665 citations · 2.1k across the 57 of their papers we have counts for
25 papers · 1 filter
Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution
Shi Chen, Lefei Zhang, Liangpei Zhang
Hyperspectral image super-resolution has attained widespread prominence to enhance the spatial resolution of hyperspectral images. However, convolution-based methods have encounter…
Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representation
Jiaqi Yang, Bo Du, Rong Liu +2
Data collected by different modalities can provide a wealth of complementary information, such as hyperspectral image (HSI) to offer rich spectral-spatial properties, synthetic ape…
Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery
Jingtao Li, Xinyu Wang, Hengwei Zhao +2
Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing…
Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process
Zhuo Zheng, Shiqi Tian, Ailong Ma +2
Understanding the temporal dynamics of Earth's surface is a mission of multi-temporal remote sensing image analysis, significantly promoted by deep vision models with its fuel -- l…
SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images
Haonan Guo, Xin Su, Chen Wu +2
Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically…
DeGMix: Efficient Multi-Task Dense Prediction with Deformable and Gating Mixer
Yangyang Xu, Yibo Yang, Bernard Ghanem +3
Convolution neural networks and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Existing studies typically…
DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space
Haonan Guo, Bo Du, Chen Wu +2
Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently…
Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision
Haonan Guo, Bo Du, Chen Wu +2
The efficacy of building footprint segmentation from remotely sensed images has been hindered by model transfer effectiveness. Many existing building segmentation methods were deve…
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation
Meng Lan, Fu Rong, Zuchao Li +2
Referring video object segmentation (RVOS) aims to segment the target object in a video sequence described by a language expression. Typical multimodal Transformer based RVOS appro…
ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration
Jiaqi Ma, Tianheng Cheng, Guoli Wang +3
Image restoration aims to reconstruct degraded images, e.g., denoising or deblurring. Existing works focus on designing task-specific methods and there are inadequate attempts at u…
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Yuxiang Wei, Zhilong Ji, Xiaohe Wu +3
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can…
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
Di Wang, Jing Zhang, Bo Du +4
The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with anno…
HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search
Di Wang, Bo Du, Liangpei Zhang +1
Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks. However, the architectures are usually optimize…
An Investigation of Face Mask Use with Busking Videos on YouTube during COVID-19: a Case Study in South Korea
Chen Wu, Xingjie Hao, Meiqi Hu +3
Wearing face mask is an effective measure to reduce the risk of COVID-19 infections and control its transmission, thus its usage survey is important for better policy decision to m…
DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
Di Wang, Jing Zhang, Bo Du +2
Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions. Prior methods suffer from limited representation ability, a…
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
Meiqi Hu, Chen Wu, Liangpei Zhang
High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, def…
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
Yi Xiao, Qiangqiang Yuan, Kui Jiang +4
Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are les…
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
Yunjie Ji, Yong Deng, Yan Gong +5
The success of ChatGPT has recently attracted numerous efforts to replicate it, with instruction-tuning strategies being a key factor in achieving remarkable results. Instruction-t…
OTAvatar: One-shot Talking Face Avatar with Controllable Tri-plane Rendering
Zhiyuan Ma, Xiangyu Zhu, Guojun Qi +2
Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not mana…
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
Jingtao Li, Xinyu Wang, Shaoyu Wang +3
Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based metho…
DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for Hyperspectral Image Restoration
Yuchun Miao, Lefei Zhang, Liangpei Zhang +1
Diffusion models have recently received a surge of interest due to their impressive performance for image restoration, especially in terms of noise robustness. However, existing di…
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
Yangyang Xu, Yibo Yang, Lefei Zhang
Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current…
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System
Hao Lv, Bing Li, Lei Zhang +2
The RRAM-based neuromorphic computing system has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and t…
DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional Networks
Lei Zhang, Xiaodong Yan, Jianshan He +2
Graph convolutional networks (GCNs) have been proved to be very practical to handle various graph-related tasks. It has attracted considerable research interest to study deep GCNs,…