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20212026
most citedSingular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning

26 citations · 56 across the 22 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception

Yanpeng Sun, Jing Hao, Ke Zhu +6

Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods for generating such captions often rely on distill…

cs.LG2024

Continual SFT Matches Multimodal RLHF with Negative Supervision

Ke Zhu, Yu Wang, Yanpeng Sun +4

Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiori…

cs.CV2024

Improving Multi-modal Large Language Model through Boosting Vision Capabilities

Yanpeng Sun, Huaxin Zhang, Qiang Chen +5

We focus on improving the visual understanding capability for boosting the vision-language models. We propose \textbf{Arcana}, a multiModal language model, which introduces two cru…

cs.CV2024

FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs

Jing Hao, Yuxiang Zhao, Song Chen +6

Multimodal Large Language Models (MLLMs) have shown promise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they hea…

cs.CV2024

CSGO: Content-Style Composition in Text-to-Image Generation

Peng Xing, Haofan Wang, Yanpeng Sun +5

The diffusion model has shown exceptional capabilities in controlled image generation, which has further fueled interest in image style transfer. Existing works mainly focus on tra…

cs.CV2024

VRP-SAM: SAM with Visual Reference Prompt

Yanpeng Sun, Jiahui Chen, Shan Zhang +7

In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmenta…