CPL: Curriculum Pseudo Labeling for Weakly Supervised Temporal Forgery Localization

In forgery detection, temporal forgery localization offers a more nuanced perspective than binary detection by providing more precise temporal boundaries of manipulations. However, its need for frame-wise annotations limits real-world practicality. Therefore, we present the task of Weakly Supervised Temporal Forgery Localization (WS-TFL), which aims to localize forgery segments in videos given only video-level labels for training. To tackle the absence of frame-wise annotations in weakly supervised settings, pseudo-label learning presents a viable solution for WS-TFL. However, pseudo labels often suffer from inaccuracy (i.e. contain noise) due to lack of supervision. Inspired by the ability of curriculum learning in handling noisy data, this work proposes Curriculum Pseudo Labeling (CPL), a simple yet effective strategy to address noise in pseudo labels. Specifically, pseudo-label learning leverages proposals generated from a base model as coarse pseudo labels to retrain the model for better performance. To refine pseudo labels and reduce noise, we design a learning curriculum that ranks them by quality, where we analyze the multiple-instance learning essence of WS-TFL and further introduce forgery prior knowledge. Experiments show that CPL consistently improves performance across different baselines, with a 35.24% and 27.63% increase in average precision to the default baseline on Lav-DF and TVIL datasets, respectively.