  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_ADDRESS>of 38.52. It emerges as one of
the top performers in the sub-track of MMVRAC in ICME 2024 Grand Challenges.
