Summary of Multi-modal Situated Reasoning in 3d Scenes, by Xiongkun Linghu et al.
Multi-modal Situated Reasoning in 3D Scenes
by Xiongkun Linghu, Jiangyong Huang, Xuesong Niu, Xiaojian Ma, Baoxiong Jia, Siyuan Huang
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 This paper proposes Multi-modal Situated Question Answering (MSQA), a large-scale dataset for embodied AI agents to understand 3D scenes. The existing datasets and benchmarks are limited in terms of modality, diversity, scale, and task scope. MSQA is collected using 3D scene graphs and vision-language models across various real-world scenarios. The dataset includes 251K situated question-answering pairs across 9 categories, covering complex situations within 3D scenes. A novel interleaved multi-modal input setting is introduced to provide text, image, and point cloud for situation and question description. This resolves ambiguity in previous single-modality conventions. The paper also introduces the Multi-modal Situated Next-step Navigation (MSNN) benchmark to evaluate models’ situated reasoning for navigation. Evaluations on MSQA and MSNN highlight the limitations of existing vision-language models and emphasize the importance of handling multi-modal interleaved inputs and situation modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps AI agents understand 3D scenes better. Right now, there are limited datasets and benchmarks to test how well these agents can reason about what they see. The researchers created a new dataset called MSQA that has lots of questions and answers about different 3D scenes. This dataset is special because it includes text, images, and point clouds to help the agents understand the situation. The paper also introduces another benchmark called MSNN to test how well the agents can navigate through these 3D scenes. The results show that existing AI models are not very good at this task and need more training data like MSQA. |
Keywords
» Artificial intelligence » Multi modal » Question answering
