CLIP-Driven Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels as supervision. Despite recent advancements incorporating transformers into WSOL have resulted in improvements, these methods often rely on category-agnostic attention maps, leading to suboptimal object localization. This paper presents a novel CLIP-Driven TRansformer (CDTR) that learns category-aware representations for accurate object localization. Specifically, we initially propose a Category-aware Stimulation Module (CSM) that embeds learnable category biases into self-attention maps, enhancing the learning process with auxiliary supervision. Additionally, an Object Constraint Module (OCM) is designed to refine object regions in a self-supervised manner, leveraging the discriminative potential of the self-attention maps provided by CSM. To create a synergistic connection between CSM and OCM, we further develop a Semantic Kernel Integrator (SKI), which generates a semantic kernel for self-attention maps. Meanwhile, we explore the CLIP model and design a Semantic Boost Adapter (SBA) to enrich object representations by integrating semantic-specific image and text representations into self-attention maps. Extensive experimental evaluations on benchmark datasets, such as CUB-200-2011 and ILSVRC highlight the superior performance of our CDTR framework.