62 citations · 141 across the 16 of their papers we have counts for
16 papers
SkyVLaM: Multimodal Large Language Model for UAV Video Understanding in Remote Sensing
Kaiwen Jing, Ruixu Jia, Bingyao Li +3
Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved remote sensing (RS) multimodal understanding. Language-conditioned segmentation is crucial f…
MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting
Mengqiu Xu, Kaixin Chen, Heng Guo +5
Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance. However, the…
Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data
Xun Zhu, Fanbin Mo, Zheng Zhang +6
The emergence of medical generalist foundation models has revolutionized conventional task-specific model development paradigms, aiming to better handle multiple tasks through join…
M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
Mengqiu Xu, Ming Wu, Kaixin Chen +11
Marine fog poses a significant hazard to global shipping, necessitating effective detection and forecasting to reduce economic losses. In recent years, several machine learning (ML…
Multi-Scale Representations by Varying Window Attention for Semantic Segmentation
Haotian Yan, Ming Wu, Chuang Zhang
Multi-scale learning is central to semantic segmentation. We visualize the effective receptive field (ERF) of canonical multi-scale representations and point out two risks in learn…
VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting
Yutong Xiong, Xun Zhu, Ming Wu +4
Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention. Recent studies have highlighted the potential of spatio-te…
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection
Shenwei Xie, Wanfeng Zheng, Zhenglin Xian +3
Automatically extracting roads from satellite imagery is a fundamental yet challenging computer vision task in the field of remote sensing. Pixel-wise semantic segmentation-based a…
SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines
Shizun Wang, Weihong Zeng, Xu Wang +7
The creation of a parameterized stylized character involves careful selection of numerous parameters, also known as the "avatar vectors" that can be interpreted by the avatar engin…
Privileged Prior Information Distillation for Image Matting
Cheng Lyu, Jiake Xie, Bo Xu +6
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are seman…
Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention
Haotian Yan, Chuang Zhang, Ming Wu
Multi-scale representations are crucial for semantic segmentation. The community has witnessed the flourish of semantic segmentation convolutional neural networks (CNN) exploiting…
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
Shizun Wang, Ming Lu, Kaixin Chen +4
With the development of Deep Neural Networks (DNNs), plenty of methods based on DNNs have been proposed for Single Image Super-Resolution (SISR). However, existing methods mostly t…
Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation
Jiaming Liu, Ming Lu, Kaixin Chen +7
Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwi…
ConTNet: Why not use convolution and transformer at the same time?
Haotian Yan, Zhe Li, Weijian Li +3
Although convolutional networks (ConvNets) have enjoyed great success in computer vision (CV), it suffers from capturing global information crucial to dense prediction tasks such a…
GINet: Graph Interaction Network for Scene Parsing
Tianyi Wu, Yu Lu, Yu Zhu +4
Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorporate the linguistic kno…
FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images
Kaiyan Chen, Ming Wu, Jiaming Liu +1
Ship detection using high-resolution remote sensing images is an important task, which contribute to sea surface regulation. The complex background and special visual angle make sh…
C-DLinkNet: considering multi-level semantic features for human parsing
Yu Lu, Muyan Feng, Ming Wu +1
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human. The challenge of human…