Topology-Aware Vision Transformers for Enhanced Scene Recognition
Yunxi Wang, Shuaiyu Liu, Qiling Li, Yazhou Ren, Xiaorong Pu
Abstract
Scene recognition (SR) is a fundamental task in computer vision (CV). In recent years, Transformer-based methods have achieved remarkable success in scene recognition tasks. Most existing approaches primarily rely on visual features, while failing to effectively model the structural relationships within scenes, which are crucial for accurate scene recognition. To this end, we propose Topology Attention Network for Scene Recognition (TANSR), an innovative method that leverages topological relationships from graphs to guide scene recognition. Specifically, Graph Attention Mask Generation Network (GAMGN) generates topology-aware masks from graph representations constructed by Graph Generation Module (GGM) and integrates them with patch embeddings by Topology Attention Guidance (TAG), enabling the transformer
BibTeX
@inproceedings{aaai2026_topologyawarevis,
title = {Topology-Aware Vision Transformers for Enhanced Scene Recognition},
author = {Yunxi Wang and Shuaiyu Liu and Qiling Li and Yazhou Ren and Xiaorong Pu},
booktitle = {AAAI 2026},
year = {2026}
}