the notable success of untargeted attacks, achieving targeted attack transferability remains a challenging endeavor. Recent investigations have demonstrated the efficacy of ensemble-based techniques. However, utilizing additional models to carry out ensemble attacks brings extra costs. To reduce the number of white-box models required, model augmentation methods augment the given network to produce a variant of diverse models, contributing useful gradients for attack. In this work, we propose Diversified Weight Pruning (DWP) as an innovative model augmentation technique specifically designed to facilitate the generation of transferable targeted attacks. In contrast to prior techniques, DWP preserves essential connections while simultaneously ensuring diversity among the pruned models, both of which are identified as pivotal factors for targeted transferability. DWP is shown effective with experiments on ImageNet under challenging conditions, with enhancements of up to 10.1%, 6.6%, and 7.0% across adversarially trained models, Non-CNN architectures, and Google Cloud vision respectively.

关键词： computer vision Image coding Costs Perturbation methods conferences computer architecture pattern recognition

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Estimating (and fixing) the Effect of Face Obfuscation in Video recognition

Estimating (and <i>fixing</i>) the Effect of Face Obfuscatio...

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

作者： Tomei, Matteo Baraldi, Lorenzo Bronzin, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Metaliquid Srl Milan Italy

ISBN: (纸本)9781665448994

Recent research has shown that faces can be obfuscated in large-scale datasets with a minimal performance impact on image classification and downstream tasks like object recognition. In this paper, we investigate the role of face obfuscation in video classification datasets and quantify a more significant reduction in performance caused by face blurring. To reduce such performance effects, we propose a generalized distillation approach in which a privacy-preserving action recognition network is trained with privileged information given by face identities. We show, through experiments performed on Kinetics-400, that the proposed approach can fully close the performance gap caused by face anonymization.

关键词： Visualization computer vision Face recognition conferences Computational modeling Pipelines Object recognition

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