34 citations · 66 across the 8 of their papers we have counts for
8 papers · 1 filter
XStreamVGGT: Extremely Memory-Efficient Streaming Vision Geometry Grounded Transformer with KV Cache Compression
Zunhai Su, Weihao Ye, Hansen Feng +5
Learning-based 3D visual geometry models have significantly advanced with the advent of large-scale transformers. Among these, StreamVGGT leverages frame-wise causal attention to d…
XStreamVGGT: Extremely Memory-Efficient Streaming Vision Geometry Grounded Transformer with KV Cache Compression
Zunhai Su, Weihao Ye, Hansen Feng +5
Learning-based 3D visual geometry models have benefited substantially from large-scale transformers. Among these, StreamVGGT leverages frame-wise causal attention for strong stream…
AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results
Alexander Yakovenko, George Chakvetadze, Ilya Khrapov +17
This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge. The task is to develop methods that denoise low-light RAW video by exploit…
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency
Hansen Feng, Lizhi Wang, Yiqi Huang +3
The rapid advancement of photography has created a growing demand for a practical blind raw image denoising method. Recently, learning-based methods have become mainstream due to t…
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
Tong Li, Lizhi Wang, Hansen Feng +2
Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance image quality. While recent advance…
Rethinking Model Redundancy for Low-light Image Enhancement
Tong Li, Lizhi Wang, Hansen Feng +3
Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light imag…
Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling
Tong Li, Hansen Feng, Lizhi Wang +2
Image denoising is a fundamental problem in computational photography, where achieving high perception with low distortion is highly demanding. Current methods either struggle with…
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling
Hansen Feng, Lizhi Wang, Yuzhi Wang +1
Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limit…