Search Results for author: Xun Zhao

Found 8 papers, 4 papers with code

Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

no code implementations16 Apr 2024 Xiao Wang, Tianze Chen, Xianjun Yang, Qi Zhang, Xun Zhao, Dahua Lin

The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.

In-Context Learning Instruction Following

XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation

no code implementations12 Oct 2023 Qiang Li, Dan Zhang, Shengzhao Lei, Xun Zhao, Shuyan Li, Porawit Kamnoedboon, Weiwei Li

The lack of standardized robustness metrics and the widespread reliance on numerous unrelated benchmark datasets for testing have created a gap between academically validated robust models and their often problematic practical adoption.

Classification

Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models

no code implementations4 Oct 2023 Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, Dahua Lin

This study serves as a clarion call for a collective effort to overhaul and fortify the safety of open-source LLMs against malicious attackers.

Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space

1 code implementation7 Jul 2022 Wenqi Shao, Xun Zhao, Yixiao Ge, Zhaoyang Zhang, Lei Yang, Xiaogang Wang, Ying Shan, Ping Luo

It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive.

Transferability

Temporally Efficient Vision Transformer for Video Instance Segmentation

3 code implementations CVPR 2022 Shusheng Yang, Xinggang Wang, Yu Li, Yuxin Fang, Jiemin Fang, Wenyu Liu, Xun Zhao, Ying Shan

To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS).

Instance Segmentation Semantic Segmentation +1

Active Learning for Open-set Annotation

1 code implementation CVPR 2022 Kun-Peng Ning, Xun Zhao, Yu Li, Sheng-Jun Huang

To tackle this open-set annotation (OSA) problem, we propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation.

Active Learning

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