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FreeStyler: A Free-Form Stylization Method via Multimodal Vector Quantization 12th

FreeStyler: A Free-Form Stylization Method via Multimodal V...

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12th International conference on Computational Visual Media, CVM 2024

作者： Liu, WuQin Lin, MinXuan Huang, HaiBin Ma, ChongYang Dong, WeiMing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Kuaishou Technology Beijing100085 China

ISBN: (纸本)9789819720910

Image stylization refers to the process of transforming an input image into a new one, while retaining its original content but in different styles. However, most existing works only support single-modal guidance, which is not ideal for real-world applications. To tackle this limitation, we propose FreeStyler, a flexible framework for image stylization that is capable of handling various input scenarios. Our approach goes beyond the traditional approach of relying on content and style images to generate a stylized image and supports situations where these references are absent. Specifically, in such cases, FreeStyler allows performing the stylization through text or audio information. the core of FreeStyler is a vector quantized style transfer framework that encodes content and style information into a shared discrete latent feature space, followed by a stylization transformer for style fusion and an image decoder for stylized image reconstruction. To enable free-form stylization, we introduce a novel pseudo-paired token predictor that can estimate tokens from varying input forms without the need for additional text or audio data. Specifically, we leverage Contrastive Language-Image Pre-training (CLIP) as prior knowledge to align discrete representations across different modalities and train the framework using an image and pseudo caption pair provided by Bootstrapping Language-Image Pre-training (BLIP). through qualitative and quantitative experiments, our method has demonstrated superior performance compared to state-of-the-art stylization methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vector quantization

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A Scalable 3D Array Architecture for Accelerating Convolutional Neural Networks 6th

A Scalable 3D Array Architecture for Accelerating Convolutio...

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6th International conference on Cognitive Systems and Signal Processing, ICCSIP 2021

作者： Ji, Yafei Wang, Xiang Zhou, Yangfan Cheng, Chen Li, Jiang Wang, Haoyuan Wang, Xuguang Liu, Xin Chinese Academy of Sciences 398 Ruoshui Road Suzhou Industrial Park Jiangsu Suzhou China Gusu Laboratory of Materials 388 Ruoshui Road Suzhou Industrial Park Jiangsu Suzhou China

ISBN: (纸本)9789811692468

Convolutional neural network (CNN) is widely used in computer vision and image recognition, and the structure of the CNN becomes more and more complex. the complexity of CNN brings challenges of performance and storage capacity for hardware implementation. To address these challenges, in this paper, we propose a novel 3D array architecture for accelerating CNN. this proposed architecture has several benefits: Firstly, the strategy of multilevel caches is employed to improve data reusage, and thus reducing the access frequency to external memory;Secondly, performance and throughout are balanced among 3D array nodes by using novel workload and weight partitioning schemes. thirdly, computing and transmission are performed simultaneously, resulting in higher parallelism and lower hardware storage requirement;Finally, the efficient data mapping strategy is proposed for better scalability of the entire system. the experimental results show that our proposed 3D array architecture can effectively improve the overall computing performance of the system. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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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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RRSR:Reciprocal Reference-Based Image Super-Resolution with Progressive Feature Alignment and Selection 17th

RRSR:Reciprocal Reference-Based Image Super-Resolution with ...

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17th European conference on computer vision (ECCV)

作者： Zhang, Lin Li, Xin He, Dongliang Li, Fu Wang, Yili Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Beijing Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Tsinghua Univ Beijing Peoples R China CASIA Natl Lab Pattern Recognit Beijing Peoples R China UCAS Sch Future Technol Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9783031197994;9783031198007

Reference-based image super-resolution (RefSR) is a promising SR branch and has shown great potential in overcoming the limitations of single image super-resolution. While previous state-of-the-art RefSR methods mainly focus on improving the efficacy and robustness of reference feature transfer, it is generally overlooked that a well reconstructed SR image should enable better SR reconstruction for its similar LR images when it is referred to as. therefore, in this work, we propose a reciprocal learning framework that can appropriately leverage such a fact to reinforce the learning of a RefSR network. Besides, we deliberately design a progressive feature alignment and selection module for further improving the RefSR task. the newly proposed module aligns reference-input images at multi-scale feature spaces and performs reference-aware feature selection in a progressive manner, thus more precise reference features can be transferred into the input features and the network capability is enhanced. Our reciprocal learning paradigm is model-agnostic and it can be applied to arbitrary RefSR models. We empirically show that multiple recent state-of-the-art RefSR models can be consistently improved with our reciprocal learning paradigm. Furthermore, our proposed model together with the reciprocal learning strategy sets new state-of-the-art performances on multiple benchmarks.

