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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition"

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On the Automated Selection of Neural Network Architectures in the Task of pattern recognition for Intelligent Transportation Systems

On the Automated Selection of Neural Network Architectures i...

引用

2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Moseva, Alesya Gorodnichev, Mikhail Moseva, Marina Kharrasov, Kamil Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

Exponential growth in the number of computing resources, their cheapening, and the wide availability of large data sets have ensured a great demand for information systems based on neural networks. Such systems are widely used in many spheres of life: in medicine, detecting diseases at early stages and helping to design new drugs;in the financial sector, detecting fraudulent transactions and calculating risks, including the development of intelligent transportation systems. Intelligent transportation systems (ITS) actively use computer vision technologies to solve such tasks as object recognition, traffic analysis, traffic forecasting and road safety. However, the development of effective neural network models for these tasks requires significant resources and high qualification of specialists. The system of automated training and selection of neural network architecture can significantly simplify and speed up this process. Data collection, selection of the necessary model and tuning of hyperparameters requires specialized knowledge and skills. This work is aimed at developing a tool for automated selection of neural network architecture, which will accelerate the development and implementation of models, reduce development costs, improve the quality and reliability of solutions, provide scalability and adaptability to changing conditions. © 2025 ieee.

关键词： Intelligent systems

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Breaking the Frame: Visual Place recognition by Overlap Prediction

Breaking the Frame: Visual Place Recognition by Overlap Pred...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Tong Wei Philipp Lindenberger Jiří Matas Daniel Barath Visual Recognition Group FEE Czech Technical University in Prague Computer Vision and Geometry Group ETH Zurich HUN-REN SZTAKI

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Visual place recognition methods struggle with occlusion and partial visual overlaps. We propose a novel visual place recognition approach based on overlap prediction, called VOP, shifting from traditional reliance on global image similarities and local features to image overlap prediction. VOP proceeds co-visible image sections by obtaining patch-level embeddings using a vision Transformer backbone and establishing patch-to-patch correspondences without requiring expensive feature detection and matching. Our approach uses a voting mechanism to assess overlap scores for potential database images. It provides a nuanced image retrieval metric in challenging scenarios. Experimental results show that VOP leads to more accurate relative pose estimation and localization results on the retrieved image pairs than state-of-the-art base-lines on a number of large-scale, real-world indoor and outdoor benchmarks. The code is available at https://***/weitong8591/***.

关键词： Measurement Location awareness Visualization Accuracy Three-dimensional displays Pose estimation Image retrieval Focusing Transformers Visual place recognition

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

引用

7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer Science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the ieee conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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MimicGait: A Model Agnostic approach for Occluded Gait recognition Using Correlational Knowledge Distillation

MimicGait: A Model Agnostic approach for Occluded Gait Recog...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Ayush Gupta Rama Chellappa Johns Hopkins University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Gait recognition is an important biometric technique over large distances. State-of-the-art gait recognition systems perform very well in controlled environments at close range. Recently, there has been an increased interest in gait recognition in the wild prompted by the collection of outdoor, more challenging datasets containing variations in terms of illumination, pitch angles and distances. An important problem in these environments is that of occlusion, where the subject is partially blocked from camera view. While important, this problem has received little attention. Thus, we propose MimicGail, a model-agnostic approach for gait recognition in the presence of occlusions. We train the network using a multi-instance correlational distillation loss to capture both inter-sequence and intra-sequence correlations in the occluded gait patterns of a subject, utilizing an auxiliary Visibility Estimation Network to guide the training of the proposed mimic network. We demonstrate the effectiveness of our approach on challenging real-world datasets like GREW, Gait3D and BRIAR. We release the code in https://***/Ayush-00/mimicgait.

关键词： Training Measurement computer vision Adaptation models Correlation Codes Estimation Lighting Cameras Gait recognition

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Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification

Fairer Analysis and Demographically Balanced Face Generation...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Alexandre Fournier-Montgieux Michaël Soumm Adrian Popescu Bertrand Luvison Hervé Le Borgne CEA LIST Université Paris-Saclay Palaiseau France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Face recognition and verification are two computer vision tasks whose performances have advanced with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive nature of face data and biases in real-world training datasets hinder their development. Generative AI addresses privacy by creating fictitious identities, but fairness problems remain. Using the existing DCFace SOTA framework, we introduce a new controlled generation pipeline that improves fairness. Through classical fairness metrics and a proposed indepth statistical analysis based on logit models and ANOVA, we show that our generation pipeline improves fairness more than other bias mitigation approaches while slightly improving raw performance.

