Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark

• URL: http://arxiv.org/abs/2409.11227v1
• Date: Tue, 17 Sep 2024 14:20:47 GMT
• Title: Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
• Authors: Clifford Broni-Bediako, Junshi Xia, Jian Song, Hongruixuan Chen, Mennatullah Siam, Naoto Yokoya,
• Abstract summary: Few-shot semantic segmentation can encourage deep learning models to learn from few labelled examples for novel classes not seen during the training. The generalized few-shot segmentation setting has an additional challenge which encourages models not only to adapt to the novel classes but also to maintain strong performance on the training base classes. We release the dataset augmenting OpenEarthMap with additional classes labelled for the generalized few-shot evaluation setting.
• Score: 18.636210870172675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage deep learning models to learn from few labelled examples for novel classes not seen during the training. The generalized few-shot segmentation setting has an additional challenge which encourages models not only to adapt to the novel classes but also to maintain strong performance on the training base classes. While previous datasets and benchmarks discussed the few-shot segmentation setting in remote sensing, we are the first to propose a generalized few-shot segmentation benchmark for remote sensing. The generalized setting is more realistic and challenging, which necessitates exploring it within the remote sensing context. We release the dataset augmenting OpenEarthMap with additional classes labelled for the generalized few-shot evaluation setting. The dataset is released during the OpenEarthMap land cover mapping generalized few-shot challenge in the L3D-IVU workshop in conjunction with CVPR 2024. In this work, we summarize the dataset and challenge details in addition to providing the benchmark results on the two phases of the challenge for the validation and test sets.

Related papers

• A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks [81.2624272756733]
  In dense retrieval, deep encoders provide embeddings for both inputs and targets. We train a small parametric corrector network that adjusts stale cached target embeddings. Our approach matches state-of-the-art results even when no target embedding updates are made during training.
  arXiv  Detail & Related papers  (2024-09-03T13:29:13Z)
• GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing [9.504503675097137]
  We introduce a generator-based contrastive learning framework (GenCo) that pre-trains backbones and simultaneously explores variants of feature samples. In fine-tuning, the auxiliary generator can be used to enrich limited labeled data samples in feature space. We demonstrate the effectiveness of our method in improving few-shot learning performance on two key remote sensing datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:59:19Z)
• Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models [7.452422412106768]
  We propose a novel method named Text2Seg for remote sensing semantic segmentation. It overcomes the dependency on extensive annotations by employing an automatic prompt generation process. We show that Text2Seg significantly improves zero-shot prediction performance compared to the vanilla SAM model.
  arXiv  Detail & Related papers  (2023-04-20T18:39:41Z)
• A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition [6.260916845720537]
  It may not always be possible to collect hundreds of labeled samples per class for training deep learning-based SAR Automatic Target Recognition (ATR) models. This work specifically tackles the few-shot SAR ATR problem, where only a handful of labeled samples may be available to support the task of interest.
  arXiv  Detail & Related papers  (2023-03-20T00:24:05Z)
• StandardSim: A Synthetic Dataset For Retail Environments [0.07874708385247352]
  We present a large-scale synthetic dataset featuring annotations for semantic segmentation, instance segmentation, depth estimation, and object detection. Our dataset provides multiple views per scene, enabling multi-view representation learning. We benchmark widely-used models for segmentation and depth estimation on our dataset, show that our test set constitutes a difficult benchmark compared to current smaller-scale datasets.
  arXiv  Detail & Related papers  (2022-02-04T22:28:35Z)
• MSeg: A Composite Dataset for Multi-domain Semantic Segmentation [100.17755160696939]
  We present MSeg, a composite dataset that unifies semantic segmentation datasets from different domains. We reconcile the generalization and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. A model trained on MSeg ranks first on the WildDash-v1 leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.
  arXiv  Detail & Related papers  (2021-12-27T16:16:35Z)
• Large-scale Unsupervised Semantic Segmentation [163.3568726730319]
  We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 40k high-quality semantic segmentation annotations for evaluation.
  arXiv  Detail & Related papers  (2021-06-06T15:02:11Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation [52.487469544343305]
  Methods for object detection and segmentation rely on large scale instance-level annotations for training. We propose an intuitive and unified semi-supervised model that is applicable to a range of supervision.
  arXiv  Detail & Related papers  (2020-06-12T22:45:47Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark [11.101588888002045]
  We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles. We analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations.
  arXiv  Detail & Related papers  (2020-01-10T14:44:23Z)

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.