3 citations · 12 across the 33 of their papers we have counts for
12 papers · 1 filter
MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models
Bei Yan, Jie Zhang, Zhiyuan Chen +2
The rapid integration of Large Vision-Language Models (LVLMs) into critical domains necessitates comprehensive moral evaluation to ensure their alignment with human values. While e…
Multi-PA: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Models
Jie Zhang, Xiangkui Cao, Zhouyu Han +2
Large Vision-Language Models (LVLMs) exhibit impressive potential across various tasks but also face significant privacy risks, limiting their practical applications. Current resea…
Semantic or Covariate? A Study on the Intractable Case of Out-of-Distribution Detection
Xingming Long, Jie Zhang, Shiguang Shan +1
The primary goal of out-of-distribution (OOD) detection tasks is to identify inputs with semantic shifts, i.e., if samples from novel classes are absent in the in-distribution (ID)…
CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation
Yifeng Xu, Zhenliang He, Shiguang Shan +1
Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approac…
T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models
Zhongqi Wang, Jie Zhang, Shiguang Shan +1
While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model ou…
Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs
Jie Zhang, Zhongqi Wang, Mengqi Lei +4
Currently many benchmarks have been proposed to evaluate the perception ability of the Large Vision-Language Models (LVLMs). However, most benchmarks conduct questions by selecting…
Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
Bei Yan, Jie Zhang, Zheng Yuan +2
Despite the outstanding performance in multimodal tasks, Large Vision-Language Models (LVLMs) have been plagued by the issue of hallucination, i.e., generating content that is inco…
VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model
Sibo Wang, Xiangkui Cao, Jie Zhang +4
The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by co…
Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox
Xingming Long, Jie Zhang, Shiguang Shan +1
Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. However, some marginal OOD samples actually have close semantic con…
Task-adaptive Q-Face
Haomiao Sun, Mingjie He, Shiguang Shan +2
Although face analysis has achieved remarkable improvements in the past few years, designing a multi-task face analysis model is still challenging. Most face analysis tasks are stu…
HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
Xiaolong Tang, Meina Kan, Shiguang Shan +3
Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory…
FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers
Zheng Yuan, Jie Zhang, Shiguang Shan +1
In recent years, the Vision Transformer (ViT) model has gradually become mainstream in various computer vision tasks, and the robustness of the model has received increasing attent…