Joint pyramid attention network for real-time semantic segmentation of urban scenes

Hu, Xuegang; Jing, Liyuan; Sehar, Uroosa

doi:10.1007/s10489-021-02446-8

Cited by 43 publications

(22 citation statements)

References 38 publications

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“…Compared with Fig. 11 (b) and (c), the loss of the acoustic features with postprocessing is much less than that without postprocessing, which proves the effectiveness of the postprocessing module [46]. Figure 11 (d) proves that the model has learned how many time steps should stop generating the predicted acoustic features.…”

Section: Methodsmentioning

confidence: 78%

“…After about 200k iteration, the loss curve converges at a very low value, which proves a satisfying performance of the model. Compared with Fig.11(b) and (c), the loss of the acoustic features with postprocessing is much less than that without postprocessing, which proves the effectiveness of the postprocessing module[46]. Figure11 (d) proves that the model has learned how many time steps should stop generating the predicted acoustic features.To evaluate the sound quality of synthesized speeches, objective and subject test are conducted.…”

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confidence: 76%

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Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

et al. 2022

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This paper proposes a framework of applying only the EGG signal for speech synthesis in the limited categories of contents scenario. EGG is a sort of physiological signal which can reflect the trends of the vocal cord movement. Note that EGG’s different acquisition method contrasted with speech signals, we exploit its application in speech synthesis under the following two scenarios. (1) To synthesize speeches under high noise circumstances, where clean speech signals are unavailable. (2) To enable dumb people who retain vocal cord vibration to speak again. Our study consists of two stages, EGG to text and text to speech. The first is a text content recognition model based on Bi-LSTM, which converts each EGG signal sample into the corresponding text with a limited class of contents. This model achieves 91.12% accuracy on the validation set in a 20-class content recognition experiment. Then the second step synthesizes speeches with the corresponding text and the EGG signal. Based on modified Tacotron-2, our model gains the Mel cepstral distortion (MCD) of 5.877 and the mean opinion score (MOS) of 3.87, which is comparable with the state-of-the-art performance and achieves an improvement by 0.42 and a relatively smaller model size than the origin Tacotron-2. Considering to introduce the characteristics of speakers contained in EGG to the final synthesized speech, we put forward a fine-grained fundamental frequency modification method, which adjusts the fundamental frequency according to EGG signals and achieves a lower MCD of 5.781 and a higher MOS of 3.94 than that without modification.

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Section: Methodsmentioning

confidence: 78%

mentioning

confidence: 76%

Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

et al. 2022

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“…Presently, the residual structure, as illustrated in Figure 2. (a), from ResNet [27] is widely used to reduce parameters and boost inference. ERFNet [16] factorized convolution effectively and realized the lightweight of the network, as depicted in Figure 2.(b).…”

Section: Multi-scale Information Interaction Modulementioning

confidence: 99%

A lightweight network with multi-scale information interaction attention for real-time semantic segmentation

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Real-time semantic segmentation is an important field in computer vision. It is widely employed in real-world scenarios such as mobile devices and autonomous driving, requiring networks to achieve a trade-off between efficiency, performance, and model size. This paper proposes a lightweight network with multi-scale information interaction attention (MSIANet) to solve this issue. Specifically, we designed a multi-scale information interaction module (MSI) is the main component of the encoder and is used to densely encode contextual semantic features. Moreover, we designed the multi-channel attention fusion module (MAF) in the decoder part, thereby realizing multi-scale information fusion through channel attention mechanism and spatial attention mechanism. We verify our method through numerous experiments and prove that our network possesses fewer parameters and faster inference speed compared to most of the existing real-time semantic segmentation methods in multiple datasets.

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“…There are many works on fusing multi-scale features. Hu et al [27] propose a joint feature pyramid (JFP) module, and built a spatial detail extraction (SDE) module, design a bilateral feature fusion (BFF) module, making full use of the correspondence between high-level features and lowlevel features. Benvcevic et al [28] propose training a neural network on polar transformations of the original dataset, such that the polar origin for the transformation is the center point of the object.…”

Section: A Related Workmentioning

confidence: 99%

Segmentation and Measurement of Superalloy Microstructure Based on Improved Nonlocal Block

Zhang

Yao

et al. 2022

IEEE Access

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The microstructure of superalloy materials has a decisive impact on its service performance. When preparing the material and photographing the microstructure, different depths of metallography perpendicular to the cut plane appear in the microstructure image. These metallographic features are major factors causing inaccurate segmentation. Aiming at the problems of traditional image processing methods, such as large noise and poor robustness of the edge extraction, the deep learning method is introduced. The receptive field of the traditional convolutional neural network method is too local to obtain remote dependencies, and dense feature extraction also brings problems such as excessive noise.To address the problem of non-tangential metallographic information in microstructure images that is not easily distinguishable. We consider how to combine and utilize the feature maps of the intermediate process as effectively as possible, and find that reusing affinity matrices from different angles and stacking them can improve the overall effect, and can solve the problem of inaccurate segmentation of metallographic images at different depths. We propose to improve and optimize the non-local attention module and further combine the module with the UNet network to form a new improved SNL-Unet image segmentation structure, which significantly raises the accuracy and efficiency of image segmentation, Additionally, we measure the characteristic parameters such as volume fraction, average thickness and the degree of rafting. The code for this paper will be available at github.com/ustbjdl1021/improved-snl-unet.

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Joint pyramid attention network for real-time semantic segmentation of urban scenes

Cited by 43 publications

References 38 publications

Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

A lightweight network with multi-scale information interaction attention for real-time semantic segmentation

Segmentation and Measurement of Superalloy Microstructure Based on Improved Nonlocal Block

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