1 citations · 3 across the 20 of their papers we have counts for
23 papers
Advanced Pixel Diffusion Model with Guided Sparse Global Refinement
Weiyi You, Jinhua Zhang, Xingyu Zhou +3
Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixel domain. However, pixel-spac…
Beyond Token-Level Cross-Entropy: Fréchet Distributional Post-Training for Autoregressive Image Generation
Jinhua Zhang, Yisong Lin, Wei Long +1
Autoregressive image generators are commonly pretrained with token-level cross-entropy under teacher forcing, yet evaluated by the distributional quality of decoded images. This cr…
Perceiving Better Moments: Cover Frame Reselection and Enhancement for Live Photos with the Live2K Dataset
Junyu Lou, Kai Chen, Weiyi You +3
Modern smartphones capture Live Photos, short video bursts surrounding a still image, offering a dynamic and engaging photographic experience. However, the cover photo and video co…
Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models
Qifan Li, Xingyu Zhou, Jinhua Zhang +2
Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact late…
ATD: Improved Transformer with Adaptive Token Dictionary for Image Restoration
Leheng Zhang, Wei Long, Yawei Li +3
Recently, Transformers have gained significant popularity in image restoration tasks such as image super-resolution and denoising, owing to their superior performance. However, bal…
From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution
Chunyu Meng, Wei Long, Shuhang Gu
Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input. Transformer-based…
Guiding a Diffusion Transformer with the Internal Dynamics of Itself
Xingyu Zhou, Qifan Li, Xiaobin Hu +2
The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover lo…
Task-Aware Image Signal Processor for Advanced Visual Perception
Kai Chen, Jin Xiao, Leheng Zhang +2
In recent years, there has been a growing trend in computer vision towards exploiting RAW sensor data, which preserves richer information compared to conventional low-bit RGB image…
Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution
Qifan Li, Jiale Zou, Jinhua Zhang +3
Vector-quantized based models have recently demonstrated strong potential for visual prior modeling. However, existing VQ-based methods simply encode visual features with nearest c…
Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement
Junyu Lou, Xiaorui Zhao, Kexuan Shi +1
Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling effici…
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
Minghao Han, Weiyi You, Jinhua Zhang +3
While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produc…
Learned Image Compression with Dictionary-based Entropy Model
Jingbo Lu, Leheng Zhang, Xingyu Zhou +3
Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the…
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
Xingyu Zhou, Wei Long, Jingbo Lu +4
Video super-resolution (VSR) can achieve better performance compared to single image super-resolution by additionally leveraging temporal information. In particular, the recurrent-…
HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement
Xiaorui Zhao, Xinyue Zhou, Peibei Cao +2
Developing effective approaches to generate enhanced results that align well with human visual preferences for high-quality well-lit images remains a challenge in low-light image e…
MVAR: Visual Autoregressive Modeling with Scale and Spatial Markovian Conditioning
Jinhua Zhang, Wei Long, Minghao Han +2
Essential to visual generation is efficient modeling of visual data priors. Conventional next-token prediction methods define the process as learning the conditional probability di…
Neural Stereo Video Compression with Hybrid Disparity Compensation
Shiyin Jiang, Zhenghao Chen, Minghao Han +1
Disparity compensation represents the primary strategy in stereo video compression (SVC) for exploiting cross-view redundancy. These mechanisms can be broadly categorized into two…
Progressive Focused Transformer for Single Image Super-Resolution
Wei Long, Xingyu Zhou, Leheng Zhang +1
Transformer-based methods have achieved remarkable results in image super-resolution tasks because they can capture non-local dependencies in low-quality input images. However, thi…
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model
Leheng Zhang, Weiyi You, Kexuan Shi +1
Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a leng…
Consistency Trajectory Matching for One-Step Generative Super-Resolution
Weiyi You, Mingyang Zhang, Leheng Zhang +3
Current diffusion-based super-resolution (SR) approaches achieve commendable performance at the cost of high inference overhead. Therefore, distillation techniques are utilized to…
Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations
Kexuan Shi, Hai Chen, Leheng Zhang +1
Implicit Neural Representations (INRs), as a versatile representation paradigm, have achieved success in various computer vision tasks. Due to the spectral bias of the vanilla mult…
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
Leheng Zhang, Yawei Li, Xingyu Zhou +2
Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks…
Improved Implicit Neural Representation with Fourier Reparameterized Training
Kexuan Shi, Xingyu Zhou, Shuhang Gu
Implicit Neural Representation (INR) as a mighty representation paradigm has achieved success in various computer vision tasks recently. Due to the low-frequency bias issue of vani…
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
Xingyu Zhou, Leheng Zhang, Xiaorui Zhao +3
Recently, Vision Transformer has achieved great success in recovering missing details in low-resolution sequences, i.e., the video super-resolution (VSR) task. Despite its superior…