Summary of From the Least to the Most: Building a Plug-and-play Visual Reasoner Via Data Synthesis, by Chuanqi Cheng et al.
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
by Chuanqi Cheng, Jian Guan, Wei Wu, Rui Yan
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper explores multi-step reasoning in vision-language models (VLMs), a challenging problem due to the scarcity of data consisting of multiple steps of visual and language processing. To overcome this challenge, the authors introduce a least-to-most visual reasoning paradigm that interleaves decomposing questions into sub-questions and invoking external tools for resolving these sub-questions. A novel data synthesis approach is also proposed, which can automatically create questions and multi-step reasoning paths for an image in a bottom-up manner. This approach divides the complex synthesis task into simple sub-tasks, relying on open-sourced models to accomplish these tasks. The synthesized data is quality guaranteed, making it reproducible and cost-efficient. With this approach, the authors construct 50k visual reasoning examples and develop a visual reasoner through supervised fine-tuning, which can enhance the reasoning abilities of existing VLMs in a plug-and-play fashion. Extensive experiments demonstrate that the visual reasoner consistently and significantly improves four VLMs on four VQA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how machines can learn to solve problems by looking at images and understanding natural language. This is important because many tasks, like image recognition, require machines to understand complex relationships between objects in an image. The authors propose a new way to train machines to reason about visual information, which involves breaking down complex questions into smaller steps and using pre-trained models to answer each step. They also create a large dataset of visual reasoning examples and show that their approach can improve the performance of existing machine learning models on four different benchmarks. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Supervised
