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bolun zheng

7 accepted papers

2026

EMR-Diff: Edge-aware Multimodal Residual Diffusion Model for Hyperspectral Image Super-resolution

CVPR 2026

Hardware constraints make it challenging to simultaneously acquire hyperspectral images (HSIs) with both high spatial and high spectral resolutions. A promising solution is to fuse low-resolution HSI (LR-HSI) with high-resolution multispectral images (HR-MSI) to generate high-resolution HSI (HR-HSI)

Cited by 0SourcecodeScholar
2025

SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity

CVPR 2025poster

Social media popularity prediction task aims to predict the popularity of posts on social media platforms, which has a positive driving effect on application scenarios such as content optimization, digital marketing and online advertising. Though many studies have made significant progress, few of t…

2024

Infrared Small Target Detection with Scale and Location Sensitivity

CVPR 2024poster

Recently infrared small target detection (IRSTD) has been dominated by deep-learning-based methods. However these methods mainly focus on the design of complex model structures to extract discriminative features leaving the loss functions for IRSTD under-explored. For example the widely used Interse…

2024

Quad Bayer Joint Demosaicing and Denoising Based on Dual Encoder Network with Joint Residual Learning

AAAI 2024technical

The recent imaging technology Quad Bayer CFA brings better imaging PSNR and higher visual quality compared to traditional Bayer CFA, but also serious challenges for demosaicing and denoising during the ISP pipeline. In this paper, we propose a novel dual encoder network, namely DRNet, to achieve joi…

Cited by 12SourcePDFScholar
2023

Improving Dynamic HDR Imaging with Fusion Transformer

AAAI 2023technical

Reconstructing a High Dynamic Range (HDR) image from several Low Dynamic Range (LDR) images with different exposures is a challenging task, especially in the presence of camera and object motion. Though existing models using convolutional neural networks (CNNs) have made great progress, challenges s…

2022

Boosting Out-of-distribution Detection with Typical Features

NeurIPS 2022accept

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD detection methods that focus on designing OOD scores or introducing diverse outlier examples to retrain the model, we delve…

Cited by 61SourcePDFScholar