Summary of Modeling Collaborator: Enabling Subjective Vision Classification with Minimal Human Effort Via Llm Tool-use, by Imad Eddine Toubal et al.
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
by Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin, Jan Dlabal, Wenlei Zhou, Enming Luo, Otilia Stretcu, Hao Xiong, Chun-Ta Lu, Howard Zhou, Ranjay Krishna, Ariel Fuxman, Tom Duerig
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A new framework is proposed to alleviate manual effort in developing classifiers for nuanced or subjective visual concepts. The traditional approach requires substantial manual effort, measured in hours, days, or even months, to identify and annotate data needed for training. Agile Modeling techniques can reduce this time, but users still spend 30 minutes or more on repetitive data labeling. This new framework uses natural language interactions to replace human labeling, reducing the total effort required by an order of magnitude. The approach leverages foundation models, such as large language models and vision-language models, to carve out the concept space through conversation and automatic labeling. This eliminates the need for crowd-sourced annotations and produces lightweight classification models deployable in cost-sensitive scenarios. Across 15 subjective concepts and two public image classification datasets, trained models outperform traditional Agile Modeling and state-of-the-art zero-shot classification models like ALIGN, CLIP, CuPL, and large visual question-answering models like PaLI-X. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to train computer vision models for recognizing subtle or subjective visual concepts. Instead of spending hours labeling images, the new framework uses conversations with AI models to define these concepts. This reduces the time and effort needed to create models that can classify images into different categories. The approach is more efficient and effective than previous methods and has applications in areas like content moderation and wildlife conservation. |
Keywords
* Artificial intelligence * Classification * Data labeling * Image classification * Question answering * Zero shot
