Publications

Find more on my Google Scholar.

Multimodal Large-Scale Pre-Training

• CLOC: Contrastive Localized Language-Image Pre-Training, ICML 2025
• Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models, ICLR 2025
• VeCLIP: Improving CLIP Training via Visual-Enriched Captions, ECCV 2024

Apple Intelligence and Foundation Model

• Updates to Apple’s On-Device and Server Foundation Language Models, Blog
• Apple Intelligence Foundation Language Models, Arxiv
• MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning, ICLR 2025
• MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains, NAACL 2025 Findings

Video Foundation Model

• StreamBridge: Turning Your Offline Video Large Model into a Proactive Streaming Assistant, Arxiv
• Breaking Down Video LLM Benchmarks: Knowledge, Spatial Perception, or True Temporal Understanding? Arxiv
• SlowFast-LLaVA-1.5: A Family of Token-Efficient Video Large Language Models for Long-Form Video Understanding, COLM 2025
• SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models, Arxiv
• ETVA: Evaluation of Text-to-Video Alignment via Fine-grained Question Generation and Answering, ICCV 2025
• STIV: Scalable Text and Image Conditioned Video Generation, ICCV 2025

2024

• D. Chen, K. Patwari, Z. Lai, X. Zhu, S. Cheung, C-N. Chuah, “Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning,” Under Review.

• A. Haydari, D. Chen, Z. Lai, M. Zhang, C-N. Chuah, “MobilityGPT: Enhanced Human Mobility Modeling with a GPT model,” Under Review.

• Z. Lai, H. Zhang, B. Zhang, W. Wu, H. Bai, A. Timofeev, X. Du, Z. Gan, J. Shan, C-N. Chuah, Y. Yang, M. Cao, “VeCLIP: Improving CLIP Training via Visual-enriched Captions,” European Conference on Computer Vision (ECCV), Oct 2024.

• Z. Lai, J. Chauhan, B. N. Dugger, C-N. Chuah, “Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data,” European Conference on Computer Vision (ECCV), Oct 2024.

• Z. Lai, J. Chauhan, D. Chen, B. Dugger, S-C. Cheung, and C-N. Chuah, “Semi-Path: An Interactive Semi-supervised Learning Framework for Gigapixel Pathology Image Analysis,” Elsevier Smart Health Journal, accepted and presented at IEEE/ACM CHASE, June 2024.

• Z. Lai, H. Bai, H. Zhang, X. Du, J. Shan, Y. Yang, C-N. Chuah, M. Cao, “Empowering Unsupervised Domain Adaptation with Large-scale Pre-trained Vision-Language Models,” WACV 2024.

• H. Siefkes, L. Cerny Oliveira, R. Koppel, W. Hogan, M. Garg, E. Manalo, N. Cresalia, Z. Lai, D. Trancredi, S. Lakshminrusimha, and C-N. Chuah, “Machine Learning Based Critical Congenital Heart Disease Screening using Dual-Site Pulse Oximetry Measurements.” Journal of American Heart Association (JAHA), 2024.

2023

• Z. Lai, S. Vesdapunt, N. Zhou, J. Wu, X. Li, C. Huynh, C-N. Chuah, “PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation,” ICCV 2023.

• Z. Lai, Z. Li, L. Cerny Oliveira, J. Chauhan, B. Dugger, and C-N. Chuah, “CLIPath: Fine-tune CLIP with Visual Feature Fusion for Pathology Image Analysis Towards Minimizing Data Collection Efforts,” ICCV 2nd Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD), Oct 2023.

• L. Cerny Oliveira, Z. Lai, D. Harvey, K. Nzenkue, L-W. Jim, C. DeCarlie, C-N. Chuah, and B. N. Dugger, “Pre-analytic variable effects on segmentation and quantification machine learning algorithms for amyloid beta analyzes on digitized human brain slides,” Journal of Neuropathology and Experimental Neurology, Jan 2023.

