Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification
Zhiguang Lu, Qianqian Xu, Shilong Bao, Zhiyong Yang, Qingming Huang
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.
BibTeX
@article{Lu_Xu_Bao_Yang_Huang_2025, title={Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34112}, DOI={10.1609/aaai.v39i18.34112}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lu, Zhiguang and Xu, Qianqian and Bao, Shilong and Yang, Zhiyong and Huang, Qingming}, year={2025}, month={Apr.}, pages={19189-19197} }