# Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization

arXiv: https://arxiv.org/abs/2003.04719 · HTML: https://mlsanity.com/paper/2003.04719/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [ULTra: Unveiling Latent Token Interpretability in Transformer-Based Understanding and Segmentation](https://mlsanity.com/paper/2411.12589/) (2024)
- [ReplaceBlock: An improved regularization method based on background information](https://mlsanity.com/paper/2203.16029/) (2022)

## Abstract (arXiv)

Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.