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Triple-Branch Asymmetric Network for Real-time Semantic Segmentation of Road Scenes

Instrumentation 2024年第2期11卷 72-82页

作者： Yazhi Zhang Xuguang Zhang Hui Yu The Communication Engineering Department Hangzhou Dianzi UniversityZhejiang 310020China The School of Creative Technologies University of PorsmouthUK

As the field of autonomous driving evolves, real-time semantic segmentation has become a crucial part of computer vision tasks. However, most existing methods use lightweight convolution to reduce the computational effort, resulting in lower accuracy. To address this problem, we construct TBANet, a network with an encoder-decoder structure for efficient feature extraction. In the encoder part, the TBA module is designed to extract details and the ETBA module is used to learn semantic representations in a high-dimensional space. In the decoder part, we design a combination of multiple upsampling methods to aggregate features with less computational overhead. We validate the efficiency of TBANet on the Cityscapes dataset. It achieves 75.1% mean Intersection over Union(mIoU) with only 2.07 million parameters and can reach 90.3 Frames Per Second(FPS).

关键词： encoder-decoder architecture lightweight convolution real-time semantic segmentation

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ATU-NET: AN ADAPTIVE TRANSFORMATION-BASED U-NET FOR MEDICAL IMAGE SEGMENTATION 31

ATU-NET: AN ADAPTIVE TRANSFORMATION-BASED U-NET FOR MEDICAL ...

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2024 International Conference on Image Processing

作者： Du, Qianyu Zhong, Baojiang Ma, Kai-Kuang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Coll Integrated Circuits Nanjing Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Both the U-Net and its variants, which produce the state-of-the-art performance in the field of medical image segmentation, are founded on an encoder-decoder architecture. However, this architecture generally processes the input image in the spatial domain only, overlooking potential insights that could be gained from other transform domains. For that, an adaptive transformation-based U-Net (ATU-Net) is proposed in this paper. Our ATU-Net is based on a novel network architecture called the adaptive transformation-encoder-decoder (ATU), which adaptively transforms the input image into a more suitable domain by training the transformation kernel for processing to make it easier for the encoder and decoder to extract key features of the image. Extensive experimental results obtained on benchmark datasets have shown that our proposed ATU-Net can deliver superior performance to the existing methods.

关键词： Deep learning medical image segmentation transform domain encoder-decoder architecture

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Enhancing E-commerce Spelling Correction with Fine-Tuned Transformer Models 24

Enhancing E-commerce Spelling Correction with Fine-Tuned Tra...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Dutta, Arnab Polushin, Gleb Zhang, Xiaoshuang Stein, Daniel eBay GmbH Dreilinden Germany eBay Inc Shanghai Peoples R China eBay GmbH Aachen Germany

ISBN: (纸本)9798400704901

In the realm of e-commerce, the process of search stands as the primary point of interaction for users, wielding a profound influence on the platform's revenue generation. Notably, spelling correction assumes a pivotal role in shaping the user's search experience by rectifying erroneous query inputs, thus facilitating more accurate retrieval outcomes. Within the scope of this research paper, our aim is to enhance the existing state-of-the-art discriminative model performance with generative modelling strategies while concurrently addressing the engineering concerns associated with real-time online latency, inherent to models of this category. We endeavor to refine LSTM-based classification models for spelling correction through a generative fine-tuning approach hinged upon pre-trained language models. Our comprehensive offline assessments have yielded compelling results, showcasing that transformer-based architectures, such as BART (developed by Facebook) and T5 (a product of Google), have achieved a 4% enhancement in F1 score compared to baseline models for the English language sites. Furthermore, to mitigate the challenges posed by latency, we have incorporated model pruning techniques like no-teacher distillation. We have undertaken the deployment of our model (English only) as an A/B test candidate for real-time e-commerce traffic, encompassing customers from the US and the UK. The model attest to a 100% successful request service rate within real-time scenarios, with median, 90th percentile, and 99th percentile (p90/p99) latencies comfortably falling below production service level agreements. Notably, these achievements are further reinforced by positive customer engagement, transactional and search page metrics, including a significant reduction in instances of search results page with low or almost zero recall. Moreover, we have also extended our efforts into fine-tuning a multilingual model, which, notably, exhibits substantial accuracy enhancements, amounti

