Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach (1703.08448v3)

Abstract: We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. Classification networks are only responsive to small and sparse discriminative regions from the object of interest, which deviates from the requirement of the segmentation task that needs to localize dense, interior and integral regions for pixel-wise inference. To mitigate this gap, we propose a new adversarial erasing approach for localizing and expanding object regions progressively. Starting with a single small object region, our proposed approach drives the classification network to sequentially discover new and complement object regions by erasing the current mined regions in an adversarial manner. These localized regions eventually constitute a dense and complete object region for learning semantic segmentation. To further enhance the quality of the discovered regions by adversarial erasing, an online prohibitive segmentation learning approach is developed to collaborate with adversarial erasing by providing auxiliary segmentation supervision modulated by the more reliable classification scores. Despite its apparent simplicity, the proposed approach achieves 55.0% and 55.7% mean Intersection-over-Union (mIoU) scores on PASCAL VOC 2012 val and test sets, which are the new state-of-the-arts.

• The paper introduces an iterative adversarial erasing method to progressively uncover discriminative object regions for semantic segmentation.
• It combines adversarial erasing with online prohibitive segmentation learning to refine segmentation masks and reduce noisy annotations.
• The approach achieves state-of-the-art results with mIoU scores of 55.0% on validation and 55.7% on the test set of PASCAL VOC 2012.

Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

Overview

Summarizing the presented paper reveals a structured approach designed to address weakly-supervised semantic segmentation through a novel adversarial erasing (AE) method. The authors identify a significant challenge with classification networks: their limited responsiveness to small and sparse discriminative object regions insufficient for dense pixel-wise segmentation tasks. To bridge this gap, they introduce an AE method combined with a complementary prohibitive segmentation learning (PSL) approach to progressively mine and expand object regions, resulting in effective segmentation.

Adversarial Erasing (AE) Approach

The AE method is central to the paper's contributions, initiating with a classification network that identifies the most responsive discriminative regions of an object. These regions are sequentially erased in an adversarial manner, continuously forcing the network to identify new discriminative segments until no significant regions remain undiscovered. Specifically, each iteration comprises the following steps:

1. Classification Training: A classification network is first trained using image-level labels.
2. Region Localization: Utilize the CAM technique to generate a heatmap highlighting discriminative regions.
3. Adversarial Erasure: Identified regions are erased, and the modified image is used to further train the network to discover new object parts.

This iterative process ensures that progressively larger and more comprehensive object regions are mined, ultimately forming a complete foreground segmentation mask. This approach facilitates the transition from sparse classification activations to dense regions necessary for meaningful segmentation.

Online Prohibitive Segmentation Learning (PSL)

To complement AE, the PSL method is introduced as a mechanism to enhance the quality of generated segmentation masks. The PSL approach leverages classification confidences to modulate category-specific response maps, generating auxiliary segmentation masks in an online fashion. Performance is optimized by combining these auxiliary masks with the initial regions identified by AE, ensuring a robust and noise-resistant training process. Essentially, the PSL approach reduces the impact of noisy and incomplete region annotations, providing a more stable structure for segmentation learning.

Experimental Evaluation and Results

The evaluation on the PASCAL VOC 2012 benchmark demonstrates the efficacy of the proposed approach. Notable results include:

• Validation Set: The proposed AE-PSL method achieves a mean Intersection-over-Union (mIoU) score of 55.0%, outperforming other weakly-supervised methods.
• Test Set: The AE-PSL method records an mIoU of 55.7%, establishing new state-of-the-art performance levels.

The comparison underscores the model's superiority, particularly against methods employing various levels of weak supervision, such as bounding boxes, scribbles, and image-level labels.

Theoretical and Practical Implications

Theoretical Implications:

• The AE-PSL approach demonstrates that adversarial manipulation of training inputs can be a powerful tool in transitioning from classification to segmentation tasks traditionally relying on dense pixel-level labels.
• It also shows the potential for iterative refining of discriminative regions to establish a more comprehensive understanding of object boundaries.

Practical Implications:

• From a practical perspective, the AE-PSL method offers a more cost-effective solution to semantic segmentation tasks, reducing the dependency on extensive, labor-intensive pixel-level annotations.
• The methodology could be particularly valuable in domains requiring rapid adaptation to new datasets where labeled data is scarce.

Future Prospects

The paper's findings open several avenues for future research, including:

• Improving the AE process via adaptive erasing strategies that account for low-level visual features, hence refining and extending erased regions more precisely.
• Integrating the AE and PSL methods into a unified framework that dynamically balances between classification confidence and segmentation accuracy.
• Applying similar adversarial approaches to other forms of weakly-supervised learning beyond segmentation, such as object detection or scene understanding.

In conclusion, the paper presents a compelling and rigorous approach to weakly-supervised semantic segmentation, leveraging adversarial training dynamics to expand discriminative regions effectively. By combining this with a robust PSL framework, the authors achieve significant advancements in performance and offer a scalable path forward for semantic segmentation research.