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Few-shot Action Recognition with Video Transformer
Published in 2023 IEEE SITIS Conference, 2023
This paper introduces a few-shot action recognition framework integrating a Video Transformer with meta-learning via ProtoNet. Extensive experiments show superior performance over baselines, with effective transfer learning capabilities in cross-domain scenarios, offering a promising approach for few-shot learning in action recognition.
Recommended citation: N. Aikyn, A. Abu, T. Zhaksylyk and N. A. Tu, "Few-shot Action Recognition with Video Transformer," 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Bangkok, Thailand, 2023, pp. 122-129, doi: 10.1109/SITIS61268.2023.00027. Published in Journal of Supercomputing (SJR Q2), 2024 This paper presents a novel framework for facial expression recognition (FER) using edge computing to address the challenges of latency, memory usage, and privacy in DL-based FER models. It empirically evaluates various landmark detection models for robust, real-time FER on IoT devices, demonstrating significant improvements in efficiency and performance. Recommended citation: Aikyn, N., Zhanegizov, A., Aidarov, T. et al. Efficient facial expression recognition framework based on edge computing. J Supercomput 80, 1935–1972 (2024). https://doi.org/10.1007/s11227-023-05548-x Published in 2024 IEEE WACV Conference, 2024 This paper proposes FedFSLAR, a federated learning framework for few-shot action recognition that combines 3D-CNN-based spatiotemporal features with meta-learning. It addresses the challenges of data scarcity and bias in federated settings and validates the effectiveness of the framework under non-IID conditions, offering notable advances for FL and FSL in action recognition. Recommended citation: N. A. Tu et al., "FedFSLAR: A Federated Learning Framework for Few-shot Action Recognition," 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Waikoloa, HI, USA, 2024, pp. 270-279, doi: 10.1109/WACVW60836.2024.00035. Published: This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown! Published: This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field. Undergraduate course, University 1, Department, 2014 This is a description of a teaching experience. You can use markdown like any other post. Workshop, University 1, Department, 2015 This is a description of a teaching experience. You can use markdown like any other post.
Download Paper | Download Slides</p> </article> </div> Efficient Facial Expression Recognition Framework Based on Edge Computing
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