Weakly supervised video moment retrieval or weakly supervised language moment retrieval aims to search the most relevant moment given a language query. In order to guide the model to capture the most matching video segments with the text description, we design a two-granularity loss function that simultaneously considers both video-level and instance-level relationships. Specifically, we first generate coarse video segments and regard each video segment as an instance. For video-level regularized multiple instance loss (MIL), we leverage the latent alignment between all intra-video segments (ie., positive bag) and text descriptions. Then, we classify these segments by regarding this procedure as a supervised learning task under noisy labels. With the instance-level regularized loss function, our model can learn to correct noisy instance-level labels so as to locate the more accurate frame boundary from all the positive instances. Comprehensive experimental results onActivityNetandDiDeModemonstrate that the proposed loss function sets a new state-of-the-art.

Moment localization in videos using natural language refers to finding the most relevant segment from the video with given a query in natural language form. In this paper, we present a new boundary-determining strategy called explicit correlation-based convolution boundary locator (ECCL), which can handle any lengths of videos and moments while leveraging fine-grained matching relationships. In this method, we first train a deep network to obtain the correlation scores between video clips and query statements. Subsequently, with the correlation scores, we utilize a convolution kernel to generate the boundary probability distribution. Finally, the start and end time indexes of the video moment are calculated with an optimization problem. Experiments on two publicly available datasets demonstrate the feasibility of ECCL.

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.