Re-Attention Transformer for Weakly Supervised Object Localization

Understand

Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.

• Existing deep network approaches are mainly based on class activation map, which focuses on highlighting discriminative local region while ignoring the full object.
• In addition, the emerging transformer-based techniques constantly put a lot of emphasis on the backdrop that impedes the ability to identify complete objects.
• To address these issues, we present a re-attention mechanism termed token refinement transformer (TRT) that captures the object-level semantics to guide the localization well.