3 papers
cs.CV2026
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
Ouxiang Li, Yuan Wang, Xinting Hu +3
Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, offensive content, a…
cs.CV2025
Accelerating Diffusion Transformer via Error-Optimized Cache
Junxiang Qiu, Shuo Wang, Jinda Lu +4
Diffusion Transformer (DiT) is a crucial method for content generation. However, it needs a lot of time to sample. Many studies have attempted to use caching to reduce the time con…
cs.CV2025
RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation
Xingrui Qin, Wentao Zhao, Chuan Cao +5
Dense depth estimation using millimeter-wave radar typically requires dense LiDAR supervision, generated via multi-frame projection and interpolation, for guiding the learning of a…