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Bichen Wu

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Papers (12)

  1. LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking cs.CV · 2019 · author #3
  2. ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation cs.CV · 2018 · author #3
  3. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search cs.CV · 2018 · author #1
  4. Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search cs.CV · 2018 · author #1
  5. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud cs.CV · 2018 · author #1
  6. Co-Design of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications cs.DC · 2018 · author #4
  7. A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving cs.CV · 2018 · author #2
  8. Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld cs.CV · 2018 · author #2
  9. Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions cs.CV · 2017 · author #1
  10. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud cs.CV · 2017 · author #1
  11. SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving cs.CV · 2016 · author #1
  12. Shallow Networks for High-Accuracy Road Object-Detection cs.CV · 2016 · author #2

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