关键词： Transformers encoder-decoder architecture Fine-Tuning Spelling Correction Knowledge Distillation BART T5 Multilingual Online Inference

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Diverter transformer-based multi-encoder-multi-decoder network model for medical retinal blood vessel image segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 93卷

作者： Wu, Chengwei Guo, Min Ma, Miao Wang, Kaiguang Shaanxi Normal Univ Sch Comp Sci Minist Educ Key Lab Modern Teaching Technol Xian 710119 Peoples R China

The retinal blood vessel is an essential part of the fundus structure. It is important to accurately analyze the structure and distribution of retinal vessels, which can help make accurate medical diagnoses. However, it is still challenging to extract detailed information due to the problems of fuzzy edges, low resolution, and lots of noise in retinal blood vessel medical images. To extract the image detail information effectively, we propose a new diverter transformer -based multi -encoder -multi -decoder network model in this paper. The network model consists of a feature encoder module and a feature decoder module. Among them, the feature encoding module consists of a diverter transformer with a diverter adaptive mechanism, three encoder units with a convolution layer and max -pooling layer, and the two decoder units in the feature decoding module consist of an inverse convolution layer and an up -sampling layer, respectively. The Local Context Module (LCNet Module) in the feature encoding module learns richer local context feature information layer by layer through changing the width of the network while downsampling;the Global encoder Module1 (G -encoder Module1) and the Global encoder Module2 (G -encoder Module2) extract the global feature representation of retinal blood vessel images by performing a max -pooling operation to transform the input data into a vector of fixed dimensions, thus helping the network model to better understand and extract the global feature representation of retinal blood vessel images. The two decoder units in the feature decoding module receive local and global feature information from three encoder units, LCNet Module, G -encoder Module1 and G -encoder Module2, respectively. decoder Module1 generates segmentation prediction by layer -by -layer up -sampling operation, and decoder Module2 recovers the feature information by downsampling and decoding operations and fuses the recovered feature information to output, obtaining the final

关键词： Medical image processing encoder-decoder architecture Local context Retinal vessel segmentation

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Joint encoder-decoder Self-Supervised Pre-training for ASR 23

Joint Encoder-Decoder Self-Supervised Pre-training for ASR

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Interspeech Conference

作者： Arunkumar, A. Umesh, S. Indian Inst Technol Madras Speech Lab Madras Tamil Nadu India

Self-supervised learning (SSL) has shown tremendous success in various speech-related downstream tasks, including Automatic Speech Recognition (ASR). The output embeddings of the SSL model are treated as powerful short-time representations of the speech signal. However, in the ASR task, the main objective is to get the correct sequence of acoustic units, characters, or byte-pair encodings (BPEs). Usually, encoder-decoder architecture works exceptionally well for a sequenceto-sequence task like ASR. Therefore, in this paper, we propose a new paradigm that exploits the power of a decoder during self-supervised learning. We use Hidden Unit BERT (HuBERT) SSL framework to compute the conventional masked prediction loss for the encoder. In addition, we have introduced a decoder in the SSL framework and proposed a target preparation strategy for the decoder. Finally, we use a multitask SSL setup wherein we jointly optimize both the encoder and decoder losses. We hypothesize that the presence of a decoder in the SSL model helps it learn an acoustic unit-based language model, which might improve the performance of an ASR downstream task. We compare our proposed SSL model with HuBERT and show up to 25% relative improvement in performance on ASR by finetuning on various LibriSpeech subsets.

关键词： Self-Supervised Learning HuBERT Automatic Speech Recognition encoder-decoder architecture

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An efficient encoder-decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data

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NEURAL NETWORKS 2021年 142卷 479-491页

作者： Khan, Faisal Hussain, Shahid Basak, Shubhajit Lemley, Joseph Corcoran, Peter Natl Univ Ireland Galway Dept Elect Engn Coll Sci & Engn Galway H91 TK33 Ireland Natl Univ Ireland Galway Data Sci Inst Galway H91 TK33 Ireland Natl Univ Ireland Galway Sch Comp Sci Galway H91 TK33 Ireland Xperi Corp Block 5Parkmore East Business Pk Galway H91V0TX Ireland

