NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

07/02/2021
by   Jerrick Liu, et al.
9

In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO andSAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15 our current baselines for each track of the competition

READ FULL TEXT
research
08/18/2021

The Multi-Modal Video Reasoning and Analyzing Competition

In this paper, we introduce the Multi-Modal Video Reasoning and Analyzin...
research
12/14/2022

Multi-Modal Domain Fusion for Multi-modal Aerial View Object Classification

Object detection and classification using aerial images is a challenging...
research
04/23/2020

Roof material classification from aerial imagery

This paper describes an algorithm for classification of roof materials u...
research
05/04/2022

Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification

Multi-modal aerial view object classification (MAVOC) in Automatic targe...
research
06/17/2017

Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text

The YouTube-8M video classification challenge requires teams to classify...
research
06/16/2023

Multi-task 3D building understanding with multi-modal pretraining

This paper explores various learning strategies for 3D building type cla...
research
11/06/2019

AIM 2019 Challenge on Image Demoireing: Dataset and Study

This paper introduces a novel dataset, called LCDMoire, which was create...

Please sign up or login with your details

Forgot password? Click here to reset