activity
20192022
most citedFasterSeg: Searching for Faster Real-time Semantic Segmentation

114 citations · 181 across the 7 of their papers we have counts for

collaborators

9 papers

astro-ph.GA20224 cited

Optical Properties of Crich (C, SiC and FeC) Dust Layered Structure of Massive Stars

Ruiqing Wu, Mengqiu Long, Xiaojiao Zhang +9

The composition and structure of interstellar dust are important and complex for the study of the evolution of stars and the \textbf{interstellar medium} (ISM). However, there is a…

cs.CV202014 cited

AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation

Xinyu Gong, Wuyang Chen, Yifan Jiang +5

We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…

cs.LG202010 cited

Automated Synthetic-to-Real Generalization

Wuyang Chen, Zhiding Yu, Zhangyang Wang +1

Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Ye…

cs.LG202029 cited

Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Xuxi Chen, Wuyang Chen, Tianlong Chen +4

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled…

cs.CV2020114 cited

FasterSeg: Searching for Faster Real-time Semantic Segmentation

Wuyang Chen, Xinyu Gong, Xianming Liu +3

We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neura…

cs.CV20199 cited

In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation

Ye Yuan, Wuyang Chen, Yang Yang +1

The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…