Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation (2206.06363v1)

Abstract: The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or part category. This paper presents MaskDistill: a novel framework for unsupervised semantic segmentation based on three key ideas. First, we advocate a data-driven strategy to generate object masks that serve as a pixel grouping prior for semantic segmentation. This approach omits handcrafted priors, which are often designed for specific scene compositions and limit the applicability of competing frameworks. Second, MaskDistill clusters the object masks to obtain pseudo-ground-truth for training an initial object segmentation model. Third, we leverage this model to filter out low-quality object masks. This strategy mitigates the noise in our pixel grouping prior and results in a clean collection of masks which we use to train a final segmentation model. By combining these components, we can considerably outperform previous works for unsupervised semantic segmentation on PASCAL (+11% mIoU) and COCO (+4% mask AP50). Interestingly, as opposed to existing approaches, our framework does not latch onto low-level image cues and is not limited to object-centric datasets. The code and models will be made available.

• The paper introduces MaskDistill, which generates object masks using self-supervised vision transformers for semantic segmentation.
• It employs clustering and confidence-based filtering to create pseudo-labels and refine segmentation performance without manual annotations.
• Experimental results show an 11% mIoU increase on PASCAL VOC and a 4% AP boost on COCO, highlighting its scalability across diverse datasets.

Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

The paper "Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation" introduces MaskDistill, a novel framework designed for unsupervised semantic segmentation through the utilization of transformers. Semantic segmentation traditionally requires labor-intensive annotations, but MaskDistill aims to reduce this dependency by leveraging data-driven processes to generate object masks.

Methodology Overview

The paper identifies three key strategies that underpin the MaskDistill framework:

1. Data-Driven Object Mask Generation: Instead of relying on handcrafted priors that are typically limited to certain scene compositions, MaskDistill employs a self-supervised vision transformer model. These transformers are capable of learning spatially structured image representations from unannotated data, which allows for the generation of object masks based on attention layers. This approach captures high-level semantics, enabling it to generalize across varied datasets.
2. Clustering for Initial Model Training: After obtaining object masks, MaskDistill clusters them to create pseudo-ground truths. These pseudo-labels are used to train an initial object segmentation model, such as Mask R-CNN. This step purposefully extracts features from the masked images to inform the segmentation model's learning process without human intervention.
3. Filtering for Improved Training Data: MaskDistill employs a filtering process based on confidence scores from the initial segmentation model to remove low-quality masks. This cleaned dataset, with high-confidence object masks, facilitates the training of a more refined semantic segmentation model capable of better accuracy and consistency.

Experimental Results and Analysis

MaskDistill demonstrates significant improvements in unsupervised semantic segmentation performance. Notably, it achieves state-of-the-art results, surpassing previous unsupervised approaches with a 11% increase in mean Intersection over Union (mIoU) on the PASCAL VOC dataset and a 4% increase in average precision on the COCO dataset.

Noteworthy aspects of MaskDistill include:

• Avoidance of Low-Level Image Cues: Unlike many existing methodologies that are susceptible to overfitting to texture or color cues, MaskDistill's approach ensures the learning of representation is firmly rooted in semantic object characteristics.
• Generality Across Diverse Datasets: Without being confined to object-centric datasets, MaskDistill demonstrates applicability across complex scenes, as evidenced by its performance on both PASCAL VOC and COCO benchmarks.

Implications and Future Directions

The MaskDistill framework holds considerable implications for the field of unsupervised learning. By minimizing reliance on annotated datasets, this method offers a scalable solution especially relevant for domains where annotations are costly or infeasible, such as in medical imaging or rapidly changing environments in autonomous driving.

Future avenues for development could include further refinement of transformer models and mask generation techniques, or additional exploration of alternative self-supervised pretraining strategies to optimize feature extraction. There's also potential to extend MaskDistill’s capabilities to more complex scene understanding tasks, involving finer granularity in segmentation.

Overall, this research supports the growing body of work demonstrating the power of transformers and self-supervised learning techniques in achieving high-quality semantic segmentation without the prohibitive costs associated with extensive dataset annotation.