# Video Temporal Grounding with Multi-Model Collaborative Learning
## 1 Introduction
Through an equitable exchange of pseudo-labels across different models, predictions from Model A furnish pseudo-supervision for Model B, while Model B's output similarly informs Model A. This bidirectional collaboration mechanism fully leverages the complementary learning capacities among models, compensates for the limitations of one-way knowledge flow, and further capitalizes on each model's strengths in boundary prediction and context modeling. To bolster the fidelity of pseudo-labels, the MMCL framework introduces a CLIP-guided pseudo-label generator. Leveraging the CLIP [21] model's cross-modal priors, this module calculates the similarity between the video and the query text to provide precise temporal constraints for the pseudo-label generator, thereby reducing noise while enhancing temporal accuracy and semantic consistency of the pseudo-labels. Moreover, to counteract gradient clashes within multi-model collaborative learning, the MMCL framework embeds an iterative training algorithm. During each optimization cycle, one model's parameters remain static while the other model's supervision and transfer objectives are refined. This alternating optimization tactic substantially alleviates gradient contention among models and ensures their respective optimization autonomy. Through adaptive calibration of consistency thresholds and transfer loss coefficients, the framework achieves a nuanced balance between cooperative learning and autonomous optimization, culminating in superior overall performance. Experimental results show that the proposed MMCL framework exhibits pronounced strengths and robust versatility in video temporal grounding. Its inherent adaptability makes the MMCL framework suitable for both heterogeneous and homogeneous model collaboration contexts, where it consistently yields appreciable performance gains. From a theoretical perspective, this study broadens the applicability of knowledge transfer and collaborative learning, addressing limitations in pseudo-label quality and model collaboration mechanisms, while offering an efficient and flexible solution for video temporal grounding. The key contributions of this work are enumerated as follows:  
* We propose a multi-model collaborative learning framework employing a bidirectional knowledge transfer scheme to foster cooperative learning across distinct models, thus transcending the confines of isolated model optimization and amplifying cross-model complementarity. * We design a CLIP-guided pseudo-label generator that harnesses CLIP's cross-modal priors to optimize pseudo-label generation, improving their quality and robustness, and bolstering the accuracy of knowledge transfer. * We present an iterative multi-model training algorithm that alternately freezes and optimizes model parameters to effectively mitigate gradient conflicts, safeguard the balance between cooperative and independent optimization, and further boost training stability and model performance. * Experimental findings reveal that our method effectively enhance the performance of various baseline methods for video temporal grounding. By incorporating both the VSLNet [22] and 2D-TAN [23] baseline models, performance on multiple datasets surpasses current mainstream approaches.