Few-shot Geometry-Aware Keypoint Localization (2303.17216v1)

Abstract: Supervised keypoint localization methods rely on large manually labeled image datasets, where objects can deform, articulate, or occlude. However, creating such large keypoint labels is time-consuming and costly, and is often error-prone due to inconsistent labeling. Thus, we desire an approach that can learn keypoint localization with fewer yet consistently annotated images. To this end, we present a novel formulation that learns to localize semantically consistent keypoint definitions, even for occluded regions, for varying object categories. We use a few user-labeled 2D images as input examples, which are extended via self-supervision using a larger unlabeled dataset. Unlike unsupervised methods, the few-shot images act as semantic shape constraints for object localization. Furthermore, we introduce 3D geometry-aware constraints to uplift keypoints, achieving more accurate 2D localization. Our general-purpose formulation paves the way for semantically conditioned generative modeling and attains competitive or state-of-the-art accuracy on several datasets, including human faces, eyes, animals, cars, and never-before-seen mouth interior (teeth) localization tasks, not attempted by the previous few-shot methods. Project page: https://xingzhehe.github.io/FewShot3DKP/}{https://xingzhehe.github.io/FewShot3DKP/

• The paper presents a novel few-shot learning framework that combines geometric constraints, keypoint uncertainty, and self-supervision to enable reliable keypoint localization with limited data.
• It enhances localization accuracy by integrating 3D geometry-aware constraints that ensure viewpoint consistency and robust handling of occlusions.
• Experimental results show competitive performance with lower normalized mean errors and higher percentage of correct keypoints on datasets like WFLW, SynthesEyes, and CarFusion.

Few-shot Geometry-Aware Keypoint Localization: An Expert Overview

The presented paper offers a significant technical contribution to the field of computer vision, specifically addressing the challenge of keypoint localization with limited training data. The traditional supervised keypoint localization approaches require large datasets with accurate annotations, which are costly and error-prone due to inconsistencies. This research proposes a novel method for few-shot keypoint localization with added geometric awareness, promising reasonable accuracy even when the amount of labeled data is significantly reduced.

Problem Statement and Methodology

Keypoint localization is crucial in diverse applications like image generation, 3D modeling, and anti-spoofing, and typically demands extensive labeled datasets. However, the labeling process is labor-intensive, often inconsistent, and may suffer from errors due to low-resolution imagery or occlusions. This motivates the need for effective few-shot learning strategies.

The proposed method leverages a small set of user-labeled 2D images, which act as semantic constraints, augmented by a larger unlabeled dataset for self-supervised learning. By introducing 3D geometry-aware constraints, the method improves upon 2D localization accuracy. Notably, the approach does not require the usual hundreds or thousands of labeled examples, as required by semi-supervised methods, making it practical for real-world applications where acquiring labeled data is challenging.

Key Contributions

1. Novel Few-Shot Learning Framework: This research presents a unique approach combining geometric constraints, keypoint uncertainty, and self-supervised learning to perform keypoint localization with limited labeled data. The general-purpose formulation supports diverse object categories with varying geometries.
2. 3D Geometry-Awareness: Introducing 3D constraints enables more accurate localization by ensuring viewpoint consistency and depth modeling, which is especially beneficial for occluded regions and complex object categories.
3. Adaptation of Transformation Equivariance: Techniques from unsupervised methods, like transformation equivariance and image reconstruction, are employed to enhance model robustness and learning flexibility.
4. Semantic Consistency and Robustness: By using few-shot labeled images as semantic shape constraints, the method achieves semantically consistent and human-interpretable results that are competitive or state-of-the-art on numerous datasets, including novel applications like mouth interior localization.

Experimental Results and Implications

The experimental results indicate that this method achieves competitive performance on several benchmarks with minimal user input. The research showcases the effectiveness on datasets such as WFLW, SynthesEyes, and CarFusion, achieving lower normalized mean errors (NME) and higher percentage of correct keypoints (PCK) compared to existing approaches under few-shot conditions.

Importantly, the ability to perform few-shot keypoint localization has significant implications for practical applications. It suggests feasibility for rapid dataset labeling, potential improvements in live tracking systems, and expansion into more complex domains without the prerequisite of extensive labeled data. The integration of 3D constraints opens further avenues for more accurate object recognition and manipulation tasks.

Future Directions

Looking ahead, this research direction could benefit from exploring increased integration with generative models for data augmentation and synthesizing labeled datasets. Enhancing the model's adaptability to asymmetrical objects and cases with extreme occlusions remains a vital future challenge. The pursuit of more generalized frameworks can potentially lead to improvements in diverse fields beyond the confines of computer vision, particularly in areas requiring detailed spatial understanding from limited data, such as robotics and autonomous vehicles.

This paper represents a distinct advancement in efficiently using limited data for achieving robust and semantically meaningful keypoint localization, setting a foundation for further exploration and development in the domain.