Key points are not available for this paper at this time.
Few-shot object detection (FSOD), a formidable task centered around developing inclusive models with annotated constrained samples, has attracted increasing interest in recent years. This discipline addresses unbalanced data distributions, which are particularly relevant to authentic scenarios. Although recent FSOD efforts have achieved considerable success in terms of localization, recognition remains a formidable obstacle. This stems from the fact that typical FSOD models evolve from general object detection frameworks predicated on extensive training data, and they underutilize and mine data information in scenarios with restricted samples, resulting in subpar performance. To address this deficiency, we introduce a groundbreaking methodology that is specifically tailored to overcome the inadequate sample challenge in FSOD tasks. Our approach incorporates a neighborhood information adaption (NIA) module that is designed to dynamically utilize information near the target, assisting in robustly performing object identification within the target domain. In addition, we propose an innovative attention mechanism called all attention, which not only encapsulates the dependencies of each position within a single feature map but also leverages correlations with other feature maps. This methodology culminates in more refined feature representations, which are particularly advantageous in situations with limited data. Comprehensive experiments conducted on the PASCAL VOC and COCO datasets illustrate that our technique achieves a substantial improvement with regard to addressing the FSOD task.
Zhu et al. (Tue,) studied this question.