Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation (2103.07246v2)

Abstract: Weakly-supervised semantic segmentation (WSSS) using image-level labels has recently attracted much attention for reducing annotation costs. Existing WSSS methods utilize localization maps from the classification network to generate pseudo segmentation labels. However, since localization maps obtained from the classifier focus only on sparse discriminative object regions, it is difficult to generate high-quality segmentation labels. To address this issue, we introduce discriminative region suppression (DRS) module that is a simple yet effective method to expand object activation regions. DRS suppresses the attention on discriminative regions and spreads it to adjacent non-discriminative regions, generating dense localization maps. DRS requires few or no additional parameters and can be plugged into any network. Furthermore, we introduce an additional learning strategy to give a self-enhancement of localization maps, named localization map refinement learning. Benefiting from this refinement learning, localization maps are refined and enhanced by recovering some missing parts or removing noise itself. Due to its simplicity and effectiveness, our approach achieves mIoU 71.4% on the PASCAL VOC 2012 segmentation benchmark using only image-level labels. Extensive experiments demonstrate the effectiveness of our approach. The code is available at https://github.com/qjadud1994/DRS.

• The paper introduces a novel DRS module that expands class activation maps into dense localization regions using a suppression controller and max-element extractor.
• It refines initial maps through an iterative localization map refinement learning strategy that recovers missing parts and filters out noise.
• The method achieves competitive performance with a 71.4% mIoU on the PASCAL VOC 2012 benchmark, demonstrating its practical effectiveness.

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

The paper "Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation" explores the problem of semantic segmentation using only image-level labels, which significantly reduces annotation costs compared to pixel-level labeling. The proposed method introduces a discriminative region suppression (DRS) module to address the limitations of existing weakly-supervised approaches that rely heavily on sparse and discriminative regions provided by class activation maps (CAMs). The DRS module effectively spreads activation from these discriminative regions to adjacent non-discriminative regions, thereby producing dense localization maps.

Key Contributions

1. DRS Module: The introduction of the DRS module is a significant step in expanding object activation regions in weakly-supervised settings. DRS consists of a max-element extractor, a suppression controller, and a suppressor. This module can be integrated into any network with minimal additional parameters. Its operation is centered around suppressing high-activation regions to indirectly emphasize adjacent areas, assisting in acquiring more comprehensive localization maps.
2. Localization Map Refinement Learning: The paper further enhances the initial localization maps through a learning strategy called localization map refinement learning. This strategy aims to self-enhance the localization maps by recovering missing parts and filtering out noise, thus refining the output of the DRS module.
3. Strong Quantitative Results: The evaluation on the PASCAL VOC 2012 segmentation benchmark reveals a mean Intersection over Union (mIoU) of 71.4%, which is competitive with state-of-the-art methods that also employ weak supervision strategies. This demonstrates the effectiveness of the proposed DRS module in producing high-quality pseudo segmentation labels.

Technical Analysis

The DRS module is distinctive because it offers a straightforward yet effective strategy for resolving the class imbalance prevalent in CAMs. By suppressing—but not eliminating—regions of high activation, DRS allows the network to attend more extensively across the target object, leading to more consistent segmentation masks. Importantly, the suppression controller is adaptable; it may either follow a learnable strategy, accommodating the network's feedback, or a fixed suppression schema to achieve varying levels of suppression, with trade-offs in training complexity and ease of implementation.

Localization map refinement further consolidates outcomes by minimizing errors inherent in the initial activation maps. This iterative improvement proves vital in increasing the robustness and precision of segmentation outputs.

Implications and Future Directions

The paper exemplifies progress in weakly-supervised learning—a critical area as the demand for extensive labeled datasets becomes increasingly burdensome. The introduction of the DRS module showcases a modular, adaptable option for future research in semantic segmentation. Leveraging DRS with enhanced refinement processes holds theoretical potential in improving other segmentation and classification tasks across various domains.

Future research may involve applying these concepts to other forms of annotations or image modalities. Furthermore, integrating the DRS framework with advanced architectures, such as transformers in vision tasks, could unlock further enhancements in segmentation accuracy and generalization.

Conclusion

The DRS approach addresses a key challenge in weakly-supervised semantic segmentation—translating coarse image-level labels into dense, pixel-wise predictions. Its design embodies simplicity and efficacy, ensuring ease of adaptation across different models, marking a notable contribution to semantic segmentation using deep learning methodologies.