1 code implementation • 6 Apr 2024 • Haibo Jin, Haoxuan Che, Hao Chen
Self-training is a simple yet effective method for semi-supervised learning, during which pseudo-label selection plays an important role for handling confirmation bias.
no code implementations • 30 Mar 2024 • Tongkun Su, Jun Li, Xi Zhang, Haibo Jin, Hao Chen, Qiong Wang, Faqin Lv, Baoliang Zhao, Yin Hu
In this work, we leverage descriptions in medical reports to design multi-granular question-answer pairs associated with different diseases, which assist the framework in pre-training without requiring extra annotations from experts.
no code implementations • 5 Feb 2024 • Haibo Jin, Ruoxi Chen, Andy Zhou, Jinyin Chen, Yang Zhang, Haohan Wang
Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses.
1 code implementation • 25 Aug 2023 • Haibo Jin, Haoxuan Che, Hao Chen
The framework leverages self-training and domain adversarial learning to address the domain gap during adaptation.
1 code implementation • 24 Aug 2023 • Haibo Jin, Haoxuan Che, Yi Lin, Hao Chen
To address these challenges, we propose diagnosis-driven prompts for medical report generation (PromptMRG), a novel framework that aims to improve the diagnostic accuracy of MRG with the guidance of diagnosis-aware prompts.
no code implementations • 18 Jul 2023 • Haibin Zheng, Jinyin Chen, Haibo Jin
Therefore, it is crucial to identify the misbehavior of DNN-based software and improve DNNs' quality.
1 code implementation • 10 Jul 2023 • Haoxuan Che, YuHan Cheng, Haibo Jin, Hao Chen
Diabetic Retinopathy (DR) is a common complication of diabetes and a leading cause of blindness worldwide.
1 code implementation • 28 Mar 2023 • Ming Xu, Qiang Ai, Ruimin Li, Yunyi Ma, Geqi Qi, Xiangfu Meng, Haibo Jin
Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control.
no code implementations • 25 Mar 2023 • Ruoxi Chen, Haibo Jin, Jinyin Chen, Haibin Zheng
To address the issues, we introduce the concept of local gradient, and reveal that adversarial examples have a quite larger bound of local gradient than the benign ones.
no code implementations • 9 Jul 2022 • Haoxuan Che, Haibo Jin, Hao Chen
However, prior works either grade DR or DME independently, without considering internal correlations between them, or grade them jointly by shared feature representation, yet ignoring potential generalization issues caused by difficult samples and data bias.
no code implementations • 8 Jul 2022 • Jinpeng Li, Haibo Jin, Shengcai Liao, Ling Shao, Pheng-Ann Heng
This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection.
1 code implementation • 12 Feb 2022 • Haibo Jin, Ruoxi Chen, Haibin Zheng, Jinyin Chen, Yao Cheng, Yue Yu, Xianglong Liu
By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training.
no code implementations • 24 Dec 2021 • Haibo Jin, Ruoxi Chen, Jinyin Chen, Yao Cheng, Chong Fu, Ting Wang, Yue Yu, Zhaoyan Ming
Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks.
no code implementations • 24 Dec 2021 • Ruoxi Chen, Haibo Jin, Jinyin Chen, Haibin Zheng, Yue Yu, Shouling Ji
From the perspective of image feature space, some of them cannot reach satisfying results due to the shift of features.
no code implementations • 27 May 2021 • Haibo Jin, Jinpeng Li, Shengcai Liao, Ling Shao
To this end, we first propose a baseline model equipped with one transformer decoder as detection head.
Ranked #5 on Face Alignment on COFW
2 code implementations • 8 Mar 2020 • Haibo Jin, Shengcai Liao, Ling Shao
The proposed model is equipped with a novel detection head based on heatmap regression, which conducts score and offset predictions simultaneously on low-resolution feature maps.
Ranked #4 on Face Alignment on COFW
1 code implementation • 9 May 2017 • Haibo Jin, Xiaobo Wang, Shengcai Liao, Stan Z. Li
However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows.