arXiv:2603.22375v1 Announce Type: cross
Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from reference trajectories. MTEO is plug-and-play with existing ODE solvers, adds no inference-time overhead, and trains only a tiny fraction of parameters. Extensive experiments across diverse datasets and backbones show state-of-the-art performance in the few-step sampling and substantially narrow the gap between distillation-based and lightweight methods. Code will be available.
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
arXiv:2603.25006v1 Announce Type: cross Abstract: Early detection of rice leaf diseases is critical, as rice is a staple crop supporting a substantial share of the