Temporal action detection is a crucial task in the field of video understanding, aiming to localize and recognize the category of actions in untrimmed videos along with their precise start and end times. Despite the success achieved by existing methods, two significant challenges remain: (1) difficulty in modeling long-range dependencies between video segments, leading to inaccurate localization of complex action boundaries; and (2) over-reliance on temporal dimension modeling, with insufficient exploration of the representational capabilities within the channel dimension, which limits model performance. To address these challenges, we propose a Multi-scale Temporal-Channel Collaborative (MTCC) detection framework. First, we construct a Long-Range Dependency Enhancement (LRDE) module based on the Mamba architecture with state space models (SSMs), which efficiently capture long-range temporal dependencies in video sequences. Second, leveraging the advantages of multi-scale CNNs, a Multi-Scale Boundary Awareness (MSBA) module is designed to extract local features and enhance the model's sensitivity to action boundaries. Finally, a Cross-Channel Information Fusion (CCIF) module is designed to extract features from the channel dimension of video. To better integrate features at different scales, a Multi-scale Detection Head (MDH) is employed to dynamically fuse the feature pyramid. Our systematic evaluations on the benchmark datasets THUMOS14 and ActivityNet-1.3 yield impressive results, demonstrating the effectiveness of our proposed method.
Yibo Zhao, Wen Zhang, Chunjie Ma et al.· ACM Transactions on Multimed...· 0 citations
Cross-distribution generalization remains a critical bottleneck in deepfake detection. While recent efforts leverage the semantic priors of large-scale visual foundation models (VFMs), a noteworthy yet underexplored challenge remains: the susceptibility of these semantic priors to environmental interference from factors such as lighting and style. Crucially, this interference establishes spurious correlations between forgery cues and environmental patterns that severely limit generalization. To address this fundamental challenge, we propose an innovative Environment-Invariant Subspace Learning (EISL) framework. The core contribution of EISL is that it aims to disentangle features into orthogonal forgery-relevant invariant factors and environment-related residual factors via a learnable low-rank projection. To facilitate robust feature disentanglement, we also design an Environmental Intervention module that generates diverse and challenging intervention pairs, simulating out-of-distribution environmental shifts to guide the model toward discovering truly invariant forgery representations. Experiments across cross-dataset, cross-generator, whole-face synthesis, and corruption settings show consistent gains and competitive or leading performance against strong detectors, demonstrating improved robustness to unseen forgery types and environmental variations. This work provides a new perspective and a valuable exploration for understanding and tackling the generalization barriers of VFMs in deepfake detection.
Shenghao Chen, Hao Jia, Chen Li et al.· 0 citations