6 citations · 12 across the 16 of their papers we have counts for
16 papers
Dynamic Training-Free Fusion of Subject and Style LoRAs
Qinglong Cao, Yuntian Chen, Chao Ma +1
Recent studies have explored the combination of multiple LoRAs to simultaneously generate user-specified subjects and styles. However, most existing approaches fuse LoRA weights us…
Learning Domain Knowledge in Multimodal Large Language Models through Reinforcement Fine-Tuning
Qinglong Cao, Yuntian Chen, Chao Ma +1
Multimodal large language models (MLLMs) have shown remarkable capabilities in multimodal perception and understanding tasks. However, their effectiveness in specialized domains, s…
Auto-Regressive Moving Diffusion Models for Time Series Forecasting
Jiaxin Gao, Qinglong Cao, Yuntian Chen
Time series forecasting (TSF) is essential in various domains, and recent advancements in diffusion-based TSF models have shown considerable promise. However, these models typicall…
Latent Knowledge-Guided Video Diffusion for Scientific Phenomena Generation from a Single Initial Frame
Qinglong Cao, Xirui Li, Ding Wang +3
Video diffusion models have achieved impressive results in natural scene generation, yet they struggle to generalize to scientific phenomena such as fluid simulations and meteorolo…
Open-Vocabulary Remote Sensing Image Semantic Segmentation
Qinglong Cao, Yuntian Chen, Chao Ma +1
Open-vocabulary image semantic segmentation (OVS) seeks to segment images into semantic regions across an open set of categories. Existing OVS methods commonly depend on foundation…
Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting
Jiaxin Gao, Qinglong Cao, Yuntian Chen +1
Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, thereby enabling efficient energy management and grid integration.…
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.…
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
Qinglong Cao, Zhengqin Xu, Chao Ma +2
Flow image super-resolution (FISR) aims at recovering high-resolution turbulent velocity fields from low-resolution flow images. Existing FISR methods mainly process the flow image…
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…
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Jiaxin Gao, Yuxiao Hu, Qinglong Cao +2
Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains. Previous representation learning-based TSF algorithms typically embrace a c…
MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
Jiaxin Gao, Qinglong Cao, Yuntian Chen
Automatic Modulation Classification (AMC) plays a vital role in time series analysis, such as signal classification and identification within wireless communications. Deep learning…
Domain-Controlled Prompt Learning
Qinglong Cao, Zhengqin Xu, Yuntian Chen +2
Large pre-trained vision-language models, such as CLIP, have shown remarkable generalization capabilities across various tasks when appropriate text prompts are provided. However,…
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…
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…
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Qinglong Cao, Yuntian Chen, Xiwen Yao +1
Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples. Currently, few-shot segmentation methods mainly focus on l…
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Yuanwei Liu, Nian Liu, Qinglong Cao +3
Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target r…