DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2609.00666v1
• Date: Tue, 01 Sep 2026 03:44:47 GMT
• Title: DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection
• Abstract summary: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision.<n>We propose a novel Dual-knowledge Guided Network (DGNet) based on multiple generalizable texts.
• Score: 43.281292792735364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly improved detection performance. However, they still suffer from two key limitations. First, a single text description simultaneously modeling both background and target leads to semantic entanglement, which contradicts the objective of background suppression and target enhancement. Second, reliance on image-specific textual prompts (requiring additional external models such as CLIP during inference) results in deployment constraints. To address these issues, we propose a novel Dual-knowledge Guided Network (DGNet) based on multiple generalizable texts. Specifically, we design a Prior-knowledge Wavelet Modulation (PWM) module, which leverages dual textual priors that separately characterize large-scale backgrounds and sparse targets to effectively disentangle and modulate entangled semantics in the frequency domain. Furthermore, we introduce a Consensus-knowledge Directional Alignment (CDA) loss, which models the initial state and the ideal target across samples as `complex background' and `bright target', respectively, thereby constructing a clear and unified directional optimization trajectory for the model. Extensive experiments on three public datasets demonstrate the superior performance of DGNet and the effectiveness of each component. The source code is available at https://github.com/iLearn-Lab/MM26-DGNet.

Related papers

• ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection [21.00991696055637]
  InfRared Small Target Detection (IRSTD) is a challenging task.<n>Current multimodal methods describe both targets and backgrounds with a single textual prompt.<n>We propose a novel Asymmetric Dual-text Guided Network (ADGNet)
  arXiv  Detail & Related papers  (2026-09-01T07:48:22Z)
• FSGNet: A Frequency-Aware and Semantic Guidance Network for Infrared Small Target Detection [9.669351353574323]
  IRSTD aims to identify and distinguish small targets from complex backgrounds.<n>U-Net suffers from semantic degradation when transferring high-level features from deep to shallow layers.<n>This paper proposes FSGNet, a lightweight and effective detection framework incorporating frequency-aware and semantic guidance mechanisms.
  arXiv  Detail & Related papers  (2026-03-26T12:38:29Z)
• UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing [77.10640210751981]
  UDPNet is a general framework that leverages depth-based priors from a large-scale pretrained depth estimation model DepthAnything V2.<n>Our proposed solution establishes a new benchmark for depth-aware dehazing across various scenarios.
  arXiv  Detail & Related papers  (2026-01-11T13:29:02Z)
• SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection [2.8563206958455467]
  We propose a novel Semantic and Frequency Guided Network (SFGNet)<n>It incorporates semantic prompts and frequency-domain features to capture camouflaged objects and improve boundary perception.<n>Extensive experiments conducted on three COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-15T03:15:31Z)
• CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection [4.731073701194089]
  Infrared small target detection (ISTD) plays a critical role in a wide range of civilian and military applications.<n>Existing methods suffer from deficiencies in the localization of dim targets and the perception of contour information under dense clutter environments.<n>We propose a contour-aware and saliency priors embedding network (CSPENet) for ISTD.
  arXiv  Detail & Related papers  (2025-05-15T03:56:36Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• Double-Dot Network for Antipodal Grasp Detection [20.21384585441404]
  This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net) It follows the recent anchor-free object detection framework, which does not depend on empirically pre-set anchors. An effective CNN architecture is introduced to localize such fingertips, and with the help of auxiliary centers for refinement, it accurately and robustly infers grasp candidates.
  arXiv  Detail & Related papers  (2021-08-03T14:21:17Z)
• Adaptive Context-Aware Multi-Modal Network for Depth Completion [107.15344488719322]
  We propose to adopt the graph propagation to capture the observed spatial contexts. We then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks.
  arXiv  Detail & Related papers  (2020-08-25T06:00:06Z)
• Suppress and Balance: A Simple Gated Network for Salient Object Detection [89.88222217065858]
  We propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
  arXiv  Detail & Related papers  (2020-07-16T02:00:53Z)

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.