STAF: A Spatio-Temporal Attention Fusion Network for Few-shot Video Classification

We propose STAF, a Spatio-Temporal Attention Fusion network for few-shot video classification. STAF first extracts coarse-grained spatial and temporal features of videos by applying a 3D Convolution Neural Networks embedding network. It then fine-tunes the extracted features using self-attention and cross-attention networks. Last, STAF applies a lightweight fusion network and a nearest neighbor classifier to classify each query video. To evaluate STAF, we conduct extensive experiments on three benchmarks (UCF101, HMDB51, and Something-Something-V2). The experimental results show that STAF improves state-of-the-art accuracy by a large margin, e.g., STAF increases the five-way one-shot accuracy by 5.3% and 7.0% for UCF101 and HMDB51, respectively.