activity
20162023
most citedRON: Reverse Connection with Objectness Prior Networks for Object Detection

66 citations · 121 across the 12 of their papers we have counts for

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16 papers · 1 filter

cs.CV20232 cited

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

Dongqi Cai, Yangyuxuan Kang, Anbang Yao +1

This paper presents Ske2Grid, a new representation learning framework for improved skeleton-based action recognition. In Ske2Grid, we define a regular convolution operation upon a…

cs.CV2023

ECT: Fine-grained Edge Detection with Learned Cause Tokens

Shaocong Xu, Xiaoxue Chen, Yuhang Zheng +4

In this study, we tackle the challenging fine-grained edge detection task, which refers to predicting specific edges caused by reflectance, illumination, normal, and depth changes,…

cs.CV2023

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

Senmao Tian, Ming Lu, Jiaming Liu +3

With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K…

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.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…