collaborators

10 papers

cs.CV2026

Trustworthy synthetic visual media: Evidence across the media lifecycle

Zexi Jia, Zhiqiang Yuan, Jie Zhou +1

Images and videos have long helped people understand what happened and how a work came into being. Generative systems complicate that role. Realistic media can now be produced and…

cs.AI2026

PAL-Bench: Evidence-Grounded Profile Reconstruction from Longitudinal Personal Albums

Qiwei Yan, Zhiqiang Yuan, Zexi Jia +4

Longitudinal personal albums are weak-schema multimodal databases: noisy perceptual records whose key facts require joins across faces, text, timestamps, locations, and repeated ev…

cs.CL2026

Fine-grained Fragment Retrieval in Multi-modal Long-form Dialogues

Hanbo Bi, Zhiqiang Yuan, Chongyang Li +7

With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. Users often need to retrieve…

cs.CV2026

CoDA: Color Distribution Probing for Efficient and Generalizable AI-Generated Image Detection

Zexi Jia, Zhiqiang Yuan, Xiaoyue Duan +3

AI-generated image detection faces a persistent trade-off between generalization and efficiency: lightweight artifact-based methods often degrade on unseen generators or domains, w…

cs.CV2026

Manifold-Optimal Guidance: A Unified Riemannian Control View of Diffusion Guidance

Zexi Jia, Pengcheng Luo, Zhengyao Fang +2

Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, an…

cs.CV2026

Evaluating Generative Models via One-Dimensional Code Distributions

Zexi Jia, Pengcheng Luo, Yijia Zhong +2

Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant…

cs.CV2026

Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity

Zhengyao Fang, Zexi Jia, Yijia Zhong +5

Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challe…

cs.CV2026

StyleDecoupler: Generalizable Artistic Style Disentanglement

Zexi Jia, Jinchao Zhang, Jie Zhou

Representing artistic style is challenging due to its deep entanglement with semantic content. We propose StyleDecoupler, an information-theoretic framework that leverages a key in…

cs.CV2025

F2RVLM: Boosting Fine-grained Fragment Retrieval for Multi-Modal Long-form Dialogue with Vision Language Model

Hanbo Bi, Zhiqiang Yuan, Zexi Jia +6

Traditional dialogue retrieval aims to select the most appropriate utterance or image from recent dialogue history. However, they often fail to meet users' actual needs for revisit…

cs.CL2025

Less Redundancy: Boosting Practicality of Vision Language Model in Walking Assistants

Chongyang Li, Zhiqiang Yuan, Hanbo Bi +2

Approximately 283 million people worldwide live with visual impairments, motivating increasing research into leveraging Visual Language Models (VLMs) to develop effective walking a…