Asim Kadav

Research Interests: Video understanding, generation and reasoning

News: Watch my lecture on Video understanding at Yale University, Topics in Deep Learning, Spring 2020.

I currently work at Tonal on machine learning and data science engineering. Previously, I worked at NEC Labs leading the eigen team. My team and I built extremely efficient image classification, video classification, video retrieval and pose tracking methods.

Email: first.lastname@tonal.com

Recent Publications | All, By area | Students

  • Hopper: Multi-hop Transformer for Spatiotemporal Reasoning. In ICLR 2021. PDF. Poster. Honglu Zhou, Asim Kadav, Farley Lai, Alexandru Niculescu-Mizil, Martin Renqiang Min, Mubbasir Kapadia, Hans Peter Graf.
  • 15 Keypoints is All You Need. Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf. In CVPR 2020.PDF. Ranked #1 in PoseTrack.
  • S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation. Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf.
    In CVPR 2020. PDF.
  • Tripping Through Time: Efficient Localization of Activities in Videos. (Spotlight) Meera Hahn, Asim Kadav, James M. Rehg, Hans Peter Graf.
    In CVPR Workship on Language and Vision, 2019. Also, appears in BMVC'20. PDF.
  • Visual Entailment: A Novel Task for Fine-Grained Image Understanding. Ning Xie, Farley Lai, Derek Doran, Asim Kadav.
    In NeurIPS Workshop on Visually-Grounded Interaction and Language, 2018 (ViGIL’18). PDF.Dataset. Check out the leaderboard!
  • Teaching Syntax using Adverserial Distraction. Juho Kim, Christopher Malon, and Asim Kadav. In FEVER-EMNLP, 2018. PDF.
  • Attend and Interact: Higher-Order Object Interactions for Video Understanding. Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, and Hans Peter Graf. In CVPR, 2018. PDF. Video NEC Video. Live Demo.
  • DeepConfig: Automating Data Center Network Topologies Management with Machine Learning. Christopher Streiffer, Huan Chen, Theophilus Benson, Asim Kadav. SIGCOMM Net AI Workshop, 2018. PDF.
  • ASAP: Asynchronous Approximate Data-Parallel Computation.
    Asim Kadav and Erik Kruus. arXiv Preprint. Dec 2016. PDF. Code.

Open-source software/datasets

  • CATER-H: A more difficult CATER dataset with longer reasoning chains, ICLR 2021. Github.
  • SNLI-VE: A novel visual entailment task using image/text pairs. Github.
  • MALT-v2: A federated learning framework over RDMA and TCP. Github. Website. PDF.
  • Numerous patches to the Linux and android kernel to improve the software/hardware layer.[1][2][3][4].

Service: (out of date)