CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation (1811.10842v2)

Abstract: Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.

• The paper introduces the Cross-Image Affinity Net (CIAN), a novel method that exploits pixel-level relationships between different images to improve weakly supervised semantic segmentation quality using only image-level labels.
• CIAN achieves new state-of-the-art performance for image-level supervision methods, scoring 64.3% mIoU on Pascal VOC 2012 validation and 65.3% on the test set.
• Leveraging cross-image relationships provides supplementary pixel information, improves consistency across representations, and enhances the effective utility of weak image-level labels.

Analysis of Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

The paper "CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation," authored by Junsong Fan et al., presents a novel approach to tackle the task of semantic segmentation using weak supervision, only relying on image-level labels. The methodology focuses on exploiting relationships across different images, a feature that previous methods have largely ignored.

The primary contribution of this research is the introduction of the Cross-Image Affinity Net (CIAN), which establishes pixel-level relationships between different images to improve segmentation quality. The authors argue that these relationships are critical for obtaining consistent and complete segmentation regions, as they allow for the propagation of supplementary information across images. The proposed affinity module is integrated into existing segmentation architectures, forming an end-to-end system that remains computationally efficient.

Strong numerical results validate the efficacy of this approach: achieving mean Intersection over Union (mIoU) scores of 64.3% on the Pascal VOC 2012 validation dataset and 65.3% on the test set. These results set a new benchmark for methods reliant solely on image-level tagging, demonstrating the modular and effective nature of the affinity-based augmentation.

The research highlights several advantages gained from cross-image relationships:

1. Supplementary Pixel Information: The affinity configuration facilitates the exploitation of supplementary contextual clues from related segments in different images, leading to refined classification results at the pixel level.
2. Consistency Across Representations: By leveraging relationships across a dataset, the model can internalize more consistent semantic representations, ultimately improving segmentation accuracy.
3. Enhanced Utility of Labels: Through cross-image propagation, the supervision derived from image-level labels is shared across multiple inputs, increasing its effective utility.

The authors support their claims with extensive experimental analysis. For instance, they demonstrate that the cross-image affinity module can be effectively combined with stronger initial seeds, proving its robustness and adaptability. Unlike other methods, which treat images independently, CIAN exhibits clear advantages through its coherent cross-image strategy.

The empirical success also prompts theoretical implications for future AI systems: the prioritization of relational understanding may steer the development of models that handle data more intuitively. As systems evolve, similar relationship-based configurations could broaden contexts and improve interpretations beyond the immediate data landscape.

In conclusion, Fan et al.'s introduction of CIAN marks a promising step toward more intelligent, relationship-aware AI systems. While their work primarily addresses the segmentation challenge, it lays the groundwork for broader applications where cross-data affinities could lead to substantial gains in machine perception tasks. Future research could explore enhancing cross-affinity through more sophisticated relational modeling, possibly facilitating even greater efficiency and accuracy in weakly supervised settings.