News

2023

• April: We will be organizing a tutorial at ICCV, Paris, France 2023. Stay tunned.
• Learning with Noisy and Unlabeled Data for Large Models beyond Categorization.

• March: 2 new papers accepted at IV 2023
• Shen et al. Hardware-Aware Latency Pruning for Real-Time 3D Object Detection.
• Clemons et al. Leaf: Legacy Networks for Flexible Inference.

• March: 3 new papers accepted at CVPR 2023
• VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
• Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
• Vision Transformers are Good Mask Auto-Labelers. Code available!
• I will be talking about data for atonomous driving at Autonomous Vehicles USA 2023, April 17th, Anaheim, California.
• Full-Stack, GPU-based Acceleration of Deep Learning tutorial at CVPR 2023. Stay tunned.
• We are organizing the Workshop on Autonomous Driving at CVPR 2023.
• We are organizing the T4V Workshop: Transformers for Vision at CVPR 2023.

2022

• Sept. 2022: 1 paper accepted at BMVC 2022.
• Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
• September 2022: 2 papers accepted at NeurIPS 2022
• Optimizing Data Collection for Machine Learning
• Structural Pruning via Latency-Saliency Knapsack
• July 2022: 1 paper accepted at ECCV 2022.
• Soft Masking for Cost-Constrained Channel Pruning
• April 2022: Our paper on Robustness in Transformers has been accepted at ICML 2022
• Understanding The Robustness in Vision Transformers
• March 2022: 7 new papers accepted at CVPR 2022
• FreeSOLO: Learning to Segment Objects without Annotations
• How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
• A-ViT: Adaptive Tokens for Efficient Vision Transformer
• Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo
• Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
• When to Prune? A Policy towards Early Structural Pruning
• Panoptic SegFormer: Delving deeper into panoptic segmentation with transformers

2021 - Older News

• Dec 2021: Presenting 2 papers at NeurIPS 2021:
  • Segformer: Simple and efficient design for semantic segmentation with transformers
  • Distilling Image Classifiers in Object Detectors
• October 2021: Presenting our active learning paper at ICCV 2021
  • Active learning for deep object detection via probabilistic modeling
• 1 new paper accepted at ICML 2021
• Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
• 2 new papers accepted at ICLR 2021
• 1 new paper accepted at ICCV 2021
• 3 new papers accepted at CVPR 2021
• 1 new paper accepted at WACV 2021
• 1 new paper accepted at IEEE-IV 2021
• 2 new papers accepted at IEEE CVPR 2020
• 1 new paper accepted at IEEE-IV 2020
• 1 new paper accepted at NeurIPS 2020
• 1 new paper accepted at WACV 2020
• 1 new paper accepted at ICLR 2020

Interns (2022)

• David Wang – PhD, Tsinghua Univ.
• Shixing Yu – PhD, Univ. Texas Austin
• Yanwei Li – PhD, CUHK
• Zhiqi Li – PhD, Nanjing Univ.
• Zetong Yang – PhD, CUHK
• Tzofi Klinghoffer – PhD, MIT
• Bingyin Zhao – PhD, Clemson Univ.
• Annamarie Bair – PhD, CMU
• Yilun Chen – PhD, CUHK
• Alex Sun – MS, Stanford
• Justin Hsu – BS, Stanford
• Arnav Joshi – BS, Stanford
• Shubhranshu Sigh – MS, CMU

Interns (2021)

• Jiayu Yang – PhD, Australian National University
• Marc Finzi – PhD, NYU
• Enze Xie – PhD, HKU
• Ryan Humble – PhD, Stanford
• Vlad Sobal – PhD, NYU
• Jianna Liu – BS, MIT
• Xinlong Wang – PhD, Univ. Adelaide
• Daquan Zhou – PhD, NUS
• Faith Johnson – PhD, Rutgers
• Javier Sagastuy Brena – PhD, Stanford
• Joshua Chen – MS, CMU (now Nvidia)
• Nithya Attaluri – BS, MIT
• Jessica Lee – MS, CMU

