Few-shot Learning

Few-shot learning is a powerful technique that addresses the challenge of training machine learning models with limited labeled data. This becomes particularly relevant in tasks like HDR deghosting and RAW image enhancement, where obtaining ground truth data is extremely challenging due to the complex nature of the problem and the absence of a definitive reference. In such scenarios, few-shot learning enables us to leverage a small set of annotated examples to generalize and adapt the model's knowledge to unseen data. By utilizing transfer learning, meta-learning, or other few-shot learning approaches, the model can learn to generalize from the limited labeled data to effectively address HDR deghosting and RAW image enhancement tasks. Few-shot learning techniques facilitate the extraction of meaningful image features and patterns that are essential for accurate deghosting and enhancement, even in situations where ground truth data is scarce or difficult to obtain. This enables the model to make informed decisions and generate visually pleasing and high-quality results, enhancing the overall image processing pipeline and providing more effective solutions for HDR deghosting and RAW image enhancement tasks.

Reference

1. K Ram Prabhakar et al., Labeled from Unlabeled: Exploiting Unlabeled Data for Few-shot Deep HDR Deghosting, CVPR 2021.
2. K Ram Prabhakar et al., Few-Shot Domain Adaptation for Low Light RAW Image Enhancement, BMVC 2021.
3. K Ram Prabhakar et al., Few-Shot Cross-Sensor Domain Adaptation between SAR and Multispectral Data, IEEE IGARSS 2022.
4. M Singh et al., A Few-shot approach to MRI-based Knee Disorder Diagnosis using Fuzzy Layers, ICVGIP 2022.