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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

International Challenge on Segment Anything in Medical Image...

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the ieee/CVF conference on computer vision and pattern recognition, CVPR 2024

ISBN: (纸本)9783031818530

The proceedings contain 16 papers. The special focus in this conference is on Segment Anything in Medical Images on Laptop. The topics include: Filters, Thresholds, and Geodesic Distances for Scribble-Based Interactive Segmentation of Medical Images;Rep-MedSAM: Towards Real-Time and Universal Medical Image Segmentation;Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets;a Light-Weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation;taking a Step Back: Revisiting Classical Approaches for Efficient Interactive Segmentation of Medical Images;ExpertsMedSAM: Faster Medical Image Segment Anything with Mixture-of-Experts;efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment;Lite Class-Prompt Tiny-VIT for Multi-modality Medical Image Segmentation;Segment Anything in Medical Images with nnUNet;SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments;RepViT-MedSAM: Efficient Segment Anything in the Medical Images;U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation;Modality-Specific Strategies for Medical Image Segmentation Using Lightweight SAM Architectures;gray’s Anatomy for Segment Anything Model: Optimizing Grayscale Medical Images for Fast and Lightweight Segmentation.

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Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Huttner, Lukas Mayr, Martin Gorges, Thomas Wu, Fei Seuret, Mathias Maier, Andreas Christlein, Vincent Friedrich-Alexander Universität Erlangen-Nürnberg Pattern Recognition Lab Erlangen91058 Germany

ISBN: (纸本)9798331536626

The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR. © 2025 ieee.

关键词： Optical character recognition

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Proceedings - 2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

Proceedings - 2025 IEEE/CVF Winter Conference on Application...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

ISBN: (纸本)9798331536626

The proceedings contain 166 papers. The topics discussed include: applying computer vision to analyze self-injurious behaviors in children with autism spectrum disorder;underwater image enhancement and object detection: are poor object detection results on enhanced images due to missing human labels?;enhancing weakly-supervised object detection on static images through (hallucinated) motion;a zero-shot learning approach for ephemeral gully detection from remote sensing using vision language models;Attrivision: advancing generalization in pedestrian attribute recognition using CLIP;human gaze improves vision transformers by token masking;SSTAR: skeleton-based spatio-temporal action recognition for intelligent video surveillance and suicide prevention in metro stations;and offline signature verification in the banking domain.

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Unified Face Matching and Physical-Digital Spoofing Attack Detection

Unified Face Matching and Physical-Digital Spoofing Attack D...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Kunwar, Arun Rattani, Ajita University of North Texas Dept. of Computer Science and Engineering Denton United States

ISBN: (纸本)9798331536626

Face recognition technology has dramatically trans-formed the landscape of security, surveillance, and authentication systems, offering a user-friendly and non-invasive biometric solution. However, despite its significant advantages, face recognition systems face increasing threats from physical and digital spoofing attacks. Current research typically treats face recognition and attack detection as distinct classification challenges. This approach necessitates the implementation of separate models for each task, leading to considerable computational complexity, particularly on devices with limited resources. Such inefficiencies can stifle scalability and hinder performance. In response to these challenges, this paper introduces an innovative unified model designed for face recognition and detection of physical and digital attacks. By leveraging the advanced Swin Transformer backbone and incorporating Hil:o attention in a convolutional neural network framework, we address unified face recognition and spoof attack detection more effectively. Moreover, we introduce augmentation techniques that replicate the traits of physical and digital spoofing cues, significantly enhancing our model ro-bustness. Through comprehensive experimental evaluation across various datasets, we showcase the effectiveness of our model in unified face recognition and spoof detection. Additionally, we confirm its resilience against unseen physical and digital spoofing attacks, underscoring its potential for real-world applications. © 2025 ieee.

关键词： digital attacks face matching physical attacks unified models

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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Huber, Marco Neto, Pedro C. Sequeira, Ana F. Damer, Naser Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Technische Universität Darmstadt Department of Computer Science Germany Inesc Tec Porto Portugal University of Porto Faculry of Engineering Portugal

ISBN: (纸本)9798331536626

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner. © 2025 ieee.

关键词： Frequency domain analysis

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Evaluating Multimodal vision-Language Model Prompting Strategies for Visual Question Answering in Road Scene Understanding

Evaluating Multimodal Vision-Language Model Prompting Strate...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Keskar, Aryan Perisetla, Srinivasa Greer, Ross University of California Merced United States

ISBN: (纸本)9798331536626

Understanding complex traffic scenes is a crucial challenge in advancing autonomous driving systems. Visual Question Answering (VQA) tasks have emerged as a promising approach to extracting actionable insights from multimodal traffic data, enabling vehicles to make accurate, real-time decisions. The MAPLM-QA dataset, introduced as part of the 2025 WACV Large Language vision Models Challenge for Autonomous Driving (LLVM-AD), offers a robust benchmark for this task, comprising 14,000 multi-modal frames combining high-resolution panoramic images and rendered Bird's Eye View (BEV) depictions of LiDAR 3D point clouds. In this work, we explore the application of NVIDIA's vision-Language Model (ViLA) to address VQAs in MAPLM-QA. By employing detailed prompt engineering tailored to the dataset, we systematically evaluate ViLA's performance, identifying strengths in certain metrics such as quality assessment while highlighting challenges in lane counting, intersection recognition, and nuanced scene understanding. Our findings illustrate the potential of vision-Language Models (VLMs) in enhancing traffic scene analysis and autonomous driving, establishing a strong foundation and analysis of limitations for future research in leveraging VLMs and multimodal datasets toward scalable, robust traffic scene understanding. © 2025 ieee.

