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20232025
most citedHow Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

16 citations · 32 across the 14 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models

Tong Wu, Yinghao Xu, Ryan Po +6

Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications. However, accurately describing desired visual attributes can…

cs.CV2024

IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations

Zhibing Li, Tong Wu, Jing Tan +3

Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours…

cs.CV2024

Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images

Zhangyang Qi, Yunhan Yang, Mengchen Zhang +7

Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, deta…

cs.CV2024

Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs

Yuxuan Qiao, Haodong Duan, Xinyu Fang +6

Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing…

cs.CV2024

Make-it-Real: Unleashing Large Multimodal Model for Painting 3D Objects with Realistic Materials

Ye Fang, Zeyi Sun, Tong Wu +4

Physically realistic materials are pivotal in augmenting the realism of 3D assets across various applications and lighting conditions. However, existing 3D assets and generative mo…

cs.CV2024★ 16 cited

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Zhe Chen, Weiyun Wang, Hao Tian +32

In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…

cs.CV2024★ 1 cited

SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization

Ying Jin, Jiaqi Wang, Dahua Lin

We consider multi-source free domain adaptation, the problem of adapting multiple existing models to a new domain without accessing the source data. Among existing approaches, meth…