230 citations · 716 across the 17 of their papers we have counts for
28 papers
PointAcc: Efficient Point Cloud Accelerator
Yujun Lin, Zhekai Zhang, Haotian Tang +2
Deep learning on point clouds plays a vital role in a wide range of applications such as autonomous driving and AR/VR. These applications interact with people in real-time on edge…
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
Ji Lin, Chuang Gan, Kuan Wang +1
The explosive growth in video streaming requires video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computationally cheap but cannot capture te…
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
Zhijian Liu, Simon Stent, Jie Li +2
Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets. In this paper, we propose LocTex th…
NAAS: Neural Accelerator Architecture Search
Yujun Lin, Mengtian Yang, Song Han
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numeric…
Efficient and Robust LiDAR-Based End-to-End Navigation
Zhijian Liu, Alexander Amini, Sibo Zhu +3
Deep learning has been used to demonstrate end-to-end neural network learning for autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably accurate i…
PatchNet -- Short-range Template Matching for Efficient Video Processing
Huizi Mao, Sibo Zhu, Song Han +1
Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition.…