Weakly- and Semi-Supervised Panoptic Segmentation (1808.03575v3)

Abstract: We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both "thing" and "stuff" classes, and thus explain all the pixels in the image. "Thing" classes are weakly-supervised with bounding boxes, and "stuff" with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.

Weakly- and Semi-Supervised Panoptic Segmentation: A Detailed Overview

The paper "Weakly- and Semi-Supervised Panoptic Segmentation," authored by Li, Arnab, and Torr from the University of Oxford, presents a novel approach to panoptic segmentation leveraging weak supervision methods. The work addresses a key challenge in image segmentation—the high cost and labor-intensive nature of pixel-perfect annotations—by utilizing bounding boxes and image-level tags to achieve segmentation tasks with substantially reduced annotation efforts.

Methodology

The authors introduce a segmentation model capable of handling both semantic and instance-level segmentation. Unlike traditional models that often produce overlapping instances within object detection-based architectures, this approach focuses on segmenting all pixels into "thing" and "stuff" classes without overlaps.

• Semantic Segmentation: In this task, the model assigns each pixel to a semantic class. "Thing" classes, such as objects are annotated with bounding boxes, while "stuff" classes, like textures and amorphous regions, are tagged at the image level.
• Instance Segmentation: Here, each pixel is labeled with both an object class and a unique instance identifier. This model combines semantic segmentation with instance recognition to achieve non-overlapping instance segmentation.

The core innovation lies in the dual supervision strategy:

1. Weak Supervision: Utilizing bounding boxes as coarse labels for "thing" classes and image-level tags for "stuff" classes. This is in contrast to existing methods that generally require dense pixel-level annotations.
2. Semi-Supervised Approach: Integrating both fully labeled images and those with weak annotations to enhance model learning.

Results and Performance

The authors' model demonstrated a significant reduction in annotation effort, with weak supervision methods accounting for only 3% of the time required for full annotations. The model achieves approximately 95% of the accuracy of fully-supervised models on benchmarks like Pascal VOC, achieving state-of-the-art results. The model presents the first weakly-supervised methodology for both semantic and instance-level segmentation on datasets like Cityscapes, underscoring its novelty and efficacy.

On the Cityscape dataset, the semantic segmentation achieved an IoU of 63.6% with weak supervision, which rose to 71.6% under full supervision, indicating strong performance close to the fully supervised benchmark. In terms of instance segmentation evaluated by PQ, the method achieved 40.5% with weak supervision and improved to 47.3% with full supervision.

Implications

The research presents clear practical implications. It provides a cost-effective solution to high-quality image segmentation, suggesting a pathway for reducing reliance on intensive manual labeling efforts. The approach could open doors for wider adoption in domains where large-scale image data is available but extensive labeling remains prohibitive.

Future Directions

Future research could delve into expanding these methods further into fully unsupervised domains or exploring other forms of minimal supervision. Examining the trade-offs between weak supervision precision and broader labeling strategies could also be crucial in refining these models for diverse applications. Investigations into applying this framework to real-world data corpus beyond benchmarks could reveal more insights into its robustness across varied data types.

In conclusion, the paper significantly advances the field of panoptic segmentation by effectively marrying reduced annotation costs with high performance, suggesting transformative potential where annotated data is sparse or costly.