Towards Label-free Scene Understanding by Vision Foundation Models
(2306.03899)Abstract
Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classification and segmentation tasks. However, the incorporation of CLIP and SAM for label-free scene understanding has yet to be explored. In this paper, we investigate the potential of vision foundation models in enabling networks to comprehend 2D and 3D worlds without labelled data. The primary challenge lies in effectively supervising networks under extremely noisy pseudo labels, which are generated by CLIP and further exacerbated during the propagation from the 2D to the 3D domain. To tackle these challenges, we propose a novel Cross-modality Noisy Supervision (CNS) method that leverages the strengths of CLIP and SAM to supervise 2D and 3D networks simultaneously. In particular, we introduce a prediction consistency regularization to co-train 2D and 3D networks, then further impose the networks' latent space consistency using the SAM's robust feature representation. Experiments conducted on diverse indoor and outdoor datasets demonstrate the superior performance of our method in understanding 2D and 3D open environments. Our 2D and 3D network achieves label-free semantic segmentation with 28.4\% and 33.5\% mIoU on ScanNet, improving 4.7\% and 7.9\%, respectively. For nuImages and nuScenes datasets, the performance is 22.1\% and 26.8\% with improvements of 3.5\% and 6.0\%, respectively. Code is available. (https://github.com/runnanchen/Label-Free-Scene-Understanding).
Overview
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The paper introduces a novel Cross-modality Noisy Supervision (CNS) framework that leverages vision foundation models like CLIP and SAM to achieve label-free scene understanding in 2D and 3D domains.
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The CNS framework addresses the dependency on large-scale annotated data by using pseudo-labeling, label refinement, and consistency regularization techniques to supervise networks without explicit labels.
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Experimental results on datasets such as ScanNet and nuScenes demonstrate significant improvements in semantic segmentation accuracy, highlighting the framework's potential for real-world applications in domains like autonomous driving and robotics.
Towards Label-free Scene Understanding by Vision Foundation Models
"Towards Label-free Scene Understanding by Vision Foundation Models" introduces a novel approach to leverage vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) for label-free scene understanding in both 2D and 3D domains. This paper addresses the challenges associated with the reliance on large-scale annotated data for tasks like image segmentation and classification, emphasizing the need for efficient methods to supervise networks without explicit labels.
Background and Motivation
Scene understanding, crucial for applications in autonomous driving, robotics, and urban planning, demands accurate recognition of objects within their contextual environments. Traditionally, this task has relied heavily on extensive, high-quality labeled data. However, obtaining such data is labor-intensive and costly, making these methods impractical for deployment in dynamic, real-world scenarios where novel objects frequently appear.
Methodological Framework
The authors propose a Cross-modality Noisy Supervision (CNS) framework that exploits the complementary strengths of CLIP and SAM models to train 2D and 3D networks without labeled data. CLIP, known for its zero-shot image classification capabilities, and SAM, noted for its robust zero-shot image segmentation performance, are harnessed in a synergistic manner. The primary innovation lies in the supervision of networks via noisy pseudo labels, refined and regularized to improve consistency and reduce noise.
Key components of the methodology include:
- Pseudo-labeling by CLIP: Leveraging CLIP to generate dense pseudo-labels for 2D image pixels, which are then projected to 3D points using the established pixel-point correspondences.
- Label Refinement by SAM: Utilizing SAM's masks to refine the noisy pseudo-labels generated by CLIP. This involves max voting within masks to ensure label consistency and reduce noise.
- Prediction Consistency Regularization: Implementing a mechanism that involves co-training 2D and 3D networks by randomly switching between pseudo labels drawn from various sources (CLIP, 2D, and 3D network predictions) to prevent overfitting to noisy labels.
- Latent Space Consistency Regularization: Imposing constraints to align 2D and 3D features within SAM's robust feature space, thereby enhancing the networks' capability to produce precise segmentations.
Experimental Results
Experiments were conducted on ScanNet, nuImages, and nuScenes datasets. The results demonstrate substantial improvements over state-of-the-art methods, with the proposed method achieving 28.4% and 33.5% mIoU for 2D and 3D semantic segmentation on ScanNet, and 26.8% mIoU for 3D segmentation on nuScenes. These improvements underscore the effectiveness of the CNS framework in handling noisy supervision and refining labels.
Implications and Future Directions
The implications of this research are significant, especially for domains that require robust scene understanding without extensive labeled data. The proposed CNS framework provides a scalable solution that can adapt to open-world scenarios, making it feasible for real-world applications where manual annotation is impractical.
Future research could explore further integration of vision foundation models and their adaptation to more complex environments. Advancements in consistent feature alignment and noise reduction techniques could enhance the generalization capabilities of these models. Moreover, extending this approach to multimodal data, including temporal sequences in video, could open new avenues for autonomous systems and smart environments.
Conclusion
This paper presents an innovative approach to label-free scene understanding by harnessing the strengths of vision foundation models like CLIP and SAM. Through a comprehensive experimental evaluation, the authors demonstrate the efficacy of their Cross-modality Noisy Supervision framework, setting a new benchmark in the domain. The proposed methods offer a promising direction for future research and practical applications in autonomous systems and beyond.
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