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arXiv · 2412.12782

Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

Zhiguang Lu·Qianqian Xu·Shilong Bao·Zhiyong Yang·Qingming Huang
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Abstract

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method.

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Zhiguang Lu, Qianqian Xu, Shilong Bao, Zhiyong Yang, Qingming Huang. 2024-12-17. Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification. https://arxiv.org/abs/2412.12782

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