Zhengfeng Lai

According to our database, Zhengfeng Lai authored at least 21 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Contrastive Localized Language-Image Pre-Training.

Hong-You Chen, Zhengfeng Lai, Haotian Zhang, Xinze Wang, Marcin Eichner, Keen You, Meng Cao, Bowen Zhang, Yinfei Yang, Zhe Gan

CoRR, 2024

Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models.

Zhengfeng Lai, Vasileios Saveris, Chen Chen, Hong-You Chen, Haotian Zhang, Bowen Zhang, Juan Lao Tebar, Wenze Hu, Zhe Gan, Peter Grasch, Meng Cao, Yinfei Yang

CoRR, 2024

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning.

Haotian Zhang, Mingfei Gao, Zhe Gan, Philipp Dufter, Nina Wenzel, Forrest Huang, Dhruti Shah, Xianzhi Du, Bowen Zhang, Yanghao Li, Sam Dodge, Keen You, Zhen Yang, Aleksei Timofeev, Mingze Xu, Hong-You Chen, Jean-Philippe Fauconnier, Zhengfeng Lai, Haoxuan You, Zirui Wang, Afshin Dehghan, Peter Grasch, Yinfei Yang

CoRR, 2024

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains.

Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe Zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang

CoRR, 2024

SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models.

Mingze Xu, Mingfei Gao, Zhe Gan, Hong-You Chen, Zhengfeng Lai, Haiming Gang, Kai Kang, Afshin Dehghan

CoRR, 2024

Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning.

Dongjie Chen, Kartik Patwari, Zhengfeng Lai, Sen-ching Samson Cheung, Chen-Nee Chuah

CoRR, 2024

MobilityGPT: Enhanced Human Mobility Modeling with a GPT model.

Ammar Haydari, Dongjie Chen, Zhengfeng Lai, Chen-Nee Chuah

CoRR, 2024

Empowering Unsupervised Domain Adaptation with Large-scale Pre-trained Vision-Language Models.

Zhengfeng Lai, Haoping Bai, Haotian Zhang, Xianzhi Du, Jiulong Shan, Yinfei Yang, Chen-Nee Chuah, Meng Cao

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

VeCLIP: Improving CLIP Training via Visual-Enriched Captions.

Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, Meng Cao

Proceedings of the Computer Vision - ECCV 2024, 2024

Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.

Zhengfeng Lai, Joohi Chauhan, Brittany N. Dugger, Chen-Nee Chuah

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions.

Zhengfeng Lai, Haotian Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, Meng Cao

CoRR, 2023

CLIPath: Fine-tune CLIP with Visual Feature Fusion for Pathology Image Analysis Towards Minimizing Data Collection Efforts.

Zhengfeng Lai, Zhuoheng Li, Luca Cerny Oliveira, Joohi Chauhan, Brittany N. Dugger, Chen-Nee Chuah

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation.

Zhengfeng Lai, Noranart Vesdapunt, Ning Zhou, Jun Wu, Cong Phuoc Huynh, Xuelu Li, Kah Kuen Fu, Chen-Nee Chuah

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis.

Zhengfeng Lai, Luca Cerny Oliveira, Runlin Guo, Wenda Xu, Zin Hu, Kelsey Mifflin, Charles DeCarli, Sen-Ching S. Cheung, Chen-Nee Chuah, Brittany N. Dugger

IEEE Access, 2022

Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data.

Zhengfeng Lai, Chao Wang, Henrry Gunawan, Sen-Ching S. Cheung, Chen-Nee Chuah

Proceedings of the International Conference on Machine Learning, 2022

SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning.

Zhengfeng Lai, Chao Wang, Sen-Ching S. Cheung, Chen-Nee Chuah

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling.

Zhengfeng Lai, Chao Wang, Luca Cerny Oliveira, Brittany N. Dugger, Sen-Ching Samson Cheung, Chen-Nee Chuah

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

A Semi-supervised Learning for Segmentation of Gigapixel Histopathology Images from Brain Tissues.

Zhengfeng Lai, Chao Wang, Zin Hu, Brittany N. Dugger, Sen-Ching S. Cheung, Chen-Nee Chuah

Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms.

Zhengfeng Lai, Pranjali Vadlaputi, Daniel J. Tancredi, Meena Garg, Robert I. Koppel, Mera Goodman, Whitnee Hogan, Nicole Cresalia, Stephan Juergensen, Erlinda Manalo, Satyan Lakshminrusimha, Chen-Nee Chuah, Heather Siefkes

Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection.

Luca Cerny Oliveira, Zhengfeng Lai, Wenbo Geng, Heather Siefkes, Chen-Nee Chuah

Proceedings of the IEEE/ACM Conference on Connected Health: Applications, 2021

2020

Automated grey and white matter segmentation in digitized Aβ human brain tissue slide images.

Zhengfeng Lai, Runlin Guo, Wenda Xu, Zin Hu, Kelsey Mifflin, Brittany N. Dugger, Chen-Nee Chuah, Sen-Ching S. Cheung

Proceedings of the 2020 IEEE International Conference on Multimedia & Expo Workshops, 2020