Summary of A Closer Look at Data Augmentation Strategies For Finetuning-based Low/few-shot Object Detection, by Vladislav Li et al.
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection
by Vladislav Li, Georgios Tsoumplekas, Ilias Siniosoglou, Vasileios Argyriou, Anastasios Lytos, Eleftherios Fountoukidis, Panagiotis Sarigiannidis
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Performance (cs.PF)
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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 This paper investigates the relationship between model performance and energy efficiency for low- and few-shot object detection. Specifically, it examines the impact of custom data augmentations and automated data augmentation selection strategies on a lightweight object detector’s performance and energy consumption. The study evaluates these methods in three benchmark datasets, considering both their performance and energy efficiency using an Efficiency Factor. Results show that while data augmentation strategies can improve model performance, they often come at the cost of increased energy usage, emphasizing the need for more energy-efficient approaches to address data scarcity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well object detectors work when there’s not much training data. It tries different ways to make the detector better and more efficient, like adding fake pictures to the training set or choosing which augmentations to use automatically. The researchers tested these methods on three datasets and looked at both how well they worked and how much energy they used. They found that making the detector better often uses up a lot of extra energy, so we need to find ways to make it work more efficiently when there’s not enough data. |
Keywords
» Artificial intelligence » Data augmentation » Few shot » Object detection
