MT-DSNet: Mix-mask teacher–student strategies and dual dynamic selection plug-in module for fine-grained image recognition

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-10-08 DOI:10.1016/j.cviu.2024.104201
Hongchun Lu, Min Han
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Abstract

The fine-grained image recognition (FGIR) task aims to classify and distinguish subtle differences between subcategories with visually similar appearances, such as bird species and the makes or models of vehicles. However, subtle interclass differences and significant intraclass variances lead to poor model recognition performance. To address these challenges, we developed a mixed-mask teacher–student cooperative training strategy. A mixed masked image is generated and embedded into a knowledge distillation network by replacing one image’s visible marker with another’s masked marker. Collaborative reinforcement between teachers and students is used to improve the recognition performance of the network. We chose the classic transformer architecture as a baseline to better explore the contextual relationships between features. Additionally, we suggest a dual dynamic selection plug-in for choosing features with discriminative capabilities in the spatial and channel dimensions and filter out irrelevant interference information to efficiently handle background and noise features in fine-grained images. The proposed feature suppression module is used to enhance the differences between different features, thereby motivating the network to mine more discriminative features. We validated our method using two datasets: CUB-200-2011 and Stanford Cars. The experimental results show that the proposed MT-DSNet can significantly improve the feature representation for FGIR tasks. Moreover, by applying it to different fine-grained networks, the FGIR accuracy can be improved without changing the original network structure. We hope that this work provides a promising approach for improving the feature representation of networks in the future.
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MT-DSNet:用于细粒度图像识别的师生混合掩码策略和双动态选择插件模块
细粒度图像识别(FGIR)任务旨在分类和区分视觉外观相似的子类别之间的细微差别,例如鸟的种类和汽车的品牌或型号。然而,细微的类间差异和显著的类内差异导致模型识别性能低下。为了应对这些挑战,我们开发了一种混合掩码师生合作训练策略。通过用一个图像的可见标记替换另一个图像的屏蔽标记,生成混合屏蔽图像并将其嵌入知识提炼网络。教师和学生之间通过合作强化来提高网络的识别性能。我们选择了经典的变换器架构作为基线,以更好地探索特征之间的上下文关系。此外,我们还提出了一个双动态选择插件,用于选择在空间和通道维度上具有分辨能力的特征,并过滤掉无关的干扰信息,以有效处理细粒度图像中的背景和噪声特征。所提出的特征抑制模块用于增强不同特征之间的差异,从而促使网络挖掘出更多具有分辨能力的特征。我们使用两个数据集验证了我们的方法:CUB-200-2011 和斯坦福汽车。实验结果表明,所提出的 MT-DSNet 可以显著改善 FGIR 任务的特征表示。此外,通过将其应用于不同的细粒度网络,可以在不改变原始网络结构的情况下提高 FGIR 的准确性。我们希望这项工作能为未来改进网络特征表示提供一种有前途的方法。
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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