37 citations · 37 across the 11 of their papers we have counts for
11 papers
Target-Aware Calibration Data Selection for Preserving Uncertainty in Quantized Language Models
Zhen Yang, Sizai Hou, Kaiwen Zheng +4
Quantization is widely used to deploy large language models, but its effect on uncertainty behavior, such as confidence, margins, and abstention, is rarely treated as a primary obj…
CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
Hanwen Zhang, Yao Liu, Die Dai +4
Fine-grained Vision-Language Pre-training (FVLP) demonstrates significant potential in 3D medical image understanding by aligning anatomy-level visual representations with correspo…
UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion
Zhaoqing Li, Haoning Xu, Jingran Su +9
We present UNISON, a latent diffusion framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, tex…
Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm
Yaofang Liu, Kangning Cui, Meng Chu +7
Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to seria…
GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning
Ziru Liu, Cheng Gong, Xinyu Fu +7
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a powerful paradigm for facilitating the self-improvement of large language models (LLMs), particularl…
Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation
Yaofang Liu, Yumeng Ren, Aitor Artola +9
The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed…
Improving Diffusion Generative Models via Truncated Karhunen--Loève Expansion
Yumeng Ren, Yaofang Liu, Aitor Artola +3
Pretrained diffusion models exhibit a well-known training-sampling mismatch, often attributed to exposure bias and related distribution-shift effects. We provide a quantitative int…
Super-resolving Real-world Image Illumination Enhancement: A New Dataset and A Conditional Diffusion Model
Yang Liu, Yaofang Liu, Jinshan Pan +4
Most existing super-resolution methods and datasets have been developed to improve the image quality in well-lighted conditions. However, these methods do not work well in real-wor…
Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
Yaofang Liu, Yumeng Ren, Xiaodong Cun +5
Diffusion models have revolutionized image generation, and their extension to video generation has shown promise. However, current video diffusion models~(VDMs) rely on a scalar ti…
VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising
Yuxiang Hui, Yang Liu, Yaofang Liu +4
Video restoration task aims to recover high-quality videos from low-quality observations. This contains various important sub-tasks, such as video denoising, deblurring and low-lig…
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Haoxin Chen, Menghan Xia, Yingqing He +9
Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source m…