Human Action Recognition (HAR) in night video sensing has become a crucial task for a wide range of applications (e.g., night surveillance and self-driving at night). Recently, Fully Supervised Learning (FSL) methods by training with large-scale labeled data achieve substantial performance in HAR. However, since the data captured by red–green–blue (RGB) sensor-based cameras in the night (low-light) scenes suffer from poor illumination, it leads to difficult annotations and limits the applications of night HAR by the FSL method. A potential solution for this issue is to transfer knowledge learned from normal-light videos to low-light ones using Semi-Supervised Learning (SSL) methods, such as Unsupervised Domain Adaptation (UDA) and self-training. Although, these SSL methods have shown promising results in datasets with the same or similar data distributions, they struggle with the dataset with large domain discrepancies, e.g., normal-light and low-light conditions. To address this issue, a new SSL method called V-DixMatch, which includes the pixel-level adaptation, the feature-level adaptation, and the Cross-Domain Video-based (CDV) self-training, is proposed in this work. Specifically, the pixel-level adaptation reduces the domain discrepancy in low-level features by pixel-level processing and the feature-level adaptation approximates domain discrepancy in high-level features through adversarial learning. Then, CDV self-training further provides a robust self-training strategy by designing a video-based Blended Frames Augmentation (BFA), which is a data augmentation method tailored for video-based data, and a pseudo-label collection, which improves the quality of pseudo-labels in self-training. Extensive experimental results demonstrate that V-DixMatch achieves state-of-the-art performance in the SSL low-light HAR, and is even comparable to some FSL methods.

Existing visual question reasoning methods usually fail to explicitly discover the inherent causal mechanism and ignore jointly modeling cross-modal event temporality and causality. In this paper, we propose a visual question reasoning framework named Cross-Modal Question Reasoning (CMQR), to discover temporal causal structure and mitigate visual spurious correlation by causal intervention. To explicitly discover visual causal structure, the Visual Causality Discovery (VCD) architecture is proposed to find question-critical scene temporally and disentangle the visual spurious correlations by attention-based front-door causal intervention module named Local-Global Causal Attention Module (LGCAM). To align the fine-grained interactions between linguistic semantics and spatial-temporal representations, we build an Interactive Visual-Linguistic Transformer (IVLT) that builds the multi-modal co-occurrence interactions between visual and linguistic content. Extensive experiments on four datasets demonstrate the superiority of CMQR for discovering visual causal structures and achieving robust question reasoning.