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SCoRe: Submodular Combinatorial Representation Learning for Real-World Class-Imbalanced Settings
In this paper, we introduce the SCoRe (Submodular Combinatorial Representation Learning) framework and propose a family of Submodular Combinatorial Loss functions to overcome these pitfalls in contrastive learning.
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection
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.
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Our method outperforms State-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.
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data, and none of the old class data.
Few-Shot Learning for Road Object Detection
Few-shot learning is a problem of high interest in the evolution of deep learning.
