Papers › Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection

2 Mar 2021CVPR 2021 1arXiv:2103.01903archive 2025-07-28

Chenchen Zhu, Fangyi Chen, Uzair Ahmed, Zhiqiang Shen, Marios Savvides

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availability. In this work, we investigate utilizing this semantic relation together with the visual information and introduce explicit relation reasoning into the learning of novel object detection. Specifically, we represent each class concept by a semantic embedding learned from a large corpus of text. The detector is trained to project the image representations of objects into this embedding space. We also identify the problems of trivially using the raw embeddings with a heuristic knowledge graph and propose to augment the embeddings with a dynamic relation graph. As a result, our few-shot detector, termed SRR-FSD, is robust and stable to the variation of shots of novel objects. Experiments show that SRR-FSD can achieve competitive results at higher shots, and more importantly, a significantly better performance given both lower explicit and implicit shots. The benchmark protocol with implicit shots removed from the pretrained classification dataset can serve as a more realistic setting for future research.

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Tasks

Few-Shot Object DetectionNovel Object DetectionObject Detectionobject-detection

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Object Detection MS-COCO (10-shot) SSR-FSD AP 11.3 #23 of 33 Archive leaderboard report
Few-Shot Object Detection MS-COCO (30-shot) SSR-FSD AP 14.7 #18 of 25 Archive leaderboard report

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