Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual Recognition

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arindam Sikdar, Yonghuai Liu, Siddhardha Kedarisetty, Yitian Zhao, Amr Ahmed, Ardhendu Behera
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Abstract

This paper presents a novel approach for Fine-Grained Visual Classification (FGVC) by exploring Graph Neural Networks (GNNs) to facilitate high-order feature interactions, with a specific focus on constructing both inter- and intra-region graphs. Unlike previous FGVC techniques that often isolate global and local features, our method combines both features seamlessly during learning via graphs. Inter-region graphs capture long-range dependencies to recognize global patterns, while intra-region graphs delve into finer details within specific regions of an object by exploring high-dimensional convolutional features. A key innovation is the use of shared GNNs with an attention mechanism coupled with the Approximate Personalized Propagation of Neural Predictions (APPNP) message-passing algorithm, enhancing information propagation efficiency for better discriminability and simplifying the model architecture for computational efficiency. Additionally, the introduction of residual connections improves performance and training stability. Comprehensive experiments showcase state-of-the-art results on benchmark FGVC datasets, affirming the efficacy of our approach. This work underscores the potential of GNN in modeling high-level feature interactions, distinguishing it from previous FGVC methods that typically focus on singular aspects of feature representation. Our source code is available at https://github.com/Arindam-1991/I2-HOFI.

Abstract Image

交织洞察力:细粒度视觉识别的高阶特征交互
本文提出了一种新颖的细粒度视觉分类(FGVC)方法,通过探索图形神经网络(GNN)来促进高阶特征交互,尤其侧重于构建区域间和区域内图形。以往的 FGVC 技术通常将全局特征和局部特征分离开来,而我们的方法则不同,在通过图学习的过程中将这两种特征无缝地结合在一起。区域间图捕捉长距离依赖关系,从而识别全局模式,而区域内图则通过探索高维卷积特征,深入研究物体特定区域内的更多细节。一项关键的创新是使用共享的 GNN,并将注意力机制与近似个性化神经预测传播(APPNP)信息传递算法相结合,提高了信息传播效率,从而获得更好的辨别能力,并简化了模型架构,提高了计算效率。此外,残差连接的引入提高了性能和训练稳定性。综合实验在基准 FGVC 数据集上展示了最先进的结果,肯定了我们方法的有效性。这项工作凸显了 GNN 在高层次特征交互建模方面的潜力,使其有别于以往通常侧重于特征表示单一方面的 FGVC 方法。我们的源代码可在 https://github.com/Arindam-1991/I2-HOFI 上获取。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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