3 citations · 4 across the 16 of their papers we have counts for
12 papers · 1 filter
UmniBench: Unified Understand and Generation Model Oriented Omni-dimensional Benchmark
Kai Liu, Leyang Chen, Wenbo Li +5
Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. However, evaluations of unified multimodal models (UMMs) rem…
VividFace: High-Quality and Efficient One-Step Diffusion For Video Face Enhancement
Shulian Zhang, Yong Guo, Long Peng +6
Video Face Enhancement (VFE) aims to restore high-quality facial regions from degraded video sequences, enabling a wide range of practical applications. Despite substantial progres…
SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal
Tingyu Yang, Jue Gong, Jinpei Guo +3
JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoratio…
QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution
Bowen Chai, Zheng Chen, Libo Zhu +3
Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models…
PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement
ZhanFeng Feng, Long Peng, Xin Di +7
Multi-frame video enhancement tasks aim to improve the spatial and temporal resolution and quality of video sequences by leveraging temporal information from multiple frames, which…
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…
OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Jinpei Guo, Yifei Ji, Zheng Chen +6
Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconst…
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…
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li +5
Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration ability. However, accuracy degrad…
One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Jianze Li, Jiezhang Cao, Yong Guo +2
Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits…
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen +5
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and compu…
Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
Jinpei Guo, Zheng Chen, Wenbo Li +2
Diffusion models have demonstrated remarkable success in image restoration tasks. However, their multi-step denoising process introduces significant computational overhead, limitin…