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20232026
most citedPromoting AI Equity in Science: Generalized Domain Prompt Learning for Accessible VLM Research

2 citations · 4 across the 10 of their papers we have counts for

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cs.CV2026

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…

cs.CV20241 cited

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…

cs.CV2024

Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics

Junyi Cao, Shanyan Guan, Yanhao Ge +3

While humans effortlessly discern intrinsic dynamics and adapt to new scenarios, modern AI systems often struggle. Current methods for visual grounding of dynamics either use pure…

cs.CV20241 cited

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…

cs.CV2024

SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images

Wenxi Li, Ruxin Zhang, Haozhe Lin +3

The advancement of deep learning in object detection has predominantly focused on megapixel images, leaving a critical gap in the efficient processing of gigapixel images. These su…

cs.CV20242 cited

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.…