Stan Sclaroff

About

Stan Sclaroff is a Professor and Dean of the College of Arts and Sciences at Boston University. He joined the BU Department of Computer Science in 1995 after completing his PhD at MIT. He has served as the Chair of the Department from 2007 to 2013, Associate Dean of the Faculty for Mathematical & Computational Sciences from 2015 to 2018, and Dean ad interim for the College of Arts and Sciences from August 2018 to May 2019. On May 17, 2019, he was appointed as Dean for the College of Arts and Sciences. His research interests encompass computer vision, pattern recognition, and machine learning. He is an expert in tracking, video-based analysis of human motion and gesture, deformable shape matching and recognition, as well as image and video database indexing, retrieval, and data mining methods. Sclaroff developed one of the first content-based image retrieval systems for the Internet, called ImageRover, years before Google Image Search appeared. His recent work focuses on human tracking algorithms, analysis and identification of hand motion related to sign language, and filtering methods for multimedia retrieval. He co-leads the Image and Video Computing research group and has been recognized as a Fellow of the IEEE and IAPR.

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Computer vision
• Machine Learning
• Algorithm
• Mathematics

Selected publications

• Temporally Distributed Networks for Fast Video Semantic Segmentation

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 204 citations

  We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation. We observe that features extracted from a certain high-level layer of a deep CNN can be approximated by composing features extracted from several shallower sub-networks. Leveraging the inherent temporal continuity in videos, we distribute these sub-networks over sequential frames. Therefore, at each time step, we only need to perform a lightweight computation to extract a sub-features g…

  DOI

• Universal Domain Adaptation through Self Supervision

  arXiv (Cornell University) · 2020 · 163 citations

  Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation framework that can handle arbitrary category shift, called Domain Adaptative Neighborhood…

  DOI

• Real-Time Semantic Segmentation With Fast Attention

  IEEE Robotics and Automation Letters · 2020 · 160 citations

  Senior authorCorresponding

  In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which incur high computational costs. In this letter, we propose a novel architecture that addresses both challenges and achieves state-of-the-art performance for semantic segmentation of high-resolution images and videos in real-time. The proposed architecture relies on our fast spatial attention, which is a simple yet effi…

  DOI

• Many-to-many Splatting for Efficient Video Frame Interpolation

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022-06-01 · 64 citations

  article

  Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion estimation (e.g. occlusions and discontinuities), most state-of-the-art interpolation approaches require subsequent refinement of the warped result to generate satisfying outputs, which drastically decreases the efficiency for multi-frame interpolation. In this work, we propose a fully differentiable Many-to-Many…

  PublisherDOI

• A Broad Study of Pre-training for Domain Generalization and Adaptation

  Lecture notes in computer science · 2022-01-01 · 57 citations

  book-chapter
  PublisherDOI

Recent grants

• Mining and Indexing Spatio-Temporal Patterns in Video Databases of Human Motion

  NSF · $405k · 2003–2007

• RI: Parameter-Sensitive and Dynamics-Aware Methods for Object Detection, Pose Estimation, and Tracking

  NSF · $404k · 2007–2011

• Estimating and Recognizing 3D Articulated Motion via Uncalibrated Cameras

  NSF · $403k · 2002–2006

Frequent coauthors

• Vittorio Murino

  1174 shared

• Giovanni Maria Farinella

  University of Catania

  1161 shared

• Sérgio Escalera

  Computer Vision Center

  1160 shared

• Cosimo Distante

  National Research Council

  1158 shared

• Emanuele Frontoni

  University of Macerata

  1158 shared

• Marcos Ortega

  1156 shared

• Pierluigi Carcagnì

  1156 shared

• Fausto Milletarì

  1156 shared

Labs

• Machine LearningPI

Education

• Ph.D.

  MIT

Awards & honors

• Fellow of the IEEE
• Fellow of the IAPR

Similar researchers at Boston University

• Kate Saenkoh-89
• Ran Canettih-78
• Mark Crovellah-64