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Localization and recognition of less-occurring road objects have been achallenge in autonomous driving applications due to the scarcity of datasamples. Few-Shot Object Detection techniques extend the knowledge fromexisting base object classes to learn novel road objects given few trainingexamples. Popular techniques in FSOD adopt either meta or metric learningtechniques which are prone to class confusion and base class forgetting. Inthis work, we introduce a novel Meta Guided Metric Learner (MGML) to overcomeclass confusion in FSOD. We re-weight the features of the novel classes higherthan the base classes through a novel Squeeze and Excite module and encouragethe learning of truly discriminative class-specific features by applying anOrthogonality Constraint to the meta learner. Our method outperformsState-of-the-Art (SoTA) approaches in FSOD on the India Driving Dataset (IDD)by upto 11 mAP points while suffering from the least class confusion of 20%given only 10 examples of each novel road object. We further show similarimprovements on the few-shot splits of PASCAL VOC dataset where we outperformSoTA approaches by upto 5.8 mAP accross all splits.

Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection