The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

Hieu Le presents Incorporating Physical Illumination Constraints into Deep Learning Shadow Detection and Removal (PhD Proposal)

Shadows provide useful cues to analyze the scene but also hamper many computer vision algorithms such as image segmentation, object detection or tracking. For those reasons, shadow detection and shadow removal have been well studied topics in computer vision. Early approaches for shadow detection and removal focus on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and slow in inference due to reliance on hand-designed image features. On the other hand, recent deep-learning approaches have achieved breakthroughs in performances for both shadow detection and removal. They learn to extract useful features automatically through training while being extremely efficient in computation. However, these models are data-dependent, opaque and ignore the physical aspects of shadows.

We propose to incorporate physical illumination constraints into deep-learning frameworks. Thus the mapping learned by the deep-network closely follows the physics of shadows, enabling the network to systematically and realistically modify shadows in images. For shadow detection, we present a novel GAN framework in which the generator can generate realistic images with attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters for a shadow image formation model that removes shadows. The system outputs shadow-free images in high-quality with no image artifacts and achieves state-of-the-art shadow removal performance. Lastly, we propose a system trained without the need for any shadow-free images in which physical constraints play pivotal roles that enable training the networks.

For Zoom information, please email events@cs.stonybrook.edu.

Predictable Autonomy for Cyber-Physical Systems by Stanley Bak, Safe Sky Analytics

ABSTRACT: Cyber-physical systems combine complex physics with complex software. Although these systems offer significant potential in fields such as smart grid design, autonomous robotics and medical systems, verification of CPS designs remains challenging. Model-based design permits simulations to be used to explore potential system behaviors, but individual simulations do not provide full coverage of what the system can do. In particular, simulations cannot guarantee the absence of unsafe behaviors, which is unsettling as many CPS are safety-critical systems.

The goal of set-based analysis methods is to explore a system's behaviors using sets of states, rather than individual states. The usual downside of this approach is that set-based analysis methods are limited in scalability, working only for very small models. This talk describes our recent process on improving the scalability of set-based reachability computation for LTI hybrid automaton models, some of which can apply to very large systems (up to one billion continuous state variables!). Lastly, we'll discuss the significant overlap of techniques used for our scalable reachability analysis methods with set-based input/output analysis of neural networks.

BIO: Stanley Bak is a computer scientist investigating the predictable design of autonomous cyber-physical systems. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems. He received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He currently helps run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performs teaching as an Adjunct Professor at Georgetown University.

Abstract:
Deep neural network (DNN) is a powerful tool for solving image processing and computer vision tasks such as image and video reconstructions, object recognition, and scene understanding, etc. However, DNN have been used for only the digital domain in the imaging pipeline, such as the feature extractor and classifier models after an image is captured and digitized. In this research, we propose a new framework called deep sensing. The proposed framework also models the analog layer to the neural network model and jointly optimizes the parameters in optics and sensor designs of a camera, as well as reconstruction and classification models by the same training strategy.

Bio:
Hajime Nagahara is a professor at D3 Center, Osaka University, since 2024. He received Ph.D. degree in system engineering from Osaka University in 2001. He was a research associate of the Japan Society for the Promotion of Science from 2001 to 2003. He was an assistant professor at the Graduate School of Engineering Science, Osaka University, Japan from 2003 to 2010. He was an associate professor in Faculty of Information Science and Electrical Engineering at Kyushu University from 2010 to 2017. He was a professor at Institute for Datability Science, Osaka University, from 2017 to 2024. He was a visiting associate professor at CREA University of Picardie Jules Verns, France, in 2005. He was a visiting researcher at Columbia University in 2007-2008 and 2016-2017. Computational photography and computer vision are his research areas. He received IPSJ Nagao Special Researcher Award in 2012, ICCP2016 Best Paper Runners-up, and SSII Takagi Award in 2016. He is a program chair for ICCP2019, General Chair for upcoming ACCV2026 Osaka, Associate Editor for IEEE Transactions on Computational Imaging in 2019-2022, Associate Editor for IEEE Transactions of Pattern Recognisiton and Machine Intelligence, and Director of Information Processing Society of Japan in 2022-2024.

Zoom: https://stonybrook.zoom.us/j/4742917886?pwd=YiW3AL5lvPlEaWrd221YfLzYhlgsRK.1

AI + Music Seminar - Details to come.

Objectives:
1. Explain the clinical radiology workflow, and highlight how AI is currently in use to impact each step
2. Describe how radiologists interact with the currently available tools, highlighting both positive andnegative examples
3. Offer a brief description of how these tools are approved, validated, and reimbursed
4. Explore the utility of cutting edge AI techniques in diagnostic radiology

Speaker:
Dr. David Payne, MD Neuroradiologist and Assistant Professor, Rush University Medical Centre

Remote Access:
Zoom: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 95617197636
Passcode: 924293

Abstract: Many unresolved legal questions over LLMs and copyright center on memorization: whether specific training data have been encoded in the model's weights during training, and whether those memorized data can be extracted in the model's outputs. While many believe that LLMs do not memorize much of their training data, recent work shows that substantial amounts of copyrighted text can be extracted from open-weight models. However, it remains an open question if similar extraction is feasible for production LLMs, given the safety measures these systems implement. We investigate this question using a two-phase procedure: (1) an initial probe to test for extraction feasibility, which sometimes uses a Best-of-N (BoN) jailbreak, followed by (2) iterative continuation prompts to attempt to extract the book. We evaluate our procedure on four production LLMs -- Claude 3.7 Sonnet, GPT-4.1, Gemini 2.5 Pro, and Grok 3 -- and we measure extraction success with a score computed from a block-based approximation of longest common substring (nv-recall). With different per-LLM experimental configurations, we were able to extract varying amounts of text. For the Phase 1 probe, it was unnecessary to jailbreak Gemini 2.5 Pro and Grok 3 to extract text (e.g, nv-recall of 76.8% and 70.3%, respectively, for Harry Potter and the Sorcerer's Stone), while it was necessary for Claude 3.7 Sonnet and GPT-4.1. In some cases, jailbroken Claude 3.7 Sonnet outputs entire books near-verbatim (e.g., nv-recall=95.8%). GPT-4.1 requires significantly more BoN attempts (e.g., 20X), and eventually refuses to continue (e.g., nv-recall=4.0%). Taken together, our work highlights that, even with model- and system-level safeguards, extraction of (in-copyright) training data remains a risk for production LLMs.

Speaker: Xinyue

Location: CS2311

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.