关键词： Reference-based image super-resolution Reciprocal learning Reference-input feature alignment

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Multi-view anal fistula disease diagnosis based on local enhancement and global fusion via MRI

Multi-view anal fistula disease diagnosis based on local enh...

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7th International conference on vision, Image and Signal Processing (ICVISP 2023)

作者： C. Tan L. Tian H. Chen P. Liao College of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu 611137 People's Republic of China School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS UK National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu 610065 People's Republic of China The Sixth People's Hospital of Chengdu Chengdu 610051 People's Republic of China

Anal fistulas often result from cryptoglandular infections or complications of conditions like Crohn's disease. Patients typically present with symptoms such as recurrent perianal abscesses, discharge, and occasional fecal incontinence. Accurate diagnosis is crucial for determining the type of fistula and planning appropriate treatment. Usually, Magnetic Resonance Imaging (MRI) is used for diagnosis. However, depending on a single perspective can become progressively obstructed, and potentially resulting in erroneous conclusions and miscarriages of justice. thus, a deep learning method for multi-view anal fistula MRI image fusion, combining convolutional neural networks (CNN) and Transformers is proposed. this model extracts feature from different angles of anal fistula MRI, achieving initial feature extraction through CNN modules. It models the local semantic relationship between the two views using a joint crossattention mechanism, exploring multi-view feature connections for local regions, and enhancing feature interaction across views. Additionally, a consistency loss is introduced by computing the cosine similarity between features from two views, maximizing the similarity between features, to constrain the feature distances, ensuring global feature consistency calculation across different views, enhancing the feature representation of single-view images. Experimental validation demonstrates that the method proposed outperforms other mainstream models and can achieve precise identification of anal fistula.

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Dual-Evidential Learning for Weakly-supervised Temporal Action Localization 1

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17th European conference on computer vision (ECCV)

作者： Chen, Mengyuan Gao, Junyu Yang, Shicai Xu, Changsheng Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Hikvis Res Inst Hangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (数字)9783031197727

ISBN: (纸本)9783031197710;9783031197727

Weakly-supervised temporal action localization (WS-TAL) aims to localize the action instances and recognize their categories with only video-level labels. Despite great progress, existing methods suffer from severe action-background ambiguity, which mainly comes from background noise introduced by aggregation operations and large intra-action variations caused by the task gap between classification and localization. To address this issue, we propose a generalized evidential deep learning (EDL) framework for WS-TAL, called Dual-Evidential Learning for Uncertainty modeling (DELU), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal. Specifically, targeting at adaptively excluding the undesirable background snippets, we utilize the video-level uncertainty to measure the interference of background noise to video-level prediction. then, the snippet-level uncertainty is further deduced for progressive learning, which gradually focuses on the entire action instances in an "easy-to-hard" manner. Extensive experiments show that DELU achieves state-of-the-art performance on thUMOS14 and ActivityNetl.2 benchmarks. Our code is available in ***/MengyuanChen21/ECCV2022-DELU.

关键词： Weakly-supervised temporal action localization Evidential deep learning Action-background ambiguity

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An Efficient Polyp Detection Framework with Suspicious Targets Assisted Training 4th

An Efficient Polyp Detection Framework with Suspicious Targe...

