2 citations · 3 across the 7 of their papers we have counts for
7 papers
PerPO: Perceptual Preference Optimization via Discriminative Rewarding
Zining Zhu, Liang Zhao, Kangheng Lin +7
This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained mul…
Hgformer: Hyperbolic Graph Transformer for Recommendation
Xin Yang, Xingrun Li, Heng Chang +8
The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effectiv…
Corner2Net: Detecting Objects as Cascade Corners
Chenglong Liu, Jintao Liu, Haorao Wei +4
The corner-based detection paradigm enjoys the potential to produce high-quality boxes. But the development is constrained by three factors: 1) Hard to match corners. Heuristic cor…
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System
Xin Yang, Heng Chang, Zhijian Lai +6
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 g…
VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model
Jinze Yang, Haoran Wang, Zining Zhu +3
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. Although recent works…
SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior
Zhongrui Yu, Haoran Wang, Jinze Yang +6
Novel View Synthesis (NVS) for street scenes play a critical role in the autonomous driving simulation. The current mainstream technique to achieve it is neural rendering, such as…
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
Hanzhang Wang, Haoran Wang, Jinze Yang +7
The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…