Attention-guided Unified Network for Panoptic Segmentation (1812.03904v2)

Abstract: This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-the-arts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks.

• The paper introduces AUNet, a unified framework that leverages proposal and mask attention modules for efficient panoptic segmentation.
• It integrates instance and semantic segmentation into a single network, enhancing both computational efficiency and segmentation precision.
• AUNet achieves notable panoptic quality improvements with 46.5% on MS-COCO and 59.0% on Cityscapes, surpassing previous benchmarks.

Attention-guided Unified Network for Panoptic Segmentation

The paper entitled "Attention-guided Unified Network for Panoptic Segmentation" introduces an innovative framework designed to address the complex problem of panoptic segmentation. Panoptic segmentation is distinguished by its simultaneous effort to segment foreground (FG) objects at the instance level and background (BG) contents at the semantic level. Traditionally tackled through separate models for instance segmentation and semantic segmentation, this paper proposes a unified framework, aptly named Attention-guided Unified Network (AUNet), that integrates these processes in a single, cohesive model.

Overview of Approach

AUNet consists of two primary branches — one dedicated to foreground elements, the "things," and the other to background elements, the "stuff." The authors emphasize the complementary nature of FG objects, leveraging them to enhance the semantic parsing of BG contents. To facilitate this integration, AUNet introduces two attention-based modules: the Proposal Attention Module (PAM) and the Mask Attention Module (MAM). These modules provide object-level and pixel-level guidance respectively, using information from FG predictions to refine BG segmentations, and vice versa.

Key Contributions

1. Unified Framework: The proposed AUNet employs a unified network architecture, which not only streamlines processing by sharing a common backbone for both FG and BG segmentation but also enhances computational efficiency.
2. Attention Modules: The integration of PAM and MAM is crucial in this research. PAM utilizes region proposals to focus on likely FG areas, applying these as attention operations to background features. MAM further refines this process, using detailed mask information to adjust semantic boundaries with greater precision.
3. Performance Metrics: The network demonstrates enhanced performance across standard benchmarks, achieving a 46.5% panoptic quality (PQ) on the MS-COCO dataset and a 59.0% PQ on the Cityscapes dataset, surpassing previous state-of-the-art results without relying on additional data or extensive modifications typically used in competitive AI models.

Implications and Future Directions

The implications of AUNet are significant for the field of computer vision, particularly in tasks requiring comprehensive scene understanding such as autonomous driving and robotics. The ability to handle FG and BG processing in a unified model not only streamlines computational demands but also opens the avenue for more robust interpretations of dynamic environments.

Future research may expand on this foundation by exploring more sophisticated attention mechanisms or integrating additional data types, such as depth information, to further enhance segmentation accuracy. Moreover, the potential for applying this unified framework to other vision tasks, such as video processing where temporal coherence is crucial, remains an exciting prospect.

In conclusion, the Attention-guided Unified Network represents a meaningful advancement in panoptic segmentation, blending the traditionally separate tasks of instance and semantic segmentation into a harmonious process. Its innovative use of attention to cross-leverage FG and BG information constitutes a significant stride in the quest for more integrated and efficient vision models.