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检索条件"任意字段=Conference on Visual Communications and Image Processing"

共 3468 条记录，以下是161-170 订阅

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Learn A Compression for Objection Detection - VAE with a Bridge

Learn A Compression for Objection Detection - VAE with a Bri...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Mei, Yixin Li, Fan Li, Li Li, Zhu Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (纸本)9781728185514

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.

关键词： image coding for machine object detection learning-based image compression

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Text-to-image Generation via Semi-Supervised Training

Text-to-Image Generation via Semi-Supervised Training

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Ji, Zhongyi Wang, Wenmin Chen, Baoyang Han, Xiao Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Macau Univ Sci & Technol Intemat Inst Next Generat Internet Macau Peoples R China

ISBN: (纸本)9781728180687

Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed and accurate descriptions of images are onerous to obtain. In this paper, we introduce a simple but effective semi-supervised approach that considers the feature of unlabeled images as " Pseudo Text Feature". Therefore, the unlabeled data can participate in the following training process. To achieve this, we design a Modality-invariant Semanticconsistent Module which aims to make the image feature and the text feature indistinguishable and maintain their semantic information. Extensive qualitative and quantitative experiments on MNIST and Oxford-102 flower datasets demonstrate the effectiveness of our semi-supervised method in comparison to supervised ones. We also show that the proposed method can be easily plugged into other visual generation models such as image translation and performs well.

关键词： image Synthesis Text-to-image Generation Semi-Supervised Learning Pseudo Feature

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Deep Motion Flow Aided Face Video De-identification

Deep Motion Flow Aided Face Video De-identification

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Wen, Yunqian Liu, Bo Xie, Rong Cao, Jingyi Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781728185514

Advances in cameras and web technology have made it easy to capture and share large amounts of face videos over to an unknown audience with uncontrollable purposes. These raise increasing concerns about unwanted identity-relevant computer vision devices invading the characters's privacy. Previous de-identification methods rely on designing novel neural networks and processing face videos frame by frame, which ignore the data feature in redundancy and continuity. Besides, these techniques are incapable of well-balancing privacy and utility, and per-frame evaluation is easy to cause flicker. In this paper, we present deep motion flow, which can create remarkable de-identified face videos with a good privacy-utility tradeoff. It calculates the relative dense motion flow between every two adjacent original frames and runs the high quality image anonymization only on the first frame. The de-identified video will be obtained based on the anonymous first frame via the relative dense motion flow. Extensive experiments demonstrate the effectiveness of our proposed de-identification method.

关键词： video de-identification video anonymization privacy protection privacy-utility tradeoff

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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

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2024 conference on visual communications and image processing

作者： Ding, Xiaoying Yue, Guanghui Zhang, Yingxue Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Med Sch Sch Biomed Engn Shenzhen Peoples R China Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects' visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： cross-device saliency detecton visual attention eye-tracking

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A Privacy-Preserving and End-to-End-Based Encrypted image Retrieval Scheme

A Privacy-Preserving and End-to-End-Based Encrypted Image Re...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Lu, Zhixun Feng, Qihua Li, Peiya Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Beijing Insitute Technol Sch Comp Sci & Technol Beijing Peoples R China Jinan Univ Coll Cyber Secur Guangzhou Peoples R China

ISBN: (纸本)9781665475921

Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, a new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.

关键词： JPEG image encryption image retrieval neural network end-to-end

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NIR image Colorization Using SPADE Generator and Grayscale Approximated Self-Reconstruction

NIR Image Colorization Using SPADE Generator and Grayscale A...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Tian Jung, Cheolkon Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China Xidian Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9781728180687

Near infrared (NIR) images are robust to ambient light and contain clear textures in low light condition. In this paper, we propose NIR image colorization using spatial adaptive denormalization (SPADE) generator and grayscale approximated self-reconstruction. Compared with traditional image to image translation methods, the proposed NIR colorization pursues photorealism rather than generative diversity. The challenge of this task is NIR-RGB mis-registration in training data. We address this problem by separately extracting NIR texture and RGB color with an end to end SPADE based model. Moreover, the proposed method facilitates a more precise synthesis with a given low light RGB reference image. Experiments on an open NIR-RGB dataset verify that the proposed method effectively preserves NIR textures and RGB colors in the synthesized results and outperforms the baselines in terms of visual quality and quantitative assessments.

