activity
20182022
most citedMAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion

10 citations · 14 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV20222 cited

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Mengze Li, Tianbao Wang, Haoyu Zhang +9

Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing…

cs.CV20221 cited

UWC: Unit-wise Calibration Towards Rapid Network Compression

Chen Lin, Zheyang Li, Bo Peng +4

This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance. Previous PTQ meth…

cs.CV2021

LUAI Challenge 2021 on Learning to Understand Aerial Images

Gui-Song Xia, Jian Ding, Ming Qian +33

This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in a…

cs.CV2021

Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition

Hui Jiang, Yunlu Xu, Zhanzhan Cheng +5

Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without add…

cs.CV2020

PolarDet: A Fast, More Precise Detector for Rotated Target in Aerial Images

Pengbo Zhao, Zhenshen Qu, Yingjia Bu +3

Fast and precise object detection for high-resolution aerial images has been a challenging task over the years. Due to the sharp variations on object scale, rotation, and aspect ra…

cs.CV202010 cited

MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion

Zehan Zhang, Ming Zhang, Zhidong Liang +4

3D vehicle detection based on multi-modal fusion is an important task of many applications such as autonomous driving. Although significant progress has been made, we still observe…