1 citations · 2 across the 10 of their papers we have counts for
12 papers
On the Adversarial Robustness of Multimodal LLM Judges
Zihan Wang, Guansong Pang, Zelin Liu +3
Multimodal Large Language Models (MLLMs) are increasingly used as automated judges, e.g., for image quality and safety assessment. However, their adversarial robustness remains lar…
MTAttack: Multi-Target Backdoor Attacks against Large Vision-Language Models
Zihan Wang, Guansong Pang, Wenjun Miao +2
Recent advances in Large Visual Language Models (LVLMs) have demonstrated impressive performance across various vision-language tasks by leveraging large-scale image-text pretraini…
SEMPO: Lightweight Foundation Models for Time Series Forecasting
Hui He, Kun Yi, Yuanchi Ma +3
The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across di…
TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Binbin Gao, Guansong Pang +3
Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. However, existing methods typically rely on a single textual space to align…
AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worlds
Qizhou Wang, Hanxun Huang, Guansong Pang +2
Speech synthesis systems can now produce highly realistic vocalisations that pose significant authenticity challenges. Despite substantial progress in deepfake detection models, th…
DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps
Hongjie Zhu, Zeyu Zhang, Guansong Pang +6
Weakly supervised semantic segmentation (WSSS) typically utilizes limited semantic annotations to obtain initial Class Activation Maps (CAMs). However, due to the inadequate coupli…
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
Wenjun Miao, Guansong Pang, Jin Zheng +1
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples…
OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learning
Wenjun Miao, Guansong Pang, Trong-Tung Nguyen +3
Class incremental learning (CIL) aims to learn a model that can not only incrementally accommodate new classes, but also maintain the learned knowledge of old classes. Out-of-distr…
Zero-Shot Out-of-Distribution Detection with Outlier Label Exposure
Choubo Ding, Guansong Pang
As vision-language models like CLIP are widely applied to zero-shot tasks and gain remarkable performance on in-distribution (ID) data, detecting and rejecting out-of-distribution…
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Tianqi Li, Guansong Pang, Xiao Bai +2
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead…
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
Wenjun Miao, Guansong Pang, Tianqi Li +2
Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are…
Simple Image-level Classification Improves Open-vocabulary Object Detection
Ruohuan Fang, Guansong Pang, Xiao Bai
Open-Vocabulary Object Detection (OVOD) aims to detect novel objects beyond a given set of base categories on which the detection model is trained. Recent OVOD methods focus on ada…