most citedPhoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks

5 citations · 13 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20203 cited

Accurate Anchor Free Tracking

Shengyun Peng, Yunxuan Yu, Kun Wang +1

Visual object tracking is an important application of computer vision. Recently, Siamese based trackers have achieved good accuracy. However, most of Siamese based trackers are not…

eess.SP2020

Low Precision Floating-point Arithmetic for High Performance FPGA-based CNN Acceleration

Chen Wu, Mingyu Wang, Xinyuan Chu +2

Low precision data representation is important to reduce storage size and memory access for convolutional neural networks (CNNs). Yet, existing methods have two major limitations:…

eess.SP20205 cited

Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks

Chen Wu, Mingyu Wang, Xiayu Li +3

Convolutional neural networks (CNNs) achieve state-of-the-art performance at the cost of becoming deeper and larger. Although quantization (both fixed-point and floating-point) has…

cs.HC20205 cited

OralCam: Enabling Self-Examination and Awareness of Oral Health Using a Smartphone Camera

Yuan Liang, Hsuan-Wei Fan, Zhujun Fang +7

Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-c…

cs.CV2019

CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion

Yuan Liang, Weinan Song, J. P. Dym +2

Label propagation is a popular technique for anatomical segmentation. In this work, we propose a novel deep framework for label propagation based on non-local label fusion. Our fra…