One Shot is Enough for Sequential Infrared Small Target Segmentation

• URL: http://arxiv.org/abs/2408.04823v2
• Date: Sun, 15 Sep 2024 15:28:01 GMT
• Title: One Shot is Enough for Sequential Infrared Small Target Segmentation
• Authors: Bingbing Dan, Meihui Li, Tao Tang, Jing Zhang,
• Abstract summary: Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information. We propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capability to sequential IRSTS. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art IRSTS methods.
• Score: 9.354927663020586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation (IRSTS) with minimal data. Inspired by the success of Segment Anything Model (SAM) across various downstream tasks, we propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capability to sequential IRSTS. Specifically, we first obtain a confidence map through local feature matching (LFM). The highest point in the confidence map is used as the prompt to replace the manual prompt. Then, to address the over-segmentation issue caused by the domain gap, we design the point prompt-centric focusing (PPCF) module. Subsequently, to prevent miss and false detections, we introduce the triple-level ensemble (TLE) module to produce the final mask. Experiments demonstrate that our method requires only one shot to achieve comparable performance to state-of-the-art IRSTS methods and significantly outperforms other one-shot segmentation methods. Moreover, ablation studies confirm the robustness of our method in the type of annotations and the selection of reference images.

Related papers

• Bridge the Points: Graph-based Few-shot Segment Anything Semantically [79.1519244940518]
  Recent advancements in pre-training techniques have enhanced the capabilities of vision foundation models. Recent studies extend the SAM to Few-shot Semantic segmentation (FSS) We propose a simple yet effective approach based on graph analysis.
  arXiv  Detail & Related papers  (2024-10-09T15:02:28Z)
• PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images [16.662173255725463]
  We propose a novel Pointly-supervised Segment Anything Model named PointSAM. We conduct experiments on RSI datasets, including WHU, HRSID, and NWPU VHR-10. The results show that our method significantly outperforms direct testing with SAM, SAM2, and other comparison methods.
  arXiv  Detail & Related papers  (2024-09-20T11:02:18Z)
• Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection [57.666055329221194]
  We investigate the adaptation of generic segmentation models, such as the Segment Anything Model (SAM), to infrared small object detection tasks. Our model demonstrates significantly improved performance in both accuracy and throughput compared to existing approaches.
  arXiv  Detail & Related papers  (2024-09-07T05:31:24Z)
• IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection [55.554484379021524]
  Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. We propose the IRSAM model for IRSTD, which improves SAM's encoder-decoder architecture to learn better feature representation of infrared small objects.
  arXiv  Detail & Related papers  (2024-07-10T10:17:57Z)
• NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation [2.2559617939136505]
  We propose a simple and training-free method to enhance the validity and robustness of the matching strategy. The core concept involves randomly dropping feature channels (setting them to zero) during the matching process. This technique mimics discarding pathological nubbles, and it can be seamlessly applied to other similarity computing scenarios.
  arXiv  Detail & Related papers  (2024-05-19T08:00:38Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models [14.292149307183967]
  This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided masks to produce coarse pixel-wise prompts for SAM.
  arXiv  Detail & Related papers  (2023-11-22T07:07:55Z)
• Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection [48.707233614642796]
  Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Deep learning based methods have achieved promising performance on SIRST detection, but at the cost of a large amount of training data. We propose the first method to achieve SIRST detection with single-point supervision.
  arXiv  Detail & Related papers  (2023-04-10T08:04:05Z)
• Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation [63.910211095033596]
  Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. We propose a simple yet versatile framework in the spirit of divide-and-conquer. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
  arXiv  Detail & Related papers  (2022-04-21T06:21:14Z)

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.