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20172026
most citedAn adsorbed gas estimation model for shale gas reservoirs via statistical learning

74 citations · 135 across the 29 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

Promoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research

Qinglong Cao, Yuntian Chen, Lu Lu +4

Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs.…

cs.CV2023

Domain Prompt Learning with Quaternion Networks

Qinglong Cao, Zhengqin Xu, Yuntian Chen +2

Prompt learning has emerged as an effective and data-efficient technique in large Vision-Language Models (VLMs). However, when adapting VLMs to specialized domains such as remote s…

cs.CV2023

AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN

Changqi Sun, Hao Xu, Yuntian Chen +1

Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models. However,it remains difficult for existing methods…

cs.CV2023

A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing

Longfeng Nie, Yuntian Chen, Mengge Du +2

Cloud types, as a type of meteorological data, are of particular significance for evaluating changes in rainfall, heatwaves, water resources, floods and droughts, food security and…

cs.CV2023

Reflection Invariance Learning for Few-shot Semantic Segmentation

Qinglong Cao, Yuntian Chen, Chao Ma +1

Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images. Existing FSS algorithms typically focus on…

cs.CV2023

Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation

Qinglong Cao, Yuntian Chen, Chao Ma +1

Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching method…