Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning (1909.13032v2)

Abstract: Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived from meta-learning on images with a single visual object. Obfuscated by a complex background and multiple objects in one image, they are hard to promote the research of few-shot object detection/segmentation. In this work, we present a flexible and general methodology to achieve these tasks. Our work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature. This simple spirit disentangles multi-object information merged with the background, without bells and whistles, enabling Faster /Mask R-CNN turn into a meta-learner to achieve the tasks. Specifically, we introduce a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster /Mask R-CNN. PRN receives images containing few-shot objects with their bounding boxes or masks to infer their class attentive vectors. The vectors take channel-wise soft-attention on RoI features, remodeling those R-CNN predictor heads to detect or segment the objects that are consistent with the classes these vectors represent. In our experiments, Meta R-CNN yields the state of the art in few-shot object detection and improves few-shot object segmentation by Mask R-CNN.

• The paper introduces Meta R-CNN, a framework that extends Faster/Mask R-CNN with meta-learning to tackle instance-level few-shot learning.
• It employs a Predictor-head Remodeling Network (PRN) that leverages class-attentive vectors to refine RoI features for detection and segmentation.
• Extensive experiments on PASCAL VOC and MS-COCO demonstrate that Meta R-CNN outperforms existing baselines in few-shot object detection and segmentation.

An Examination of Meta R-CNN for Instance-level Few-shot Learning

The focal point of the paper, "Meta R-CNN: Towards General Solver for Instance-level Few-shot Learning," is the development of a flexible and robust framework aimed at enhancing the capabilities of few-shot learning in object detection and segmentation tasks. The authors propose Meta R-CNN, an extension of the Faster R-CNN and Mask R-CNN architectures, that leverages meta-learning paradigms to address the complexities associated with instance-level tasks in few-shot learning contexts.

The paper identifies a significant challenge in extending traditional meta-learning approaches to few-shot object detection and segmentation: the presence of multiple objects within a single image obscured by complex backgrounds. To address this, Meta R-CNN employs meta-learning over RoI features rather than full image features, effectively disentangling complex multi-object information from the background. This approach transforms Faster/Mask R-CNN into a meta-learner capable of efficiently detecting and segmenting objects in classes with limited data.

In the Meta R-CNN framework, a pivotal component is the Predictor-head Remodeling Network (PRN), which is designed to infer class-attentive vectors using few-shot objects along with their bounding boxes or masks. These vectors facilitate channel-wise soft attention on RoI features, thus remodeling the R-CNN predictor heads to detect or segment objects corresponding to the represented classes. The PRN seamlessly integrates with Faster/Mask R-CNN by sharing the main backbone architecture, ensuring computational efficiency and consistency in operation.

The robustness of the proposed Meta R-CNN is empirically validated through extensive experiments across multiple benchmarks, such as PASCAL VOC and MS-COCO. The framework convincingly demonstrates state-of-the-art performance in few-shot object detection and segmentation, consistently outperforming existing baselines, including a modified YOLO model for few-shot detection. For instance, in few-shot detection, Meta R-CNN achieved notable improvements across several test scenarios with variations in the number of shots, asserting its effectiveness and adaptability.

The implications of this research are multifaceted. Practically, Meta R-CNN shows considerable promise in reducing the dependency on extensive labeled datasets for training complex models, thereby mitigating the labor-intensive data annotation processes currently predominant in computer vision tasks. Theoretically, it expands the applicability of meta-learning principles beyond traditional recognition tasks to more granular and complex object detection and segmentation tasks. This contribution enriches the field's understanding of how learning to learn can be extended effectively to instance-level problems.

Speculatively, the adoption of the Meta R-CNN methodology in future research could lead to significant advancements in AI systems capable of rapid adaptation to novel visual concepts using minimal data. Such capabilities align well with emerging demands for models that robustly perform under data-scarce environments, a crucial aspect for real-world applications like autonomous vehicles or medical imaging, where new cases may not be extensively represented in training datasets.

In conclusion, the paper provides a well-articulated advancement in few-shot learning methodologies for object detection and segmentation, furnishing a new pathway for developing generalizable, efficient, and effective vision systems. The Meta R-CNN framework underscores the potential of meta-learning as a tool to bridge the gap between classical recognition tasks and the more intricate demands of instance-level learning problems.