Summary of Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-language Model, by Jaehyuk Heo et al.
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model
by Jaehyuk Heo, Pilsung Kang
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: The paper proposes a novel active learning (AL) strategy, CLIPNAL, to minimize cost losses and improve model performance on open-set data. Unlike previous methods, CLIPNAL does not require out-of-distribution (OOD) samples, instead leveraging linguistic and visual information from in-distribution (ID) data using a pre-trained vision-language model. The approach consists of two stages: first, it detects and excludes OOD data; second, it selects highly informative ID data for annotation by human experts. Experimental results on various datasets demonstrate that CLIPNAL achieves the lowest cost loss and highest performance across all scenarios. This approach has implications for practical applications where minimizing annotation costs is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: The paper introduces a new way to choose what data to label when we have lots of data, but most of it isn’t important. This approach helps us save time and money by only labeling the most useful data. It works by first getting rid of any data that’s not like the rest, then picking the most helpful data to label. The results show that this method is better than others at finding the right data to label and using those labels to make accurate predictions. |
Keywords
» Artificial intelligence » Active learning » Language model
