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arXiv 2024年

作者： Devkatte, Trinetra Dubey, Shiv Ram Singh, Satish Kumar Hadid, Abdenour Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad India Sorbonne Centre for Artificial Intelligence Sorbonne University Abu Dhabi United Arab Emirates

Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt to new, unseen data remains a challenge. This paper addresses this problem by proposing an incremental super-resolution using GANs with knowledge distillation (ISR-KD) for face to cartoon. Previous research in this area has not investigated incremental learning, which is critical for real-world applications where new data is continually being generated. The proposed ISR-KD aims to develop a novel unified framework for facial super-resolution that can handle different settings, including different types of faces such as cartoon face and various levels of detail. To achieve this, a GAN-based super-resolution network was pre-trained on the CelebA dataset and then incrementally trained on the iCartoonFace dataset, using knowledge distillation to retain performance on the CelebA test set while improving the performance on iCartoonFace test set. Our experiments demonstrate the effectiveness of knowledge distillation in incrementally adding capability to the model for cartoon face super-resolution while retaining the learned knowledge for facial hallucination tasks in GANs. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Unsupervised Deep Graph Matching Based on Cycle Consistency

arXiv

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arXiv 2023年

作者： Tourani, Siddharth Khan, Muhammad Haris Rother, Carsten Savchynskyy, Bogdan Computer Vision and Learning Lab IWR Heidelberg University Germany Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images. Contrary to the standard supervised approach, our method does not require ground truth correspondences between keypoint pairs. Instead, it is self-supervised by enforcing consistency of matchings between images of the same object category. As the matching and the consistency loss are discrete, their derivatives cannot be straightforwardly used for learning. We address this issue in a principled way by building our method upon the recent results on black-box differentiation of combinatorial solvers. This makes our method exceptionally flexible, as it is compatible with arbitrary network architectures and combinatorial solvers. Our experimental evaluation suggests that our technique sets a new state-of-the-art for unsupervised deep graph matching. © 2023, CC BY.

关键词： Network architecture

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Multi-view Transformer-Based Network for Prerequisite Learning in Concept Graphs 23rd

Multi-view Transformer-Based Network for Prerequisite Learn...

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23rd International Semantic Web Conference, ISWC 2024

作者： Wang, Zhichun Shao, Yifeng Peng, Boci Li, Bangui Li, Yun Wang, Qianren Li, Nijun School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing China School of Intelligence Science and Technology Peking University Beijing China School of Computer Science and Engineering Beihang University Beijing China Advanced Cognitive AI Lab Shanghai Huawei Technologies Shanghai China

ISBN: (纸本)9783031778438

Prerequisite learning is the task of identifying prerequisite relations among concepts, which is important for many AI-based educational applications. Previous approaches explore different kinds of learning resources to obtain useful features for predicting prerequisites. Early approaches use handcrafted features while recent approaches use neural networks to encode information of concepts. To further improve the results of prerequisite learning, we build concept graphs to include comprehensive information about concepts from open data and learning resources, and propose a Multi-view Transformer-based Network (MTN) to encode multi-view features of concepts in the graph. Multi-view features are fused to make accurate predictions of concept prerequisites. We evaluate our approach on four public datasets and compare it with recently published approaches. The results show that our approach can achieve state-of-the-art results in the task of prerequisite learning. The source code and data are available at https://***/kg-bnu/MTN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Unsupervised Pre-training with Language-vision Prompts for Low-Data Instance Segmentation

arXiv

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arXiv 2024年

作者： Zhang, Dingwen Li, Hao He, Diqi Liu, Nian Cheng, Lechao Wang, Jingdong Han, Junwei The Brain and Artificial Intelligence Lab. Northwestern Polytechnical University Shaanxi Xi’an710000 China The Inception Institute of Artificial Intelligence United Kingdom The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Computer Vision Baidu Inc. China

