activity
20192026
most citedSFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition

131 citations · 219 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CV2026

CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling

Xinran Duan, Guozhang Li, Yaoyao Zhong +3

Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from t…

cs.CV2026

SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models

Yuhang Su, Mei Wang, Yaoyao Zhong +4

While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature…

cs.AI2025

SMART: Self-Generating and Self-Validating Multi-Dimensional Assessment for LLMs' Mathematical Problem Solving

Yujie Hou, Mei Wang, Yaoyao Zhong +3

Large Language Models (LLMs) have achieved remarkable performance across a wide range of mathematical benchmarks. However, concerns remain as to whether these successes reflect gen…

cs.CV2024

Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation

Xuannan Liu, Yaoyao Zhong, Weihong Deng +4

The blooming of social media and face recognition (FR) systems has increased people's concern about privacy and security. A new type of adversarial privacy cloak (class-universal)…

cs.CV2023★ 2 cited

AdvCloak: Customized Adversarial Cloak for Privacy Protection

Xuannan Liu, Yaoyao Zhong, Xing Cui +3

With extensive face images being shared on social media, there has been a notable escalation in privacy concerns. In this paper, we propose AdvCloak, an innovative framework for pr…

cs.CV2023★ 4 cited

Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation

Xuannan Liu, Yaoyao Zhong, Yuhang Zhang +2

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