  Few-shot object detection (FSOD) localizes and classifies objects in an image
given only a few data samples. Recent trends in FSOD research show the adoption
of metric and meta-learning techniques, which are prone to catastrophic
forgetting and class confusion. To overcome these pitfalls in metric learning
based FSOD techniques, we introduce Attention Guided Cosine Margin (AGCM) that
facilitates the creation of tighter and well separated class-specific feature
clusters in the classification head of the object detector. Our novel Attentive
Proposal Fusion (APF) module minimizes catastrophic forgetting by reducing the
intra-class variance among co-occurring classes. At the same time, the proposed
Cosine Margin Cross-Entropy loss increases the angular margin between confusing
classes to overcome the challenge of class confusion between already learned
(base) and newly added (novel) classes. We conduct our experiments on the
challenging India Driving Dataset (IDD), which presents a real-world
class-imbalanced setting alongside popular FSOD benchmark PASCAL-VOC. Our
method outperforms State-of-the-Art (SoTA) approaches by up to 6.4 mAP points
on the IDD-OS and up to 2.0 mAP points on the IDD-10 splits for the 10-shot
setting. On the PASCAL-VOC dataset, we outperform existing SoTA approaches by
up to 4.9 mAP points.
