7 papers
Manifold-Constrained Noise Optimization for Diverse Diffusion Sampling
Qitan Shi, Cheng Jin, Ziyuan Liu +1
Few-step distilled diffusion models generate high-quality images quickly, but often lose per-prompt diversity, producing near-identical samples across random seeds. Optimizing the…
JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering
Ziyuan Liu, Ruifei Zhu, Ouqiao Ma +1
Remote sensing change detection (CD) traditionally focuses on pixel-level binary segmentation, which identifies where changes occur but neither what nor why. To bridge this semanti…
Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models
Jiawei Zhang, Ziyuan Liu, Leon Yan +2
The distortion-perception (D-P) tradeoff is a fundamental phenomenon of Bayesian inverse problems, which characterizes the inherent tension between distortion performance and perce…
Counteraction-Aware Multi-Teacher On-Policy Distillation for General Capability Recovery with Domain Preservation
Tianlei Chen, Jiao Ou, Ziyuan Liu +3
Domain specialization can improve LLM behavior in vertical domains, but often weakens the general capabilities inherited from the original model. Recent Multi-Teacher On-Policy Dis…
Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation
Jiawei Zhang, Ziyuan Liu, Leon Yan +2
Diffusion-based inverse algorithms have shown remarkable performance across various inverse problems, yet their reliance on numerous denoising steps incurs high computational costs…
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
Ziyuan Liu, Ruifei Zhu, Long Gao +3
Change detection (CD) in remote sensing images plays a vital role in Earth observation. However, the scarcity of high-resolution, comprehensive open-source datasets and the difficu…