AdaMML: Adaptive Multi-Modal Learning for
Efficient Video Recognition

Rameswar Panda1,†	Chun-Fu (Richard) Chen1,†	Quanfu Fan1	Ximeng Sun2

Kate Saenko1,2	Aude Oliva1,3	Rogerio Feris1

†: Equal Contribution

1 MIT-IBM Watson AI Lab	2 Boston University	3 MIT

ICCV 2021

Abstract

Multi-modal learning, which focuses on utilizing various modalities to improve the performance of a model, is widely used in video recognition. While traditional multi-modal learning offers excellent recognition results, its computational expense limits its impact for many real-world applications. In this paper, we propose an adaptive multi-modal learning framework, called AdaMML, that selects on-the-fly the optimal modalities for each segment conditioned on the input for efficient video recognition. Specifically, given a video segment, a multi-modal policy network is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on four challenging diverse datasets demonstrate that our proposed adaptive approach yields 35%-55% reduction in computation when compared to the traditional baseline that simply uses all the modalities irrespective of the input, while also achieving consistent improvements in accuracy over the state-of-the-art methods.

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Qualitative Results

Qualitative examples showing the effectiveness of AdaMML in selecting the right modalities per video segment (marked by green borders)

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Paper & Code

		Rameswar Panda*, Chun-Fu (Richard) Chen*, Quanfu Fan, Ximeng Sun, Kate Saenko, Aude Oliva, Rogerio Feris AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition International Conference on Computer Vision (ICCV), 2021 [PDF] [Supp] [Poster] [Slides] [Code]

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