222 citations · 325 across the 7 of their papers we have counts for
7 papers
Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders
Yitong Jiang, Hongjun Wang, Collin McCarthy +15
Vision foundation models are bottlenecked by the quadratic cost of self-attention, which limits usable resolution and increases the cost of large-scale pretraining. Subquadratic al…
AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion
Yitong Jiang, Zhaoyang Zhang, Tianfan Xue +1
We present AutoDIR, an innovative all-in-one image restoration system incorporating latent diffusion. AutoDIR excels in its ability to automatically identify and restore images suf…
Learning Image-Adaptive Codebooks for Class-Agnostic Image Restoration
Kechun Liu, Yitong Jiang, Inchang Choi +1
Recent work on discrete generative priors, in the form of codebooks, has shown exciting performance for image reconstruction and restoration, as the discrete prior space spanned by…
Real-time Controllable Denoising for Image and Video
Zhaoyang Zhang, Yitong Jiang, Wenqi Shao +4
Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness. In traditional filter-based denoising methods, this c…
HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors
Yitong Jiang, Inchang Choi, Jun Jiang +1
We propose a novel high dynamic range (HDR) video reconstruction method with new tri-exposure quad-bayer sensors. Thanks to the larger number of exposure sets and their spatially u…
Revisiting Shadow Detection: A New Benchmark Dataset for Complex World
Xiaowei Hu, Tianyu Wang, Chi-Wing Fu +3
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on…
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
Xiaowei Hu, Yitong Jiang, Chi-Wing Fu +1
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employ…