ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation

• URL: http://arxiv.org/abs/2608.02468v1
• Date: Mon, 03 Aug 2026 16:37:12 GMT
• Title: ISRS-DETR: Detection-Guided Click Propagation for Remote Sensing Interactive Segmentation
• Abstract summary: We propose ISRS-DETR, a detection-guided interactive segmentation framework.<n>It injects object-level evidence into both training and inference.<n>It achieves state-of-the-art accuracy while substantially reducing Number Clicks per Image.
• Score: 9.07529044229261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interactive segmentation reduces the prohibitive cost of pixel-level annotation by allowing users to delineate objects with a few clicks. However, applying this paradigm directly to remote sensing imagery is non-trivial: ultra-high resolutions, small object sizes, and sparse spatial distributions all degrade segmentation quality. Recent work has addressed the resolution barrier and achieved competitive results in interactive segmentation for remote sensing (ISRS). However, they treat all instances of a class within an image as a single objective target. Consequently, interactions spent on one object contribute nothing to its same-class neighbours, and satisfactory masks may demand up to 40 clicks per image, hindering the practicality of these frameworks. We observe that remote sensing scenes exhibit markedly strong inter-object correlation, meaning a single clicked object is highly informative about the rest of its category. Building on this, we propose ISRS-DETR, a detection-guided interactive segmentation framework that injects object-level evidence into both training and inference. Our ISRS-DETR employs an RF-DETR decoder with the interactive segmentation backbone to localise co-occurring same-class objects, and introduces a Dynamic Top-K Click Selection strategy that retains only reliable proposals and converts each into a simulated click, so one user interaction propagates across an entire class. Experiments on three standard remote sensing benchmarks show that ISRS-DETR achieves state-of-the-art accuracy while substantially reducing Number of Clicks per Image (NoC-I). All codes and data splits will be released for reproducibility upon acceptance.

Related papers

• ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings [9.399068602086821]
  We propose a novel interactive segmentation framework that operates directly on sparse, randomly downsampled 3D points.<n>Our framework consists of a point Transformer-based encoder and a hierarchical mask decoder, which integrates multi-level crop-and-merge operations.<n>Our model improves the mIoU metric by over 20 percent compared to strong baselines.
  arXiv  Detail & Related papers  (2026-05-09T12:51:25Z)
• 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)
• FocSAM: Delving Deeply into Focused Objects in Segmenting Anything [58.042354516491024]
  The Segment Anything Model (SAM) marks a notable milestone in segmentation models. We propose FocSAM with a pipeline redesigned on two pivotal aspects. First, we propose Dynamic Window Multi-head Self-Attention (Dwin-MSA) to dynamically refocus SAM's image embeddings on the target object. Second, we propose Pixel-wise Dynamic ReLU (P-DyReLU) to enable sufficient integration of interactive information from a few initial clicks.
  arXiv  Detail & Related papers  (2024-05-29T02:34:13Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Deep learning-based interactive segmentation in remote sensing [2.328864986845849]
  This study aims to bridge the gap between click-based interactive segmentation and remote sensing image analysis.<n>We assessed the performance of five state-of-the-art interactive segmentation methods on two high-resolution aerial imagery datasets.<n>Building upon these findings, we developed a dedicated online tool called SegMap for interactive segmentation of remote sensing data.
  arXiv  Detail & Related papers  (2023-08-25T04:49:49Z)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation [2.482735440750151]
  We propose a click-based and mask-guided interactive image segmentation framework containing three novel components. The proposed framework offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. Our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach.
  arXiv  Detail & Related papers  (2023-03-09T23:20:35Z)
• Contour-based Interactive Segmentation [4.164728134421114]
  We consider a natural form of user interaction as a loose contour, and introduce a contour-based interactive segmentation method. We demonstrate that a single contour provides the same accuracy as multiple clicks, thus reducing the required amount of user interactions.
  arXiv  Detail & Related papers  (2023-02-13T13:35:26Z)
• Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction [74.52904110197004]
  We propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
  arXiv  Detail & Related papers  (2022-01-25T12:44:23Z)
• Interactive Object Segmentation with Dynamic Click Transform [27.709779682559883]
  We propose a Dynamic Click Transform Network(DCT-Net), consisting of Spatial-DCT and Feature-DCT, to better represent user interactions. We demonstrate the effectiveness of our proposed method and achieve favorable performance compared to the state-of-the-art on three standard benchmark datasets.
  arXiv  Detail & Related papers  (2021-06-19T10:13:37Z)
• FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points [70.65563691392987]
  We present a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
  arXiv  Detail & Related papers  (2020-04-04T22:25:47Z)

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.