Interns (2020)

• Jiwoong Choi PhD, Seoul Nat. University (now NVIDIA)
• Ismail Elezi PhD TUM (now PostDoc TUM)
• Shuxuan Guo - PhD. EPFL
• Marvin Kim – MS CMU (now Waymo)
• Lilian Luong – BS MIT
• Cynthia Liu – MS, MIT (now Cerebras)
• Nadine Chang – PhD CMU
• Yerlan Idelbayav – PhD UC Merced (now Amazon)
• Xinnan Du – MS CMU (now Goldman Sachs)
• Hoang Vu Nguyen – PhD Stony Brooks Univ.

Interns (2019)

• Maying Shen – MS CMU (now Nvidia)
• Michael Zhang – BS Hardvard (now PhD Stanford)
• Akshay Chawla – MS CMU (now Vicarious)
• Kashyap Chitta – MS CMU (now PhD student MPI)
• Tony Wang – BS MIT
• Wenbo Guo – PhD Penn. State Univ.
• Nikhil Murthy – BS MIT

Interns (2018)

• Jiaming Zeng – PhD Stanford (now IBM Research)
• Yousef Hindy – MS Stanford (now NewCo)
• Kashyap Chitta – MS CMU (now PhD student MPI)

Invited Talks

• Scaling-up Deep Learning for Autonomous Driving, MVA'19 Invited Tutorial, Tokyo, Japan
• Scaling up Deep Learning for Autonomous Driving, ECCV 2018 workshop, Munich, Germany
• Efficient ConvNets for Perception in Autonomous Driving, ICML'2017, Sydney, Australia
• Efficient Deep Model Selection. GTC'17, San Jose, CA, US
• Efficient Networks for Real Time Scene Understanding. GTCx Australia'16. Melbourne, Australia
• Large-scale scene understanding in constrained platforms. NVIDIA RoadShow. Canberra, Australia
• Compacting ConvNets for end to end learning. Deep Learning workshop ACRA 2015

Awards and Recognitions

• SegFormer Ranked 3 in NeurIPS 2021 Top-10 Influential Papers.
• 2020 Outstanding research for "ErfNet: Efficient Residual Factorized ConvNEt for Real-Time Semantic Segmentaiton".
• Road Detection based on Illuminance-Invariance awarded: 2010-2019 Top Ten Research Papers
• Best Paper award for Efficient ConvNet for Real-time Semantic Segmentation at IV 2017
• Outstanding Reviewer Awards at ICCV 2021
• Awarded 2019 Top Reviewers at NeurIPS 2019
• Outstanding Reviewer Awards at CVPR 2017
• Best Paper Award for Exploiting Sparsity for Real Time Video Labelling, at CVPR workshop on Computer Vision for Vehicle Technology 2013
• Best Poster Award for Synchronization of video sequences from free-moving cameras, at IbPria 2007
• Ramon y Cajal Fellowship, Spain, 2016
• ERC - Fellow Grant, 2011
• Extraordinary Doctorate award, Barcelona, Spain, 2010-2011
• Best PhD award 2010, Barcelona, Spain

Relevant Workshop Organization

• Transformers for Vision at CVPR 2022
• Deep-Vision Workshop at CVPR, 2014 to 2020
• Tutorial on Scaling up Deep Learning for Autonomous Driving at IV 2020
• Beyond Supervised Learning at IV 2022
• Embodied AI Workshop at CVPR
• Workshop on Autonomous Driving at CVPR, 2019 to 2022
• Color in Computer Vision at ECCV / ICCV 2013 to 2017
• Computer Vision for Road Scene Understanding at ECCV / ICCV 2014 to 2018
• Computer Vision in Vehicle Technology, at CVPR 2015 to 2018
• Deep-Driving at IV 2014 to 2017