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 IoU@0.3 of 38.52. It emerges as one of
the top performers in the sub-track of MMVRAC in ICME 2024 Grand Challenges.

提出了一种新颖的位置恢复训练框架（Port），该框架在动物行为数据中解决了时间定位的挑战，通过基于真实信息的重建部分和双重对齐方法，使模型能够专注于特定的时间区域。在动物王国数据集上的大量实验表明，Port 的有效性，其在 ICME 2024 大挑战赛的子任务 MMVRAC 中取得了 IoU@0.3 为 38.52 的成绩，成为顶级表现之一。