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

共 3470 条记录，以下是681-690 订阅

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Mask-Guided Transformer for Human-Object Interaction Detection

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

引用

IEEE visual communications and image processing (VCIP)

作者： Daocheng Ying Hua Yang Jun Sun Shanghai Key Lab of Digital Media Processing and Transmission Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

ISBN: (纸本)9781665475938

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： visual communication image processing Focusing Predictive models Transformers Decoding

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Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model Compression

Explicit-NeRF-QA: A Quality Assessment Database for Explicit...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuke Xing Qi Yang Kaifa Yang Yiling Xu Zhu Li Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai China Department of Computer Science and Electrical Engineering University of Missouri-Kansas City USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In recent years, Neural Radiance Fields (NeRF) have demonstrated significant advantages in representing and synthesizing 3D scenes. Explicit NeRF models facilitate the practical NeRF applications with faster rendering speed, and also attract considerable attention in NeRF compression due to its huge storage cost. To address the challenge of the NeRF compression study, in this paper, we construct a new dataset, called Explicit-NeRF-QA. We use 22 3D objects with diverse geometries, textures, and material complexities to train four typical explicit NeRF models across five parameter levels. Lossy compression is introduced during the model generation, pivoting the selection of key parameters such as hash table size for InstantNGP and voxel grid resolution for Plenoxels. By rendering NeRF samples to processed video sequences (PVS), a large scale subjective experiment with lab environment is conducted to collect subjective scores from 21 viewers. The diversity of content, accuracy of mean opinion scores (MOS), and characteristics of NeRF distortion are comprehensively presented, establishing the heterogeneity of the proposed dataset. The state-of-the-art objective metrics are tested in the new dataset. Best Pearson correlation, which is around 0.85, is collected from the full-reference objective metric. All tested no-reference metrics report very poor results with 0.4 to 0.6 correlations, demonstrating the need for further development of more robust no-reference metrics. The dataset, including NeRF samples, source 3D objects, multiview images for NeRF generation, PVSs, MOS, is made publicly available at the following location: https://***/YukeXing/Explicit-NeRF-QA.

关键词： Measurement Three-dimensional displays Correlation Accuracy image coding visual communication Video sequences Neural radiance field Distortion Rendering (computer graphics)

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Contrast Restoration of Hazy image in HSV Space 13

Contrast Restoration of Hazy Image in HSV Space

引用

13th International conference on Wireless communications and Signal processing, WCSP 2021

作者： Chen, Kai Liu, Juping Chen, Chuheng Wang, Zhe Ju, Mingye Nanjing University of Posts and Telecommunications Nanjing210003 China Monash University VIC3800 Australia

ISBN: (纸本)9781665407854

In this paper, a new haze restoration algorithm is proposed to recover hazy images. Hazy images always suffer from color distortion, detail-information loss and contrast reduction, which degrade the visual quality of the image seriously. To address the issue, this paper introduces an effective and robust hazy image enhancement method without any dedicated hardware or prior knowledge. Firstly, the scene depth can be estimated based on the dark channel prior. Additionally, the saturation and brightness are adaptively corrected according to the scene depth. Finally, the gradient algorithm is used to obtain a better restored result. A large number of experiments are conducted, which demonstrates the superior performance of the proposed method compared with other state-of-the-art techniques in terms of both subjective and objective evaluations. © 2021 IEEE.

关键词： image enhancement

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Learning Features by Minimizing the Interframe Differences 19th

Learning Features by Minimizing the Interframe Differences

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19th Chinese conference on image and Graphics Technologies and Applications, IGTA 2024

作者： Zhao, Dong Zhang, Dan School of Computer Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China

ISBN: (纸本)9789819799183

In this paper, we present a novel yet intuitive unsupervised feature learning approach, referred to as Minimizing Interframe Differences (MID). The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of the frames, the differences between the before and after frames can be minimally encoded. MID is implemented with a difference encoding module and a frame reconstruction module. The former tries to minimize the difference between frames, while the latter first maps the difference encoding to optical flow and then realizes frame reconstruction based on the learned optical flow. All the modules are jointly learned in an end-to-end way through the reconstruction loss. We verify the feasibility of MID in the image datasets by replacing the natural transformation of videos to artificially parameterized transformation for images. Experimental results show that MID is able to predict optical flow accurately based on the minimum difference coding and learn a powerful visual representation that greatly enhances other visual tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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SalCrop: Spatio-temporal Saliency Based Video Cropping

SalCrop: Spatio-temporal Saliency Based Video Cropping

引用

IEEE visual communications and image processing (VCIP)

作者： Kao Zhang Yan Shang Songnan Li Shan Liu Zhenzhong Chen Wuhan University China Tencent Media Lab Tencent China Tencent Media Lab Tencent America USA

ISBN: (纸本)9781665475938

Video cropping is a key research task in video processing field. In this paper, a spatio-temporal saliency based video cropping framework (SalCrop) is introduced including four core modules: video scene detection module, video saliency prediction module, adaptive cropping module, and video codec module. It can automatically reframe videos in the desired aspect ratios. In addition, a large-scale video cropping dataset (VCD) is built for training and testing. Experiments on the VCD test dataset show that our SalCrop outperforms the state-of-the-art algorithms with high efficiency. Besides, a FFmpeg video filter is developed based on the framework, which can be widely used in different scenarios. A demo is available at: https://***/smartcontent/videoCrop (access token: test_token).

关键词： Training visual communication image processing Filtering algorithms Prediction algorithms Video codecs Task analysis

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CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based Action Recognition

CdCLR: Clip-Driven Contrastive Learning for Skeleton-Based A...

