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20232026
most citedHealthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation

8 citations · 15 across the 18 of their papers we have counts for

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cs.CV2025

Bi-Level Optimization for Self-Supervised AI-Generated Face Detection

Mian Zou, Nan Zhong, Baosheng Yu +2

AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative tech…

cs.CV2025

Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation

Zhiyao Ren, Yibing Zhan, Baosheng Yu +1

Text-to-image generation has become increasingly popular, but achieving the desired images often requires extensive prompt engineering. In this paper, we explore how to decode text…

cs.CV20251 cited

Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies

Mian Zou, Baosheng Yu, Yibing Zhan +1

The detection of AI-generated faces is commonly approached as a binary classification task. Nevertheless, the resulting detectors frequently struggle to adapt to novel AI face gene…

cs.CV2024

Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach

Mian Zou, Baosheng Yu, Yibing Zhan +2

In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (i.e., face forgery) detection. The prevailing appr…

cs.CV20241 cited

Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID

Wentao Tan, Changxing Ding, Jiayu Jiang +3

Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming, restricting…

cs.CV2024

Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method

Mian Zou, Baosheng Yu, Yibing Zhan +2

In recent years, deep learning has greatly streamlined the process of manipulating photographic face images. Aware of the potential dangers, researchers have developed various tool…