关键词： Damage detection

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ChildACT: Child Action recognition Dataset in RGB Data 20

ChildACT: Child Action Recognition Dataset in RGB Data

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20th Annual ACM/ieee International conference on Human-Robot Interaction, HRI 2025

作者： Sandygulova, Anara Yershov, Andrey Zhanatkyzy, Aida Telisheva, Zhansaule School of Engineering and Digital Sciences Nazarbayev University Department of Robotics and Mechatronics Astana Kazakhstan

ISBN: (纸本)9798350378931

Human action recognition is concerned with analysing, understanding and synthesizing kinematic and kinetic data. Data-driven computer vision solutions have advanced the progress in the field of action recognition while it would not be possible without large and publicly available datasets. While there are numerous datasets available for adult action recognition, child-centered action recognition efforts are still scarce. This paper presents a dataset that consists of video recordings of 200 children of different age and gender groups performing seven actions (boxing, waving, clapping, running, jogging, walking towards the camera, and walking from side to side). Two camera perspectives are provided, with a top view and a frontal view in RGB format. The dataset provides an opportunity for kinematic analysis of children motion while it also aims to bridge the performance gap between activity recognition systems for adults and children. © 2025 ieee.

关键词： pattern recognition systems

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Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery

Interactive Rotated Object Detection for Novel Class Detecti...

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2025 ieee/CVF Winter conference on Applications of computer vision workshops, WACVW 2025

作者： Burges, Marvin Dias, Philipe Ambrozio Woody, Carson Walters, Sarah Lunga, Dalton Tu Wien Vienna Vienna Austria Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798331536626

In this paper, we propose IRTR-DETR, an Interactive and Real-Time Rotated DEtection TRansformer that extends IRT-DETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTDETR but introduces rotation-aware heads for improved detection of objects with arbitrary orientations. Similarly to IRTDETR, IRTR-DETR can be trained with a small labeled sample set in an interactive setting, but we show that it can also be pretrained on related but not identical data-such as a building damage dataset-before being applied to tasks like identifying buildings under construction. We demonstrate the efficacy of our approach on the publicly available Tiny-DOTA and xBD dataset, as well as two study-cases on proprietary datasets of greenhouses and houses under construction ('waffle homes'). Detecting greenhouses is highly relevant in the context of damage assessment, while 'waffle homes' aid understanding typical floorplans and building codes in different areas, both thereby supporting population modeling, emergency response, and policy planning. Our method outperforms the state of the art in interactive rotated object detection on the Tiny-DOTA dataset by 5.7 percent, and improves upon the non interactive RTDETR by 7.85 to 19.39 percent (depending on the number of provided samples) while maintaining its real-time efficiency. © 2025 ieee.

关键词： pattern recognition

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Research on Extracting Clothing Style Images Based on computer vision Technology

Research on Extracting Clothing Style Images Based on Comput...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Xu, Linan Kuang, Min Wuhan Institute of Design and Engineering Hubei Wuhan430000 China

ISBN: (纸本)9798331504205

Deep learning-based image recognition and generation technology is a key aspect of visual artificial intelligence, and the rapid development of AI technology is driving the clothing industry towards digitization. How to automatically and accurately obtain relevant clothing demand information to meet the severe market challenges has become a focal point for major clothing e-commerce platforms. This paper employs deep learning techniques to construct CNN classification models and object detection models for the automatic recognition of multiple features, including clothing color, style, and patterns. The study utilizes an improved lightweight VGGNet network for recognizing colors and styles, combined with YOLOv3, Faster R-CNN, and SSD object detection algorithms to achieve pattern recognition and localization. Through comparative experiments, the recognition accuracy of the improved lightweight VGGNet in identifying clothing styles and colors reached 96.49%;in clothing pattern detection, YOLOv3 achieved the highest mean Average Precision (mAP) of 86.66%. Among the three categories of patterns, texture patterns demonstrated the best detection performance with a mAP of 96.14%, followed by animal patterns with an mAP of 83.69%, and text patterns with an mAP of 79.80%. The results indicate that the graphic recognition algorithm proposed in this paper can effectively extract and identify patterns on clothing, thereby promoting the digital transformation of the clothing industry. © 2025 ieee.

关键词： Garment industry

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Emotion recognition in Visual Art on Color and Texture Analysis using Improved vision Transformer 1

Emotion Recognition in Visual Art on Color and Texture Analy...

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1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Suma, K.G. Balasm, Zaed Sasirekha, N. Veera Manikandan, P. Sandhya, K. Vellore Institute of Technology School of Computer Science and Engineering Department of Ai & Ml Andhra Pradesh India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electrical and Electronics Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Nowadays, Emotion recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual-Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively. © 2025 ieee.

关键词： Local binary pattern

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