Team sports auto-narrative requires simultaneous modeling of fine-grained individual actions and uncovering of spatio-temporal dependency structures of frequent group interactions, and then accurate mapping of these complex interaction details into long and detailed commentary. We propose a novel framework - Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) for fine-grained team sports auto-narrative task. A multi-granular interaction module is proposed to extract among-subjects' interactive actions in a progressive way for encoding both intra- and inter-team interactions. Based on the above multi-granular representations, a multi-granular attention module is developed to consider action/event descriptions of multiple spatio-temporal resolutions. Both modules are integrated seamlessly and work in a collaborative way to generate the final narrative. In the meantime, we collect a new video dataset called Sports Video Narrative dataset (SVN). It is a novel direction as it contains 6K team sports videos with 10K ground-truth narratives. Furthermore, as previous metrics, DO NOT cope with fine-grained sports narrative task well, we hence develop a novel evaluation metric named Fine-grained Captioning Evaluation (FCE), which measures how accurate the generated linguistic description reflects fine-grained action details as well as the overall spatio-temporal interactional structure. Extensive experiments on our SVN dataset have demonstrated the effectiveness of the proposed framework for fine-grained team sports video auto-narrative.

Face anti-spoofing is a critical aspect of ensuring security in the context of human-robot interaction and collaboration. Recently, disentangled-based data augmentation methods have achieved great success in face anti-spoofing tasks. The underlying assumption of those methods is that the liveness information could be completely disentangled and the labeling of the augmented data could totally depend on the liveness-related feature branch. However, we observe that it is almost impossible to extract the liveness-related information completely, which makes the current labeling strategy inaccurate. In this paper, we rethink the disentangling process and propose a novel generative-based data augmentation framework without forcing liveness information encoded into any specific feature space. Specifically, the original images are decomposed into statistic feature space and spatial feature space with liveness information preserving. With these two feature spaces, synthesized liveness-preserving images are generated with the Cartesian product to further approach the distribution of real face anti-spoofing data. Along with the original samplings, the augmented data are fed to a ResNet-based classifier with our proposed pseudo-label strategy for liveness information augmentation. Both qualitative and quantitative experiments demonstrate promising results to show the effectiveness of our proposed method.