Summary of Mamba Fusion: Learning Actions Through Questioning, by Zhikang Dong et al.
Mamba Fusion: Learning Actions Through Questioning
by Zhikang Dong, Apoorva Beedu, Jason Sheinkopf, Irfan Essa
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 introduces MambaVL, a novel video language model that leverages selective state space modality fusion to efficiently capture long-range dependencies and learn joint representations for vision and language data. The model utilizes a shared state transition matrix across both modalities, allowing it to capture information about actions from multiple perspectives within the scene. To guide the model toward relevant cues, the paper proposes a question-answering task that provides critical information about actions, objects, and environmental context. As a result, MambaVL achieves state-of-the-art performance in action recognition on the Epic-Kitchens-100 dataset and outperforms baseline methods in action anticipation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MambaVL is a new way for computers to understand videos and words together. It’s like having a superpower that lets you see what’s happening in a video and understand what people are saying about it at the same time. This helps the computer do tasks like recognizing actions or anticipating what will happen next. The paper shows that MambaVL is really good at doing these things, especially when compared to other models. |
Keywords
» Artificial intelligence » Language model » Question answering
