News
- Nov 7, 2024: our paper ERIC has won ACM BuildSys'24
Best paper award !!!
- Oct 2024: 1 paper on adapting foundation models for video understanding was accepted to WACV'25.
- Sep 2024: 1 paper on building efficient computer vision system was accepted to ACM BuildSys'24
as Best paper candidate!
- June 2024: Coordinated the 4th Open World Vision Workshop at CVPR'24.
- June 2024: I will present our paper in CVPR'24, Seattle.
- June 2024: Our paper "The Neglected Tails in Vision-Language Models" was accepted to ICML 2024 DMLR Oral.
- Mar 2024: I was awarded TAMU CSE department travel grant.
- Mar 2024: I received TAMU CSE Graduate Teaching Assistant Excellence Award (1 each year).
- Mar 2024: I passed Ph.D. qualify exam with 99% percentile.
- Feb 2024: 1 paper on improving Vision Language Models for zero-shot recognition was accepted to CVPR'24.
Research
My research focuses on computer vision with Vision Language Models, addressing a central question:
"how to adapt pretrained foundation models to better serve specific downstream tasks, with none or limited labeled data?".
This question drives my efforts to identify and analyze the limitations of foundation models, examining their pretraining data
to understand the origins of these limitations. My prior work includes developing advanced prompting and retrieval-augmented
learning techniques for zero-shot recognition, as well as stage-wise retrieval-augmented finetuning methods for few-shot recognition.
I am also exploring the application of foundation models for video understanding, with specific interests in detecting unusual activities,
such as failure actions or behaviors indicative of autism in children.
In addition, I have broad interest in cyber-physical systems, including developing efficient machine learning systems on resource-constrained
edge devices. Applications include efficient vision system for precision residential irrigation and voice assistance system for emergency medical services.
Beyond these areas, my research extends to machine learning for healthcare and geoscience. This includes developing safe reinforcement learning
algorithms for personalized medicine; enhancing the fairness of cardiovascular disease (CVD) risk prediction models for underrepresented populations;
advancing geoscientific methods for characterizing subsurface fracture distribution and integrating 4D seismic data for more accurate reservoir
model calibration.
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Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning
Tian Liu, Huixin Zhang, Shubham Parashar, Shu Kong
arxiv /
project /
code
We explore retrieval-augmented learning for solving few-shot recognition,
and propose stage-wise retrieval-Augmented fineTuning (SWAT) method to mitigate the imbalanced distribution and domain gap issues,
outperforming SOTA methods by >6% accuracy.
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UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark
Hasnat Md Abdullah, Tian Liu, Kangda Wei, Shu Kong, Ruihong Huang
[WACV 2025]
arxiv /
code
We explore multimodal foundation models for Unusual Activities Localization (UAL) in video data.
We compiled the first UAL benchmark dataset and proposed VLM-LLM framework to synergize multimodal
foundational models for better video understanding.
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ERIC: Estimating Rainfall with Commodity Doorbell Camera for Precision Residential Irrigation
Tian Liu, Liuyi Jin, Radu Stoleru, Amran Haroon, Charles Swanson, Kexin Feng
[ACM BuildSys 2024]
Best paper award
paper /
arxiv /
video presentation /
slides /
code
We develop efficient vision system to estimate hyperlocal rainfall from doorbell camera for precision residential irrigation,
saving > 9,000 gallons of water per month.
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The Neglected Tails in Vision-Language Models
Shubham Parashar*, Zhiqiu Lin*, Tian Liu* (*co-first authors), Xiangjue Dong,
Yanan Li, Deva Ramanan, James Caverlee, Shu Kong
[CVPR 2024, ICML 2024 DMLR Oral]
paper /
DMLR /
arxiv /
project /
code
We expose the long-tailed concept distributions in VLMs' pretraining data
and reveal failues of SOTA multimodal systems (e.g. GPT-4V, DALL-E 3).
We propose retrieval-augmented learning, achieving SOTA zero-shot recognition performance.
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EMSAssist: An End-to-End Mobile Voice Assistant at the Edge for
Emergency Medical Services
Liuyi Jin, Tian Liu, Amran Haroon, Radu Stoleru, Michael Middleton, Ziwei Zhu, Theodora Chaspari
[ACM MobiSys 2023]
paper /
workshop paper /
presentation /
slides /
app demo /
code
We build the first end-to-end mobile voice assistant system to assist Emergency Medical Technicians in selecting proper protocols for critical medical intervention.
