# Video Temporal Grounding with Multi-Model Collaborative Learning
## 1 Introduction  
Video Temporal Grounding (VTG) seeks to accurately pinpoint the corresponding time segment in an untrimmed video based on natural language queries [1]. As a pivotal task at the intersection of computer vision and natural language processing, VTG lays the groundwork for a variety of downstream video comprehension tasks, such as video dialogue systems [2], video relationship detection [3, 4, 5], and video question answering systems [6, 7, 8]. The majority of traditional VTG strategies concentrate on refining a singlemodel, typified by the Span-based Model and the 2D-Map Model shown in Figure 1, both of which display distinct advantages and limitations in particular contexts [9]. The Span-based Model explicitly forecasts the probabilistic distribution of a target interval's start and end points, showcasing commendable capabilities in delineating precise temporal boundaries. Nevertheless, given its dependence on localized feature extraction, it exhibits a diminished aptitude for holistic semantic context modeling, thereby restricting its effectiveness in handling semantically intricate videos [10; 11]. In comparison, the 2D-Map Model devises a two-dimensional temporal mapping, partitioning the entire video span into a 2D matrix in which every element corresponds to a prospective temporal clip. Operating within a two-dimensional framework, this model maximizes the score region, thereby adeptly capturing the contextual interplay among temporal segments. However, the expansive search space for candidate clips renders its computational overhead substantial, ultimately undermining boundary prediction precision [12; 13; 14]. Despite the impressive performance of these models in specific settings, their independent optimization strategies fail to fully exploit the complementary nature among models, disregarding the potential for collaborative learning and thereby limiting further performance enhancement. In recent years, knowledge flow, which encompasses knowledge distillation and transfer, has increasingly become a critical strategy for achieving multi-model cooperative optimization in VTG endeavors. Through the supervised transfer of extensive knowledge from large-scale models to more compact ones, knowledge distillation empowers lightweight models to attain robust performance under constrained computational budgets [9; 15; 16]. By leveraging cross-modal alignment and data exchange, knowledge transfer fortifies the synergy between visual and textual representations, facili  
Figure 1: An illustration of localizing a temporal moment in an untrimmed video using a language query. The 2D-MAP Model and the Span-based Model employ pseudo-labels to facilitate knowledge transfer and achieve collaborative learning across models. Leveraging an iterative training algorithm, they strike a dynamic balance between collaborative learning and independent optimization. tating effective multimodal co-modeling [17; 18]. Nevertheless, existing work often restricts knowledge flow to a unidirectional paradigm, such as transferring insights from larger models to smaller counterparts or employing simplistic cross-modal alignments. Such approaches generate pseudo-labels of limited quality and are prone to gradient conflicts in multi-model collaboration [19; 20], thus failing to fully harness the potential of bidirectional cooperative learning and dynamic knowledge sharing. These shortcomings have emerged as a key bottleneck restricting further advancements in VTG performance. To solve the aforementioned deficiencies, this study introduces a Multi-Model Collaborative Learning (MMCL) framework, illustrated in Figure 1. Within this framework, a bidirectional collaborative learning paradigm is instituted across multiple models, yielding significant enhancements in the performance of video temporal grounding. Concretely, to surmount the constraints of one-way knowledge transfer, the MMCL architecture adopts a bidirectional knowledge transmission scheme.