1 citations · 1 across the 8 of their papers we have counts for
10 papers
TRIG: Toward Measuring Texture Realism in Image Generation
Zhiwei Xu, Jiali Cui, Zhiqiang Lao +2
This paper studies the backbone used by metrics for evaluating the quality of generated images. Although generative models have advanced rapidly, some outputs still exhibit unreali…
LEAP: Learned Block-wise Evidence Retrieval for Long Audio-Video Perception
Juyi Lin, Zhiqiang Lao, Jiali Cui +9
Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, whereas uniform temporal compression…
A General-Purpose VLM Can Teach an Astronomy Foundation Model to Better Recognize Galaxy Morphology
Dichang Zhang, Jiaqi Deng, Yixuan Shao +7
Existing astronomy foundation models provide strong galaxy representations, but adapting them to new survey conditions and survey-specific morphology recognition tasks still requir…
Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision
Jiali Cui, Zhiqiang Lao, Heather Yu
Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM b…
ShaLa: Multimodal Shared Latent Space Modelling
Jiali Cui, Yan-Ying Chen, Yanxia Zhang +1
This paper presents a novel generative framework for learning shared latent representations across multimodal data. Many advanced multimodal methods focus on capturing all combinat…
Learning Multimodal Latent Generative Models with Energy-Based Prior
Shiyu Yuan, Jiali Cui, Hanao Li +1
Multimodal generative models have recently gained significant attention for their ability to learn representations across various modalities, enhancing joint and cross-generation c…
Learning Latent Space Hierarchical EBM Diffusion Models
Jiali Cui, Tian Han
This work studies the learning problem of the energy-based prior model and the multi-layer generator model. The multi-layer generator model, which contains multiple layers of laten…
Learning Energy-based Model via Dual-MCMC Teaching
Jiali Cui, Tian Han
This paper studies the fundamental learning problem of the energy-based model (EBM). Learning the EBM can be achieved using the maximum likelihood estimation (MLE), which typically…
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior
Jiali Cui, Ying Nian Wu, Tian Han
This paper studies the fundamental problem of multi-layer generator models in learning hierarchical representations. The multi-layer generator model that consists of multiple layer…
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator
Jiali Cui, Ying Nian Wu, Tian Han
This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on…