Weakly-Supervised Temporal Action Alignment Driven by Unbalanced Spectral Fused Gromov-Wasserstein Distance

Temporal action alignment aims at segmenting videos into clips and tagging each clip with a textual description, which is an important task of video semantic analysis. Most existing methods, however, rely on supervised learning to train their alignment models, whose applications are limited because of the common insufficiency issue of labeled videos. To mitigate this issue, we propose a weakly-supervised temporal action alignment method based on a novel computational optimal transport technique called unbalanced spectral fused Gromov-Wasserstein (US-FGW) distance. Instead of using videos with known clips and corresponding textual tags, our method just needs each training video to be associated with a set of (unsorted) texts while does not require the fine-grained correspondence between the frames and the texts. Given such weakly-supervised video-text pairs, our method trains the representation models of the video frames and the texts jointly in a probabilistic or deterministic autoencoding architecture and penalizes the US-FGW distance between the distribution of visual latent codes and that of textual latent codes. We compute the US-FGW distance efficiently by leveraging the Bregman ADMM algorithm. Furthermore, we generalize classic contrastive learning framework and reformulate it based on the proposed US-FGW distance, which provides a new viewpoint of contrastive learning for our problem. Experimental results show that our method and its variants outperform state-of-the-art weakly-supervised temporal action alignment methods, whose results are even comparable to those derived by supervised learning methods on some specific evaluation measurements. The code is available at https://github.com/hhhh1138/Temporal- Action-Alignment-USFGW.