Papers › Feature Fusion Vision Transformer for Fine-Grained Visual Categorization

Feature Fusion Vision Transformer for Fine-Grained Visual Categorization

6 Jul 2021arXiv:2107.02341archive 2025-07-28

Jun Wang, Xiaohan Yu, Yongsheng Gao

The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the discriminative parts or integrating the attention mechanism via CNN-based approaches.However, these methods enhance the computational complexity and make the modeldominated by the regions containing the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA performance on general image recognition tasks. Theself-attention mechanism aggregates and weights the information from all patches to the classification token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation token in the deep layer pays more attention to the global information, lacking the local and low-level features that are essential for FGVC. In this work, we proposea novel pure transformer-based framework Feature Fusion Vision Transformer (FFVT)where we aggregate the important tokens from each transformer layer to compensate thelocal, low-level and middle-level information. We design a novel token selection mod-ule called mutual attention weight selection (MAWS) to guide the network effectively and efficiently towards selecting discriminative tokens without introducing extra param-eters. We verify the effectiveness of FFVT on three benchmarks where FFVT achieves the state-of-the-art performance.

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Code

Markin-Wang/FFVT officialmentioned in papermentioned on GitHubpytorchNOASSERTION report

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Tasks

Fine-Grained Image Classification Fine-Grained Visual Categorization

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Fine-Grained Image Classification	CUB-200-2011	FFVT	Accuracy	91.6%	#2 of 12	Archive leaderboard	report
Fine-Grained Image Classification	CUB-200-2011	FFVT	Accuracy	91.6	#10 of 30	Archive leaderboard	report
Fine-Grained Image Classification	Stanford Dogs	FFVT	Accuracy	91.5%	#14 of 24	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Vision Transformer

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