Content Feature Contrastive Disentangling for Video Corpus Moment Retrieval

Video Corpus Moment Retrieval (VCMR) retrieves relevant untrimmed videos from a corpus and localizes target moments using natural language queries. While existing models achieve impressive accuracy, they suffer from a critical vulnerability: a disproportionate reliance on intrinsic dataset biases, particularly spurious dependencies on temporal locations. This over-reliance severely hinders their generalization capabilities. To address this, we propose a debiased inference framework rooted in a Structural Causal Model (SCM), implemented via a novel approach termed Content Disentangling with Contrastive Learning (CDCL). CDCL applies causal intervention by explicitly decoupling confounded video features into task-relevant content representations and bias-inducing confounders. Furthermore, we integrate a contrastive disentangling mechanism to fundamentally enhance the representational purity of the isolated content features. Experiments on the constructed TVR-OOD benchmark demonstrate consistent improvements for two representative late-fusion baselines. At VCMR Recall@10 with IoU = 0.5, CDCL improves XML from 10.60% to 12.32% and ReLoCLNet from 13.57% to 14.23%. These results demonstrate the effectiveness of combining content–location disentanglement with query-guided contrastive learning under the evaluated temporal distribution shift.