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ML-FDA: Meta-Learning via Feature Distribution Alignment for Few-Shot Learning

ML-FDA: Meta-Learning via Feature Distribution Alignment for...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuewen Li Binghao Liu Shuchang Lyu Lijiang Chen Qi Zhao Wenquan Feng Department of Electronic and Information Engineering Beihang University Beijing China

ISBN: (纸本)9781665475938

Computer vision tasks suffer from the high cost of collecting large amounts of labeled data. Few-shot Learning (FSL) is a dominant approach to solve this problem because it provides an insight to learn the knowledge of novel categories with few training samples. In FSL task, Meta-learning and metric learning have achieved impressive results. However, the performance of this task is still limited by large intra-class variance and small inter-class distance caused by limited number of few samples. To solve this problem, In this paper, we propose a new method, which integrates meta-learning and metric learning techniques. Specifically, we first propose a feature representation module (FR) to construct representative support class prototypes and query features. Then, we design bias loss to minimize the bias between support and query samples. Furthermore, we design an intra-class loss to minimize the distance between query class prototype and each query sample. We denote this model as ML-FDA and validate it on standard few-shot classification benchmark datasets (MiniimageNet, CIFAR-FS, FC100). The results show that our method improves the performance over other same paradigm methods and achieves the best performance on most benchmarks. The ablation study and visulization analysis also demonstrate the effectiveness of our method.

关键词： Measurement Training Computer vision Costs visual communication image processing Prototypes

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ACCR: Auto-labeling for Ancient Chinese Handwritten Characters Recognition on CNN

ACCR: Auto-labeling for Ancient Chinese Handwritten Characte...

引用

IEEE visual communications and image processing (VCIP)

作者： Peikun Wu Xin Yang Fuhao Guo Li Wang Cihan Ruan School of Mathematical Sciences Nankai University Tianjin China China Institute of Science and Technology Information Beijing China Department of Computer Science Engineering Santa Clara University Santa Clara U.S

ISBN: (纸本)9781665475938

Chinese Character Recognition(CCR) is a critical application of Optical Character Recognition(OCR), a vital area of pattern recognition. Research on CCR in the past decades mainly focused on the modern Chinese characters, but not on the ancient ones. Compared to modern Chinese characters, ancient characters are more diverse and multiple ancient characters can correspond to one modern character. When doing recognition, the unique features of ancient Chinese characters cause a significant amount of time on manual labeling. This paper proposes an automatic labeling algorithm based on a semi-supervised dictionary training neural network that drastically decreases human effort. We first created an offline training set as a dictionary including 8,226 Chinese characters from ancient documents in modern fonts. And put the set into the network. Then we recursively retrained the network on an unlabeled data set of about 1.3 million characters images segmented from ancient documents resulting in a very high accuracy rate of 98.96 %. This work is one part of our wide recognition of ancient documents with handwritten Chinese characters project.

关键词： Training Handwriting recognition image recognition Dictionaries visual communication Neural networks Manuals

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Deep Learning Based EBCOT Source Symbol Prediction Technique for JPEG2000 image Compression Architecture

Deep Learning Based EBCOT Source Symbol Prediction Technique...

引用

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

作者： Wang, I-Hsiang Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781728180687

In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings.

关键词： image compression JPEG2000 machine learning deep learning adaptive arithmetic coding

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Near-lossless Point Cloud Geometry Compression Based on Adaptive Residual Compensation

Near-lossless Point Cloud Geometry Compression Based on Adap...

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IEEE visual communications and image processing (VCIP)

作者： Dingquan Li Jing Wang Ge Li Peng Cheng Laboratory Shenzhen China Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9781665475938

Point cloud compression (PCC) is a crucial enabler for immersive multimedia applications since point cloud is one of the most primitive forms for representing 3D scenes and objects. Recently, some approaches are proposed to improve the average reconstruction quality of octree-based Geometry-based Point Cloud Compression (G-PCC). However, it is noticed that these approaches suffer considerable loss in terms of point-to-point (D1) Hausdorff distance when compared to G-PCC (octree). Here we introduce a near-lossless point cloud geometry compression method based on adaptive residual compensation by adding and removing points with large errors. It allows controlling of D1 Hausdorff (D1h) distance and maintains a great improvement in average reconstruction performance over G-PCC. Experimental results verify the effectiveness of our method, where our method achieves an average of 78.5% D1 and 11.4% D1h Bjontegaard-delta bitrate savings over the octree-based G-PCC on solid point clouds of the MPEG Cat1A dataset.

关键词： Point cloud compression Geometry image coding Three-dimensional displays visual communication Octrees Bit rate

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History-parameter-based Affine Model Inheritance

History-parameter-based Affine Model Inheritance

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IEEE visual communications and image processing (VCIP)

作者： Kai Zhang Li Zhang Zhipin Deng Na Zhang Yang Wang Bytedance Inc.

