Shlens, J.[Jonathon]
* 2013: Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
* 2016: Rethinking the Inception Architecture for Computer Vision
* 2017: Pixel Recursive Super Resolution
* 2017: YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
* 2018: Dataset and Architecture for Visual Reasoning with a Working Memory, A
* 2018: Learning Transferable Architectures for Scalable Image Recognition
* 2018: Progressive Neural Architecture Search
* 2018: Recurrent Segmentation for Variable Computational Budgets
* 2019: Attention Augmented Convolutional Networks
* 2019: Do Better ImageNet Models Transfer Better?
* 2019: Learned Representation for Scalable Vector Graphics, A
* 2020: Improving 3D Object Detection Through Progressive Population Based Augmentation
* 2020: Learning Data Augmentation Strategies for Object Detection
* 2020: Naive-Student: Leveraging Semi-supervised Learning in Video Sequences for Urban Scene Segmentation
* 2020: Randaugment: Practical automated data augmentation with a reduced search space
* 2020: Scalability in Perception for Autonomous Driving: Waymo Open Dataset
* 2020: Streaming Object Detection for 3-d Point Clouds
* 2021: Bottleneck Transformers for Visual Recognition
* 2021: Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
* 2021: Scaling Local Self-Attention for Parameter Efficient Visual Backbones
* 2022: PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
* 2023: Perceptual Grouping in Contrastive Vision-Language Models
Includes: Shlens, J.[Jonathon] Shlens, J.[Jon] Shlens, J.
22 for Shlens, J.
Shlesinger, T.
* 2017: What is the Space of Attenuation Coefficients in Underwater Computer Vision?
Shlezinger, N.
* 2020: Joint Radar-Communication Strategies for Autonomous Vehicles: Combining Two Key Automotive Technologies