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27th International conference on pattern recognition, ICPR 2024

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

ISBN: (纸本)9783031781827

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. the code is released at https://***/TRINETRA-DEVKATTE/ISR-KD-Network. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Identifying Alzheimer’s Disease Across Cognitive Impairment Spectrum Using Acoustic Features Only 27th

Identifying Alzheimer’s Disease Across Cognitive Impairment...

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27th International conference on pattern recognition, ICPR 2024

作者： Jon, Hyo Jin Jung, Hyuntaek Jin, Longbin Kim, Eun Yi AI&Computer Vision Lab Konkuk University Seoul Korea Republic of

ISBN: (纸本)9783031781940

Early diagnosis of Alzheimer’s Disease (AD) holds key in delaying cognitive decline and managing the progression of dementia. the Mini-Mental State Examination (MMSE) serves as a valuable tool for evaluating cognitive impairment, aiding in the identification of AD severity and early-stage diagnosis. Traditionally, prediction models aiming to predict MMSE scores from speech have utilized the mean squared error (MSE) as the loss function. However, MSE tends to bias predictions towards the average, which limits its ability to discriminate AD severity effectively. To address this limitation, we propose a novel Weighted MSE-CE loss function aimed at improving AD discrimination by accurately predicting MMSE scores. It reaches the goal through the integration of cross entropy (CE) into mean squared error (MSE), leveraging the Bernoulli penalty and distance-based weights. Furthermore, our approach relies solely on language-agnostic acoustic features, eliminating the requirement for transcription. Consequently, it gains the potential to be applied to individuals speaking various languages. Experimental results on the ADReSSo dataset demonstrate that our method reduces the RMSE to 4.55, outperforming other acoustic-based approaches. Additionally, our predictions achieved an impressive 77.46% accuracy in AD detection, highlighting its effectiveness in assessing AD severity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mean square error

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Attention-Based Patch Matching and Motion-Driven Point Association for Accurate Point Tracking 27th

Attention-Based Patch Matching and Motion-Driven Point Asso...

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27th International conference on pattern recognition, ICPR 2024

作者： Zang, Han Xu, Tianyang Zhu, Xue-Feng Song, Xiaoning Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031784439

Point tracking can be regarded as a transfer and extension of keypoints representation and matching. In contrast to matching the salient points like corners or spots, which are easily detected and described by detector-based approaches, point tracking tasks are capable of handling arbitrary points on physical surfaces, including nonrigid or weakly-textured surfaces. Additionally, keypoint matching lacks a direct mechanism to handle occlusion in tracking tasks. therefore, we propose to use a detector-free local feature-matching model based on the transformer structure to perform patch matching, incorporating occlusion prediction and introducing an uncertainty estimate for extended robustness. Besides, using a coarse-to-fine strategy, we generate coarse predictions at the patch level and refine them to obtain accurate coordinates at the sub-pixel level by motion-driven point association. After fine-tuning the model on the training set of the Perception Test benchmark, our model APM-MPAPT outperforms the competitors in the benchmark on the corresponding validation set, with promising performance improvement against the baseline. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Benchmarking

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Energy Minimization Methods in computer vision and pattern recognition - 7th International conference, EMMCVPR 2009, Proceedings

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7th International conference on Energy Minimization Methods in computer vision and pattern recognition, EMMCVPR 2009

ISBN: (纸本)3642036406

the proceedings contain 36 papers. the topics discussed include: multi-level moves for MRFs with truncated convex priors;detection and segmentation of independently moving objects from dense scene flow;efficient global minimization for the multiphase Chan-Vese model of image segmentation;bipartic graph matching computation on GPU;pose invariant face matching using MRF energy minimization framework;parallel hidden hierarchical fields for multi-scale reconstruction;general search algorithms for energy minimization problems;complex diffusion on scalar and vector valued image graphs;a PDE approach to coupled super-resolution with non-parametric motion;hierarchical pairwise segmentation using dominant sets and anisotropic diffusion kernels;and tracking as segmentation of spatial-temporal volumes by anisotropic weighted TV.

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3rd International Workshop on computer vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International conference on pattern recognition, ICPR 2018

3rd International Workshop on Computer Vision for Analysis o...

