3 citations · 5 across the 22 of their papers we have counts for
26 papers
FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision-Language Model
Jianze Li, Kai Liu, Ziqing Zhang +4
Aesthetic image cropping improves composition by balancing subjects, context, and image boundaries. Large vision-language models bring semantic knowledge to this task, but their sc…
PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution
Bingtian Qiao, Yue Shi, Yong Guo +2
Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realistic details. However, existin…
Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding
Zihan Zhou, Libo Zhu, Jue Gong +4
Flicker-banding (FB), arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color…
DiffST: Spatiotemporal-Aware Diffusion for Real-World Space-Time Video Super-Resolution
Zheng Chen, Ruofan Yang, Jin Han +5
Diffusion-based models have shown strong performance in video super-resolution (VSR) and video frame interpolation (VFI). However, their role in the coupled space-time video super-…
LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
Tianxing Wu, Zheng Chen, Cirou Xu +5
One-Step Diffusion Models have demonstrated promising capability and fast inference in video super-resolution (VSR) for real-world. Nevertheless, the substantial model size and hig…
Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution
Xun Zhang, Kaicheng Yang, Hongliang Lu +3
Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their heavy inference burden hinders rea…
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…
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
Tianao Zhang, Zhiteng Li, Xianglong Yan +3
Diffusion large language models (dLLMs), which offer bidirectional context and flexible masked-denoising generation, are emerging as a compelling alternative to autoregressive (AR)…
Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution
Jinpei Guo, Yifei Ji, Shengwei Wang +8
Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, sin…
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…
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…
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Zheng Chen, Zichen Zou, Kewei Zhang +4
Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely sl…
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…
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Jianze Li, Haotong Qin +4
Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced…
BiDense: Binarization for Dense Prediction
Rui Yin, Haotong Qin, Yulun Zhang +5
Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In th…
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution
Junbo Qiao, Jincheng Liao, Wei Li +7
State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from h…
Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
Jianze Li, Jiezhang Cao, Zichen Zou +5
Diffusion models have demonstrated excellent performance for real-world image super-resolution (Real-ISR), albeit at high computational costs. Most existing methods are trying to d…