Leveraging Hidden Positives for Unsupervised Semantic Segmentation (2303.15014v1)

Abstract: Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP.

• The paper proposes a novel framework for unsupervised semantic segmentation by leveraging two types of hidden positives: global (GHP) and local (LHP).
• The methodology identifies GHPs using both fixed and dynamically trained features and enforces LHP consistency via gradient propagation based on Vision Transformer attention scores.
• This approach achieves state-of-the-art performance on multiple datasets like COCO-stuff, Cityscapes, and Potsdam-3, highlighting its potential for applications with limited labeled data.

Leveraging Hidden Positives for Unsupervised Semantic Segmentation

The paper "Leveraging Hidden Positives for Unsupervised Semantic Segmentation" by Seong et al. presents an advanced approach to tackle unsupervised semantic segmentation by introducing methods to discover and utilize semantically meaningful relationships within image data. The paper addresses the challenge posed by the absence of pixel-level annotations in unsupervised settings and proposes a novel framework that harnesses hidden positives for enhancing segmentation tasks.

The methodology is centered on the use of contrastive learning, where the researchers excavate two types of hidden positives: global hidden positives (GHP) and local hidden positives (LHP). It builds upon recent advances using Vision Transformers (ViT) and a segmentation head-in-training to improve the accuracy of segmentation.

Key Contributions

1. Identification of Global Hidden Positives (GHP): The paper introduces a sophisticated mechanism for identifying GHP using both task-agnostic and task-specific criteria. The task-agnostic perspectives rely on feature similarities defined by a fixed pre-trained backbone, while task-specific positives are extracted using features generated by a dynamically trained segmentation head. The progressive increase in the usage of task-specific features allows the model to capture semantic nuances more accurately and adapt its segmentation strategies over time.
2. Local Hidden Positives (LHP) Strategy: To ensure semantic consistency among adjacent patches, the paper proposes a gradient propagation approach. This approach is based on the inherent assumption that nearby patches often share the same semantics, promoting local consistency in segmentation outcomes. By propagating loss gradients according to attention scores derived from the ViT architecture, the model strengthens semantic coherence in the localized regions of images.
3. Achieving State-of-the-Art (SOTA) Performance: The proposed framework sets new benchmarks in unsupervised semantic segmentation across several datasets including COCO-stuff, Cityscapes, and Potsdam-3. The results starkly highlight the effectiveness of utilizing hidden positives, especially when combined with the domain-specific benefits of the segmentation head.

Practical Implications

The findings of this research provide valuable advancements for applications where labeled data is scarce or impractical to obtain. For instance, this approach could be transformative in domains such as medical imaging, autonomous driving, and earth observation, where manual annotation is either cost-prohibitive or infeasible due to the vast quantities of data.

Theoretical Implications and Future Directions

From a theoretical standpoint, the approach emphasizes the potential of contrastive learning enhanced by hidden positives for unsupervised semantic tasks, suggesting routes for further optimization of feature learning models. Moreover, future work could explore the integration of these techniques with other self-supervised learning paradigms or extend them into novel architectures like multi-modal transformers for more complex dataset types.

In conclusion, this paper successfully demonstrates a robust method for alleviating the dependence on pre-labeled datasets in semantic segmentation and sets a precedent for further exploration into leveraging implicit relationships in image data. As AI continues to advance, strategies akin to those presented by this research will be pivotal in pushing the boundaries of unsupervised learning capabilities.