Cross-token Guidance Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to train an object locator using only image-level annotations. Recent progress in WSOL has been predominantly driven by visual transformer architectures, which effectively model long-range feature dependencies through self-attention mechanisms and multilayer perceptrons. However, existing transformer-based approaches typically depend solely on location tokens for localization, neglecting valuable semantic insights provided by class tokens, resulting in diffuse activations and imprecise localization. In this paper, we propose a novel framework named Cross-token Guidance TRansformer (CGTR), which enhances semantic coherence and localization precision by facilitating effective interaction between class and location tokens. Specifically, we introduce an Attention Regulation Module (ARM) to globally refine spatial activations using semantic guidance from class tokens, mitigating contextual biases. Additionally, we develop a Filter Regulation Module (FRM) that applies local structural refinement guided by semantic information, enriching the semantic representation of localization maps. Extensive experimental evaluations demonstrate the effectiveness of the proposed CGTR, achieving substantial and consistent performance gains compared with previous approaches on both the CUB-200-2011 and ILSVRC datasets.