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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 7: AAAI-22 Technical Tracks 7

Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks

February 1, 2023

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Authors

Jun-Tae Lee

Qualcomm AI Research


Hyunsin Park

Qualcomm AI Research


Sungrack Yun

Qualcomm AI Research


Simyung Chang

Qualcomm Korea YH


DOI:

10.1609/aaai.v36i7.20698


Abstract:

It has been crucial to leverage the rich information of multiple modalities in many tasks. Existing works have tried to design multi-modal networks with descent multi-modal fusion modules. Instead, we focus on improving generalization capability of multi-modal networks, especially the fusion module. Viewing the multi-modal data as different projections of information, we first observe that bad projection can cause poor generalization behaviors of multi-modal networks. Then, motivated by well-generalized network's low sensitivity to perturbation, we propose a novel multi-modal training method, multi-head modularization (MHM). We modularize a multi-modal network as a series of uni-modal embedding, multi-modal embedding, and task-specific head modules. Also, for training, we exploit multiple head modules learned with different datasets, swapping each other. From this, we can make the multi-modal embedding module robust to all the heads with different generalization behaviors. In testing phase, we select one of the head modules not to increase the computational cost. Owing to the perturbation of head modules, though including one selected head, the deployed network is more well-generalized compared to the simply end-to-end learned. We verify the effectiveness of MHM on various multi-modal tasks. We use the state-of-the-art methods as baselines, and show notable performance gain for all the baselines.

Topics: AAAI

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HOW TO CITE:

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks Proceedings of the AAAI Conference on Artificial Intelligence (2022) 7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks AAAI 2022, 7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang (2022). Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. 2022. Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. (2022) "Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.7354-7362

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang, "Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks", AAAI, p.7354-7362, 2022.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. "Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. "Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 7354-7362.

Jun-Tae Lee||Hyunsin Park||Sungrack Yun||Simyung Chang. Multi-Head Modularization to Leverage Generalization Capability in Multi-Modal Networks. AAAI[Internet]. 2022[cited 2023]; 7354-7362.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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