7 citations · 12 across the 6 of their papers we have counts for
6 papers
POA: Pre-training Once for Models of All Sizes
Yingying Zhang, Xin Guo, Jiangwei Lao +7
Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a…
Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
Ziyuan Huang, Kaixiang Ji, Biao Gong +6
This paper introduces Chain-of-Sight, a vision-language bridge module that accelerates the pre-training of Multimodal Large Language Models (MLLMs). Our approach employs a sequence…
SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding
Junwei Luo, Zhen Pang, Yongjun Zhang +8
Remote Sensing Large Multi-Modal Models (RSLMMs) are developing rapidly and showcase significant capabilities in remote sensing imagery (RSI) comprehension. However, due to the lim…
Enhancing DETRs Variants through Improved Content Query and Similar Query Aggregation
Yingying Zhang, Chuangji Shi, Xin Guo +4
The design of the query is crucial for the performance of DETR and its variants. Each query consists of two components: a content part and a positional one. Traditionally, the cont…
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Qingpei Guo, Furong Xu, Hanxiao Zhang +6
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence. Nevertheless, VLM models supporting multi-language, e.g., in both Chinese and…
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Xin Guo, Jiangwei Lao, Bo Dang +13
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation. Nevertheless, these works primarily focus on a singl…