Summary of Learn: a Unified Framework For Multi-task Domain Adapt Few-shot Learning, by Bharadwaj Ravichandran et al.
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
by Bharadwaj Ravichandran, Alexander Lynch, Sarah Brockman, Brandon RichardWebster, Dawei Du, Anthony Hoogs, Christopher Funk
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a unified framework that combines domain adaptation and few-shot learning in computer vision. The framework is designed to handle three tasks: image classification, object detection, and video classification. It’s highly modular, allowing for the inclusion of domain adaptation with or without few-shot learning, depending on the algorithm used. The framework also supports incremental n-shot tasks and can be scaled up to traditional many-shot tasks. Additionally, it incorporates Self-Supervised Learning (SSL) pre-training configurations. To demonstrate its capabilities, the authors provide benchmarks across various algorithms, datasets, and task settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize objects or classify images, but with only a few examples. That’s the idea behind few-shot learning. Domain adaptation is another technique that helps computers learn from one domain (like images of cats) and apply what they learned to another domain (like images of dogs). But what if you could combine both techniques into one framework? That’s exactly what this paper does, creating a unified system that can handle image classification, object detection, and video classification tasks. It’s like having a superpowerful tool that can adapt to different situations. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Few shot » Image classification » N shot » Object detection » Self supervised
