Text-IRSTD: Leveraging Semantic Text to Promote Infrared Small Target Detection in Complex Scenes

• URL: http://arxiv.org/abs/2503.07249v1
• Date: Mon, 10 Mar 2025 12:33:07 GMT
• Title: Text-IRSTD: Leveraging Semantic Text to Promote Infrared Small Target Detection in Complex Scenes
• Authors: Feng Huang, Shuyuan Zheng, Zhaobing Qiu, Huanxian Liu, Huanxin Bai, Liqiong Chen,
• Abstract summary: We introduce a novel approach leveraging semantic text to guide infrared small target detection, called Text-IRSTD.<n>We propose a progressive cross-modal semantic interaction decoder (PCSID) to facilitate information fusion between texts and images.<n>In addition, we construct a new benchmark consisting of 2,755 infrared images of different scenarios with fuzzy semantic textual annotations, called FZDT.
• Score: 3.399048100638418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target detection is currently a hot and challenging task in computer vision. Existing methods usually focus on mining visual features of targets, which struggles to cope with complex and diverse detection scenarios. The main reason is that infrared small targets have limited image information on their own, thus relying only on visual features fails to discriminate targets and interferences, leading to lower detection performance. To address this issue, we introduce a novel approach leveraging semantic text to guide infrared small target detection, called Text-IRSTD. It innovatively expands classical IRSTD to text-guided IRSTD, providing a new research idea. On the one hand, we devise a novel fuzzy semantic text prompt to accommodate ambiguous target categories. On the other hand, we propose a progressive cross-modal semantic interaction decoder (PCSID) to facilitate information fusion between texts and images. In addition, we construct a new benchmark consisting of 2,755 infrared images of different scenarios with fuzzy semantic textual annotations, called FZDT. Extensive experimental results demonstrate that our method achieves better detection performance and target contour recovery than the state-of-the-art methods. Moreover, proposed Text-IRSTD shows strong generalization and wide application prospects in unseen detection scenarios. The dataset and code will be publicly released after acceptance of this paper.

Related papers

• Boosting Weakly-Supervised Referring Image Segmentation via Progressive Comprehension [40.21084218601082]
  This paper focuses on a challenging setup where target localization is learned directly from image-text pairs.<n>We propose a novel Progressive Network (PCNet) to leverage target-related textual cues for progressively localizing the target object.<n>Our method outperforms SOTA methods on three common benchmarks.
  arXiv  Detail & Related papers  (2024-10-02T13:30:32Z)
• Exploring Fine-Grained Image-Text Alignment for Referring Remote Sensing Image Segmentation [27.13782704236074]
  We propose a new referring remote sensing image segmentation method to fully exploit the visual and linguistic representations. The proposed fine-grained image-text alignment module (FIAM) would simultaneously leverage the features of the input image and the corresponding texts. We evaluate the effectiveness of the proposed method on two public referring remote sensing datasets including RefSegRS and RRSIS-D.
  arXiv  Detail & Related papers  (2024-09-20T16:45:32Z)
• ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformer [88.61312640540902]
  We introduce Explicit Synergy-based Text Spotting Transformer framework (ESTextSpotter) Our model achieves explicit synergy by modeling discriminative and interactive features for text detection and recognition within a single decoder. Experimental results demonstrate that our model significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-08-20T03:22:23Z)
• Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval [12.057465578064345]
  The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions. We propose a novel TIPR framework to build fine-grained interactions and alignment between person images and the corresponding texts.
  arXiv  Detail & Related papers  (2023-07-18T08:23:46Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Fine-Grained Semantically Aligned Vision-Language Pre-Training [151.7372197904064]
  Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and texts. We introduce LO, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions.
  arXiv  Detail & Related papers  (2022-08-04T07:51:48Z)
• Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection [65.30079184700755]
  This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network.
  arXiv  Detail & Related papers  (2022-03-30T11:44:56Z)
• SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition [73.61592015908353]
  We propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism. The design results in a concise framework that requires neither additional rectification module nor character-level annotation.
  arXiv  Detail & Related papers  (2022-03-19T01:14:42Z)
• Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds [155.388487263872]
  We propose a new infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. We also design a feature enhancement module to learn more features of small-dim targets.
  arXiv  Detail & Related papers  (2021-09-29T12:23:41Z)
• Text-based Person Search in Full Images via Semantic-Driven Proposal Generation [42.25611020956918]
  We propose a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals.
  arXiv  Detail & Related papers  (2021-09-27T11:42:40Z)
• DGST : Discriminator Guided Scene Text detector [11.817428636084305]
  This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection. Experiments on standard datasets demonstrate that the proposed D GST brings noticeable gain and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-28T01:47:36Z)

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.