|
Baris Sarper Tezcan I am a Ph.D. student in Computer Science at Purdue University, advised by Prof. Daniel Aliaga. My research interests span computer vision, vision-language learning, generative AI, and remote sensing. I am particularly interested in visual foundation models for geospatial and multimodal understanding, including open-vocabulary recognition, segmentation, visual reasoning, synthetic data generation, and evaluation beyond fixed label sets. Before joining Purdue, I received my B.Sc. in Computer Engineering from Middle East Technical University (METU), where I also pursued a double major in Mathematics. During my undergraduate studies, I worked on weakly supervised semantic segmentation with Prof. R. Gokberk Cinbis and developed a broader interest in data-efficient learning, representation learning, and generative models. |
|
Publications
|
Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns
Baris Sarper Tezcan, Hrishikesh Viswanath, Rubab Saher, and Daniel Aliaga CVPR 2026 EarthVision Workshop |
|
Can Your Model Separate Yolks with a Water Bottle? Benchmarking Physical Commonsense Understanding in Video Generation Models
Enes Sanli*, Baris Sarper Tezcan*, Erkut Erdem, and Aykut Erdem (*equal contribution) MILA World Modeling Workshop, 2025 Extended version under review via ACL Rolling Review, 2026 |
|
Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation
Liu He, Yizhi Song, Ruiqi Xu*, Baris Sarper Tezcan*, Xiao Zeng, Albert Y. C. Chen, Lu Xia, Shashwat Verma, Sankalp Dayal, Min Sun, Cheng-Hao Kuo, and Daniel Aliaga (*equal contribution) Under review at Machine Vision and Applications, 2026 |
Projects
|
Weakly Supervised Semantic Segmentation Research
Baris Sarper Tezcan, supervised by R. Gokberk Cinbis Middle East Technical University, 2022-2023 report Analyzed confident region thresholds in AffinityNet-style WSSS pipelines on PASCAL VOC 2012. The project focused on selecting foreground and background pseudo-label thresholds more systematically under image-level supervision. |
|
DiffDis ICCV 2023 Paper Implementation
Baris Sarper Tezcan Deep Generative Models Course, 2024 code / paper Implemented "DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability" as a working reproduction for a course project, filling in missing implementation details from the paper. |
|
RoboDetection
Baris Sarper Tezcan and team METU Computer Engineering Graduation Project, 2024 project page Led a five-person team building a robot dog simulation for disaster response, including remote control, ROS networking, autonomous person detection and tracking, and real-time 2D map generation. |
Experience
| 2023 | Computer Vision / Machine Learning Intern, Kuartis. Studied weather classification, semantic occupancy prediction, horizon detection, OpenCV trackers, dataset curation, NAS, and hyperparameter optimization. report |
| 2022 | Research Intern, ROMER Research Center. Implemented and compared feature-based image matching methods including SIFT, SURF, FAST, ORB, and SuperGlue, with RANSAC-based outlier removal. report |
Awards
| 2024 | METU Graduation Project Competition, 3rd Place |
| 2021 | TEKNOFEST Artificial Intelligence in Transportation Competition, 2nd Place |
| 2020 | TUBITAK Unmanned Aerial Vehicle Competition, 7th Place |