引用

IEEE visual communications and image processing (VCIP)

作者： Rong Gao Xin Liu Jingyu Yang Huanjing Yue School of Electrical and Information Engineering Tianjin University China Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Finland

ISBN: (纸本)9781665475938

In this study, we propose a Clip-Driven Contrastive Learning for Skeleton-Based Action Recognition (CdCLR). In-stead of considering sequences as instances, CdCLR extracts clips from the sequences as new instances. Aim to implement inherent supervision-guided contrastive learning through joint optimal training of sequences discrimination, clips discrimination, and order verification. Mining abundant positive/negative pairs inside sequence while learning inter-and intra-sequence semantic repre-sentations. Extensive experiments on the NTU RGB+D 60, UCLA and iMiGUE datasets present that CdCLR exhibits superior performance under various evaluation protocols and reaches state-of-the-art. Our code is available at https://***/Erich-G/CdCLRI.

关键词： Training Protocols image recognition Codes visual communication Semantics

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A efficient predictive wavelet transform for LiDAR point cloud attribute compression

A efficient predictive wavelet transform for LiDAR point clo...

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IEEE visual communications and image processing (VCIP)

作者： Yueru Chen Jing Wang Ge Li Peng Cheng Lab Shenzhen China Peking University Shenzhen Graduate School China

ISBN: (纸本)9781665475938

In this paper, a new predictive wavelet transform (PWT) is proposed to solve LiDAR point clouds attribute compression. Our method is a combination of predictive coding and Haar wavelet transform. Based on the spatial information, a hierarchical predictive transform tree is designed to represent 3D irregular data points efficiently. Each level node is classified as a predictive node (P-node) or a transform node (T-node) according to the distances to its adjacent nodes. Then in a top-down coding process, the Haar transform is applied to all T-node pairs, and predictive coding is processed on all P-nodes alternately. It is shown by experimental results that the proposed PWT method offers better R-D performances compared with state-of-the-art methods.

关键词： Wavelet transforms Point cloud compression image coding Laser radar Three-dimensional displays visual communication Predictive coding

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Lightweight image Classification Algorithm Based on Dual Knowledge Distillation 23

Lightweight Image Classification Algorithm Based on Dual Kno...

引用

Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Peng Xiao Ying Liu Yu Hao Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Deep learning models perform exceptionally well in image processing; however, the increasing number of parameters and computational burdens make it difficult to apply these models on mobile devices. To address this issue, a widely adopted approach is using knowledge distillation to compress and optimize deep learning models. Traditional knowledge distillation trains a student network with soft targets from a teacher network. This paper proposes a Novel Dual Knowledge Distillation (NDKD) network by integrating Convolutional Block Attention Module (CBAM) distillation and feature distillation. Specifically, a new CBAM module and a hash layer are first added to improve the teacher-student network. Next, attention distillation loss is computed to transfer CBAM knowledge from the teacher module to the student network to enhance the student network's ability to capture low-level visual features. Finally, feature distillation loss is constructed by hashing layers to transfer semantic knowledge from the teacher network to the student network to enhance the student network's high-level semantic ability. Our method is compared with other knowledge distillation methods on benchmarking datasets including the CIFAR-10, SVNM, EMNIST, and Tire Pattern image dataset CIIP-TPID. Experimental results show that our method outperforms other methods on adapted datasets.

关键词： Convolutional Block Attention Module Deep learning image processing knowledge distillation

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Fast CU Partition Method Based on Extra Trees for VVC Intra Coding

Fast CU Partition Method Based on Extra Trees for VVC Intra ...

引用

IEEE visual communications and image processing (VCIP)

作者： Kaijie Wang Hong Liang Saiping Zhang Fuzheng Yang School of Telecommunications Engineering Xidian University Xi'an China

ISBN: (纸本)9781665475938

In this paper, we propose a method to skip unnecessary CU encoding modes for VVC Intra coding based on the extra trees model. Two extra tree models with calculated features are used to simplify the encoding process, where the first model determines whether to early terminate the partition and the best partition direction and the second model selects the better partition mode between the binary and ternary partition modes. Experimental results show that our proposed method can save encoding time from 34.68% to 46.70 % with only from 0.81% to 1.65% increase of BDBR compared to VVC reference software (VTM 10.0). Besides, the method gets a great tradeoff when applied on VVenC 1.0, an efficient encoder ofVVC, at both preset slower and preset medium.

关键词： image coding visual communication Software Distance measurement

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Autoencoder-based intra prediction with auxiliary feature

Autoencoder-based intra prediction with auxiliary feature

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IEEE visual communications and image processing (VCIP)

作者： Luhang Xu Yue Yu Haoping Yu Dong Wang Department of Standards Research OPPO Nanjing JiangSu China

ISBN: (纸本)9781665475938

A set of auto encoders is trained to perform intra prediction for block-based video coding. Each auto encoder consists of an encoding network and a decoding network. Both encoding network and decoding networks are jointly optimized and integrated into the state-of-the-art VVC reference software VTM-11.0 as an additional intra prediction mode. The simulation is conducted under common test conditions with all intra config-urations and the test results show 1.55%, 1.04%, and 0.99% of Y, U, V components Bjentegaard-Delta bit rate saving compared to VTM-11.0 anchor, respectively. The overall relative decoding running time of proposed autoencoder-based intra prediction mode on top of VTM-11.0 are 408% compared to VTM-11.0.

关键词： Video coding Training image coding visual communication Bit rate Software Decoding

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