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4th chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhang, Zhipeng Xiao, Li Zhuang, Fuzhen Ma, Ling Chang, Yuan Wang, Yuanyuan Jiang, Huiqin He, Qing Zhengzhou Univ Henan Inst Adv Technol Zhengzhou Peoples R China Chinese Acad Sci Inst Comp Technol Chinese Acad Sci CAS Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Chinese Acad Sci Ningbo Huamei Hosp Ningbo Peoples R China Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China Xiamen Inst Data Intelligence Xiamen Peoples R China Zhengzhou Univ Affiliated Hosp 1 Zhengzhou Peoples R China

ISBN: (纸本)9783030880132;9783030880125

Automatic polyp detection during colonoscopy is beneficial for reducing the risk of colorectal cancer. However, due to the various shapes and sizes of polyps and the complex structures in the intestinal cavity, some normal tissues may display features similar to actual polyps. As a result, traditional object detection models are easily confused by such suspected target regions and lead to false-positive detection. In this work, we propose a multi-branch spatial attention mechanism based on the one-stage object detection framework, YOLOv4. Our model is further jointly optimized with a top likelihood and similarity to reduce false positives caused by suspected target regions. A similarity loss is further added to identify the suspected targets from real ones. We then introduce a Cross Stage Partial Connection mechanism to reduce the parameters. Our model is evaluated on the private colonic polyp dataset and the public MICCAI 2015 grand challenge dataset including the CVCClinic 2015 and Etis-Larib, both of the results show our model improves performance by a large margin and with less computational cost.

关键词： Polyp detection Suspected target Semi-supervised learning

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A Self-Attention Based Method for Facial Expression recognition 2021

A Self-Attention Based Method for Facial Expression Recognit...

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7th International conference on Computing and Artificial Intelligence, ICCAI 2021

作者： Ling, Xufeng Liang, Jingxin Yang, Jie Shanghai Normal University Tianhua College Ai School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781450389501

We present a self-attention-based method termed as vision Transformer (ViT) to efficiently classify the human facial expressions. Our work can be divided into two contributions. First, the facial expression image is divided to N∗N patches, each of which corresponds to word, and the whole image data is used as a paragraph that composed of n words. Second, we design a learnable module, the ViT, with sequence length of L, latent dimension, and 12 attention layers which are integrated together as a unified framework. We also train the proposed model on the normalized and augmented version of FER2013plusdataset. We show empirically that ViT has superior performance compared to alternative approaches. © 2021 ACM.

关键词： Face recognition

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Multi-scale Residual Pyramid Attention Network for Monocular Depth Estimation 25

Multi-scale Residual Pyramid Attention Network for Monocular...

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25th International conference on pattern recognition (ICPR)

作者： Liu, Jing Zhang, Xiaona Li, Zhaoxin Mao, Tianlu Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang Hebei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China

ISBN: (纸本)9781728188089

Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end multi-scale residual pyramid attention network (MRPAN) to mitigate these problems. First, we propose a multi-scale attention context aggregation (MACA) module, which consists of spatial attention module (SAM) and global attention module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover complex structures in the scene. then we proposed an improved residual refinement module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promising performance in object boundaries and local details compared with other state-of-the-art methods.

关键词： monocular depth estimation convolutional neural networks multi-scale attention module context

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Segmentation of thyroid Nodules Based on Hyperspectral Images

Segmentation of Thyroid Nodules Based on Hyperspectral Image...

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IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Junjie Wang Chenglong Tao Jian Du Bingliang Hu Zhoufeng Zhang Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Xi'an China University of Chinese Academy of Sciences Beijing China Key Laboratory of Biomedical Spectroscopy of Xi'an Xi'an China

thyroid nodules are a common thyroid disease, and early detection and treatment have a significant impact on the prognosis of patients. Hyperspectral image technology has significant advantages in medical image analysis due to its high-resolution and multi band characteristics, especially in the recognition and classification of thyroid nodules, which has important application value. But currently, there is no very effective method for segmenting hyperspectral images of thyroid nodules. Deep learning algorithms have shown superior performance in recent years in computer vision and pattern recognition tasks, including medical image analysis. this paper proposes the use of VGG model for segmentation of hyperspectral images of thyroid nodule tissue. Hyperspectral images can obtain spatial and spectral information of organizations, and VGG models can segment images through deep networks. Using hyperspectral and VGG, we found that thyroid nodule tissue can be well distinguished at the pixel level.

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