Learning to Fuse Things and Stuff (1812.01192v2)

Abstract: We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.

Learning to Fuse Things and Stuff: A Detailed Review

The paper "Learning to Fuse Things and Stuff" introduces a novel end-to-end approach for panoptic segmentation, which integrates instance (things) and semantic (stuff) segmentation into a unified framework. Panoptic segmentation, a concept originally introduced by Kirillov et al., aims to provide a comprehensive scene understanding by combining semantic segmentation, classifying each pixel into a category, and instance segmentation, which identifies and delineates each object instance.

Key Contributions

1. Architecture Design: The proposed model, TASCNet, leverages a shared backbone network based on ResNet, with a Feature Pyramid Network (FPN) to harmoniously integrate feature extraction for both tasks. This architecture is designed to handle the dual segmentation task comprehensively, ensuring that both tasks benefit from shared feature representation while remaining efficient in terms of parameterization.
2. Things and Stuff Consistency (TASC): A significant innovation in this paper is the introduction of a differentiable TASC loss function, which serves to enforce consistency between the predictions of instance and semantic segmentation tasks. This loss ensures alignment between these tasks by encouraging a minimal residual between the tasks’ output distributions.
3. Unified Training and Inference: The model is trained in a single pass, allowing it to learn efficiently without the need for separate, task-specific training phases. This unified approach also simplifies the inference pipeline, facilitating quicker deployment in real-world applications.

Experimental Results

The paper demonstrates the efficacy of TASCNet across various benchmark datasets, including Cityscapes, Mapillary Vistas, and COCO. These datasets present diverse scenes and challenges, providing a robust platform for evaluating the model’s performance.

• Cityscapes: TASCNet achieves competitive results compared to state-of-the-art methods with a significantly smaller backbone (ResNet-50), showcasing its efficiency.
• Mapillary Vistas: This dataset, with its broad range of scenes and high-resolution images, further validates the robustness of TASCNet, as it consistently performs well in terms of Panoptic Quality (PQ) metrics.
• COCO: TASCNet shows superior performance in the COCO test-dev set, underscoring the model’s scalability and its capability to manage large and complex datasets.

Implications and Future Directions

The integration of instance and semantic segmentation into a single task with the panoptic segmentation framework has substantial implications for computer vision. By achieving high performance with a unified model, this approach promises to streamline applications in autonomous driving, robotics, and augmented reality, where comprehensive scene understanding is crucial.

The proposed TASC constraint represents a methodological advancement that could be beneficial for broader multi-task learning paradigms, potentially applicable to other domains requiring task consistency.

Future work could explore enhancements in handling rare classes more effectively and better managing the trade-offs inherent in fusing semantic and instance segmentation tasks. Moreover, improving metrics like PQ to better balance things and stuff classes could further enhance evaluation practices in panoptic segmentation.

Overall, "Learning to Fuse Things and Stuff" contributes a salient methodological approach with practical implications, advancing the capabilities of dense scene understanding in dynamic and complex environments. The results and methodology presented provide a robust groundwork for further exploration and enhancement in the field of panoptic segmentation and beyond.