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Safe Reinforcement Learning with Contextual Information: Theory and Applications
Junyu Cao, Esmaeil Keyvanshokooh, Tian Liu
[Under review]
preprint /
code
We develop a safe reinforcement learning algorithm for personalized medical prescription considering patient's contextual information
(e.g. age, gender, race etc.),
achieveing sub-linear regret with zero safety violation.
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A Machine Learning-based Hybrid Model for Fracture Parameterization and
Distribution Prediction in Unconventional Reservoirs
Tian Liu, Ruxin Zhang
[Journal of Computers and Geotechnics 2024]
paper
We develop Variational Autoencoder (VAE) model for fracture parameterization
and distribution prediction using reservoir production data.
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Integration of Time-lapse Seismic Data using the Onset Time Approach: the Impact of Seismic Survey Frequency
Tian Liu, Hongquan Chen, Gill Hetz, Akhil Datta-Gupta
[Journal of Petroleum Science and Engineering 2020]
1st Place of TAMU Student Paper Contest,
3rd Place of International Championship
JPSE journal paper /
ATCE paper
We develop onset-time approach for efficient
and robust integration of 4D seismic data for reservoir model calibration, achieveing 2x error reduction and 6x speedup
compared to traditional amplitude-matching methods.
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Workshop Papers/Presentations
(* denotes equal contribution)
- Y. Yang*, T. Liu*, S. J. Lee, C.-Y. Liao, H. Shao, F. Pasquel, M. B. Weber, E. Keyvanshokooh,
G.-G. P. Garcia. "Development and Fairness Evaluation of CVD Risk Prediction Models for
Patients with Type-2 Diabetes". Society for Medical Decision Making Annual Meeting, Boston,
MA, October 2024.
- Y. Yang*, T. Liu*, S. J. Lee, C.-Y. Liao, H. Shao, F. Pasquel, M. B. Weber, E. Keyvanshokooh,
G.-G. P. Garcia. "Survival Modeling for CVD Risk Estimation Among a Diverse Cohort with
Type-2 Diabetes". AI for Health Equity Symposium AIM-AHEAD Annual Meeting, Atlanta,
GA, August 2024.
- S. Parashar*, Z. Lin*, T. Liu*, X. Dong, Y. Li, D. Ramanan,
J. Caverlee, and S. Kong, "The Neglected Tails in Vision-Language Models".
ICML 2024 Workshop on Data-centric Machine Learning Research (DMLR):
Datasets for Foundation Models, Vienna, Austria, July 2024.
- L. Jin, T. Liu, A. Haroon, R. Stoleru, M. Middleton, Z. Zhu,
T. Chaspari, "Demo: EMSAssist -- An End-to-End Mobile Voice Assistant at the Edge for
Emergency Medical Services". The 21st IEEE International Conference on Mobile Systems, Applications
and Services (MobiSys), 2023, Helsinki, Finland, June 2023.
Teaching Assistance
- CSCE670: Information Storage and Retrieval, Spring 2025
- CSCE606: Software Engineering, Fall 2023
- CSCE313: Introduction to Computer Systems, Summer 2023
- CSCE110: Programming I, Summer 2023
Professional Services
- Coordinator of 4th Open World Vision Workshop at CVPR'24.
- Reviewer for Pattern Recognition, WACV'25
- Reviewer for IEEE Internet of Things Journal
- Reviewer for Applied Thermal Engineering, Geoenergy Science and Enginerring, SPE Journal
Selected Awards
- ACM BuildSys, Best Paper Award, 2024
- TAMU CSE Department Travel Grant, 2024
- TAMU CSE Department Graduate Teaching Assistant Excellence Award (1 each year), 2024
- 1st place of SPE Student Paper Contest in TAMU, 1st place of Gulf Coast Region, 3rd place of International Championship, 2018
- 2nd place of SPE Petrobowl Knowledge Contest in North American Region, 2017
- 1st place of SPE Petrobowl Knowledge Contest in Asia-Pacific Region, 2015
- Dean's Award (4 out of 296), China University of Petroleum Beijing, 2014
- National Scholarship, Ministry of Education of China, 2012
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