54 citations · 79 across the 23 of their papers we have counts for
13 papers · 1 filter
Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction
Yi Ai, Yuanhao Cai, Yulun Zhang +2
Reconstructing a three-dimensional hyperspectral cube from a two-dimensional compressed measurement is a severely ill-posed inverse problem. Existing deep unfolding networks (DUNs)…
Asymmetric VAE for One-Step Video Super-Resolution Acceleration
Jianze Li, Yong Guo, Yulun Zhang +1
Diffusion models have significant advantages in the field of real-world video super-resolution and have demonstrated strong performance in past research. In recent diffusion-based…
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Yuanhao Cai, He Zhang, Xi Chen +11
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Anoth…
QuantFace: Efficient Quantization for Face Restoration
Jiatong Li, Libo Zhu, Haotong Qin +5
Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations hamper the widespread adoption of these models. In this work, we pr…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Xianglong Yan, Zhiteng Li, Tianao Zhang +4
Large language models (LLMs) have demonstrated remarkable performance, but their long-context reasoning remains constrained by the excessive memory required for the Key-Value (KV)…
HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance
Jue Gong, Tingyu Yang, Jingkai Wang +5
Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB), making restoration challenging. Existing research l…
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Zhiteng Li, Hanxuan Li, Junyi Wu +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…
QuantCache: Adaptive Importance-Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation
Junyi Wu, Zhiteng Li, Zheng Hui +3
Recently, Diffusion Transformers (DiTs) have emerged as a dominant architecture in video generation, surpassing U-Net-based models in terms of performance. However, the enhanced ca…
One-Step Diffusion Model for Image Motion-Deblurring
Xiaoyang Liu, Yuquan Wang, Zheng Chen +4
Currently, methods for single-image deblurring based on CNNs and transformers have demonstrated promising performance. However, these methods often suffer from perceptual limitatio…
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Haotong Qin, Kaicheng Yang +5
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…
Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Jue Gong, Jingkai Wang, Zheng Chen +4
Human body restoration, as a specific application of image restoration, is widely applied in practice and plays a vital role across diverse fields. However, thorough research remai…
AdaSVD: Adaptive Singular Value Decomposition for Large Language Models
Zhiteng Li, Mingyuan Xia, Jingyuan Zhang +5
Large language models (LLMs) have achieved remarkable success in natural language processing (NLP) tasks, yet their substantial memory requirements present significant challenges f…