Few-Shot Video Classification via Representation Fusion and Promotion Learning

Publication Date: 10/2/2023

Event: ICCV 2023

Reference: pp. 19311-19320, 2023

Authors: Haifeng Xia, NEC Laboratories America, Inc., Tulane Univeristy; Kai Li, NEC Laboratories America, Inc.; Martin Renqiang Min, NEC Laboratories America, Inc.; Zhengming Ding, Tulane Univeristy

Abstract: Recent few-shot video classification (FSVC) works achieve promising performance by capturing similarity across support and query samples with different temporal alignment strategies or learning discriminative features via Transformer block within each episode. However, they ignore two important issues: a) It is difficult to capture rich intrinsic action semantics from a limited number of support instances within each task. b) Redundant or irrelevant frames in videos easily weaken the positive influence of discriminative frames. To address these two issues, this paper proposes a novel Representation Fusion and Promotion Learning (RFPL) mechanism with two sub-modules: meta-action learning (MAL) and reinforced image representation (RIR). Concretely, during training stage, we perform online learning for seeking a task-shared meta-action bank to enrich task-specific action representation by injecting global knowledge. Besides, we exploit reinforcement learning to obtain the importance of each frame and refine the representation. This operation maximizes the contribution of discriminative frames to further capture the similarity of support and query samples from the same category. Our RFPL framework is highly flexible that it can be integrated with many existing FSVC methods. Extensive experiments show that RFPL significantly enhances the performance of existing FSVC models when integrated with them.

Publication Link: https://openaccess.thecvf.com/content/ICCV2023/papers/Xia_Few-Shot_Video_Classification_via_Representation_Fusion_and_Promotion_Learning_ICCV_2023_paper.pdf

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