Few-shot action recognition aims to recognize novel action classes (query) using just a few samples (support). The majority of current approaches follow the metric learning paradigm, which learns to compare the similarity between videos. Recently, it has been observed that directly measuring this similarity is not ideal since different action instances may show distinctive temporal distribution, resulting in severe misalignment issues across query and support videos. In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and action evolution misalignment. We address them sequentially through a Two-stage Action Alignment Network (TA2N). The first stage locates the action by learning a temporal affine transform, which warps each video feature to its action duration while dismissing the action-irrelevant feature (e.g. background). Next, the second stage coordinates query feature to match the spatial-temporal action evolution of support by performing temporally rearrange and spatially offset prediction. Extensive experiments on benchmark datasets show the potential of the proposed method in achieving state-of-the-art performance for few-shot action recognition.

Key frame extraction can facilitate rapid browsing and efficient video indexing in many applications. However, to be effective, key frames must preserve sufficient video content while also being compact and representative. This study proposes a syncretic key frame extraction framework that combines sparse selection (SS) and mutual information‐based agglomerative hierarchical clustering (MIAHC) to generate effective video summaries. In the proposed framework, the SS algorithm is first applied to the original video sequences to obtain optimal key frames. Then, using content‐loss minimisation and representativeness ranking, several candidate key frames are efficiently selected and grouped as initial clusters. A post‐processor – an improved MIAHC – subsequently performs further processing to eliminate redundant images and generate the final key frames. The proposed framework overcomes issues such as information redundancy and computational complexity that afflict conventional SS methods by first obtaining candidate key frames instead of accurate key frames. Subsequently, application of the improved MIAHC to these candidate key frames rather than the original video not only results in the generation of accurate key frames, but also reduces the computation time for clustering large videos. The results of comparative experiments conducted on two benchmark datasets verify that the performance of the proposed SS–MIAHC framework is superior to that of conventional methods.