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- Jingyu YangSuggested from co-authorship
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Works35 from public data
- SARS-CoV-2 Omicron strain exhibits potent capabilities for immune evasion and viral entrance262
It is shown Omicron variant causes serious immune escape from the convalescent sera from COVID-19 patients, still needs angiotensin-converting enzyme 2 (ACE2) as its receptor and exhibits a significantly increased infectivity, which indicates a significant immune Escape from an existed protection established by virus infection or vaccination.
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- UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results38
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
- NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results30
This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.
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- Learning to See Low-Light Images via Feature Domain Adaptation22
A single-stage network empowered by Feature Domain Adaptation (FDA) to decouple the denoising and color mapping tasks in raw LLIE achieves state-of-the-art performance with fewer computing costs (60% FLOPs of the two-stage method DNF).
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- Multilevel feature fusion dilated convolutional network for semantic segmentation20
A multilevel feature fusion dilated convolution network (Refine-DeepLab) is proposed, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated Convolution operation.
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- Efficient Attention Pyramid Network for Semantic Segmentation15
An efficient attention pyramid network (EAPNet) for semantic segmentation is proposed and a residual attention fusion block (RAFB) is proposed, whose purpose is to simultaneously focus on meaningful low-level feature maps and spatial location information.
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- DSDNet: Raw Domain Demoiréing via Dual Color-Space Synergy8
A single-stage raw domain demoiréing framework, Dual-Stream Demoiréing Network (DSDNet), which leverages the synergy of raw and YCbCr images to remove moiré while preserving luminance and color fidelity and develops a Luminance-Chrominance Adaptive Transformer (LCAT), which decouples luminance and chrominance representations.
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- Efficient HDR Reconstruction From Real-World Raw Images7
This work proposes RepUNet, a lightweight and efficient HDR network leveraging structural re-parameterization for fast and robust inference, and designs a new computational raw HDR data formation pipeline and construct a new raw HDR dataset, RealRaw-HDR.
- Any-to-Bokeh: Arbitrary-Subject Video Refocusing with Video Diffusion Model6
This work proposes a novel one-step video bokeh framework that converts arbitrary input videos into temporally coherent, depth-aware bokeh effects and leverages a multi-plane image (MPI) representation constructed through a progressively widening depth.
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- CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching3
The key idea is to explicitly decouple the camera directive and then coherently integrate two critical information streams: a directive representation that captures the photographer's intent, and a parameter embedding that encodes precise camera settings.
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- Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions2
The UGSAM method, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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