AI RESEARCH
Knowledge-Preserved Model Tuning in Null-Space for Robust Spatio-Temporal Video Grounding
arXiv CS.CV
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ArXi:2606.03539v1 Announce Type: new Spatio-Temporal Video Grounding aims to localize object tubes based on textual queries. While recent methods have achieved remarkable success, they mainly focus on high-quality(HQ) inputs, neglecting the widespread presence of low-quality(LQ) videos in real-world scenarios. Although tuning methods like LoRA can adapt to degraded inputs, they inevitably disrupt pre-trained knowledge. To address this, we propose Null-Space Tuning