recent news

  1. I gave three keynotes at CVPR 2021 workshops:

  2. "Video Understanding with Language Models," EPIC@CVPR2021, The Eight International Workshop on Egocentric Perception, Interaction and Computing.
    [video link]

  3. "Vision using Sight... but also Sound and Speech," MULA, The Fourth Multimodal Learning and Applications Workshop. [video link]

  4. "Space-Time Models for Segmentation, Tracking and Recognition in Video," RVSU, Robust Video Scene Understanding Workshop. [video link]

  1. A Facebook AI blog article describing our new research on video understanding. Code and pretrained models are available here.

  1. Two new papers to be presented at ICML 2021:

  2. Is Space-Time Attention All You Need for Video Understanding?,
    with Gedas Bertasius, and Heng Wang. 

  3. Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks ,
    with Maxwell Aladago.

  1. Two new papers presented at CVPR 2021:

  2. VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs,
    with Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, and Devi Parikh. 

  3. Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories,
    with Xitong Yang, Haoqi Fan, Larry Davis, and Heng Wang.

  1. Two papers presented at NeurIPS 2020:

  2. Spotlight presentation:
    Self-Supervised Learning by Cross-Modal Audio-Video Clustering,
    with Humam Alwassel, Dhruv Mahajan, Bernard Ghanem, and Du Tran. 

  3. COBE: Contextualized Object Embeddings from Narrated Instructional Video,
    with Gedas Bertasius.

  1. Three papers presented at WACV 2021:

  2. Only Time Can Tell: Discovering Temporal Data for Temporal Modeling,
    with Laura Sevilla-Lara, Shengxin Zha, Zhicheng Yan, Vedanuj Goswami, and Matt Feiszli.

  3. Supervoxel Attention Graphs for Long-Range Video Modeling,
    with Yang Wang, Gedas Bertasius, Tae-Hyun Oh, Abhinav Gupta, and Minh Hoai.

  4. Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification,
    with J. Wei, A. Suriawinata, B. Ren, X. Liu, M. Lisovsky, L. Vaickus, C. Brown, M. Baker, M. Nasir- Moin, N. Tomita, J. Wei, and S. Hassanpour.

  1. New paper presented at BMVC 2020:

  2. Attentive Action and Context Factorization,
    with Yang Wang, Vinh Tran, Gedas Bertasius, and Minh Hoai.

  1. MaskProp nominated for CVPR 2020 paper award and ranked first in one of the EPIC-Kitchens CVPR 2020 challenges:

  2. Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation, by Gedas Bertasius and Lorenzo Torresani, was one of the 29 paper award nominees at CVPR 2020 (out of 1470 accepted papers).

  3. Our software submission based on this paper received the First Place Award on Unseen Kitchens and the Third Place Award on Seen Kitchens at the IEEE CVPR 2020 EPIC-Kitchens Object Detection in Video Challenge.

  1. Three papers presented at CVPR 2020:

  2. Oral presentation:
    Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation,
    with Gedas Bertasius.

  3. Listen to Look: Action Recognition by Previewing Audio,
    with Ruohan Gao, Tae-Hyun Oh, and Kristen Grauman.

  4. Correlation Networks for Video Classification,
    with Heng Wang, Du Tran, and Matt Feiszli.

  1. New paper presented at AISTATS 2020:

  2. Stein Variational Inference for Discrete Distributions,
    with Jun Han, Fan Ding, Xianglong Liu, Jian Peng, and Qiang Liu.

research overview

My research interests are in computer vision and machine learning. My current work is primarily focused on learning representations for image and video recognition. You can read more about the research of my group here.

previous affiliations

  1. Fulbright U.S. Scholar at Ashesi University in Ghana.

  2. Microsoft Research Cambridge, Machine Learning and Perception

  3. Riya/

  4. New York University, Computer Science

  5. Stanford University, Computer Science

  6. DigitalPersona

  7. IRST

  8. University of Milan, Computer Science

Lorenzo Torresani

Professor of Computer Science

Visual Learning Group

Dartmouth College

Research Scientist

Facebook AI Research (FAIR)