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3rd International Workshop on computer vision for Analysis of Underwater Imagery, CVAUI 2018, 7th International Workshop on Computational Forensics, IWCF 2018, and International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018 held at 24th International conference on pattern recognition, ICPR 2018

ISBN: (纸本)9783030057916

the proceedings contain 16 papers. the special focus in this conference is on computer vision for Analysis of Underwater Imagery. the topics include: Categorization of Document Image Tampering Techniques and How to Identify them;overview of the Multimedia Information Processing for Personality & Social Networks Analysis Contest;handwritten Texts for Personality Identification Using Convolutional Neural Networks;recognition of Apparent Personality Traits from Text and Handwritten Images;multimodal Database of Emotional Speech, Video and Gestures;from Text to Speech: A Multimodal Cross-Domain Approach for Deception Detection;marine Snow Removal Using a Fully Convolutional 3D Neural Network Combined with an Adaptive Median Filter;strategies for Tackling the Class Imbalance Problem in Marine Image Classification;an Online Platform for Underwater Image Quality Evaluation;tracking Sponge Size and Behaviour with Fixed Underwater Observatories;enhancement of Low-Lighting Underwater Images Using Dark Channel Prior and Fast Guided Filters;Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network;single Image Plankton 3D Reconstruction from Extended Depth of Field Shadowgraph;a Novel Method for Race Determination of Human Skulls.

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7th National conference on computer vision, pattern recognition, Image Processing, and Graphics, NCVPRIPG 2019

7th National Conference on Computer Vision, Pattern Recognit...

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7th National conference on computer vision, pattern recognition, Image Processing, and Graphics, NCVPRIPG 2019

ISBN: (纸本)9789811586965

the proceedings contain 58 papers. the special focus in this conference is on computer vision, pattern recognition, Image Processing, and Graphics. the topics include: Deep Learn Bananas: A Transfer Learning for Banana Variety Classification;a Framework for Lane Prediction Based on Vehicle Detection and Tracking;detector-SegMentor Network for Skin Lesion Localization and Segmentation;choroid Disease Classification Using Convolutional Neural Network;hyper vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit;texture Classification by Local Rajan Transform Based Descriptor;domain Decomposition Based Preconditioned Solver for Bundle Adjustment;deep Dictionary Learning for Inpainting;A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis;structure Preserving Image Inpainting Using Edge Priors with Contextual Attention;preface;fast Stereo Depth Estimation in Smartphone Devices with Narrow Baseline;exploring Temporal Differences in 3D Convolutional Neural Networks;PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network;unsupervised Domain Adaptation for Remote Sensing Images Using Metric Learning and Correlation Alignment;computationally Efficient Super-Resolution Approach for Real-World Images;Accurate Damage Dimension Estimation in AI Driven Vehicle Inspection System;a Deep Learning Based Framework for Distracted Driver Detection;RECAL: Reuse of Established CNN Classifier Apropos Unsupervised Learning Paradigm;Pose Estimation of UAVs Using Stereovision;U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments;emphasizing Similar Feature Representations to Defend Against Adversarial Attacks;single Storage Semi-Global Matching for Real Time Depth Processing;iSalGAN - An Improvised Saliency GAN;putting Jewellery and Accessories on a 3D Face Model Generated from 2D Image.

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Freestyle 3D-Aware Portrait Synthesis Based on Compositional Generative Priors 27th

Freestyle 3D-Aware Portrait Synthesis Based on Compositiona...

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27th International conference on pattern recognition, ICPR 2024

作者： Ma, Tianxiang Zhao, Kang Sun, Jianxin Zhang, Yingya Dong, Jing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CRIPAC MAISInstitute of Automation Chinese Academy of Sciences Beijing China Alibaba Group Hangzhou China

ISBN: (纸本)9783031781711

Efficiently generating a freestyle 3D portrait with high quality and 3D-consistency is a promising yet challenging task. the portrait styles generated by most existing methods are usually restricted by their 3D generators, which are learned in specific facial datasets, such as FFHQ. To get the diverse 3D portraits, one can build a large-scale multi-style database to retrain a 3D-aware generator, or use a off-the-shelf tool to do the style translation. However, the former is time-consuming due to data collection and training process, the latter may destroy the multi-view consistency. To tackle this problem, we propose a novel text-driven 3D-aware portrait synthesis framework that can generate out-of-distribution portrait styles. Specifically, for a given portrait style prompt, we first composite two generative priors, a 3D-aware GAN generator and a text-guided image editor, to quickly construct a few-shot stylized portrait set. then we map the special style domain of this set to our proposed 3D latent feature generator and obtain a 3D representation containing the given style information. Finally we use a pre-trained 3D renderer to generate view-consistent stylized portraits from the 3D representation. Extensive experimental results show that our method is capable of synthesizing high-quality 3D portraits with specified styles in a few minutes, outperforming the state-of-the-art. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D Portrait Synthesis Text-to-3D Generation

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Effects of Primary Capsule Shapes and Sizes in Capsule Networks 27th

Effects of Primary Capsule Shapes and Sizes in Capsule Ne...

