Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models

• URL: http://arxiv.org/abs/2510.07135v1
• Date: Wed, 08 Oct 2025 15:29:48 GMT
• Title: Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
• Abstract summary: We present the first structured benchmark for evaluating few-shot adaptation methods on RSVLMs.<n>We conduct comprehensive experiments across ten remote sensing scene classification datasets.<n>Our findings reveal that models with similar zero-shot performance can exhibit markedly different behavior under few-shot adaptation.
• Score: 20.81142541450895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data regimes, such as few-shot learning, remains insufficiently explored. In this work, we present the first structured benchmark for evaluating few-shot adaptation methods on RSVLMs. We conduct comprehensive experiments across ten remote sensing scene classification datasets, applying five widely used few-shot adaptation strategies to three state-of-the-art RSVLMs with varying backbones. Our findings reveal that models with similar zero-shot performance can exhibit markedly different behavior under few-shot adaptation, with some RSVLMs being inherently more amenable to such adaptation than others. The variability of performance and the absence of a clear winner among existing methods highlight the need for the development of more robust methods for few-shot adaptation tailored to RS. To facilitate future research, we provide a reproducible benchmarking framework and open-source code to systematically evaluate RSVLMs under few-shot conditions. The source code is publicly available on Github: https://github.com/elkhouryk/fewshot_RSVLMs

Related papers

• Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing [55.291219073365546]
  Open-Vocabulary Remote Sensing Image (OVRSIS) is an emerging task that adapts Open-Vocabulary (OVS) to the remote sensing (RS) domain.<n>textbfRSKT-Seg is a novel open-vocabulary segmentation framework tailored for remote sensing.<n> RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2x faster inference through efficient aggregation.
  arXiv  Detail & Related papers  (2025-09-15T15:24:49Z)
• Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts [13.21626568246313]
  We analyze whether vision-language foundation models can be adapted to target datasets with very different distributions and classes.<n>We propose a novel prompt-tuning method, PromptMargin, for adapting such large-scale VLMs directly on the few target samples.<n>PromptMargin effectively tunes the text as well as visual prompts for this task, and has two main modules.
  arXiv  Detail & Related papers  (2025-05-21T13:26:56Z)
• Realistic Test-Time Adaptation of Vision-Language Models [23.972884634610413]
  Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance.<n>Previous works on transductive or test-time adaptation (TTA) often make strong assumptions about the data distribution.<n>Our work challenges these favorable deployment scenarios, and introduces a more realistic evaluation framework.
  arXiv  Detail & Related papers  (2025-01-07T12:17:25Z)
• Low-Rank Few-Shot Adaptation of Vision-Language Models [13.803180972839213]
  We introduce Low-Rank Adaptation (LoRA) in few-shot learning for Vision-Language Models (VLMs) Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times. Our results do not dismiss the potential of prompt-learning and adapter-based research.
  arXiv  Detail & Related papers  (2024-05-28T19:16:59Z)
• Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition [72.35438297011176]
  We propose a novel method to realize seamless adaptation of pre-trained models for visual place recognition (VPR) Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time.
  arXiv  Detail & Related papers  (2024-02-22T12:55:01Z)
• Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation [51.99752147380505]
  This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs) We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence. Our framework widens performance discrepancies both between different models and within the same model across various tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:40:06Z)
• Revisiting Few-Shot Object Detection with Vision-Language Models [49.79495118650838]
  We revisit the task of few-shot object detection (FSOD) in the context of recent foundational vision-language models (VLMs) We propose Foundational FSOD, a new benchmark protocol that evaluates detectors pre-trained on any external data. We discuss our recent CVPR 2024 Foundational FSOD competition and share insights from the community.
  arXiv  Detail & Related papers  (2023-12-22T07:42:00Z)
• Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples [61.66967790884943]
  Referring video object segmentation (RVOS) relies on sufficient data for a given scene. In more realistic scenarios, only minimal annotations are available for a new scene. We propose a model with a newly designed cross-modal affinity (CMA) module based on a Transformer architecture. CMA module builds multimodal affinity with a few samples, thus quickly learning new semantic information, and enabling the model to adapt to different scenarios.
  arXiv  Detail & Related papers  (2023-09-05T08:34:23Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Few Shot Activity Recognition Using Variational Inference [9.371378627575883]
  We propose a novel variational inference based architectural framework (HF-AR) for few shot activity recognition. Our framework leverages volume-preserving Householder Flow to learn a flexible posterior distribution of the novel classes. This results in better performance as compared to state-of-the-art few shot approaches for human activity recognition.
  arXiv  Detail & Related papers  (2021-08-20T03:57:58Z)
• Few-shot Classification via Adaptive Attention [93.06105498633492]
  We propose a novel few-shot learning method via optimizing and fast adapting the query sample representation based on very few reference samples. As demonstrated experimentally, the proposed model achieves state-of-the-art classification results on various benchmark few-shot classification and fine-grained recognition datasets.
  arXiv  Detail & Related papers  (2020-08-06T05:52:59Z)

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.