In recent times, following the paradigm of DETR (DEtection TRansformer), query-based end-to-end instance segmentation (QEIS) methods have exhibited superior performance compared to CNN-based models, particularly when trained on large-scale datasets. Nevertheless, the effectiveness of these QEIS methods diminishes significantly when confronted with limited training data. This limitation arises from their reliance on substantial data volumes to effectively train the pivotal queries/kernels that are essential for acquiring localization and shape priors. To address this problem, we propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-vision Prompts (UPLVP), which improves QEIS models’ instance segmentation by bringing language-vision prompts to queries/kernels. Our method consists of three parts: (1) Masks Proposal: Utilizes language-vision models to generate pseudo masks based on unlabeled images. (2) Prompt-Kernel Matching: Converts pseudo masks into prompts and injects the best-matched localization and shape features to their corresponding kernels. (3) Kernel Supervision: Formulates supervision for pre-training at the kernel level to ensure robust learning. With the help of our pre-training method, QEIS models can converge faster and perform better than CNN-based models in low-data regimes. Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models’ performance can be significantly improved when pre-trained with our method. Code will be available at: https://***/lifuguan/UPLVP. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Causal Discovery via Conditional Independence Testing with Proxy Variables 41

Causal Discovery via Conditional Independence Testing with P...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Mingzhou Sun, Xinwei Qiao, Yu Wang, Yizhou School of Computer Science Peking University China Peking University China Sch. of Data Science Fudan University China Dep. of Automation Shanghai Jiao Tong University China Inst. for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Peking University China Nat'l Key Lab of General Artificial Intelligence Peking University China

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced proxy variables to adjust for the bias caused by unobserveness. However, these methods were either limited to categorical variables or relied on strong parametric assumptions for identification. In this paper, we propose a novel hypothesis-testing procedure that can effectively examine the existence of the causal relationship over continuous variables, without any parametric constraint. Our procedure is based on discretization, which under completeness conditions, is able to asymptotically establish a linear equation whose coefficient vector is identifiable under the causal null hypothesis. Based on this, we introduce our test statistic and demonstrate its asymptotic level and power. We validate the effectiveness of our procedure using both synthetic and real-world data. Code is publicly available at https://***/lmz123321/proxy_causal_discovery. Copyright 2024 by the author(s)

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Analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray 6th

Analyzing Pulmonary Abnormality with Superpixel Based Graph...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Pradhan, Ronaj Santosh, Kc Applied Artificial Intelligence Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States

ISBN: (纸本)9783031530845

In recent years, the utilization of graph-based deep learning has gained prominence, yet its potential in the realm of medical diagnosis remains relatively unexplored. The challenge arises from the inherent irregular and unordered nature of physiological data, making it challenging to depict intricate patterns using conventional methods. Our research focuses on abnormality screening: classification of Chest X-Ray (CXR) as Tuberculosis positive or negative, using Graph Neural Networks (GNN) that uses Region Adjacency Graphs (RAGs) and each superpixel serves as a dedicated graph node. By integrating residual and concatenation structures, our approach effectively captures crucial features and relationships among superpixels, facilitating advancements in tuberculosis identification. Through the amalgamation of state-of-the-art neural network architectures and innovative graph-based representations, our work introduces a new perspective to medical image analysis. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph neural networks

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Effects on Hand Gesture Recognition Accuracy after Severe Cranial Trauma

Effects on Hand Gesture Recognition Accuracy after Severe Cr...

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IEEE Ecuador Technical Chapters Meeting (ETCM)

作者： Jonathan A. Zea Luis Gabriel Macias Santillán Lorena Isabel Barona López Ángel Leonardo Valdivieso Caraguay Marco E. Benalcázar Departamento de Informática y Ciencias de la Computación Escuela Politécnica Nacional Artificial Intelligence and Computer Vision Research Lab Quito Ecuador

