activity
20162021
most citedRON: Reverse Connection with Objectness Prior Networks for Object Detection

66 citations · 119 across the 9 of their papers we have counts for

collaborators

18 papers

eess.IV20212 cited

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation

Jiaming Liu, Ming Lu, Kaixin Chen +7

Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwi…

cs.AR2020

AccSS3D: Accelerator for Spatially Sparse 3D DNNs

Om Ji Omer, Prashant Laddha, Gurpreet S Kalsi +6

Semantic understanding and completion of real world scenes is a foundational primitive of 3D Visual perception widely used in high-level applications such as robotics, medical imag…

cs.CV2020

LID 2020: The Learning from Imperfect Data Challenge Results

Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32

Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…

cs.CV20202 cited

CASNet: Common Attribute Support Network for image instance and panoptic segmentation

Xiaolong Liu, Yuqing Hou, Anbang Yao +2

Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentat…

cs.LG2020

On Connections between Regularizations for Improving DNN Robustness

Yiwen Guo, Long Chen, Yurong Chen +1

This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically,…

cs.CV2019

Learning Two-View Correspondences and Geometry Using Order-Aware Network

Jiahui Zhang, Dawei Sun, Zixin Luo +6

Establishing correspondences between two images requires both local and global spatial context. Given putative correspondences of feature points in two views, in this paper, we pro…