LDPNet: A Lightweight Densely Connected Pyramid Network for Real-Time Semantic Segmentation

Hu, Xuegang; Jing, Liyuan

doi:10.1109/access.2020.3038864

Cited by 9 publications

(2 citation statements)

References 34 publications

(46 reference statements)

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“…It focused much more on inference efficiency rather than accurate pixel classification. Similarly, LDPNet achieved a 71.1% mIoU in 2020 [53]. Finally, looking at the benchmark section on the official Cityscapes dataset hosting website at the time of writing, several anonymous mIoU reports show that the latest research could be achieving around 85-87% mIoU.…”

Section: Dataset Configuration and Comparisonsmentioning

confidence: 94%

Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

Naudé,

Myburgh

2023

Sensors

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Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.

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Section: Dataset Configuration and Comparisonsmentioning

confidence: 94%

Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

Naudé,

Myburgh

2023

Sensors

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“…Recently, deep learning has been successfully developed to tackle various computer vision tasks, for example, image super resolution [11], restoration [12, 23], medical image fusion [13] and medical image segmentation [14]. Inspired by such success, various convolutional neural networks (CNNs) based models have been exploited to reconstruct MRI images from undersampled

k

‐space measurements [15].…”

Section: Introductionmentioning

confidence: 99%

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

Zhang,

Zhou,

Wan

et al. 2024

IET Image Processing

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Magnetic resonance imaging (MRI) is a non‐interposition imaging technique that provides rich anatomical and physiological information. Yet it is limited by the long imaging time. Recently, deep neural networks have shown potential to significantly accelerate MRI. However, most of these approaches ignore the correlation between adjacent slices in MRI image sequences. In addition, the existing deep learning‐based methods for MRI are mainly based on convolutional neural networks (CNNs). They fail to capture long‐distance dependencies due to the small receptive field. Inspired by the feature similarity in adjacent slices and impressive performance of Transformer for exploiting the long‐distance dependencies, a novel multislice texture transformer network is presented for undersampled MRI reconstruction (TTMRI). Specifically, the proposed TTMRI is consisted of four modules, namely the texture extraction, correlation calculation, texture transfer and texture synthesis. It takes three adjacent slices as inputs, in which the middle one is the target image to be reconstructed, and the other two are auxiliary images. The multiscale features are extracted by the texture extraction module and their inter‐dependencies are calculated by the correlation calculation module, respectively. Then the relevant features are transferred by the texture transfer module and fused by the texture synthesis module. By considering inter‐slice correlations and leveraging the Transformer architecture, the joint feature learning across target and adjacent slices are encouraged. Moreover, TTMRI can be stacked with multiple layers to recover more texture information at different levels. Extensive experiments demonstrate that the proposed TTMRI outperforms other state‐of‐the‐art methods in both quantitative and qualitative evaluationsions.

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An improved residual learning model and its application to hardware image classification

Zhang,

Li,

Cheng

et al. 2024

Vis Comput

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LDPNet: A Lightweight Densely Connected Pyramid Network for Real-Time Semantic Segmentation

Cited by 9 publications

References 34 publications

Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

An improved residual learning model and its application to hardware image classification

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