DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2409.19599v4
• Date: Sat, 01 Mar 2025 17:31:31 GMT
• Title: DATransNet: Dynamic Attention Transformer Network for Infrared Small Target Detection
• Authors: Chen Hu, Yian Huang, Kexuan Li, Luping Zhang, Chang Long, Yiming Zhu, Tian Pu, Zhenming Peng,
• Abstract summary: Infrared small target detection (ISTD) is widely used in civilian and military applications.<n>ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds.<n>We propose the Dynamic Attention Transformer Network (DATransNet) to extract and preserve detailed information vital for small targets.
• Score: 12.291732476567192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target detection (ISTD) is widely used in civilian and military applications. However, ISTD encounters several challenges, including the tendency for small and dim targets to be obscured by complex backgrounds. To address this issue, we propose the Dynamic Attention Transformer Network (DATransNet), which aims to extract and preserve detailed information vital for small targets. DATransNet employs the Dynamic Attention Transformer (DATrans), simulating central difference convolutions (CDC) to extract gradient features. Furthermore, we propose a global feature extraction module (GFEM) that offers a comprehensive perspective to prevent the network from focusing solely on details while neglecting the global information. We compare the network with state-of-the-art (SOTA) approaches and demonstrate that our method performs effectively. Our source code is available at https://github.com/greekinRoma/DATransNet.

Related papers

• DRCT: Saving Image Super-resolution away from Information Bottleneck [7.765333471208582]
  Vision Transformer-based approaches for low-level vision tasks have achieved widespread success. Dense-residual-connected Transformer (DRCT) is proposed to mitigate the loss of spatial information. Our approach surpasses state-of-the-art methods on benchmark datasets.
  arXiv  Detail & Related papers  (2024-03-31T15:34:45Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• 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)
• SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection [46.049401912285134]
  Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. Existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks.
  arXiv  Detail & Related papers  (2024-01-28T06:41:15Z)
• Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection [8.388564430699155]
  Infrared small target detection based on deep learning offers unique advantages in separating small targets from complex and dynamic backgrounds. The features of infrared small targets gradually weaken as the depth of convolutional neural network (CNN) increases. We propose improved dense nested attention network (IDNANet), which is based on the transformer architecture.
  arXiv  Detail & Related papers  (2023-11-15T07:29:24Z)
• Understanding Deep Gradient Leakage via Inversion Influence Functions [53.1839233598743]
  Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. We propose a novel Inversion Influence Function (I$2$F) that establishes a closed-form connection between the recovered images and the private gradients. We empirically demonstrate that I$2$F effectively approximated the DGL generally on different model architectures, datasets, attack implementations, and perturbation-based defenses.
  arXiv  Detail & Related papers  (2023-09-22T17:26:24Z)
• Salient Object Detection in Optical Remote Sensing Images Driven by Transformer [69.22039680783124]
  We propose a novel Global Extraction Local Exploration Network (GeleNet) for Optical Remote Sensing Images (ORSI-SOD) Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-15T07:14:43Z)
• Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection [95.84616822805664]
  We introduce CNNs-assisted Transformer architecture and propose a novel RGB-D SOD network with Point-aware Interaction and CNN-induced Refinement. In order to alleviate the block effect and detail destruction problems brought by the Transformer naturally, we design a CNN-induced refinement (CNNR) unit for content refinement and supplementation.
  arXiv  Detail & Related papers  (2023-08-17T11:57:49Z)
• EFLNet: Enhancing Feature Learning for Infrared Small Target Detection [20.546186772828555]
  Single-frame infrared small target detection is considered to be a challenging task. Due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small target. We propose an enhancing feature learning network (EFLNet) to address these problems.
  arXiv  Detail & Related papers  (2023-07-27T09:23:22Z)
• ABC: Attention with Bilinear Correlation for Infrared Small Target Detection [4.7379300868029395]
  CNN based deep learning methods are not effective at segmenting infrared small target (IRST) We propose a new model called attention with bilinear correlation (ABC) ABC is based on the transformer architecture and includes a convolution linear fusion transformer (CLFT) module with a novel attention mechanism for feature extraction and fusion.
  arXiv  Detail & Related papers  (2023-03-18T03:47:06Z)
• Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution [64.25751738088015]
  Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks. Transformers that need to incorporate contextual information to extract features dynamically are neglected. We propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer.
  arXiv  Detail & Related papers  (2022-07-06T16:32:29Z)
• Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-based Localization [0.24596929878045565]
  We introduce a new technique to produce visual explanations for the predictions of a CNN. Our method can be applied to any layer of the network, and like Integrated Gradients it is not affected by the problem of vanishing gradients. Compared to Grad-CAM, heatmaps produced by our algorithm are better focused in the areas of interest, and their numerical computation is more stable.
  arXiv  Detail & Related papers  (2022-05-22T18:30:38Z)
• 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)
• MobileSal: Extremely Efficient RGB-D Salient Object Detection [62.04876251927581]
  This paper introduces a novel network, methodname, which focuses on efficient RGB-D salient object detection (SOD) We propose an implicit depth restoration (IDR) technique to strengthen the feature representation capability of mobile networks for RGB-D SOD. With IDR and CPR incorporated, methodnameperforms favorably against sArt methods on seven challenging RGB-D SOD datasets.
  arXiv  Detail & Related papers  (2020-12-24T04:36:42Z)
• Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection [63.18846475183332]
  We aim to develop an efficient and compact deep network for RGB-D salient object detection. We propose a progressively guided alternate refinement network to refine it. Our model outperforms existing state-of-the-art approaches by a large margin.
  arXiv  Detail & Related papers  (2020-08-17T02:55:06Z)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

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.