The 1st Agriculture-Vision Challenge: Methods and Results

• URL: http://arxiv.org/abs/2004.09754v2
• Date: Thu, 23 Apr 2020 17:24:31 GMT
• Title: The 1st Agriculture-Vision Challenge: Methods and Results
• Authors: Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Junhee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt B. Salberg, Alexandre Barbosa, Rodrigo Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng, Van Thong Huynh, Soo-Hyung Kim, In-Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay Talbar, Jianyu Tang
• Abstract summary: The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. This paper provides a summary of notable methods and results in the challenge.
• Score: 144.57794061346974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task; the link can be found here.

Related papers

• The VoxCeleb Speaker Recognition Challenge: A Retrospective [75.40776645175585]
  The VoxCeleb Speaker Recognition Challenges (VoxSRC) were a series of challenges and workshops that ran annually from 2019 to 2023. The challenges primarily evaluated the tasks of speaker recognition and diarisation under various settings. We provide a review of these challenges that covers: what they explored; the methods developed by the challenge participants and how these evolved.
  arXiv  Detail & Related papers  (2024-08-27T08:57:31Z)
• Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble [20.631333392618327]
  The Agriculture-Vision Challenge at CVPR 2024 aims at leveraging semantic segmentation models to produce pixel level semantic segmentation labels. We propose a mosaic data augmentation with a rare class sampling strategy to enrich long-tail class samples. Our methodology achieved a mean Intersection over Union (mIoU) score of 0.547 on the test set, securing second place in this challenge.
  arXiv  Detail & Related papers  (2024-06-18T04:59:04Z)
• A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition [2.7624021966289605]
  We propose an approach that combines the effectiveness of the Segment AnyThing Model (SAM) for instance segmentation with prompt input from object detection models. Our best-performing model achieved a PQ+ score of 81.33 based on the evaluation metrics of the competition.
  arXiv  Detail & Related papers  (2023-09-24T08:34:12Z)
• NICE: CVPR 2023 Challenge on Zero-shot Image Captioning [149.28330263581012]
  NICE project is designed to challenge the computer vision community to develop robust image captioning models. Report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries.
  arXiv  Detail & Related papers  (2023-09-05T05:32:19Z)
• Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis [11.133807938044804]
  We release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) We extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. We demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-04T17:35:24Z)
• Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models [11.615548490321123]
  We propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582.
  arXiv  Detail & Related papers  (2022-06-23T18:02:12Z)
• LUAI Challenge 2021 on Learning to Understand Aerial Images [113.42987112252851]
  This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021. Using DOTA-v2.0 and GID-15 datasets, this challenge proposes three tasks for oriented object detection, horizontal object detection, and semantic segmentation of common categories in aerial images.
  arXiv  Detail & Related papers  (2021-08-30T14:03:54Z)
• Batch Exploration with Examples for Scalable Robotic Reinforcement Learning [63.552788688544254]
  Batch Exploration with Examples (BEE) explores relevant regions of the state-space guided by a modest number of human provided images of important states. BEE is able to tackle challenging vision-based manipulation tasks both in simulation and on a real Franka robot.
  arXiv  Detail & Related papers  (2020-10-22T17:49:25Z)
• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis [110.30849704592592]
  We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers.
  arXiv  Detail & Related papers  (2020-01-05T20:19:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.