SpikeTAD proposes the first SNN-based end-to-end TAD model, reporting 67.2% mAP on THUMOS14 and 37.42% on ActivityNet-1.3 with extremely low power consumption.
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Adapts MDVLMs to TAL via planned training objective and step-level IoU reward, reporting gains over autoregressive baselines on ActivityNet and THUMOS datasets.
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SpikeTAD: Spiking Neural Networks for End-to-End Temporal Action Detection
SpikeTAD proposes the first SNN-based end-to-end TAD model, reporting 67.2% mAP on THUMOS14 and 37.42% on ActivityNet-1.3 with extremely low power consumption.
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Masked Diffusion Vision-Language Models for Temporal Action Localization
Adapts MDVLMs to TAL via planned training objective and step-level IoU reward, reporting gains over autoregressive baselines on ActivityNet and THUMOS datasets.