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17th European Conference on computer vision, ECCV 2022

作者： Ignatov, Andrey Malivenko, Grigory Timofte, Radu Treszczotko, Lukasz Chang, Xin Ksiazek, Piotr Lopuszynski, Michal Pioro, Maciej Rudnicki, Rafal Smyl, Maciej Ma, Yujie Li, Zhenyu Chen, Zehui Xu, Jialei Liu, Xianming Jiang, Junjun Shi, XueChao Xu, Difan Li, Yanan Wang, Xiaotao Lei, Lei Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Li, Jiaqi Wang, Yiran Huang, Zihao Cao, Zhiguo Conde, Marcos V. Sapozhnikov, Denis Lee, Byeong Hyun Park, Dongwon Hong, Seongmin Lee, Joonhee Lee, Seunggyu Chun, Se Young Computer Vision Lab ETH Zürich Zürich Switzerland AI Witchlabs Zollikerberg Switzerland University of Wuerzburg Wuerzburg Germany TCL Research Europe Warsaw Poland Harbin Institute of Technology Harbin China Xiaomi Inc. Beijing China Tencent GY-Lab Shenzhen China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031250651

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 m. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image segmentation

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CUSUM Based Concept Drift Detector for Data Stream Clustering 20

CUSUM Based Concept Drift Detector for Data Stream Clusterin...

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4th International Conference on Big Data and Internet of Things, BDIOT 2020

作者： Namitha, K. Santhosh Kumar, G. Artificial Intelligence and Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kochi India

ISBN: (纸本)9781450375504

The last few decades mark an unprecedented growth in the number of applications producing high-speed data streams. Learning from such fast data streams has many inherent challenges. The dynamic change in the concept of the stream is a significant challenge to be handled by the learning systems. This problem termed concept drift is given due focus in data stream classification scenarios. But, data stream clustering algorithms usually treat concept drift implicitly as part of the learning process. The need for explicit drift detection and adaptation is often neglected. This paper discusses a statistical method of change detection for data stream clustering problems. The change detection is done based on CUSUM test. On identifying a change, the model is re-built using the recent samples from the stream. The change detection process has been validated using real and synthetic datasets. © 2020 ACM.

关键词： Learning systems

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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2D Semantic Segmentation of the Prostate Gland in Magnetic Resonance Images using Convolutional Neural Networks

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IFAC-PapersOnLine 2021年第15期54卷 394-399页

作者： Silvia P. Vacacela Marco E. Benalcázar Artificial Intelligence and Computer Vision Research Lab Department of Computer Science and Informatics Escuela Politécnica Nacional Quito Ecuador

Convolutional Neural Networks is one of the most commonly used methods for automatic prostate segmentation. However, few studies focus on the segmentation of the two main zones of the prostate: the central gland and the peripheral zone. This work proposes and evaluates two models for 2D semantic segmentation of these two zones of the prostate. The first model (Model-A) uses an encoder-decoder architecture based on the global U-net and the local U-net architectures. The global U-net segments the whole prostate, whereas the local U-net segments the central gland. The peripheral zone is obtained by subtracting the central gland from the whole prostate. On the other hand, the second model (Model-B) uses an encoder-classifier architecture based on the VGG16 network. Model-B performs segmentation by classifying each pixel of a Magnetic Resonance Image (MRI) into three categories: background, central gland, and peripheral zone. Both models are tested using MRIs from the dataset NCI-ISBI 2013 Challenge. The experimental results show a superior segmentation performance for Model-A, encoder-decoder architecture, (DSC = 96.79% ± 0.15% and IoU = 93.79% ± 0.29%) compared to Model-B, encoder-classifier architecture, (DSC = 92.50%± 1.19% and IoU = 86.13% ±2.02%).

关键词： Convolutional Neural Networks Prostate Segmentation Central Gland Peripheral Zone MRIs Encoder-Decoder U-net Encoder-Classifier VGG16 NCI-ISBI 2013

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EFFICIENT ONLINE labEL CONSISTENT HASHING FOR LARGE-SCALE CROSS-MODAL RETRIEVAL

EFFICIENT ONLINE LABEL CONSISTENT HASHING FOR LARGE-SCALE CR...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Yi, Jinhan Liu, Xin Cheung, Yiu-Ming Xu, Xing Fan, Wentao He, Yi Department of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Lab. of Computer Vision and Pattern Recognition Fujian Key Lab. of Big Data Intelligence and Security China Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong School of Computer Science and Engineering University of Electronic Science and Technology of China China Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9781665438643

Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic association, while lacking flexibility to learn the hash functions for varying streaming data. (3) Existing supervised methods always require much computation time or accumulate large quantization loss to learn hash codes. To address above challenges, we present an efficient Online label Consistent Hashing (OLCH) for cross-modal retrieval, which aims to incrementally learn hash codes for the current arriving data, while updating the hash functions at a streaming manner. To be specific, an online semantic representation learning framework is designed to adaptively preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward-backward splitting is developed to optimize the hash functions. Accordingly, the hash codes are adaptively learned online with the high discriminative capability, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts. © 2021 IEEE computer Society. All rights reserved.

