EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding (2406.01429v2)

Abstract: Unsupervised Domain Adaptation has been an efficient approach to transferring the semantic segmentation model across data distributions. Meanwhile, the recent Open-vocabulary Semantic Scene understanding based on large-scale vision LLMs is effective in open-set settings because it can learn diverse concepts and categories. However, these prior methods fail to generalize across different camera views due to the lack of cross-view geometric modeling. At present, there are limited studies analyzing cross-view learning. To address this problem, we introduce a novel Unsupervised Cross-view Adaptation Learning approach to modeling the geometric structural change across views in Semantic Scene Understanding. First, we introduce a novel Cross-view Geometric Constraint on Unpaired Data to model structural changes in images and segmentation masks across cameras. Second, we present a new Geodesic Flow-based Correlation Metric to efficiently measure the geometric structural changes across camera views. Third, we introduce a novel view-condition prompting mechanism to enhance the view-information modeling of the open-vocabulary segmentation network in cross-view adaptation learning. The experiments on different cross-view adaptation benchmarks have shown the effectiveness of our approach in cross-view modeling, demonstrating that we achieve State-of-the-Art (SOTA) performance compared to prior unsupervised domain adaptation and open-vocabulary semantic segmentation methods.

• The paper introduces a cross-view geometric constraint on unpaired data to translate features between varying camera perspectives.
• It employs a geodesic flow-based correlation metric to capture semantic structural changes across different views.
• The study presents a view-conditioned prompting mechanism that boosts open-vocabulary segmentation, yielding state-of-the-art results on multiple benchmarks.

Overview of EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding

The paper addresses a critical challenge in computer vision: the generalization of models for semantic segmentation across different viewpoints, particularly in the field of Unsupervised Domain Adaptation (UDA) and open-vocabulary segmentation. The authors introduce EAGLE, a novel framework designed to enhance performance by leveraging cross-view geometric correlations. This work presents three significant contributions: the introduction of a Cross-view Geometric Constraint on Unpaired Data, a Geodesic Flow-based Correlation Metric, and a view-conditioned prompting mechanism for open-vocabulary segmentation networks.

The problem of generalizing semantic segmentation models across changing camera views is not adequately addressed by existing UDA and open-vocabulary models. Current methods often falter in conditions where there is a significant change in viewpoint, such as from car-mounted cameras to drone views, due to their inability to model geometric structural changes. EAGLE proposes to solve this by explicitly modeling these changes, using the geometric information embedded in images and segmentation masks.

The experimental results on benchmarks like SYNTHIA to UAVID, GTA to UAVID, and BDD to UAVID showcase the effectiveness of the proposed approach. The EAGLE framework achieves State-of-the-Art (SOTA) performance, outperforming previous methods by a substantial margin. These results are indicative of the framework's capacity for efficient cross-view modeling in semantic scene understanding.

Key Contributions

1. Cross-view Geometric Constraint on Unpaired Data: The paper introduces a methodology that imposes geometric constraints on unpaired data. This approach allows the modeling of geometric transformations between source and target views without the need for paired datasets. By establishing a correlation between the structural geometry of images and segmentation outputs, it provides a mechanism to translate features from one view to another effectively.
2. Geodesic Flow-based Correlation Metric: EAGLE utilizes a Geodesic Flow-based Metric to measure structural changes across views. This metric efficiently captures the semantic structural changes along a geodesic path on a Grassmann manifold between domains, enabling robust cross-view feature representation and domain adaptation.
3. View-condition Prompting: A novel view-conditioned prompting mechanism is introduced to enhance the modeling capacity of open-vocabulary segmentation networks. This mechanism improves the contextual learning and adaptability of the learning model by embedding view-specific information into the network prompting structure.

Implications and Future Directions

The methodological innovations presented in EAGLE provide significant practical and theoretical implications. Practically, this framework can enhance the robustness and generalizability of computer vision models in real-world applications where cameras capture scenes from various views, such as autonomous driving and drone-based surveillance systems. Theoretically, this work enriches the understanding of how geometric relationships between different views can be modeled and leveraged within neural networks for better domain adaptation.

Looking forward, the approach can potentially inspire future research directions to explore more complex relationships between viewpoints and to develop more sophisticated methods for multi-view learning in AI. Further investigation could focus on extending this framework to cover other tasks beyond semantic segmentation and experimenting with hyper-parameter tuning and optimization of geometric constraints. Additionally, exploring the impact of these models on more diverse datasets provides another potential avenue for future work.

Overall, EAGLE represents a significant advancement in domain adaptation and cross-view scene understanding, aligning well with ongoing efforts to develop more adaptable and perceptive AI systems.