Coarse Correspondence Elicit 3D Spacetime Understanding in Multimodal Language Model

• URL: http://arxiv.org/abs/2408.00754v1
• Date: Thu, 1 Aug 2024 17:57:12 GMT
• Title: Coarse Correspondence Elicit 3D Spacetime Understanding in Multimodal Language Model
• Authors: Benlin Liu, Yuhao Dong, Yiqin Wang, Yongming Rao, Yansong Tang, Wei-Chiu Ma, Ranjay Krishna,
• Abstract summary: Multimodal language models (MLLMs) are increasingly being implemented in real-world environments. Despite their potential, current top models within our community still fall short in adequately understanding spatial and temporal dimensions. We introduce Coarse Correspondence, a training-free, effective, and general-purpose visual prompting method to elicit 3D and temporal understanding.
• Score: 52.27297680947337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal language models (MLLMs) are increasingly being implemented in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. Despite their potential, current top models within our community still fall short in adequately understanding spatial and temporal dimensions. We introduce Coarse Correspondence, a simple, training-free, effective, and general-purpose visual prompting method to elicit 3D and temporal understanding in multimodal LLMs. Our method uses a lightweight tracking model to find object correspondences between frames in a video or between sets of image viewpoints. It selects the most frequent object instances and visualizes them with markers with unique IDs in the image. With this simple approach, we achieve state-of-the-art results on 3D understanding benchmarks including ScanQA (+20.5\%) and a subset of OpenEQA (+9.7\%), and on long-form video benchmarks such as EgoSchema (+6.0\%). We also curate a small diagnostic dataset to evaluate whether MLLMs can reason about space from a described viewpoint other than the camera viewpoint. Again, Coarse Correspondence improves spatial perspective-taking abilities but we highlight that MLLMs struggle with this task. Together, we demonstrate that our simple prompting method can significantly aid downstream tasks that require 3D or temporal reasoning.

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