Depth estimation from a single image frame is a fundamental challenge in computer vision, with many applications such as augmented reality, action recognition, image understanding, and autonomous driving. Large and diverse training sets are required for accurate depth estimation from a single image frame. Due to challenges in obtaining dense ground-truth depth, a new 3D pipeline of 100 synthetic virtual human models is presented to generate multiple 2D facial images and corresponding ground truth depth data, allowing complete control over image variations. To validate the synthetic facial depth data, we propose an evaluation of state-of-the-art depth estimation algorithms based on single image frames on the generated synthetic dataset. Furthermore, an improved encoder-decoder based neural network is presented. This network is computationally efficient and shows better performance than current state-of-the-art when tested and evaluated across 4 public datasets. Our training methodology relies on the use of synthetic data samples which provides a more reliable ground truth for depth estimation. Additionally, using a combination of appropriate loss functions leads to improved performance than the current state-of-the-art network performances. Our approach clearly outperforms competing methods across different test datasets, setting a new state-of-the-art for facial depth estimation from synthetic data. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Depth estimation Facial depth 2.5D dataset Hybrid loss function Convolution neural network encoder-decoder architecture

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PMED-Net: Pyramid Based Multi-Scale encoder-decoder Network for Medical Image Segmentation

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IEEE ACCESS 2021年 9卷 55988-55998页

作者： Khan, Abbas Kim, Hyongsuk Chua, Leon Jeonbuk Natl Univ Div Elect & Informat Engn Jeonju 54896 South Korea Jeonbuk Natl Univ Core Res Inst Intelligent Robots Jeonju 54896 South Korea Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for medical image segmentation. Different variants of encoder-decoder networks are in practice for segmenting the medical images and U-Net is the most widely used one. However, the existing architectures for segmenting medical images have millions of parameters that require enormous computations which results in memory and cost-inefficiency. To overcome such limitations, we come up with the idea of training small networks in a cascaded form for coarse-to-fine prediction. The proposed adaptive network is extended up to six pyramid levels, and at each level, features are extracted at different scales of the input image. Each lightweight encoder-decoder network is trained independently to minimize loss, where succeeding level networks further refine the prior predictions. Evaluation and comparison of our architecture were performed on four different publicly available medical image segmentation datasets: International Skin Imaging Collaboration (ISIC) challenge 2018 dataset, brain tumor dataset, nuclei dataset, and X-ray dataset. The experimental results of the PMED-Net are either better or on par with other state-of-the-art networks in terms of IoU, F1-Score, and sensitivity metrics. Moreover, PMED-Net is efficient in terms of parameterized complexity as it has 1/21.3, 1/21.1, 1/14.0, 1/11.6, 1/11.2, 1/6.64, and 1/4.95 times fewer parameters than SegNet, U-Net, BCDU-Net, CU-Net, FCN-8s, ORED-Net, and MultiResUNet respectively. The pre-trained models, datasets information, and implementation details are available at https://***/kabbas570/Pyramid-Based-encoder-decoder.

关键词： Image segmentation Decoding Feature extraction Medical diagnostic imaging Training Diseases Deep learning Convolutional neural networks encoder-decoder architecture medical image processing semantic segmentation

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CLR2G: Cross-modal Contrastive Learning on Radiology Report Generation 24

CLR2G: Cross-modal Contrastive Learning on Radiology Report ...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Xue, Hongchen Ma, Qingzhi Liu, Guanfeng Qu, Jianfeng Liu, Yuanjun Liu, An Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Macquarie Univ Sch Comp Sydney NSW Australia

