Temporal Grounding

Temporal grounding in video aims to precisely locate video segments corresponding to natural language descriptions, bridging the gap between visual and linguistic modalities. Current research focuses on improving the accuracy and efficiency of this localization, particularly for long videos and complex queries, employing techniques like large language models (LLMs), vision-language models (VLMs), and transformer-based architectures with enhanced temporal modeling. This work is significant for advancing video understanding and has implications for applications such as video summarization, content retrieval, and assistive technologies for the visually impaired. Addressing challenges like compositional generalization and mitigating biases in training data remains a key focus.