{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-attention-locating-algorithm-for","title":"An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification","arxiv_id":null,"date":"2025-01-28","proceeding":"IEEE Transactions on Circuits and Systems for Video Technology 2025 1","authors":["Yueting Huang","Zhenzhe Hechen","Mingliang Zhou","Zhengguo Li","Sam Kwong"],"abstract":"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.","url_abs":"https://ieeexplore.ieee.org/document/10855837","url_pdf":"https://ieeexplore.ieee.org/document/10855837","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-attention-locating-algorithm-for","repo_url":"https://github.com/yueting-huang/fal-vit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fine-grained-image-classification-on-nabirds","task":"Fine-Grained Image Classification","dataset":"NABirds","model":"FAL-ViT","rank_in_archive_order":11,"of":30,"metrics":{"Accuracy":"91.1%"},"uses_additional_data":false},{"leaderboard":"/sota/fine-grained-image-classification-on-stanford-1","task":"Fine-Grained Image Classification","dataset":"Stanford Dogs","model":"FAL-ViT","rank_in_archive_order":15,"of":24,"metrics":{"Accuracy":"91.1%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}