ISBN: (纸本)9798400704369

The automatic generation of radiological imaging reports aims to produce accurate and coherent clinical descriptions based on X-ray images. This facilitates clinicians in completing the arduous task of report writing and advances clinical automation. The primary challenge in radiological imaging report generation lies in accurately capturing and describing abnormal regions in the images under data bias conditions, resulting in the generation of lengthy texts containing image details. Existing methods mostly rely on prior knowledge such as medical knowledge graphs, corpora, and image databases to assist models in generating more precise textual descriptions. However, these methods still struggle to identify rare anomalies in the images. To address this issue, we propose a two-stage training model, named CLR2G, based on cross-modal contrastive learning. This model delegates the task of capturing anomalies, particularly those challenging for the generative model trained with cross-entropy loss under data bias conditions, to a specialized abnormality capture component. Specifically, we employ a semantic matching loss function to train additional abnormal image and text encoders through cross-modal contrastive learning, facilitating the capture of 13 common anomalies. We utilize the anomalous image features, text features and their confidence probabilities as a posteriori knowledge to help the model generate accurate image reports. Experimental results demonstrate the state-of-the-art performance of our method on two widely used public datasets, IU-Xray and MIMIC-CXR.

关键词： Medical Data Mining Radiology Image Report Generation encoder-decoder architecture Contrastive Learning

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Crack analysis of tall concrete wind towers using an ad-hoc deep multiscale encoder-decoder with depth separable convolutions under severely imbalanced data

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Deng, Jianghua Hua, Linxin Lu, Ye Song, Yang Singh, Amardeep Che, Jiao Li, Yang Changzhou Inst Technol Sch Civil Engn & Architecture 666 Liaohe Rd Changzhou 213002 Peoples R China Monash Univ Dept Civil Engn Melbourne Australia Changzhou Inst Technol Sch Photoelect Engn Changzhou Peoples R China

An accurate and timely cracking assessment, including the presence, location and crack geometric feature measurement, is crucial for evaluating concrete wind towers. Therefore, the early identification of cracks is a critical procedure in promptly evaluating structural integrity. This study proposed an ad-hoc encoder-decoder network based on DeepLabv3+ with depth separable convolutions to automatically segment cracks from real-world images captured from various concrete wind towers. The combined advantages of the improved DeepLabv3+ and the lightweight MobileNet v2 are suitable as a benchmark due to their high performance and universality. Four experiments were conducted to determine the model design choice and crack feature measurement capability: (1) six parametric tests using various pre-trained base networks and algorithm optimisers, (2) the influence of complex background noise (i.e., handwriting script) on crack segmentation performance, (3) comparative studies with cutting-edge pixel-wise segmentation models and (4) crack feature measurement (i.e., length and width). The research outcome demonstrated that DeepLabv3+ with MobileNet v2 can potentially be applied for efficient and accurate crack segmentation in concrete wind towers with complex backgrounds.

关键词： Structural health monitoring concrete cracks depth separable convolutions encoder-decoder architecture automated inspection for wind towers

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Open-ended remote sensing visual question answering with transformers

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第18期43卷 6809-6823页

作者： Al Rahhal, Mohamad M. Bazi, Yakoub Alsaleh, Sara O. Al-Razgan, Muna Mekhalfi, Mohamed Lamine Al Zuair, Mansour Alajlan, Naif King Saud Univ Coll Appl Comp Sci Appl Comp Sci Dept Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Comp Engn Dept Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh Saudi Arabia Fdn Bruno Kessler Digital Ind Ctr Technol Vis Unit Trento Italy King Saud Univ Coll Appl Comp Sci Appl Comp Sci Dept PO Box 51178 Riyadh 11543 Saudi Arabia

Visual question answering (VQA) has been attracting attention in remote sensing very recently. However, the proposed solutions remain rather limited in the sense that the existing VQA datasets address closed-ended question-answer queries, which may not necessarily reflect real open-ended scenarios. In this paper, we propose a new dataset named VQA-TextRS that was built manually with human annotations and considers various forms of open-ended question-answer pairs. Moreover, we propose an encoder-decoder architecture via transformers on account of their self-attention property that allows relational learning of different positions of the same sequence without the need of typical recurrence operations. Thus, we employed vision and natural language processing (NLP) transformers respectively to draw visual and textual cues from the image and respective question. Afterwards, we applied a transformer decoder, which enables the cross-attention mechanism to fuse the earlier two modalities. The fusion vectors correlate with the process of answer generation to produce the final form of the output. We demonstrate that plausible results can be obtained in open-ended VQA. For instance, the proposed architecture scores an accuracy of 84.01% on questions related to the presence of objects in the query images.

关键词： Visual question answering remote sensing open-set dataset vision transformers encoder-decoder architecture

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