{"list":"other","total":53,"rows":[{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1807.10221","title":"Unified Perceptual Parsing for Scene Understanding","created":"2018-07-26","cited_by_count":1271},{"id":"2301.08243","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","created":"2023-01-19","cited_by_count":1039},{"id":"2309.16588","title":"Vision Transformers Need Registers","created":"2023-09-28","cited_by_count":871},{"id":"2207.08051","title":"SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery","created":"2022-07-17","cited_by_count":408},{"id":"1912.12180","title":"Axial Attention in Multidimensional Transformers","created":"2019-12-20","cited_by_count":358},{"id":"2212.14532","title":"Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning","created":"2022-12-30","cited_by_count":308},{"id":"2505.06708","title":"Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free","created":"2025-05-10","cited_by_count":283},{"id":"2403.13298","title":"Rotary Position Embedding for Vision Transformer","created":"2024-03-20","cited_by_count":236},{"id":"2103.16607","title":"Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data","created":"2021-03-30","cited_by_count":215},{"id":"2011.09980","title":"Geography-Aware Self-Supervised Learning","created":"2020-11-19","cited_by_count":190},{"id":"2311.00566","title":"CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders","created":"2023-11-01","cited_by_count":169},{"id":"2211.15660","title":"SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding","created":"2022-11-28","cited_by_count":167},{"id":"2212.08013","title":"FlexiViT: One Model for All Patch Sizes","created":"2022-12-15","cited_by_count":152},{"id":"2412.02732","title":"Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications","created":"2024-12-03","cited_by_count":144},{"id":"2107.07933","title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","created":"2021-07-16","cited_by_count":143},{"id":"2206.13188","title":"Self-supervised Learning in Remote Sensing: A Review","created":"2022-06-27","cited_by_count":137},{"id":"2507.22291","title":"AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data","created":"2025-07-29","cited_by_count":137},{"id":"2306.03831","title":"GEO-Bench: Toward Foundation Models for Earth Monitoring","created":"2023-06-06","cited_by_count":124},{"id":"2302.04476","title":"Towards Geospatial Foundation Models via Continual Pretraining","created":"2023-02-09","cited_by_count":109},{"id":"2504.11171","title":"TerraMind: Large-Scale Generative Multimodality for Earth Observation","created":"2025-04-15","cited_by_count":104},{"id":"2304.14065","title":"Lightweight, Pre-trained Transformers for Remote Sensing Timeseries","created":"2023-04-27","cited_by_count":103},{"id":"2106.02795","title":"Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding","created":"2021-06-05","cited_by_count":93},{"id":"2406.11519","title":"HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model","created":"2024-06-17","cited_by_count":73},{"id":"2412.04204","title":"PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models","created":"2024-12-05","cited_by_count":68},{"id":"2404.08351","title":"OmniSat: Self-Supervised Modality Fusion for Earth Observation","created":"2024-04-12","cited_by_count":63},{"id":"2503.11849","title":"Towards a Unified Copernicus Foundation Model for Earth Vision","created":"2025-03-14","cited_by_count":53},{"id":"2404.01260","title":"Bridging Remote Sensors with Multisensor Geospatial Foundation Models","created":"2024-04-01","cited_by_count":46},{"id":"2205.09616","title":"Masked Image Modeling with Denoising Contrast","created":"2022-05-19","cited_by_count":45},{"id":"2408.08447","title":"SpectralEarth: Training Hyperspectral Foundation Models at Scale","created":"2024-08-15","cited_by_count":42},{"id":"2506.20380","title":"TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis","created":"2025-06-25","cited_by_count":39},{"id":"2503.10845","title":"Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation","created":"2025-03-13","cited_by_count":34},{"id":"2312.10114","title":"FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring","created":"2023-12-15","cited_by_count":31},{"id":"2312.02199","title":"USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery","created":"2023-12-02","cited_by_count":28},{"id":"2507.13812","title":"SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing","created":"2025-07-18","cited_by_count":26},{"id":"2506.06281","title":"TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation","created":"2025-06-06","cited_by_count":25},{"id":"2511.13655","title":"OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation","created":"2025-11-17","cited_by_count":24},{"id":"2407.15837","title":"Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning","created":"2024-07-22","cited_by_count":20},{"id":"2408.11000","title":"SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining","created":"2024-08-20","cited_by_count":18},{"id":"2501.08111","title":"EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision","created":"2025-01-14","cited_by_count":11},{"id":"2406.18554","title":"Planted: a dataset for planted forest identification from multi-satellite time series","created":"2024-05-24","cited_by_count":9},{"id":"2506.19585","title":"SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images","created":"2025-06-24","cited_by_count":9},{"id":"2506.07080","title":"FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping","created":"2025-06-08","cited_by_count":6},{"id":"2204.07052","title":"CroCo: Cross-Modal Contrastive learning for localization of Earth Observation data","created":"2022-04-14","cited_by_count":4},{"id":"2502.17066","title":"DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications","created":"2025-02-24","cited_by_count":4},{"id":"2512.05025","title":"RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation","created":"2025-12-04","cited_by_count":4},{"id":"2602.23678","title":"Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand","created":"2026-02-27","cited_by_count":4},{"id":"2504.18770","title":"PyViT-FUSE: A Foundation Model for Multi-Sensor Earth Observation Data","created":"2025-04-26","cited_by_count":2},{"id":"2508.10894","title":"MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data","created":"2025-08-14","cited_by_count":2},{"id":"2506.13542","title":"Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars","created":"2025-06-16","cited_by_count":1}],"next_cursor":null}