Weakly-Supervised Temporal Action Localization by Inferring Snippet-Feature Affinity

Weakly-supervised temporal action localization aims to locate action regions and identify action categories in untrimmed videos, only taking video-level labels as the supervised information. Pseudo label generation is a promising strategy to solve the challenging problem, but most existing methods are limited to employing snippet-wise classification results to guide the generation, and they ignore that the natural temporal structure of the video can also provide rich information to assist such a generation process. In this paper, we propose a novel weakly-supervised temporal action localization method by inferring snippet-feature affinity. First, we design an affinity inference module that exploits the affinity relationship between temporal neighbor snippets to generate initial coarse pseudo labels. Then, we introduce an information interaction module that refines the coarse labels by enhancing the discriminative nature of snippet-features through exploring intra- and inter-video relationships. Finally, the high-fidelity pseudo labels generated from the information interaction module are used to supervise the training of the action localization network. Extensive experiments on two publicly available datasets, i.e., THUMOS14 and ActivityNet v1.3, demonstrate our proposed method achieves significant improvements compared to the state-of-the-art methods.

Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer

Video denoising aims to recover high-quality frames from the noisy video. While most existing approaches adopt convolutional neural networks(CNNs) to separate the noise from the original visual content, however, CNNs focus on local information and ignore the interactions between long-range regions. Furthermore, most related works directly take the output after spatio-temporal denoising as the final result, neglecting the fine-grained denoising process. In this paper, we propose a Dual-stage Spatial-Channel Transformer (DSCT) for coarse-to-fine video denoising, which inherits the advantages of both Transformer and CNNs. Specifically, DSCT is proposed based on a progressive dual-stage architecture, namely a coarse-level and a fine-level to extract dynamic feature and static feature, respectively. At both stages, a Spatial-Channel Encoding Module(SCEM) is designed to model the long-range contextual dependencies at spatial and channel levels. Meanwhile, we design a Multi-scale Residual Structure to preserve multiple aspects of information at different stages, which contains a Temporal Features Aggregation Module(TFAM) to summarize the dynamic representation. Extensive experiments on four publicly available datasets demonstrate our proposed DSCT achieves significant improvements compared to the state-of-the-art methods.