4 citations · 8 across the 8 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Unsupervised Recognition of Unknown Objects for Open-World Object Detection
Ruohuan Fang, Guansong Pang, Lei Zhou +2
Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known a…
SimROD: A Simple Adaptation Method for Robust Object Detection
Rindra Ramamonjison, Amin Banitalebi-Dehkordi, Xinyu Kang +2
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label…