MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining

• URL: http://arxiv.org/abs/2403.13430v2
• Date: Thu, 30 May 2024 03:23:13 GMT
• Title: MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
• Authors: Di Wang, Jing Zhang, Minqiang Xu, Lin Liu, Dongsheng Wang, Erzhong Gao, Chengxi Han, Haonan Guo, Bo Du, Dacheng Tao, Liangpei Zhang,
• Abstract summary: 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.
• Score: 73.81862342673894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP.

Related papers

• A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset [44.94304541427113]
  We propose a multitask deep learning model to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-23T11:14:54Z)
• Contrastive Learning for Multi-Object Tracking with Transformers [79.61791059432558]
  We show how DETR can be turned into a MOT model by employing an instance-level contrastive loss. Our training scheme learns object appearances while preserving detection capabilities and with little overhead. Its performance surpasses the previous state-of-the-art by +2.6 mMOTA on the challenging BDD100K dataset.
  arXiv  Detail & Related papers  (2023-11-14T10:07:52Z)
• Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks [55.36987468073152]
  This paper proposes a novel Dual-Guided Spatial-Channel-Temporal (DG-SCT) attention mechanism. The DG-SCT module incorporates trainable cross-modal interaction layers into pre-trained audio-visual encoders. Our proposed model achieves state-of-the-art results across multiple downstream tasks, including AVE, AVVP, AVS, and AVQA.
  arXiv  Detail & Related papers  (2023-11-09T05:24:20Z)
• Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach [20.86345962679122]
  Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks. We propose a novel Optimal tranSport-based suBmOdular tRaNsferability metric (OSBORN) to estimate the transferability of an ensemble of models to a downstream task.
  arXiv  Detail & Related papers  (2023-09-05T17:57:31Z)
• An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training [79.78201886156513]
  We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks.
  arXiv  Detail & Related papers  (2023-06-29T17:59:57Z)
• A Billion-scale Foundation Model for Remote Sensing Images [5.065947993017157]
  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.
  arXiv  Detail & Related papers  (2023-04-11T13:33:45Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• MulT: An End-to-End Multitask Learning Transformer [66.52419626048115]
  We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks. Our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads.
  arXiv  Detail & Related papers  (2022-05-17T13:03:18Z)
• Self-Supervised Visual Representation Learning Using Lightweight Architectures [0.0]
  In self-supervised learning, a model is trained to solve a pretext task, using a data set whose annotations are created by a machine. We critically examine the most notable pretext tasks to extract features from image data. We study the performance of various self-supervised techniques keeping all other parameters uniform.
  arXiv  Detail & Related papers  (2021-10-21T14:13:10Z)
• Multi-task pre-training of deep neural networks for digital pathology [8.74883469030132]
  We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. We show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance.
  arXiv  Detail & Related papers  (2020-05-05T08:50:17Z)

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.