Temporal grounding is crucial in multimodal learning, but it poses challenges when applied to animal behavior data due to the sparsity and uniform distribution of moments. To address these challenges, we propose a novel Positional Recovery Training framework (Port), which prompts the model with the start and end times of specific animal behaviors during training. Specifically, Port enhances the baseline model with a Recovering part to predict flipped label sequences and align distributions with a Dual-alignment method. This allows the model to focus on specific temporal regions prompted by ground-truth information. Extensive experiments on the Animal Kingdom dataset demonstrate the effectiveness of Port, achieving an [email protected] of 38.52. It emerges as one of the top performers in the sub-track of MMVRAC in ICME 2024 Grand Challenges.

翻译：时间定位是多模态学习中的关键任务，但在应用于动物行为数据时，由于时间点的稀疏性和均匀分布特征，该任务面临挑战。为解决这些问题，我们提出了一种新颖的位置恢复训练框架（Port），该框架在训练过程中通过特定动物行为的起止时间对模型进行提示。具体而言，Port通过恢复部件增强基线模型，用于预测翻转标签序列，并采用双重对齐方法实现分布对齐。这使得模型能够聚焦于真实信息所提示的特定时间区域。在Animal Kingdom数据集上的大量实验证明了Port的有效性，其[email protected]达到38.52%，在ICME 2024大挑战赛MMVRAC子赛道中位列顶尖方法之一。