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An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification

28 Jan 2025IEEE Transactions on Circuits and Systems for Video Technology 2025 1archive 2025-07-28

Yueting Huang, Zhenzhe Hechen, Mingliang Zhou, Zhengguo Li, Sam Kwong

Fine-grained visual classification (FGVC) is a challenging task characterized by interclass similarity and intraclass diversity and has broad application prospects. Recently, several methods have adopted the vision Transformer (ViT) in FGVC tasks since the data specificity of the multihead self-attention (MSA) mechanism in ViT is beneficial for extracting discriminative feature representations. However, these works focus on integrating feature dependencies at a high level, which leads to the model being easily disturbed by low-level background information. To address this issue, we propose a fine-grained attention-locating vision Transformer (FAL-ViT) and an attention selection module (ASM). First, FAL-ViT contains a two-stage framework to identify crucial regions effectively within images and enhance features by strategically reusing parameters. Second, the ASM accurately locates important target regions via the natural scores of the MSA, extracting finer low-level features to offer more comprehensive information through position mapping. Extensive experiments on public datasets demonstrate that FAL-ViT outperforms the other methods in terms of performance, confirming the effectiveness of our proposed methods. The source code is available at https://github.com/Yueting-Huang/FAL-ViT.

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yueting-huang/fal-vit officialmentioned in paperpytorch report

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Tasks

Fine-Grained Image Classification Specificity

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Fine-Grained Image Classification	NABirds	FAL-ViT	Accuracy	91.1%	#11 of 30	Archive leaderboard	report
Fine-Grained Image Classification	Stanford Dogs	FAL-ViT	Accuracy	91.1%	#15 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 Focus Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer Vision Transformer

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