Search Results for author: Jinze Yang

Found 7 papers, 2 papers with code

PerPO: Perceptual Preference Optimization via Discriminative Rewarding

1 code implementation5 Feb 2025 Zining Zhu, Liang Zhao, Kangheng Lin, Jinze Yang, En Yu, Chenglong Liu, Haoran Wei, Jianjian Sun, Zheng Ge, Xiangyu Zhang

This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs).

Corner2Net: Detecting Objects as Cascade Corners

no code implementations24 Nov 2024 Chenglong Liu, Jintao Liu, Haorao Wei, Jinze Yang, Liangyu Xu, Yuchen Guo, Lu Fang

Two separate corners preserve few instance semantics, so it is difficult to guarantee getting both two class-specific corners on the same heatmap channel.

Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System

no code implementations25 Jun 2024 Xin Yang, Heng Chang, Zhijian Lai, Jinze Yang, Xingrun Li, Yu Lu, Shuaiqiang Wang, Dawei Yin, Erxue Min

Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years.

Contrastive Learning Recommendation Systems +2

VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model

1 code implementation3 Jun 2024 Jinze Yang, Haoran Wang, Zining Zhu, Chenglong Liu, Meng Wymond Wu, Mingming Sun

In this paper, we focus on resolving the problem of image outpainting, which aims to extrapolate the surrounding parts given the center contents of an image.

Image Outpainting Language Modeling +3

SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior

no code implementations29 Mar 2024 Zhongrui Yu, Haoran Wang, Jinze Yang, Hanzhang Wang, Zeke Xie, Yunfeng Cai, Jiale Cao, Zhong Ji, Mingming Sun

To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data.

3DGS Autonomous Driving +3

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

no code implementations11 Jan 2024 Hanzhang Wang, Haoran Wang, Jinze Yang, Zhongrui Yu, Zeke Xie, Lei Tian, Xinyan Xiao, Junjun Jiang, Xianming Liu, Mingming Sun

In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.

Style Transfer

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