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Patch-wise Features for Blur Image Classification
Images captured through smartphone cameras often suffer from degradation, blur being one of the major ones, posing a challenge in processing these images for downstream tasks.
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.
Enhancing Object Detection in Adverse Conditions using Thermal Imaging
Autonomous driving relies on deriving understanding of objects and scenes through images.
