6 papers · 1 filter
UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks
Liang Yao, Fan Liu, Shengxiang Xu +6
The development of multi-modal learning for Unmanned Aerial Vehicles (UAVs) typically relies on a large amount of pixel-aligned multi-modal image data. However, existing datasets f…
Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning
Fan Liu, Liang Yao, Chuanyi Zhang +4
Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream a…
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
Guangwenjie Zou, Liang Yao, Fan Liu +5
Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model prun…
Robust Noisy Correspondence Learning via Self-Drop and Dual-Weight
Fan Liu, Chenwei Dong, Chuanyi Zhang +2
Many researchers collect data from the internet through crowd-sourcing or web crawling to alleviate the data-hungry challenge associated with cross-modal matching. Although such pr…
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
Shiyu Miao, Delong Chen, Fan Liu +4
The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where…
Making Large Vision Language Models to be Good Few-shot Learners
Fan Liu, Wenwen Cai, Jian Huo +3
Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that involves recognizing novel classes from limited data. While previous methods have focuse…