Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments

• URL: http://arxiv.org/abs/2207.10785v1
• Date: Thu, 21 Jul 2022 23:28:52 GMT
• Title: Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
• Authors: Khoi D. Nguyen, Quoc-Huy Tran, Khoi Nguyen, Binh-Son Hua, Rang Nguyen
• Abstract summary: We present a novel method for few-shot video classification, which performs appearance and temporal alignments. Our approach achieves similar or better results than previous methods on both datasets.
• Score: 17.673345523918947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining the temporal similarity score between the videos. Moreover, we introduce a few-shot video classification framework that leverages the above appearance and temporal similarity scores across multiple steps, namely prototype-based training and testing as well as inductive and transductive prototype refinement. To the best of our knowledge, our work is the first to explore transductive few-shot video classification. Extensive experiments on both Kinetics and Something-Something V2 datasets show that both appearance and temporal alignments are crucial for datasets with temporal order sensitivity such as Something-Something V2. Our approach achieves similar or better results than previous methods on both datasets. Our code is available at https://github.com/VinAIResearch/fsvc-ata.

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