2022

• Z. Lai, C. Wang, H. Gunawan, S-C. Cheung, and C-N. Chuah, “Smoothed Adaptive Weighting for Imbalanced Semi-Supervised Learning: Improve Reliability Against Unknown Distribution Data,” The 39th International Conference on Machine Learning (ICML), July 17-23, 2022.
• Z. Lai, C. Wang, S-C. Cheung, and C-N. Chuah, “SaR: Self-Adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-Supervised Learning,” CVPR Workshop on Learning with Limited Labeled Data for Image and Video Understanding (L3DIVU), June 20, 2022. PDFPoster (Best Paper Award)
• Z. Lai, L. Cerny Oliveira, R. Guo, W. Xu, Z. Hu, K. Mifflin, C. DeCarlie, S-C. Cheung, C-N. Chuah, and B. N. Dugger, “BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis,” IEEE Access. PDF
• Z. Lai, L. Cerny Oliveira, D. Harvey, K. Nzenkue, LW. Jin, C. DeCarli, C-N. Chuah, and B. N. Dugger, “Generalizability of Deep Learning Frameworks for Amyloid-beta Deposit Assessment, Evaluation of Pre-analytic Variables,” Journal of Neuropathology and Experimental Neurology. 2022.

2021

• Z. Lai, C. Wang, L. Cerny Oliveira, B. Dugger, S-C. Cheung, and C.N. Chuah, “Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling,” ICCV Workshop on Computational Challenges in Digital Pathology (CDpath), Oct 11, 2021. PDF
• Z. Lai, C. Wang, Z. Hu, B. N. Dugger, S-C. Cheung, C-N. Chuah, “A Semi-supervised Learning for Segmentation of Gigapixel Histopathology Images from Brain Tissues,” 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Oct 31-Nov 4, 2021. PDF
• Z. Lai, P. Vadlaputi, D. J. Tancredi, M. Garg, R. I. Koppel, M. Goodman, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, S. Lashminrusimha, C-N. Chuah, and H. Siefkes, “Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms,” 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Oct 31-Nov 4, 2021. PDF
• K. Doshi, G. Rehm, P. Vadlaputi, Z. Lai, S. Lakshminrusimha, C-N. Chuah, and H. M Siefkes, “A Novel System to Collect Dual Pulse Oximetry Data for Critical Congenital Heart Disease Screening Research,” Journal of Clinical and Translational Science, pp. 1-25, October 2020. PDF
• L. Cerny Oliveira, Z. Lai, W. Geng, H. Siefkes and C-N. Chuah, “A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection,” IEEE/ACM 1st Workshop on Artificial Intelligence and Internet of Things for Digital Health (AIIoT4DH), co-located with IEEE/ACM Conference on Connected Health Applications, Systems, and Engineering Technologies (CHASE), Dec 16-18, 2021. PDF

2020

• Z. Lai, K. Guo, W. Xu, Z. Hu, B. Dugger, S. Cheung, and C-N. Chuah, “Automated Grey and White Matter Segmentation in Digitized Ab Human Brain Tissue Slide Images,” IEEE ICME 2020 Workshop on Multimedia Services and Technologies for Smart Health (MUST-SH), July 2020. PDF

Abstract & Poster

• Z. Lai, L. Cerny Oliveira, D. Harvey, K. Nzenkue, L-W. Jin, C. DeCarli, C-N. Chuah, and B. N. Dugger, “Generalizability of Deep Learning Frameworks for Amyloid Beta Deposit Assessment, Evaluation of Pre-analytic Variables,” American Association of Neuropathologists (AANP) Annual Meeting, June 2022. (Platform Presentation)
• Z. Lai, P. Vadlaputi, D. Tancredi, M. Garg, R. Koppel, M. Goodman, M, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, S. Lakshminrusimha, C. Chuah, and H. Siefkes, “Machine Learning Algorithm Combining Pulse Oximetry Features for Critical Congenital Heart Disease Screening,” Pediatric Academic Society, May 2021.
• P. Vadlaputi, Z. Lai, M. Garg, R. Koppel, M. Goodman, M, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, C. Chuah, S. Lakshminrusimha, and H. Siefkes, “Simple Automation Reduces False Positive Rate in Perfusion Index Critical Congenital Heart Disease (CCHD) Screening,” Eastern Society for Pediatric Research Annual Meeting, March 2021.