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27th International conference on pattern recognition, ICPR 2024

作者： Tapper, William Carneiro, Gustavo Hussein, Mohammad Evans, Phillip thomas, Spencer A. Centre for Vision Speech and Signal Processing The University of Surrey 388 Stag Hill Surrey GuildfordGU2 7XH United Kingdom National Physical Laboratory Hampton Road TeddingtonTW11 0LW United Kingdom

ISBN: (纸本)9783031781681

Capsule networks are a relatively unexplored type of neural network architecture that preserve spatial information of the input by replacing the pooling layers with convolutional strides and dynamic routing, which allow part-whole relationships of the data to be retained. One disadvantage is the computational complexity of dynamic routing, where each capsule must route to all capsules in a layer. It is common practice to use many capsules with a smaller feature space and there has been little attention in the exploration of using fewer, wider capsules. this reduces the number of routes the network must make, making the network train faster while still accounting for the same learnable space. this paper presents an ablation study on a 3-layer capsule network architecture by changing the primary capsule dimensions to assess the impact on performance and training time. Experiments were performed on capsule networks with capsule sizes: 32×8, 8×32, 16×8 and 8×16 (number × width), on 11 benchmark datasets: MNIST, CIFAR-10, PCAM, fashionMNIST, BreastMNIST, BloodMNIST, OrganMNIST, PathMNIST and OCTMNIST and SVHN. For all of our datasets we observe capsule network structures that obtain accuracy that exceeds that of the 32×8 structures and are least 40% faster to train. Training time reductions ranging from 17% to 75% are reported, alongside accuracy improvements of up to 16%. these results lead us to propose treating the primary capsule dimensions as a hyperparameter to optimise accuracy and resource (e.g., memory, processing) utilisation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network architecture

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Energy Minimization Methods in computer vision and pattern recognition: 7th International conference, EMMCVPR 2009 Bonn, Germany, August 24-27, 2009 Proceedings - Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2009年 5681 LNCS卷 VI页

作者： Cremers, Daniel Boykov, Yuri Blake, Andrew Schmidt, Frank R. Universität Bonn Institut für Informatik III Römerstraße 164 53117 Bonn Germany Computer Science Department University of Western Ontario London ON N6A 5A5 Canada Microsoft Research J.J. Thomson Avenue CB3 OFB Cambridge United Kingdom

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Self-prior Guided Mamba-UNet Networks for Medical Image Super-Resolution 27th

Self-prior Guided Mamba-UNet Networks for Medical Image Supe...

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27th International conference on pattern recognition, ICPR 2024

作者： Ji, Zexin Zou, Beiji Kui, Xiaoyan Vera, Pierre Ruan, Su School of Computer Science and Engineering Central South University Changsha410083 China Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South University Changsha410083 China University of Rouen-Normandy LITIS - QuantIF UR 4108 Rouen76000 France Department of Nuclear Medicine Henri Becquerel Cancer Center Rouen France

ISBN: (纸本)9783031781940

In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural networks (CNNs) or Transformers. CNNs-based methods fail to capture long-range dependencies, while Transformer-based approaches face heavy calculation challenges due to their quadratic computational complexity. Recently, State Space Models (SSMs) especially Mamba have emerged, capable of modeling long-range dependencies with linear computational complexity. Inspired by Mamba, our approach aims to learn the self-prior multi-scale contextual features under Mamba-UNet networks, which may help to super-resolve low-resolution medical images in an efficient way. Specifically, we obtain self-priors by perturbing the brightness inpainting of the input image during network training, which can learn detailed texture and brightness information that is beneficial for super-resolution. Furthermore, we combine Mamba with Unet network to mine global features at different levels. We also design an improved 2D-Selective-Scan (ISS2D) module to divide image features into different directional sequences to learn long-range dependencies in multiple directions, and adaptively fuse sequence information to enhance super-resolved feature representation. Both qualitative and quantitative experimental results demonstrate that our approach outperforms current state-of-the-art methods on two public medical datasets: the IXI and fastMRI. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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