Mamba Fusion: Learning Actions Through Questioning

• URL: http://arxiv.org/abs/2409.11513v1
• Date: Tue, 17 Sep 2024 19:36:37 GMT
• Title: Mamba Fusion: Learning Actions Through Questioning
• Authors: Zhikang Dong, Apoorva Beedu, Jason Sheinkopf, Irfan Essa,
• Abstract summary: Video Language Models (VLMs) are crucial for generalizing across diverse tasks and using language cues to enhance learning. We introduce MambaVL, a novel model that efficiently captures long-range dependencies and learn joint representations for vision and language data. MambaVL achieves state-of-the-art performance in action recognition on the Epic-Kitchens-100 dataset.
• Score: 12.127052057927182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Language Models (VLMs) are crucial for generalizing across diverse tasks and using language cues to enhance learning. While transformer-based architectures have been the de facto in vision-language training, they face challenges like quadratic computational complexity, high GPU memory usage, and difficulty with long-term dependencies. To address these limitations, we introduce MambaVL, a novel model that leverages recent advancements in selective state space modality fusion to efficiently capture long-range dependencies and learn joint representations for vision and language data. MambaVL utilizes a shared state transition matrix across both modalities, allowing the model to capture information about actions from multiple perspectives within the scene. Furthermore, we propose a question-answering task that helps guide the model toward relevant cues. These questions provide critical information about actions, objects, and environmental context, leading to enhanced performance. 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.

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