This study examines the impact of severe cranial trauma on the accuracy of Hand Gesture Recognition (HGR) models based on electromyography (EMG) signals. The study focuses on a single subject, who suffered left-sided hemiplegia, and exhibits limited mobility and spasticity in the left hand. EMG samples corresponding to six hand gestures from his both forearms were collected using the Myo Armband. The 2023HGR5-CNN-LSTM HGR model was used to evaluate the recognition accuracy of these samples. Results showed significantly lower HGR performance in the left side (10.67% recognition accuracy) compared to the right side (56% recognition accuracy). As the selected model was trained with EMG data only from right arms, a channel mapping to adapt the EMG signals from the left side was also developed. Unfortunately, the channel mapping implemented did not improve the recognition accuracy (9.33%). These findings highlight the challenges in achieving high performance HGR for individuals with cranial injuries. It might also point out that developing myoelectric prostheses using datasets from unaffected individuals may be inadequate for training robust models.

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A Multimodal Spatio-temporal Model for Micro-Video Emotion Classification 24

A Multimodal Spatio-temporal Model for Micro-Video Emotion C...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Xue, Junxiao Wang, Jie Zheng, Yuanxun Wang, Ke Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Zhengzhou University Yellow River Laboratory Zhengzhou450001 China Zhengzhou University School of Water Conservancy Engineering Zhengzhou450001 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350341010

Spatio-temporal features are information unique to video compared to images, and are an important source of features for video content analysis. The introduction of spatio-temporal features has brought a significant change to affective computing, which is no longer limited to single-image and ignores temporal affective changes. However, existing affective computing models still do not introduce spatio-temporal features. For example, facial emotion classification still uses single-image as input to the model and ignores spatio-temporal features. Therefore, we propose a novel neural network that takes video as the input of the model and extracts feature information containing spatio-temporal features in it. The extracted feature information consists of two modalities, which are audio spatio-temporal feature information and image spatio-temporal feature information. Decision-level fusion of the two modal information is performed to achieve emotion classification. Where a feature extractor based on VGG implementation is used as the audio emotion feature extraction module, which extracts the spectrogram feature information while focusing on its temporal feature information. And using the feature extractor implemented based on I3D as the image emotion feature extraction module, which consists of 3D convolutional blocks. Compared with 2D convolutional blocks, it not only focuses on image features, but also performs convolutional extraction for both spatial features and temporal features of the images within the blocks to obtain image features containing spatio-temporal features. By fusing multiple spatio-temporal feature classifiers in the best way, our model achieves an accuracy of 62.41%. The experimental results show that feature information containing spatio-temporal features is more effective in sentiment classifiers than feature information containing only image features. © 2023 IEEE.

关键词： Feature extraction

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CNNRF-Ensemble: A Multifaced Approach For Predicting White Spot Syndrome Virus In Shrimp Farming 4

CNNRF-Ensemble: A Multifaced Approach For Predicting White S...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Tanvir, Kazi Al Huda, Sadi Opee, Shaiful Ajam Kabir, Sayem Ali, Asraf Bhuiyan, Touhid School of Advanced Sciences Vellore Institute of Technology Department of Mathematics Tamil Nadu Vellore India Department of Computer Science Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh School of Information Technology Washington University of Science and Technology AlexandriaVA22314 United States

ISBN: (纸本)9798331530938

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries, filling the rapid growth demand, especially in Bangladesh. However, the White Spot Syndrome Virus(WSSV) creates and constantly threatens the global shrimp population, impacting the economic and ecological systems. Recently, researchers have tried to predict disease, but no studies have achieved significant accuracy using ML and DL algorithms. In this study, we have proposed an optimized CNNRF-Ensemble model for predicting WSSV using multifaceted data, including geographical features, environmental attributes, farm characteristics, operational practices, and socioeconomic indicators. Our proposed model achieved 95.714% accuracy in sequential model and afterwards, combining with RandomForestClassifier in Ensemble learning we obtained 99.99% accuracy. Our study suggested deep learning as an accurate, easy and low-cost method to predict shrimp WSSV, which helps to predict viruses and create awareness of shrimp firming. © 2025 IEEE.

关键词： Shellfish

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