Segment Anything Model 3 (SAM 3) can segment objects from text prompts and track them across video, but its memory and computation costs make deployment difficult. Post-training quantization (PTQ) reduces these costs by using fewer bits. However, a quantized model can select the wrong object or lose track of its target...
Xi Xiao, Yun-Bei Zhang, Lin Zhao et al.· 0 citations
As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses often rely on detecting or reasoning about injected malicious instructions and can therefore remain vulnerable to adaptive attacks that optimize the injection itself. Di...
Michael Cardei, Anjun Hu, Chen-Rui Ma et al.· 0 citations
Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an intermediate state. This paper introduces Posterior Flow Matching (PFM), a general generative modeling framework that learns conditional velocity means and componentwise var...
Chen-Rui Ma, Xi Xiao, Lin Zhao et al.· 0 citations
Dynamic Hub-and-Spoke Memory is proposed, a training-free framework that represents distant history as structured textual memory while preserving the recent frames as visual tokens for fine-grained perception in streaming video understanding.
Xin-Ru Jiang, Lin Zhao, Xi Xiao et al.· 4 citations
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