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Enhancing vision-Language Few-Shot Adaptation with Negative Learning

Enhancing Vision-Language Few-Shot Adaptation with Negative ...

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2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Zhang, Ce Stepputtis, Simon Sycara, Katia Xie, Yaqi School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9798331510831

Large-scale pre-trained vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-efficient manner. However, when only a few labeled samples are available, adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work, we propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of representative positive features defining 'what is a {CLASS} ', SimNL discovers a complementary set of negative features that define 'what is not a {CLASS}', providing additional insights that supplement the positive features to enhance task-specific recognition capability. Further, we identify that current adaptation approaches are particularly vulnerable to potential noise in the fewshot sample set. To mitigate this issue, we introduce a plug-and-play few-shot instance reweighting technique to suppress noisy outliers and amplify clean samples for more stable adaptation. Our extensive experimental results across 15 datasets validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks while achieving competitive computational efficiency. Code is available at https://***/zhangce01/SimNL. © 2025 ieee.

关键词： Zero-shot learning

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

Interactive Rotated Object Detection for Novel Class Detecti...

引用

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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SpectFormer: Frequency and Attention is what you need in a vision Transformer

SpectFormer: Frequency and Attention is what you need in a V...

引用

2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Patro, Badri N. Namboodiri, Vinay P. Agneeswaran, Vijay S. Microsoft United States University of Bath United Kingdom

ISBN: (纸本)9798331510831

vision transformers have been applied successfully for image recognition tasks. There have been either multi-headed self-attention based (ViT [12], DelT [54]) simi-lar to the original work in textual models or more re-cently based on spectral layers (Fnet [29], GFNet [46], AFNO [15]). We hypothesize that spectral layers cap-ture high-frequency information such as lines and edges, while attention layers capture token interactions. We inves-tigate this hypothesis through this work and observe that indeed mixing spectral and multi-headed attention layers provides a better transformer architecture. We thus pro-pose the novel Spectformer architecture for vision trans-formers that has initial spectral and deeper multi-headed attention layers. We believe that the resulting representation allows the transformer to capture the feature repre-sentation appropriately and it yields improved performance over other transformer representations. For instance, it im-proves the top-1 accuracy by 2% on ImageNet compared to both GFNet-H and LiT. SpectFormer-H-S reaches 84.25% top-1 accuracy on ImageNet-1 K (state of the art for small version). Further, Spectformer-H-L achieves 85.7% which is the state of the art for the comparable base version of the transformers. We further validated the SpectFormer per-formance in other scenarios such as transfer learning on standard datasets such as ClFAR-10, ClFAR-100, Oxford-lIlT-flower, and Standford Car datasets. We then investigate its use in downstream tasks such as object detection and instance segmentation on the MS-COCO dataset and ob-serve that Spectformer shows consistent performance that is comparable to the best backbones and can be further optimized and improved. The source code is available on this website https://***/badripatro/SpectFormers. © 2025 ieee.

关键词： Image segmentation

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Learning Visual Grounding from Generative vision and Language Model

Learning Visual Grounding from Generative Vision and Languag...

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2025 ieee/cvf Winter conference on Applications of computer vision, WACV 2025

作者： Wang, Shijie Kim, Dahun Taalimi, Ali Sun, Chen Kuo, Weicheng Brown University United States Google DeepMind United Kingdom Google Cloud United States

ISBN: (纸本)9798331510831

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we ex-plore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text an-notation of visual grounding data. We find that grounding knowledge already exists in generative VLM and can be elicited by proper prompting. We thus prompt a VLM to generate object-level descriptions by feeding it object regions from existing object detection datasets. We fur-ther propose attribute modeling to explicitly capture the im-portant object attributes, and spatial relation modeling to capture inter-object relationship, both of which are common linguistic pattern in referring expression. Our constructed dataset (500K images, 1M objects, 16M referring expressions) is one of the largest grounding datasets to date, and the first grounding dataset with purely model-generated queries and human-annotated objects. To verify the qual-ity of this data, we conduct zero-shot transfer experiments to the popular RefCoco benchmarks for both referring expression comprehension (REC) and segmentation (RES) tasks. On both tasks, our model significantly outperform the state-of-the-art approaches without using human anno-tated visual grounding data. Our results demonstrate the promise of generative VLM to scale up visual grounding in the real world. © 2025 ieee.

关键词： Image segmentation

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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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U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmentation

U-MedSAM: Uncertainty-Aware MedSAM for Medical Image Segmen...

引用

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

作者： Wang, Xin Liu, Xiaoyu Huang, Peng Huang, Pu Hu, Shu Zhu, Hongtu Albany United States School of Physics and Electronics Shandong Normal University Jinan China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Department of Computer and Information Technology Purdue University West Lafayette United States University of North Carolina at Chapel Hill Chapel Hill United States

ISBN: (纸本)9783031818530

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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Segment Anything in Medical Images with nnUNet

Segment Anything in Medical Images with nnUNet

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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

作者： Stock, Raphael Kirchhoff, Yannick Rokuss, Maximilian R. Ravindran, Ashis Maier-Hein, Klaus Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031818530

In this paper, we present an enhanced medical image segmentation approach leveraging the nnUNet framework, specifically tailored to integrate bounding box prompts for improved segmentation accuracy in resource-constrained environments. By incorporating these prompts as binary masks in an additional input channel, we enable more precise and context-aware segmentation. Our methodology employs a 2D slice-wise approach optimized for CPU-based inference through just-in-time (JIT) compiled functions, ensuring efficient processing on standard clinical equipment. Our solution demonstrates robust performance, achieving an average Dice Similarity Coefficient (DSC) of 80.98% and a Normalized Surface Dice (NSD) of 83.23% across multiple modalities in the validation set. This indicates its practical applicability and effectiveness in real-world clinical settings, where computational resources may be limited. By focusing on both accuracy and efficiency, our approach makes advanced segmentation technology accessible to a broader range of healthcare providers, facilitating enhanced clinical decision-making and patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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

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

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ieee Winter Applications and computer vision Workshops (WACVW)

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

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

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.

关键词： Training Handwriting recognition computer vision Adaptation models Runtime Text recognition Databases Transfer learning Training data Transformers

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