activity
20152022
most citedFasterSeg: Searching for Faster Real-time Semantic Segmentation

114 citations · 241 across the 6 of their papers we have counts for

collaborators

10 papers

cs.CV202272 cited

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Zhenyu Li, Xuyang Wang, Xianming Liu +1

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, some methods reformulate it as a classification-regression task to…

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.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.CV2019

Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network

Tao Hu, Lichao Huang, Xianming Liu +1

More powerful feature representations derived from deep neural networks benefit visual tracking algorithms widely. However, the lack of exploitation on temporal information prevent…

cs.CV2019

Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

Wenxiang Zuo, Qiang Li, Xianming Liu

As a real scenes sensing approach, depth information obtains the widespread applications. However, resulting from the restriction of depth sensing technology, the depth map capture…

cs.CV2018

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

Ding Liu, Bihan Wen, Jianbo Jiao +3

Image denoising and high-level vision tasks are usually handled independently in the conventional practice of computer vision, and their connection is fragile. In this paper, we co…