Summary of Coarse Correspondences Boost Spatial-temporal Reasoning in Multimodal Language Model, by Benlin Liu et al.
Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model
by Benlin Liu, Yuhao Dong, Yiqin Wang, Zixian Ma, Yansong Tang, Luming Tang, Yongming Rao, Wei-Chiu Ma, Ranjay Krishna
First submitted to arxiv on: 1 Aug 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 novel approach is proposed for enhancing multimodal language models’ (MLLMs) ability to interpret 3D spaces and comprehend temporal dynamics. The method, called Coarse Correspondences, leverages 2D images as input without modifying the architecture or requiring task-specific fine-tuning. A lightweight tracking model identifies primary object correspondences between frames in a video or across different image viewpoints, which are then conveyed to MLLMs through visual prompting. This simple training-free approach brings substantial gains to GPT4-V/O on four benchmarks that require spatial-temporal reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal language models can now better understand 3D spaces and moving objects thanks to a new technique called Coarse Correspondences. This method helps MLLMs by giving them information about what’s happening in 2D images, without needing special training or changes to their design. The result is better performance on tasks that involve understanding spatial and temporal relationships. |
Keywords
* Artificial intelligence * Fine tuning * Prompting * Tracking
