16 citations · 45 across the 15 of their papers we have counts for
5 papers · 1 filter
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection
Ashutosh Agarwal, Anay Majee, Anbumani Subramanian +1
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-lea…
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Anay Majee, Anbumani Subramanian, Kshitij Agrawal
Localization and recognition of less-occurring road objects have been a challenge in autonomous driving applications due to the scarcity of data samples. Few-Shot Object Detection…
Multi-Domain Incremental Learning for Semantic Segmentation
Prachi Garg, Rohit Saluja, Vineeth N Balasubramanian +3
Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model. A simple fine-tuning experiment perfo…
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Anuj Tambwekar, Kshitij Agrawal, Anay Majee +1
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,…
Few-Shot Learning for Road Object Detection
Anay Majee, Kshitij Agrawal, Anbumani Subramanian
Few-shot learning is a problem of high interest in the evolution of deep learning. In this work, we consider the problem of few-shot object detection (FSOD) in a real-world, class-…