SeiT++: Masked Token Modeling Improves Storage-efficient Training

Memory-Efficient Fine-Tuning for Quantized Diffusion Model

Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation

Preventing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval

Self-Supervised Vision Transformers Are Efficient Segmentation Learners for Imperfect Labels

Precision matters: Precision-aware ensemble for weakly supervised semantic segmentation

Local Expert Diffusion Models for Efficient Training in Denoising Diffusion Probabilistic Models

TADA: Timestep-Aware Data Augmentation for Diffusion Models

Few-shoot Font Generation with Weakly Supervised Localized Representations

Long-tail Mixup for Extreme Multi-label Classification