6.5k citations · 10.4k across the 118 of their papers we have counts for
6 papers · 2 filters
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Zhen Dong, Zhewei Yao, Yaohui Cai +4
Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However…
Multi-source Domain Adaptation for Semantic Segmentation
Sicheng Zhao, Bo Li, Xiangyu Yue +5
Simulation-to-real domain adaptation for semantic segmentation has been actively studied for various applications such as autonomous driving. Existing methods mainly focus on a sin…
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Sicheng Zhao, Zizhou Jia, Hui Chen +3
Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these…
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao +3
We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is tr…
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
Zhen Dong, Zhewei Yao, Amir Gholami +2
Model size and inference speed/power have become a major challenge in the deployment of Neural Networks for many applications. A promising approach to address these problems is qua…
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
Bernie Wang, Virginia Wu, Bichen Wu +1
LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor for autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of autonom…