Hypergraph-guided Intra- and Inter-category Relation Modeling for Fine-grained Visual Recognition
Abstract: Fine-grained Visual Recognition (FGVR) aims to distinguish objects within similar subcategories. Humans adeptly perform this challenging task by leveraging both intra-category distinctiveness and inter-category similarity. However, previous methods failed to combine these two complementary dimensions and mine the intrinsic interrelationship among various semantic features. To address the above limitations, we propose HI2R, a Hypergraph-guided Intra- and Inter-category Relation Modeling approach, which simultaneously extracts the intra-category structural information and inter-category relation information for more precise reasoning. Specifically, we exploit a Hypergraph-guided Structure Learning (HSL) module, which employs hypergraphs to capture high-order structural relations, transcending traditional graph-based methods that are limited to pairwise linkages. This advancement allows the model to adapt to significant intra-category variations. Additionally, we propose an Inter-category Relation Perception (IRP) module to improve feature discrimination across categories by extracting and analyzing semantic relations among them. Our objective is to alleviate the robustness issue associated with exclusive reliance on intra-category discriminative features. Furthermore, a random semantic consistency loss is introduced to direct the model's attention to commonly overlooked yet distinctive regions, which indirectly enhances the representation ability of both HSL and IRP modules. Both qualitative and quantitative results demonstrate the effectiveness and usefulness of our proposed HI2R model.
Primary Subject Area: [Content] Media Interpretation
Relevance To Conference: This work contributes significantly to the field of multimedia and multimodal processing by addressing a key challenge in Fine-grained Visual Recognition (FGVR) – the discernment of subtle differences within broad categories. FGVR is a critical component in multimedia processing, where distinguishing fine details in images or videos is essential for various applications like species identification in biodiversity, product recognition in e-commerce, or facial expression analysis in human-computer interaction. The Hypergraph-guided Intra- and Inter-category Relation Discovery Transformer (HI2RD) represents a substantial advancement in this domain. It integrates two crucial dimensions – intra-category feature distinctiveness and inter-category feature similarity – which humans naturally employ but have been largely overlooked in previous FGVR methods. This integration is particularly beneficial in FGVR, where differences are often subtle and challenging to detect. In multimedia and multimodal processing, the ability to recognize and analyze fine-grained details is paramount. The HI2RD model enhances this capability by providing a more sophisticated and nuanced approach to feature analysis in visual data. Its effectiveness is not only theoretical but also proven through both qualitative and quantitative evaluations, indicating its potential for wide application in various multimedia domains.
Submission Number: 1161
Loading