Zero-shot temporal event localisation: Label-free, training-free, domain-free

Temporal event localisation (TEL) has recently attracted increasing attention due to the rapid development of video platforms. Existing methods are based on either fully/weakly supervised or unsupervised learning, and thus they rely on expensive data annotation and time‐consuming training. Moreover, these models, which are trained on specific domain data, limit the model generalisation to data distribution shifts. To cope with these difficulties, the authors propose a zero‐shot TEL method that can operate without training data or annotations. Leveraging large‐scale vision and language pre‐trained models, for example, CLIP, we solve the two key problems: (1) how to find the relevant region where the event is likely to occur; (2) how to determine event duration after we find the relevant region. Query guided optimisation for local frame relevance relying on the query‐to‐frame relationship is proposed to find the most relevant frame region where the event is most likely to occur. Proposal generation method relying on the frame‐to‐frame relationship is proposed to determine the event duration. The authors also propose a greedy event sampling strategy to predict multiple durations with high reliability for the given event. The authors’ methodology is unique, offering a label‐free, training‐free, and domain‐free approach. It enables the application of TEL purely at the testing stage. The practical results show it achieves competitive performance on the standard Charades‐STA and ActivityCaptions datasets.