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D-Index & Metrics

Computer Science

D-Index
33
Citations
9858
World Ranking
12381
National Ranking
5017

Overview

Hamed Pirsiavash is affiliated with the University of California, Davis in the United States. Their research primarily falls within the field of Computer Science, with a significant focus on Artificial Intelligence and Computer Vision and Pattern Recognition as leading subfields. Additional areas of study include Radiology, Nuclear Medicine and Imaging, Biophysics, and Cancer Research.

The scientist's work encompasses several key topics, notably Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, and Adversarial Robustness in Machine Learning. Further research themes include Advanced Neural Network Applications, Human Pose and Action Recognition, Advanced Image and Video Retrieval Techniques, and applications of AI in COVID-19 diagnosis.

Hamed Pirsiavash has contributed extensively to the academic literature, with publications appearing predominantly in venues such as arXiv (Cornell University), Maryland Shared Open Access Repository (USMAI Consortium), and major conferences including the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and the IEEE/CVF International Conference on Computer Vision (ICCV). The Proceedings of the AAAI Conference on Artificial Intelligence also counts among the frequent platforms of publication.

  • A Cookbook of Self-Supervised Learning, 2023, arXiv (Cornell University)
  • COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning, 2020, arXiv (Cornell University)
  • Backdoor Attacks on Self-Supervised Learning, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • ISD: Self-Supervised Learning by Iterative Similarity Distillation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • A collective AI via lifelong learning and sharing at the edge, 2024, Nature Machine Intelligence

Their frequent co-authors include Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Soheil Kolouri, K L Navaneet, and Ali Abbasi, reflecting established collaborative networks within the research community.

Hamed Pirsiavash's contributions cover a broad spectrum of machine learning and artificial intelligence research, with a distinct emphasis on self-supervised learning methods, video and image analysis, and the robustness and security of AI models. Their work extends to the development and application of advanced neural network techniques as well as multimodal approaches integrating visual and textual data.

Best Publications

  • Generating Videos with Scene Dynamics

    Carl Vondrick;Hamed Pirsiavash;Antonio Torralba

  • Globally-optimal greedy algorithms for tracking a variable number of objects

    Hamed Pirsiavash;Deva Ramanan;Charless C. Fowlkes

  • A large-scale benchmark dataset for event recognition in surveillance video

    Sangmin Oh;Anthony Hoogs;Amitha Perera;Naresh Cuntoor

  • Detecting activities of daily living in first-person camera views

    Hamed Pirsiavash;Deva Ramanan

  • Generating Videos with Scene Dynamics

    Carl Vondrick;Hamed Pirsiavash;Antonio Torralba

  • Anticipating Visual Representations from Unlabeled Video

    Carl Vondrick;Hamed Pirsiavash;Antonio Torralba

  • Representation Learning by Learning to Count

    Mehdi Noroozi;Hamed Pirsiavash;Paolo Favaro

  • Hidden-Trigger Backdoor Attacks

    Aniruddha Saha;Akshayvarun Subramanya;Hamed Pirsiavash

  • Weakly Supervised Cascaded Convolutional Networks

    Ali Diba;Vivek Sharma;Ali Pazandeh;Hamed Pirsiavash

  • Parsing IKEA Objects: Fine Pose Estimation

    Joseph J. Lim;Hamed Pirsiavash;Antonio Torralba

  • Boosting Self-Supervised Learning via Knowledge Transfer

    Mehdi Noroozi;Ananth Vinjimoor;Paolo Favaro;Hamed Pirsiavash

  • A Cookbook of Self-Supervised Learning

    Unknown

  • Assessing the Quality of Actions

    Hamed Pirsiavash;Carl Vondrick;Antonio Torralba

  • Adaptive Token Sampling for Efficient Vision Transformers

    Unknown

  • Parsing Videos of Actions with Segmental Grammars

    Hamed Pirsiavash;Deva Ramanan

  • Learning Aligned Cross-Modal Representations from Weakly Aligned Data

    Lluis Castrejon;Yusuf Aytar;Carl Vondrick;Hamed Pirsiavash

  • Bilinear classifiers for visual recognition

    Hamed Pirsiavash;Deva Ramanan;Charless C. Fowlkes

  • Anticipating the future by watching unlabeled video.

    Carl Vondrick;Hamed Pirsiavash;Antonio Torralba

  • Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks

    Arsalan Mousavian;Hamed Pirsiavash;Jana Kosecka

  • Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

    Soheil Kolouri;Aniruddha Saha;Hamed Pirsiavash;Heiko Hoffmann

  • Cross-Modal Scene Networks

    Yusuf Aytar;Lluis Castrejon;Carl Vondrick;Hamed Pirsiavash

  • TalkMiner: a lecture webcast search engine

    John Adcock;Matthew Cooper;Laurent Denoue;Hamed Pirsiavash

  • COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

    Simon Ging;Mohammadreza Zolfaghari;Hamed Pirsiavash;Thomas Brox

  • Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks

    Arsalan Mousavian;Hamed Pirsiavash;Jana Kosecka

  • AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video

    Sangmin Oh;Anthony Hoogs;Amitha Perera;Naresh Cuntoor

Frequent Co-Authors

Carl Vondrick
Carl Vondrick Columbia University
Deva Ramanan
Deva Ramanan Carnegie Mellon University
Paolo Favaro
Paolo Favaro University of Bern
Ramesh Jain
Ramesh Jain University of California, Irvine
Luc Van Gool
Luc Van Gool Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Laurent Denoue
Laurent Denoue Fuji Xerox (Japan)
Charless C. Fowlkes
Charless C. Fowlkes University of California, Irvine

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