MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation

• URL: http://arxiv.org/abs/2206.08970v1
• Date: Fri, 17 Jun 2022 18:55:05 GMT
• Title: MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation
• Authors: Bo Peng, Hongchen Liu, Hang Zhou, Yuchuan Gou, Jui-Hsin Lai
• Abstract summary: This work proposes an adaptive real-time multimodal regression and generation framework for the MultiEarth Matrix Completion Challenge in CVPR 2022. It achieves superior performance on unseen test queries in this challenge with an LPIPS of 0.2226, a PSNR of 123.0372, and an SSIM of 0.6347.
• Score: 10.918741492506502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Earth observation satellites have been continuously monitoring the earth environment for years at different locations and spectral bands with different modalities. Due to complex satellite sensing conditions (e.g., weather, cloud, atmosphere, orbit), some observations for certain modalities, bands, locations, and times may not be available. The MultiEarth Matrix Completion Challenge in CVPR 2022 [1] provides the multimodal satellite data for addressing such data sparsity challenges with the Amazon Rainforest as the region of interest. This work proposes an adaptive real-time multimodal regression and generation framework and achieves superior performance on unseen test queries in this challenge with an LPIPS of 0.2226, a PSNR of 123.0372, and an SSIM of 0.6347.

Related papers

• GeoPlant: Spatial Plant Species Prediction Dataset [4.817737198128259]
  Species Distribution Models (SDMs) predict species across space from spatially explicit features. We have designed and developed a new European-scale dataset for SDMs at high spatial resolution (10-50 m) The dataset comprises 5M heterogeneous Presence-Only records and 90k exhaustive Presence-Absence survey records.
  arXiv  Detail & Related papers  (2024-08-25T20:09:46Z)
• MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark [63.878793340338035]
  Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. Existing datasets for this task are either synthetically generated or artificially constructed within a controlled camera network setting. We present MTMMC, a real-world, large-scale dataset that includes long video sequences captured by 16 multi-modal cameras in two different environments.
  arXiv  Detail & Related papers  (2024-03-29T15:08:37Z)
• SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery [35.550999964460466]
  We present SkySense, a generic billion-scale model, pre-trained on a curated multi-modal Remote Sensing dataset with 21.5 million temporal sequences. To our best knowledge, SkySense is the largest Multi-Modal to date, whose modules can be flexibly combined or used individually to accommodate various tasks.
  arXiv  Detail & Related papers  (2023-12-15T09:57:21Z)
• Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows [0.0]
  We present novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023. One of the models secured the top position among 293 competitors. We introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge.
  arXiv  Detail & Related papers  (2023-09-17T17:59:59Z)
• The Second Monocular Depth Estimation Challenge [93.1678025923996]
  The second edition of the Monocular Depth Estimation Challenge (MDEC) was open to methods using any form of supervision. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%.
  arXiv  Detail & Related papers  (2023-04-14T11:10:07Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation [152.60469768559878]
  SHIFT is the largest multi-task synthetic dataset for autonomous driving. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Our dataset and benchmark toolkit are publicly available at www.vis.xyz/shift.
  arXiv  Detail & Related papers  (2022-06-16T17:59:52Z)
• NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results [173.32437855731752]
  The challenge was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations.
  arXiv  Detail & Related papers  (2022-05-25T10:20:06Z)
• MultiEarth 2022 -- Multimodal Learning for Earth and Environment Workshop and Challenge [17.4371831579002]
  The goal of the Challenge is to provide a common benchmark for multimodal information processing. This paper presents the challenge guidelines, datasets, and evaluation metrics for the three sub-challenges.
  arXiv  Detail & Related papers  (2022-04-15T20:59:02Z)
• Large-Scale Spatio-Temporal Person Re-identification: Algorithm and Benchmark [100.77540900932763]
  We contribute a novel Large-scale Spatio-Temporal (LaST) person re-ID dataset, including 10,860 identities with more than 224k images. LaST presents more challenging and high-diversity reID settings, and significantly larger spatial and temporal ranges.
  arXiv  Detail & Related papers  (2021-05-31T16:05:51Z)
• EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts [12.795776149170978]
  Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts. We define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. We introduce EarthNet 2021, a new curated dataset containing target-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables.
  arXiv  Detail & Related papers  (2020-12-11T11:21:00Z)

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.