AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition-Reference-Cited by-同舟云学术

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AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Panda Rameswar¹,Chen Chun-Fu Richard¹,Fan Quanfu¹,Sun Ximeng²,Saenko Kate¹,Oliva Aude¹,Feris Rogerio¹

Affiliation:

1. MIT-IBM Watson AI Lab

2. Boston University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711022.pdf?arnumber=9711022

Reference64 articles.

1. Tiny video networks;piergiovanni,2019

2. MFAS: Multimodal Fusion Architecture Search

3. Deciding how to decide: Dynamic routing in artificial neural networks;mcgill;ICML,2017

4. TSM: Temporal Shift Module for Efficient Video Understanding

5. Motion Feature Network: Fixed Motion Filter for Action Recognition

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1. Embodied Human Activity Recognition;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

2. CAD - Contextual Multi-modal Alignment for Dynamic AVQA;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

3. Increasing Importance of Joint Analysis of Audio and Video in Computer Vision: A Survey;IEEE Access;2024

4. Noise-Tolerant Learning for Audio-Visual Action Recognition;IEEE Transactions on Multimedia;2024

5. Disentangled Graph Variational Auto-Encoder for Multimodal Recommendation With Interpretability;IEEE Transactions on Multimedia;2024

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