关键词： Measurement Training computer vision Analytical models Privacy Face recognition Prevention and mitigation Pipelines Analysis of variance Synthetic data

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Detective Networks: Enhancing Disaster recognition in Images Through Attention Shifting Using Optimal Masking

Detective Networks: Enhancing Disaster Recognition in Images...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Narongthat Thanyawet Photchara Ratsamee Yuki Uranishi Haruo Takemura Osaka University Osaka Japan Osaka Institute of Technology Osaka Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Aerial investigation is used for surveying damage and identifying post-disaster events through imagery data. However, the challenge lies in detecting disaster-related areas within aerial or shipborne images, as these can appear as minor regions, making recognition difficult. To address this challenge, we introduce the Detective Network (DeNet), designed to optimally mask images, thereby shifting the attention of machine learning models towards these small yet crucial regions. Utilizing the concepts of patch and anchor box, DeNet incorporates a masking candidate layer and a masking layer to facilitate optimal masking. Our experimental findings are compelling; by preprocessing images with DeNet before analysis using an image captioning model, we achieved a remarkable accuracy of 92.91% in landslide detection from side-view image captions and 87.50% for shipborne view detection. The result demonstrates the efficacy of DeNet in enhancing the recognition of disaster-related areas in challenging imaging conditions.

关键词： Visualization computer vision Image recognition Accuracy Target recognition Disasters Imaging Machine learning Terrain factors Decoding

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Channel Propagation Networks for Refreshable vision Transformer

Channel Propagation Networks for Refreshable Vision Transfor...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Junhyeong Go Jongbin Ryu Ajou University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In this paper, we introduce the Channel Propagation method, which aims to increase the channels of the vision Transformer systematically. Skip connections are commonly acknowledged as a propagation approach that improves the stability of the performance in vision Transformers. Nevertheless, it is important to note that these skip connections may give rise to the problem of over-smoothing, wherein similar features are represented in multiple layers. To tackle this matter, our proposed approach for Channel Propagation in vision Transformers retains the present signal information while concurrently propagating location-specific signals in a newly introduced channel dimension. On the other hand, the proposed Channel Propagation method effectively maintains the integrity of identity representation while simultaneously including patch-wise location-specific supervision by introducing a new channel dimension. The inclusion of this approach in vision Transformers mitigates the issue of over-smoothing while also improving the performance of visual recognition tasks. In our experiments, we confirm that the proposed method is effective for various visual recognition tasks. Specifically, our method demonstrates enhanced performance when implemented on vision Transformer models; the classification accuracy is increased considerably for plain and hierarchical architectures on the ImageNet dataset.

关键词： Hands computer vision Visualization Redundancy Buildings computer architecture Artificial neural networks Transformers Stability analysis Image classification

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DocTTT: Test-Time Training for Handwritten Document recognition Using Meta-Auxiliary Learning

DocTTT: Test-Time Training for Handwritten Document Recognit...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Wenhao Gu Li Gu Ziqiang Wang Ching Yee Suen Yang Wang Department of Computer Science and Software Engineering Concordia University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Despite recent significant advancements in Handwritten Document recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder (MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets.

关键词： Training Metalearning Adaptation models Handwriting recognition Visualization Technological innovation computer vision Text recognition Computational modeling Layout

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Importance-Guided Interpretability and Pruning for Video Transformers in Driver Action recognition

Importance-Guided Interpretability and Pruning for Video Tra...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Raquel Panadero Dominik Schörkhuber Margrit Gelautz Computer Vision Lab TU Wien Vienna Austria

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recently, transformers have gained prominence in video action recognition due to their ability to capture spatio-temporal dependencies. Despite their effectiveness, the interpretability of their self-attention mechanisms remains limited, posing obstacles in understanding model decisions, impacting transparency and bias identification. Additionally, the computational demands of transformer architectures, particularly the self-attention mechanism, present practical difficulties. To tackle both challenges, we adapt existing interpretability techniques and introduce a layer pruning method guided by importance metrics. In the context of driver action recognition, our findings highlight the efficacy of the applied head importance metrics in pinpointing crucial attention heads and identifying key visual cues essential for recognizing driver behavior. Experimental results, conducted on three mainstream video transformers, demonstrate the effectiveness of the proposed pruning technique with significantly reduced computational costs and only slight performance degradation by removing low-relevance layers. Specifically, on our DriverActionInsight (DAI) dataset, we achieve a 23.5% FLOPs saving in compressing Video Swin with less than a 1 % decrease in Top-1 accuracy.

关键词： Measurement Degradation Visualization computer vision Adaptation models Computational modeling computer architecture Transformers Computational efficiency Driver behavior

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An Encoder-Agnostic Weakly Supervised Method For Describing Textures

An Encoder-Agnostic Weakly Supervised Method For Describing ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Shangbo Mao Deepu Rajan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advances in Large Language Models (LLMs) have enabled the semantic description of textures in natural language, aiming to capture them in richer detail. However, most methods are confined to either depending on supervised training with pairs of images and manually annotated visual attributes that most texture datasets lack or using vision-Language Models (VLMs) such as CLIP. In this paper, we develop an encoder-agnostic Weakly supervised Texture Description Generator (WTDG) that employs a novel Scaled Ranked Kullback-Leibler divergence (SR-KL) loss between image and text modalities. Within the SR-KL loss formulation, we leverage category information, which is always available as ground-truths for all benchmark texture recognition datasets. We further extend our proposed WTDG to assist in texture recognition by using its generated texture descriptions. Thus, we develop a multimodal framework, called $T e x^2$ , which is adept at simultaneous generation of texture description and recognition. Our approach exhibits promising performance in describing and recognizing textures on benchmark datasets.

关键词： Training Visualization computer vision Accuracy Large language models Semantics Natural languages Benchmark testing Generators

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