Learnable Negative Proposals Using Dual-Signed Cross-Entropy Loss for Weakly Supervised Video Moment Localization

Most existing methods for weakly supervised video moment localization use rule-based negative proposals. However, the rule-based ones have a limitation in capturing various confusing locations throughout the entire video. To alleviate the limitation, we propose learning-based negative proposals which are trained using a dual-signed cross-entropy loss. The dual-signed cross-entropy loss is controlled by a weight that changes gradually from a minus value to a plus one. The minus value makes the negative proposals be trained to capture query-irrelevant temporal boundaries (easy negative) in the earlier training stages, whereas the plus one makes them capture somewhat query-relevant temporal boundaries (hard negative) in the later training stages. To evaluate the quality of negative proposals, we introduce a new evaluation metric to measure how well a negative proposal captures a poorly-generated positive proposal. We verify that our negative proposals can be applied with negligible additional parameters and inference costs, achieving state-of-the-art performance on three public datasets.