Summary of Vocot: Unleashing Visually Grounded Multi-step Reasoning in Large Multi-modal Models, by Zejun Li et al.
VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models
by Zejun Li, Ruipu Luo, Jiwen Zhang, Minghui Qiu, Xuanjing Huang, Zhongyu Wei
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 novel multi-step reasoning framework, VoCoT, is proposed to tackle complex tasks by leveraging large multi-modal models (LMMs). VoCoT’s key features include object-centric reasoning paths and visually grounded representations of object concepts. This approach is adapted for LMMs through an instruction-tuning dataset. A VoCoT-based model, VolCano, is developed with 7B parameters and limited input image resolution, achieving excellent performance in benchmarks like CLEVR and EmbSpatial, outperforming SOTA models including GPT-4V. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computers think more like humans is being explored. A team of researchers created a system that can reason about objects and scenes in a series of steps, rather than just looking at things individually. This approach allows the computer to understand complex situations better. The team tested their idea with large models that can process multiple types of data, like images and text. They found that this new way of thinking helped the computer perform better on tasks that require complex reasoning, like understanding scenes from multiple angles. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Multi modal
