Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations (2406.10114v1)

Abstract: Part-aware panoptic segmentation (PPS) requires (a) that each foreground object and background region in an image is segmented and classified, and (b) that all parts within foreground objects are segmented, classified and linked to their parent object. Existing methods approach PPS by separately conducting object-level and part-level segmentation. However, their part-level predictions are not linked to individual parent objects. Therefore, their learning objective is not aligned with the PPS task objective, which harms the PPS performance. To solve this, and make more accurate PPS predictions, we propose Task-Aligned Part-aware Panoptic Segmentation (TAPPS). This method uses a set of shared queries to jointly predict (a) object-level segments, and (b) the part-level segments within those same objects. As a result, TAPPS learns to predict part-level segments that are linked to individual parent objects, aligning the learning objective with the task objective, and allowing TAPPS to leverage joint object-part representations. With experiments, we show that TAPPS considerably outperforms methods that predict objects and parts separately, and achieves new state-of-the-art PPS results.

Task-Aligned Part-Aware Panoptic Segmentation Through Joint Object-Part Representations

The paper "Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations" by Daan de Geus and Gijs Dubbelman addresses the problem of Part-aware Panoptic Segmentation (PPS). PPS is a complex task in computer vision that aims to provide a rich, multi-level understanding of visual scenes by not only segmenting and classifying foreground objects and background regions but also identifying and linking parts within these objects to their parent objects.

Motivation and Challenges

Current PPS methods typically adopt a two-step approach: separate object-level and part-level segmentation. This separation introduces a fundamental misalignment between the learning objectives of the network and the ultimate task goal of PPS, which is to predict part-level segments within individual objects. This misalignment has several drawbacks:

1. Conflicting Representations: Networks simultaneously learn that object-level instances must be distinct while parts of these instances get grouped together, leading to conflicting feature representations.
2. Incompatibilities: Predictions made separately for object and part segments can be inconsistent, requiring additional, often computationally expensive post-processing steps to resolve conflicts.
3. Unleveraged Complementary Information: The potential complementary information between objects and their parts is not fully exploited, which could otherwise enhance segmentation performance.

Proposed Method: TAPPS

The authors propose Task-Aligned Part-aware Panoptic Segmentation (TAPPS) to address these limitations. TAPPS employs a unified set of shared queries to jointly predict object-level and part-level segments, thereby directly aligning the learning task with the PPS objective.

Architecture

The TAPPS framework leverages the mask classification paradigm, a robust approach in segmentation models such as Mask2Former. The architecture comprises:

1. Shared Queries: A single set of queries simultaneously represents objects and their parts. Each query predicts an object-level segment and the corresponding part-level segments.
2. Joint Object and Part Segmentation (JOPS) Head: This head processes each query to predict the object class, object-level mask, and the masks for all part-level segments compatible with the object class.

By utilizing these shared queries, TAPPS ensures that its learning objectives are perfectly aligned with PPS objectives, removing conflicts in the learned features and improving instance separability.

Experimental Results

Datasets and Metrics

The authors conduct rigorous evaluations on Cityscapes-PP and Pascal-PP datasets. Performance is measured using PartPQ, which evaluates segmentation quality at both object and part levels. Additional metrics include PartSQ for part-level segmentation quality and PQ for overall panoptic segmentation quality.

Findings

• Significant Performance Gains: TAPPS significantly outperforms the baseline method and existing state-of-the-art models across multiple metrics. For instance, on Pascal-PP, TAPPS achieves a PartPQ improvement of +6.3, reaching 60.4 with a Swin-B backbone, demonstrating substantial advancements in handling a more complex scene setup.
• Better Part Segmentation: TAPPS shows marked improvements in part-level segmentation quality (PartSQ), thanks to its ability to leverage joint object-part representations.
• Enhanced Object-Level Segmentation: There is a noticeable improvement in the Panoptic Quality (PQ) for thing classes, indicating better object instance separability due to learned object-instance-aware representations.

Implications and Future Directions

The results underscore the benefit of aligning learning objectives with task objectives in complex vision tasks like PPS. The proposed TAPPS framework not only enhances segmentation accuracy but also simplifies the network design by eliminating the need for post-processing.

The theoretical implications are profound; the incorporation of shared, multi-task queries into segmentation models offers a promising avenue for future research. This could extend to more hierarchical and flexible class structures or even be adapted for other tasks requiring joint predictions at multiple granularity levels.

Conclusion

In conclusion, the paper presents a well-validated method, TAPPS, which ingeniously aligns learning and task objectives for PPS. The method's ability to outperform state-of-the-art alternatives and its potential for broader applicability mark a significant step forward in comprehensive scene understanding, offering a unified approach that leverages the intricate relationship between objects and their parts. The promising results invite further exploration into more abstract and nuanced segmentation tasks, potentially catalyzed by such innovative methodological alignments.