Multi-Scale Positive Sample Refinement for Few-Shot Object Detection (2007.09384v1)

Abstract: Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited. Unlike previous attempts that exploit few-shot classification techniques to facilitate FSOD, this work highlights the necessity of handling the problem of scale variations, which is challenging due to the unique sample distribution. To this end, we propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It generates multi-scale positive samples as object pyramids and refines the prediction at various scales. We demonstrate its advantage by integrating it as an auxiliary branch to the popular architecture of Faster R-CNN with FPN, delivering a strong FSOD solution. Several experiments are conducted on PASCAL VOC and MS COCO, and the proposed approach achieves state of the art results and significantly outperforms other counterparts, which shows its effectiveness. Code is available at https://github.com/jiaxi-wu/MPSR.

• The paper presents Multi-scale Positive Sample Refinement (MPSR) to tackle scale variance in few-shot object detection, achieving a 16.2% mAP boost on challenging datasets.
• The approach integrates an auxiliary branch into Faster R-CNN with FPN to generate and refine multi-scale positive samples efficiently.
• Comprehensive experiments on PASCAL VOC and MS COCO validate that MPSR significantly outperforms state-of-the-art FSOD models in both accuracy and inference efficiency.

Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

The paper by Jiaxi Wu et al. introduces a method known as Multi-scale Positive Sample Refinement (MPSR) for enhancing the performance of Few-Shot Object Detection (FSOD). This approach addresses the primary challenge in FSOD linked to the limited availability of labeled data across varying object scales. Unlike traditional object detection methods that are data-intensive, MPSR focuses on refining scale variation management within FSOD paradigms, filling a significant gap in the field where few-shot methodologies largely target classification and neglect the issue of scale variance.

Proposed Method: Multi-scale Positive Sample Refinement (MPSR)

The core of the method is the generation of multi-scale positive samples, envisioned as object pyramids, which are subsequently refined at multiple scales. By integrating MPSR as an auxiliary branch into the Faster R-CNN architecture augmented with Feature Pyramid Networks (FPN), the authors leverage the FPN’s capacity to handle scale variations better. This approach, however, goes further by creating object pyramids manually and refining predictions, eschewing the drawbacks of traditional scale handling methods, which tend to increase negative sample noise significantly in a few-shot context.

Contributions

The contributions of MPSR are multi-faceted:

1. Scale Enrichment in FSOD: MPSR uniquely addresses the sparsity in scale distribution, a specific complication of few-shot learning paradigms.
2. Inference Efficiency: The proposed method enhances model training without additional inference cost, as it does not introduce extra weights.
3. Comprehensive Validation: The paper provides extensive experimental results on prominent datasets—PASCAL VOC and MS COCO—where MPSR significantly outperforms state-of-the-art models, proving its efficacy.

Strong Numerical Results

The paper demonstrates remarkably strong numerical results with MPSR. On the PASCAL VOC dataset, MPSR shows substantial improvements over baselines and existing state-of-the-art methods. For example, MPSR achieves an increase of 16.2% in mean Average Precision (mAP) for 1-shot FSOD tasks on one class split, showing its capability in handling extreme data sparsity scenarios. On MS COCO, MPSR raises the mAP, showing a notable improvement over prior methods tailored for the few-shot setting.

Theoretical and Practical Implications

Theoretically, this research emphasizes the necessity of addressing spatial scale variances within FSOD. This insight extends the frontier of FSOD research beyond mere classification performance into areas of localization and scale sensitivity, hereby broadening the scope for FSOD improvements. Practically, the approach substantiates FSOD solutions that can be deployed efficiently in scenarios with stringent data annotation constraints, thereby endorsing its applicability in fields like wildlife conservation and medical imaging, where data labeling can be challenging and resource-intensive.

Future Developments in AI

The insights offered by this research open pathways for further exploration into FSOD models that can intelligently adapt to scale and feature variations with minimal supervision. A promising future direction could involve the integration of MPSR with emerging object detection frameworks that possess more sophisticated context understanding and feature extraction capabilities. This could include amalgamations with transformer-based architectures or the incorporation of semi-supervised learning tactics to further mitigate the dependency on large labeled datasets.

In summary, the proposition of MPSR by Jiaxi Wu et al. brings a novel, efficient, and effective dimension to the field of FSOD by addressing the issues related to scale variance, backed by strong empirical evidence and fostering a foundation for continued innovation in the field.