Search Results for author: Lixiong Qin

Found 5 papers, 3 papers with code

Faceptor: A Generalist Model for Face Perception

3 code implementations14 Mar 2024 Lixiong Qin, Mei Wang, Xuannan Liu, Yuhang Zhang, Wei Deng, Xiaoshuai Song, Weiran Xu, Weihong Deng

This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.

Age Estimation Attribute +3

FakeNewsGPT4: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs

no code implementations4 Mar 2024 Xuannan Liu, Peipei Li, Huaibo Huang, Zekun Li, Xing Cui, Jiahao Liang, Lixiong Qin, Weihong Deng, Zhaofeng He

In this paper, we propose FakeNewsGPT4, a novel framework that augments Large Vision-Language Models (LVLMs) with forgery-specific knowledge for manipulation reasoning while inheriting extensive world knowledge as complementary.

Fake News Detection Informativeness +1

Open-Set Facial Expression Recognition

no code implementations23 Jan 2024 Yuhang Zhang, Yue Yao, Xuannan Liu, Lixiong Qin, Wenjing Wang, Weihong Deng

Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes.

Facial Expression Recognition Facial Expression Recognition (FER) +1

SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation

1 code implementation22 Aug 2023 Lixiong Qin, Mei Wang, Chao Deng, Ke Wang, Xi Chen, Jiani Hu, Weihong Deng

To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks.

Age Estimation Attribute +2

Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation

1 code implementation ICCV 2023 Xuannan Liu, Yaoyao Zhong, Yuhang Zhang, Lixiong Qin, Weihong Deng

Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples.

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