LCSegNet: An Efficient Semantic Segmentation Network for Large-Scale Complex Chinese Character Recognition

Wu, Xiang-Ping; Chen, Qingcai; Xiao, Yulun; Li, Wei; Liu, Xin; Hu, Baotian

doi:10.1109/tmm.2020.3025696

Cited by 21 publications

(6 citation statements)

References 71 publications

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“…Fusing Attention Interim used global average pooling and feature fusion into the block to build the smooth connection between decoder and encoder. Another semantic segmentation network has been designed by [54] to recognize the complex Chinese character. They combined three modules, i.e., FCN-ResNet50, Wubi-like label coding, and CRF, to develop LCSegNet.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Shahzad¹,

Umar

Shirazi

et al. 2021

IEEE Access

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Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not feasible for pixel-level analysis. The convolutional neural network (CNN) model required a large dataset with detailed pixellevel information for the semantic segmentation of red blood cells in the deep learning domain. In current research work, we address these problems by proposing a multi-level deep convolutional encoder-decoder network along with two state-of-theart healthy and Anaemic-RBC datasets. The proposed multi-level CNN model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixellevel counting of healthy and anaemic-RBC elements along with morphological analysis. For experimental purposes, we proposed two state-of-the-art RBC datasets, i.e., Healthy-RBCs and Anaemic-RBCs dataset. Each dataset contains 1000 images, ground truth masks, relevant, complete blood count (CBC), and morphology reports for performance evaluation. The proposed model results were evaluated using crossmatch analysis with ground truth mask by finding IoU, individual training, validation, testing accuracies, and global accuracies using a 05-fold training procedure. This model got training, validation, and testing accuracies as 0.9856, 0.9760, and 0.9720 on the Healthy-RBC dataset and 0.9736, 0.9696, and 0.9591 on an Anaemic-RBC dataset. The IoU and BFScore of the proposed model were 0.9311, 0.9138, and 0.9032, 0.8978 on healthy and anaemic datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Anothersemanticsegmentationnetworkhasbeendesigned by [54] to recognize the complex Chinese character. They combined three modules, i.e., FCN-ResNet50, Wubi-like label coding, and CRF, to develop LCSegNet.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Shahzad¹,

Umar

Shirazi

et al. 2021

IEEE Access

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“…The depth of the models has been proven by He et al ([11]) to be an important factor affecting precision. Common models ( [12], [13]) are at least a few dozen layers deep. Recognizing that knowledge based solely on logits may lack persuasiveness, efforts are made to provide as much information as possible.…”

Section: Introductionmentioning

confidence: 99%

Category Contrastive Distillation with Self-Supervised Classification

Chen,

Xu,

Zheng

et al. 2024

Preprint

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Knowledge distillation aims to transfer the knowledge from one model to another, which is widely used in model compression and transfer learning. Recent knowledge distillation algorithms mainly focused on the inter-sample or intra-sample knowledge, which haven't efficiently exploited supervised labels. In this work, we introduce the self-supervised tasks into conventional knowledge distillation framework to construct a new category-based knowledge distillation algorithm. To be specific, two new memory banks are constructed to store the category embedding predicted by the teacher and student models, respectively. Then, the student model can absorb the knowledge from the teacher's memory bank instead of the model itself. It utilizes the cross-batch knowledge based on the contrast of category embedding among various batches.Moreover, additional self-supervised tasks with various data augmentation strategies are are designed to combine with the knowledge distillation process, which guides the model training in a multi-task manner. In the experiments, our proposed algorithm outperforms most of the sample-based knowledge distillation on both CIFAR-100 and ImageNet datasets.

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“…Semantic segmentation is a basic task of computer vision and aims at classifying each pixel according to its semantic [1]. It has been used in a variety of fields including autonomous driving [2,3], medical image analysis [4,5] and character recognition [6]. For the application in videos like autonomous driving, an effective and real-time semantic segmentation algorithm is necessary.…”

Section: Introductionmentioning

confidence: 99%

Refinement Co‐supervision network for real‐time semantic segmentation

Dong

Zhao

Zheng

et al. 2023

IET Computer Vision

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Semantic segmentation is a fundamental technology for autonomous driving. It has a high demand for inference speed and accuracy. However, a good trade‐off between accuracy and latency is yet not present in existing semantic segmentation approaches. Due to the limitation of speed, the authors cannot increase the number of network layers without limit and cannot design modules like in the networks without real‐time. It is a challenging problem how to design a model with good performance under limited resources. To alleviate these issues, in this study, the authors propose a refinement co‐supervision network (RCNet), which is real‐time on a high‐resolution image (1024×2048). The authors first construct the context refinement module, which can provide low computation cost way for obtaining the large receptive field and context information. Furthermore, a boundary co‐supervision mechanism is proposed. It strengthens the optimisation of easily neglected boundaries and small targets. Experimental results reveal that the proposed RCNet outperforms seven representative semantic segmentation methods.

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LCSegNet: An Efficient Semantic Segmentation Network for Large-Scale Complex Chinese Character Recognition

Cited by 21 publications

References 71 publications

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network

Category Contrastive Distillation with Self-Supervised Classification

Refinement Co‐supervision network for real‐time semantic segmentation

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