6 papers
SkyNative: A Native Multimodal Architecture for Remote Sensing Vision-Language Understanding
Xiao Yang, Ronghao Fu, Zhiwen Lin +7
Remote sensing vision-language models (RS-VLMs) commonly employ a pretrained vision encoder and a projection module to map image features into the token space of a large language m…
GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning
Xiao Yang, Ronghao Fu, Zhuoran Duan +3
Vision-language pretraining models have made significant progress in bridging remote sensing imagery with natural language. However, existing approaches often fail to effectively i…
OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks
Ronghao Fu, Haoran Liu, Weijie Zhang +4
Vision-Language Models (VLMs) have demonstrated effective perception and reasoning capabilities on general-domain tasks, leading to growing interest in their application to Earth o…
Dual Relation Mining Network for Zero-Shot Learning
Jinwei Han, Yingguo Gao, Zhiwen Lin +4
Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-…
Anchor-based Robust Finetuning of Vision-Language Models
Jinwei Han, Zhiwen Lin, Zhongyisun Sun +5
We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as…
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding
Yi Xin, Junlong Du, Qiang Wang +2
Large-scale pre-trained models have achieved remarkable success in various computer vision tasks. A standard approach to leverage these models is to fine-tune all model parameters…