H
Hamed Pirsiavash
Researcher at University of Maryland, Baltimore County
Publications - 97
Citations - 9265
Hamed Pirsiavash is an academic researcher from University of Maryland, Baltimore County. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 33, co-authored 85 publications receiving 7398 citations. Previous affiliations of Hamed Pirsiavash include Massachusetts Institute of Technology & Fuji Xerox.
Papers
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Proceedings ArticleDOI
Generating Videos with Scene Dynamics
TL;DR: A generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background is proposed that can generate tiny videos up to a second at full frame rate better than simple baselines.
Proceedings ArticleDOI
Globally-optimal greedy algorithms for tracking a variable number of objects
TL;DR: A near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects andlinear in the sequence length is given which results in state-of-the-art performance.
Proceedings ArticleDOI
Detecting activities of daily living in first-person camera views
Hamed Pirsiavash,Deva Ramanan +1 more
TL;DR: This work presents a novel dataset and novel algorithms for the problem of detecting activities of daily living in firstperson camera views, and develops novel representations including temporal pyramids and composite object models that exploit the fact that objects look different when being interacted with.
Posted Content
Generating Videos with Scene Dynamics
TL;DR: The authors proposed a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background, which can generate tiny videos up to a second at full frame rate better than simple baselines.
Proceedings ArticleDOI
A large-scale benchmark dataset for event recognition in surveillance video
Sangmin Oh,Anthony Hoogs,A. G. Amitha Perera,Naresh P. Cuntoor,Chia-Chih Chen,Jong Taek Lee,Saurajit Mukherjee,Jake K. Aggarwal,Hyungtae Lee,Larry S. Davis,Eran Swears,Xioyang Wang,Qiang Ji,Kishore K. Reddy,Mubarak Shah,Carl Vondrick,Hamed Pirsiavash,Deva Ramanan,Jenny Yuen,Antonio Torralba,Bi Song,Anesco Fong,Amit K. Roy-Chowdhury,Mita Desai +23 more
TL;DR: A new large-scale video dataset designed to assess the performance of diverseVisual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage is introduced.