
A novel multi-modal spiking saliency mamba model that effectively captures fine-grained relationships between video and language, leverages relevant slots to enhance memory for long-term dependencies, and utilizes spiking neural networks to accurately identify salient moments, leading to significant improvements in temporal video grounding.


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multi-modal-spiking-saliency-mamba-for-efficient-temporal-video-grounding


Multi-Modal Spiking Saliency Mamba for Efficient Temporal Video Grounding


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SnAG is a simple and efficient model that achieves state-of-the-art performance on video grounding benchmarks, especially for long videos with many queries, by leveraging late fusion and video-centric training.


snag-a-scalable-and-accurate-model-for-video-grounding


SnAG: A Scalable and Accurate Model for Video Grounding
