Active Research Themes

Low-supervision Learning via Knowledge Transfer from Pretrained Models

This research theme investigates methods that can use a small amount of supervision to build a machine learning system. In particular, we are interested in transferring knowledge learned from a pre-trained model to achieve this goal. At the application level, this project encompasses a wide range of applications, examples including image or text classification, dense prediction such as semantic or instance segmentation, information extraction in NLP, content generation, etc.

Related Publications

• Hai-Ming Xu, Lingqiao Liu*, Ehsan Abbasnejad: Progressive Class Semantic Matching for Semi-supervised Text Classification. NAACL-HLT 2022: 3003-3013 2022 (Oral)

• Qiaoyang Luo, Lingqiao Liu*, Yuhao Lin, Wei Zhang: Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification. ACL/IJCNLP (Findings) 2021: 2773-2782

• Qiaoyang Luo, Lingqiao Liu*: Zero-shot Slot Filling with Slot-Prefix Prompting and Attention Relationship Descriptor. AAAI 2023

• Ziqin Zhou, Yinjie Lei, Bowen Zhang, Lingqiao Liu*, Yifan liu: ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation. CVPR 2023

Life-long Maintenance for Machine Learning systems

Building a real-world machine learning system can be more complicated than a one-shot training process. Continual learning and adaptation in the deploying environment, monitoring, and correcting system error could also be essential features that an ideal machine learning system should have. In this theme, we will explore various techniques to perform life-long maintenance for machine learning systems, including continual learning, source-free adaptation, model editing, etc.

Related Publications

• Yuxuan Ding, Lingqiao Liu*, Chunna Tian, Jingyuan Yang, Haoxuan Ding, Don’t Stop Learning: Towards Continual Learning for the CLIP Model, arxiv 2022

Generalisable Machine Learning Systems

The goal of this research theme is to build machine learning systems that are generalizable across different test environments and tasks. This encompasses domain generalization, cross-task generalization, and compositional generalization.

Related Publications

• Bingliang Jiao, Lingqiao Liu, Liying Gao, Guosheng Lin, Lu Yang, Shizhou Zhang, Peng Wang, Yanning Zhang: Dynamically Transformed Instance Normalization Network for Generalizable Person Re-identification. ECCV 2022

• Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu*, Jue Wang: Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection. CVPR 2022

• Duo Peng, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Jun Liu: Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation. IEEE Trans. Image Process. 30: 6594-6608 (2021)

• Liang Chen, Yong Zhang, Yibing Song, Jue Wang, Lingqiao Liu*: OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training. NeurIPS 2022

• Liang Chen, Yong Zhang, Yibing Song, Ying Shan, Lingqiao Liu*: Improved Test-Time Adaptation for Domain Generalization. CVPR 2023

Rationale-guided Machine Learning

The most machine learning system is based on the principle of Empirical Risk Minimization. Any features and classifiers that contribute to risk minimization will be acquired from the learning process. In this research theme, we consider ``prediction rationale’’ – clues about why a certain decision is made in the learning process. We are investigating how to represent rationale and how to put forward various regularizations on the rationale clues. This is expected to lead to more generalizable or more data-efficient machine learning systems.

Related Publications

• Yangyang Shu, Baosheng Yu, Haiming Xu, Lingqiao Liu*: Improving Fine-grained Visual Recognition in Low Data Regimes via Self-boosting Attention Mechanism. ECCV 2022

• Yangyang Shu, Anton van den Hengel, Lingqiao Liu*: Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems. CVPR 2023