A Billion-scale Foundation Model for Remote Sensing Images

• URL: http://arxiv.org/abs/2304.05215v4
• Date: Mon, 12 Aug 2024 03:33:12 GMT
• Title: A Billion-scale Foundation Model for Remote Sensing Images
• Authors: Keumgang Cha, Junghoon Seo, Taekyung Lee,
• Abstract summary: Three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. This paper examines the effect of increasing the number of model parameters on the performance of foundation models in downstream tasks. To the best of our knowledge, this is the first billion-scale foundation model in the remote sensing field.
• Score: 5.065947993017157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. Recently, research in the remote sensing field has focused primarily on the pretraining method and the size of the dataset, with limited emphasis on the number of model parameters. This paper addresses this gap by examining the effect of increasing the number of model parameters on the performance of foundation models in downstream tasks such as rotated object detection and semantic segmentation. We pretrained foundation models with varying numbers of parameters, including 86M, 605.26M, 1.3B, and 2.4B, to determine whether performance in downstream tasks improved with an increase in parameters. To the best of our knowledge, this is the first billion-scale foundation model in the remote sensing field. Furthermore, we propose an effective method for scaling up and fine-tuning a vision transformer in the remote sensing field. To evaluate general performance in downstream tasks, we employed the DOTA v2.0 and DIOR-R benchmark datasets for rotated object detection, and the Potsdam and LoveDA datasets for semantic segmentation. Experimental results demonstrated that, across all benchmark datasets and downstream tasks, the performance of the foundation models and data efficiency improved as the number of parameters increased. Moreover, our models achieve the state-of-the-art performance on several datasets including DIOR-R, Postdam, and LoveDA.

Related papers

• OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery [0.3926357402982764]
  Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities. We pair high-performance computing resources including Frontier supercomputer, America's first exascale system, and high-resolution optical RS data to pretrain billion-scale FMs.
  arXiv  Detail & Related papers  (2024-10-25T20:55:12Z)
• YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary [12.39040757106137]
  We introduce an innovative em textbfRetriever-emtextbfDictionary (RD) module to address this issue. This architecture enables YOLO-based models to efficiently retrieve features from a Dictionary that contains the insight of the dataset.
  arXiv  Detail & Related papers  (2024-10-20T09:38:58Z)
• Stanceformer: Target-Aware Transformer for Stance Detection [59.69858080492586]
  Stance Detection involves discerning the stance expressed in a text towards a specific subject or target. Prior works have relied on existing transformer models that lack the capability to prioritize targets effectively. We introduce Stanceformer, a target-aware transformer model that incorporates enhanced attention towards the targets during both training and inference.
  arXiv  Detail & Related papers  (2024-10-09T17:24:28Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining [73.81862342673894]
  Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. We conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. Our models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection.
  arXiv  Detail & Related papers  (2024-03-20T09:17:22Z)
• Towards A Foundation Model For Trajectory Intelligence [0.0]
  We present the results of training a large trajectory model using real-world user check-in data. Our approach follows a pre-train and fine-tune paradigm, where a base model is pre-trained via masked trajectory modeling. Our empirical analysis utilizes a comprehensive dataset of over 2 billion check-ins generated by more than 6 million users.
  arXiv  Detail & Related papers  (2023-11-30T00:34:09Z)
• CHORUS: Foundation Models for Unified Data Discovery and Exploration [6.85448651843431]
  We show that foundation models are highly applicable to the data discovery and data exploration domain. We show that a foundation-model-based approach outperforms the task-specific models and so the state of the art. This suggests a future direction in which disparate data management tasks can be unified under foundation models.
  arXiv  Detail & Related papers  (2023-06-16T03:58:42Z)
• GEO-Bench: Toward Foundation Models for Earth Monitoring [139.77907168809085]
  We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
  arXiv  Detail & Related papers  (2023-06-06T16:16:05Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• Towards Efficient Task-Driven Model Reprogramming with Foundation Models [52.411508216448716]
  Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task.
  arXiv  Detail & Related papers  (2023-04-05T07:28:33Z)

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.