关键词： Hash functions

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vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Akbar Sheikh Akbari Arvind Kumar B Ramachandra Reddy Koushlendra Kumar Singh Masahiro Takei Leeds Beckett University UK Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur Jharkhand India Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Mechanical Engineering Chiba University Chiba Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets.

关键词： computer vision Analytical models Accuracy Lungs Lung cancer Imaging Transformers Convolutional neural networks Reliability Testing

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Correction to: DNACoder: a CNN-LSTM attention-based network for genomic sequence data compression (Neural Computing and Applications, (2024), 36, 29, (18363-18376), 10.1007/s00521-024-10130-4)

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Neural Computing and Applications 2025年

作者： Sheena, K.S. Nair, Madhu S. Artificial Intelligence & Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kerala Kochi682022 India

In this article, the Eq. (2) and Eq. (3) were incorrectly displayed and it should have been displayed as given below (Formula presented.) (Formula presented.) where n is the total number of key-value pairs in the sequence The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Self-Ensembling Depth Completion Via Density-Aware Consistency

SSRN

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SSRN 2023年

作者： Zhang, Xuanmeng Zheng, Zhedong Jiang, Minyue Ye, Xiaoqing The ReLER Laboratory Australian Artificial Intelligence Institute University of Technology SydneyNSW2007 Australia The Sea-NExT Joint Lab The Department of Computer Science School of Computing National University of Singapore Singapore117417 Singapore The Department of Computer Vision Technology Baidu Inc. Beijing100085 China

Depth completion can predict a dense depth map by taking a sparse depth map and the aligned RGB image as input, but the acquisition of ground truth annotations is labor-intensive and non-scalable. Therefore, we resort to semi-supervised learning, where we only need to annotate a few images and leverage massive unlabeled data without ground truth labels to facilitate model learning. In this paper, we propose SEED, a SElf-Ensembling Depth completion framework to enhance the generalization of the model on unlabeled data. Specifically, SEED contains a pair of the teacher and student models, which are given high-density and low-density sparse depth maps as input respectively. The main idea underpinning SEED is to enforce the density-aware consistency by encouraging consistent prediction across different-density input depth maps. One empirical challenge is that the pseudodepth labels produced by the teacher model inevitably contain wrong depth values, which would mislead the convergence of the student model. To resist the noisy labels, we propose an automatic method to measure the reliability of the generated pseudo-depth labels adaptively. By leveraging the discrepancy of prediction distributions, we model the pixel-wise uncertainty map as the prediction variance and rectify the training process from noisy labels explicitly. To our knowledge, we are among the early semi-supervised attempts on the depth completion task. Extensive experiments on both outdoor and indoor datasets demonstrate that SEED consistently improves the performance of the baseline model by a large margin and even is on par with several fully-supervised methods. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

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arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

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On the analysis of HEVC Intra Prediction Mode Decision Variants 3

On the analysis of HEVC Intra Prediction Mode Decision Varia...

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3rd International Conference on Computing and Network Communications, CoCoNet 2019

作者： Nair, Preethi S. Nair, Madhu S. Department of Computer Science University of Kerala Kariavattom Thiruvananthapuram Kerala695581 India Artificial Intelligence and Computer Vision Lab Department of Computer Science Cochin University of Science and Technology Kochi Kerala682022 India

This paper presents a comparative analysis of some major High Efficiency Video Coding (HEVC) intra prediction mode decision methods reported in the literature. Intra coding in HEVC is based on spatial sample prediction and can be described as a collection of many elements including quad tree partitioning, 35 intra prediction modes, reference sample smoothing, boundary sample filtering process, intra mode coding and so on. Each of these elements results in an improved coding efficiency in HEVC intra coding. In this review, we provide an extensive and up-to-date discussion of the research proposals regarding a variety of HEVC intra prediction mode decision strategies. Moreover, some most significant research contributions on fast intra mode decision methods in HEVC to improvise the encoder efficiency are reviewed in detail along with a comparative performance analysis of the reviewed proposals in terms of some standard evaluation metrics such as bit rate, PSNR and time savings. In addition, this paper point out some research directions towards further developments in the intra prediction for the betterment of the encoder efficacy. © 2020 The Authors. Published by Elsevier B.V.

关键词： Efficiency

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