Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation Model Achieves State-of-the-Art Results

Core Concepts

The proposed LIDAPS model achieves state-of-the-art performance on multiple panoptic UDA benchmarks by incorporating a novel instance-aware cross-domain mixing strategy (IMix) and CLIP-based domain alignment (CDA) to effectively adapt both the semantic and instance segmentation branches.

Abstract

The paper addresses the task of unsupervised domain adaptation (UDA) for panoptic segmentation, which aims to transfer knowledge from a labeled source domain to an unlabeled target domain. The key contributions are:

IMix: A novel instance-aware cross-domain mixing strategy that pastes high-confidence predicted instances from the target domain onto source images. This improves instance segmentation performance by reducing confirmation bias.

CDA: A CLIP-based domain alignment module that regularizes the semantic branch to mitigate the catastrophic forgetting caused by IMix.

LIDAPS: An end-to-end model that combines IMix and CDA, achieving state-of-the-art results on four popular panoptic UDA benchmarks.

The paper first establishes a baseline panoptic UDA model using a mean-teacher framework and semantic cross-domain mixing. It then introduces IMix to adapt the instance segmentation branch, which leads to a drop in semantic performance due to catastrophic interference. CDA is proposed to address this by aligning the semantic features with CLIP embeddings.
The ablation studies demonstrate the effectiveness of the individual components. IMix significantly boosts instance segmentation (+8.5% mAP) while CDA improves semantic segmentation (+1.6% mIoU). The combination of these two modules in LIDAPS achieves the best panoptic performance, outperforming previous state-of-the-art methods by up to 3.6% mPQ.

Stats

The model is evaluated on four panoptic UDA benchmarks: SYNTHIA→Cityscapes, SYNTHIA→Mapillary Vistas, Cityscapes→Mapillary Vistas, and Cityscapes→Foggy Cityscapes.

Quotes

"While previous SOTA methods for panoptic UDA such as EDAPS [56] achieve good semantic segmentation performance, they struggle to predict correct object boundaries and thus instance segmentation masks."
"We propose employing CLIP-based domain alignment (CDA) to act as a regularizer on the semantic branch."
"Our proposed LIDAPS, while improving instance segmentation, is also able to enhance the semantic quality through CDA."

Key Insights Distilled From

Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation

by Elham Amin M... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03799.pdf

Deeper Inquiries

How can the proposed instance-aware mixing strategy (IMix) be extended to other dense prediction tasks beyond panoptic segmentation, such as instance detection or instance-level video segmentation

The proposed instance-aware mixing strategy (IMix) can be extended to other dense prediction tasks beyond panoptic segmentation by adapting the concept to suit the specific requirements of each task. For instance detection, IMix can be modified to focus on individual instances within an image, similar to how it operates in panoptic segmentation. By cutting high-confidence predicted instances from the target domain and pasting them onto source images, the model can learn to detect instances more accurately across different domains. Additionally, for instance-level video segmentation, IMix can be applied temporally to ensure consistency in instance segmentation across frames. By incorporating temporal information and adapting the mixing strategy to account for motion and object continuity, IMix can enhance the performance of instance-level video segmentation models in diverse scenarios.

What are the potential limitations of relying on CLIP embeddings for domain alignment, and how could the approach be further improved to handle more diverse or challenging target domains

While relying on CLIP embeddings for domain alignment offers several advantages, there are potential limitations to consider. One limitation is the generalization capability of CLIP embeddings across diverse target domains. CLIP embeddings may not capture domain-specific nuances or variations effectively, leading to suboptimal alignment in challenging or highly diverse target domains. To address this limitation, the approach could be further improved by incorporating domain-specific adaptation techniques. This could involve fine-tuning the CLIP model on domain-specific data or incorporating domain-specific features into the alignment process to enhance the robustness of the alignment across different domains. Additionally, exploring ensemble methods with multiple pre-trained language models or incorporating domain-specific prompts could help improve the adaptability of CLIP-based domain alignment in more diverse target domains.

Given the success of language-guided models in various computer vision tasks, how could the integration of language be further leveraged to improve unsupervised domain adaptation beyond the semantic alignment proposed in this work

The integration of language in unsupervised domain adaptation can be further leveraged to improve semantic alignment and adaptation beyond the scope of the proposed work. One potential direction is to explore the use of language prompts for guiding the adaptation process in a more dynamic and context-aware manner. By incorporating dynamic language prompts that adapt to the characteristics of the target domain, the model can learn to align semantic features more effectively and adapt to domain shifts more efficiently. Additionally, leveraging language for task-specific guidance, such as providing task-specific prompts for instance segmentation or object detection, can help improve the performance of unsupervised domain adaptation models in a task-oriented manner. By exploring the synergies between language guidance and domain adaptation, further advancements can be made in enhancing the adaptability and generalization capabilities of unsupervised domain adaptation models.