Single-Stage Semantic Segmentation from Image Labels (2005.08104v1)

Abstract: Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. This is in contrast to earlier work that used only a single stage $-$ training one segmentation network on image labels $-$ which was abandoned due to inferior segmentation accuracy. In this work, we first define three desirable properties of a weakly supervised method: local consistency, semantic fidelity, and completeness. Using these properties as guidelines, we then develop a segmentation-based network model and a self-supervised training scheme to train for semantic masks from image-level annotations in a single stage. We show that despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines, substantially outperforming earlier single-stage methods.

• The paper introduces a novel single-stage framework that produces semantic segmentation masks from image-level labels using weak supervision.
• It employs innovations like Normalised Global Weighted Pooling and Pixel-Adaptive Mask Refinement to ensure local consistency and semantic fidelity.
• Empirical results on PASCAL VOC 2012 show that the method achieves competitive accuracy while reducing model complexity and training duration.

Insights from "Single-Stage Semantic Segmentation from Image Labels"

The paper entitled "Single-Stage Semantic Segmentation from Image Labels" by Nikita Araslanov and Stefan Roth introduces a novel approach to performing semantic segmentation in a weakly supervised setting. Unlike traditional methods that rely on pixel-level annotations, this research ambitiously aims to derive semantic segmentation purely from image-level labels. This paper is of particular interest due to its potential applicability in scenarios where obtaining detailed annotations is infeasible or cost-prohibitive.

Method Overview

This research identifies three fundamental properties of effective weakly supervised semantic segmentation: local consistency, semantic fidelity, and completeness. Using these guiding principles, the authors propose a single-stage framework that integrates a novel segmentation-based network model with a self-supervised training mechanism. The core innovation is how the model produces semantically meaningful segmentation masks from image-level annotations in one stage, sidestepping the complexity typically associated with multi-stage training pipelines.

Innovative Components

The authors introduce several key elements to achieve optimal segmentation performance:

1. Normalised Global Weighted Pooling (nGWP): This mechanism is critical in transforming classification scores, using pixel-level confidence predictions to produce reliable segmentation outcomes. By employing a focal mask penalty, the approach ensures the derived masks are not only accurate but also complete.
2. Pixel-Adaptive Mask Refinement (PAMR): This module iteratively adjusts segmentation masks using pixel-level affinity based on image appearance. This refinement ensures masks maintain local consistency, leading to more precise boundary predictions.
3. Stochastic Gate (SG): SG serves as a regularisation technique, balancing the feature representations from different network layers to mitigate the propagation of inaccurate pseudo-labels. This mechanism enables the network to better generalise from the imperfect supervision inherent in weakly labelled data.

Empirical Results

The paper presents thorough evaluations on the PASCAL VOC 2012 benchmark. The proposed approach demonstrates competitive performance, achieving segmentation quality comparable to state-of-the-art methods that employ multiple stages and additional data. Notably, the presented method surpasses earlier single-stage methods significantly, both statistically and in the quality of generated segmentations.

Implications and Future Work

The implications of this research extend beyond semantic segmentation, primarily in enhancing weakly supervised learning paradigms. By proving that high-quality segmentation is feasible from minimal supervision, this work encourages pursuing similar methodologies across other computer vision tasks. Furthermore, the reduction in model complexity and training duration heralds a promising direction in efficient deep learning methodologies.

For future explorations, expanding the method's applicability to other datasets and domains could further validate its versatility. Additionally, integrating more advanced self-supervised signals or exploring unsupervised pretraining could further enhance segmentation performance without increasing annotation costs.

Conclusion

Araslanov and Roth's research presents a compelling case for revisiting single-stage approaches to weakly supervised semantic segmentation. The simplicity and robustness of their framework provide a valuable alternative to existing complex multi-stage solutions. By effectively addressing the common challenges in weakly supervised learning, this paper sets the stage for future work aiming to bridge the gap between minimal supervision and high-fidelity segmentation performance.