关键词： NIR colorization mis-registered data deep learning generative adversarial networks multi-modal synthesis

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Aerial image Classification with Label Splitting and Optimized Triplet Loss Learning

Aerial Image Classification with Label Splitting and Optimiz...

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IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Liao, Rijun Li, Zhu Bhattacharyya, Shuvra S. York, George Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA Univ Maryland Dept Elect & Comp Engn College Pk MD USA Univ Maryland UMIACS College Pk MD 20742 USA US Air Force Acad Dept Elect & Comp Engn Colorado Springs CO USA US Air Force Acad UAS Res Ctr Colorado Springs CO 80840 USA

ISBN: (纸本)9781728185514

With the development of airplane platforms, aerial image classification plays an important role in a wide range of remote sensing applications. The number of most of aerial image dataset is very limited compared with other computer vision datasets. Unlike many works that use data augmentation to solve this problem, we adopt a novel strategy, called, label splitting, to deal with limited samples. Specifically, each sample has its original semantic label, we assign a new appearance label via unsupervised clustering for each sample by label splitting. Then an optimized triplet loss learning is applied to distill domain specific knowledge. This is achieved through a binary tree forest partitioning and triplets selection and optimization scheme that controls the triplet quality. Simulation results on NWPU, UCM and AID datasets demonstrate that proposed solution achieves the state-of-the-art performance in the aerial image classification.

关键词： Aerial image Classification Label Splitting Semantic Label Appearance Label Optimized Triplet Loss

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Semantic Attribute Guided image Aesthetics Assessment

Semantic Attribute Guided Image Aesthetics Assessment

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Duan, Jiachen Chen, Pengfei Li, Leida Wu, Jinjian Shi, Guangming Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781665475921

image aesthetics assessment (IAA) measures the perceived beauty of images using a computational approach. People usually assess the aesthetics of an image according to semantic attributes, e.g., lighting, color, object emphasis, etc. However, the state-of-the-art IAA approaches usually follow the data-driven framework without considering the rich attributes contained in images. With this motivation, this paper presents a new semantic attribute guided IAA model, where the attention maps of semantic attributes are employed to enhance the representation ability of general aesthetic features for more effective aesthetics assessment. Specifically, we first design an attribute attention generation network to obtain the attention maps for different semantic attributes, which are utilized to weight the general aesthetic features, producing the semantic attribute-enhanced feature representations. Then, the Graph Convolutional Network (GCN) is employed to further investigate the inherent relationship among the enhanced aesthetic features, producing the final image aesthetics prediction. Extensive experiments and comparisons on three public IAA databases demonstrate the effectiveness of the proposed method.

关键词： image aesthetics assessment semantic attributes aesthetic reasoning graph convolutional network

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Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized image Compression

Spatial-Channel Context-Based Entropy Modeling for End-to-en...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Chongxin Luo, Jixiang Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728180687

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.

关键词： End-to-end optimized image compression context-based entropy coding artificial neural networks

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Mask-Guided Transformer for Human-Object Interaction Detection

Mask-Guided Transformer for Human-Object Interaction Detecti...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Ying, Daocheng Yang, Hua Sun, Jun Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665475921

Human-object interaction (HOI) detection is a meaningful research topic on human activity understanding. Recent works have made significant progress by focusing on efficient triplet matching and leveraging image-wide features based on encoder-decoder architecture. However, the ability to gather relevant contextual information about human is limited and different sub-tasks in HOI detection are not differentiated by specific decoupling in previous methods. To this end, we propose a new transformer-based method for HOI detection, namely, Mask-Guided Transformer (MGT). Our model, which is composed of five parallel decoders with a shared encoder, not only emphasizes interactive regions by applying body features, but also disentangles the prediction of instance and interaction. We achieve a favorable result at 63.3 mAP on the well-known HOI detection dataset V-COCO.

关键词： Transformers Object detection Human and object interaction HOI detection

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