ISBN: (纸本)9781665475938

In VVC, affine motion compensation (AMC) is a powerful coding tool to address non-translational motion, while history-based motion vector prediction (HMVP) is an efficient approach to compress motion vectors. However, HMVP was designed for translational motion vectors, without considering control point motion vectors (CPMV) for AMC. This paper presents a method of history-parameter-based affine model inheritance (HAMI), to utilize history information to represent CPMV more efficiently. With HAMI, affine parameters of previously affine-coded block are stored in a first history-parameter table (HPT). New affine-merge, affine motion vector prediction candidates and regular-merge candidates can be constructed with affine parameters fetched from the first HPT and base MVs fetched from neighbouring blocks in a “base-parameter-decoupled” way. New affine merge candidates can also be generated in a “base-parameter-coupled” way from a second HPT, which stores base MV information together with corresponding affine parameters. Besides, pair-wised affine merge candidates are generated by two existing affine merge candidates. Experimental results show that HAMI provides an average BD-rate saving about 0.34 % with a negligible change on the running time, compared with ECM-3.1 in random access configurations. HAMI has been adopted into ECM.

关键词： image coding visual communication Simulation Motion compensation Encoding Complexity theory History

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Azimuth Adjustment Considering LiDAR Calibration for the Predictive Geometry Compression in G-PCC

Azimuth Adjustment Considering LiDAR Calibration for the Pre...

引用

IEEE visual communications and image processing (VCIP)

作者： Youguang Yu Wei Zhang Fuzheng Yang School of Telecommunications Engineering Xidian University China Artificial Intelligence Research Center Peng Cheng Laboratory China

ISBN: (纸本)9781665475938

Point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have played a significant role in many applications. To efficiently store and transmit such a huge amount of data, MPEG designed the Geometry-based Point Cloud Compression (G-PCC) standard, which includes a dedicated Pre-dictive Profile for LiDAR point clouds. In this scheme, Cartesian coordinates are mapped to the spherical representation using the elevation-related LiDAR calibration parameters to better characterize the spherical acquisition pattern of the spinning LiDAR device. As such, stronger spatial correlations exist among neighbouring nodes in the predictive structure, resulting in higher compression efficiency. However, it should be mentioned that the azimuth-related calibration parameters, which are unused, also impact the accuracy and correctness of the mapped spherical coordinates. In this paper, an azimuth adjustment method is proposed taking into account this impact. Experimental results show that the proposed azimuth adjustment can consistently improve the coding efficiency of G-PCC. Furthermore, a LiDAR calibration parameter estimation method is proposed in case the azimuth-related parameters are absent. Results show that the proposed calibration parameter estimation can precisely approximate the ground truth.

关键词： Point cloud compression Geometry Laser radar image coding Parameter estimation Azimuth visual communication

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Enhanced motion list reordering for video coding

Enhanced motion list reordering for video coding

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IEEE visual communications and image processing (VCIP)

作者： Yang Wang Kai Zhang Na Zhang Zhipin Deng Li Zhang Multimedia Lab Bytedance Inc.

ISBN: (纸本)9781665475938

In video coding, motion information consisting of motion vectors and reference index is typically involved in motion compensation. Motion list is widely used to efficiently compress the motion information, in which a motion index indicating the motion information is signaled. And the compression efficiency can be improved by template matching based motion list reordering. Besides, the motion information is further refined before being used in motion compensation by motion refinement process such as decoder-side motion vector refinement and template matching. However, the original motion information of the motion list rather than the refined motion information is used in the motion list reordering, which limits the coding performance. Therefore, an enhanced motion list reordering (EMLR) approach is proposed in this paper, in which the refined motion information is used in the motion list reordering. To derive the refined motion information, a dedicated motion refinement with a simplified version of motion refinement process is proposed. Furthermore, a simplified version of EMLR with two fast algorithms (EMLR-S) is proposed. Experimental results demonstrate that EMLR can achieve 0.19% BD-rate saving on average, and EMLR-S can achieve 0.1 % BD-rate saving with negligible coding complexity change compared to ECM-4.0 under random access configuration.

关键词： Video coding image coding visual communication Motion compensation Complexity theory Decoding Indexes

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Efficient Interpolation Filters for Chroma Motion Compensation in Video Coding

Efficient Interpolation Filters for Chroma Motion Compensati...

引用

IEEE visual communications and image processing (VCIP)

作者： Xi Xie Kai Zhang Li Zhang Meng Wang Junru Li Shiqi Wang Department of Computer Science City University of Hong Kong Hong Kong China Bytedance Inc. San Diego CA USA Beijing Bytedance Technology Co Ltd Beijing CN

ISBN: (纸本)9781665475938

Interpolation filters for motion compensation play an important role in video coding. Until recently, interpolation filters for luma motion compensation (MC) draw most of the attention while interpolation filters for chroma motion compen-sation are less noticeable. This paper comprehensively analyzes different types of interpolation filters and presents 6-tap DCT based chroma interpolation filters for chroma MC. The proposed interpolation filters are designed considering multiple factors, comprising the characteristics of the chroma component, the frequency response of interpolation filter and the expenses of computational complexity. The experimental results show the proposed method can achieve 0.05%, 1.23% and 1.25% BD-rate reductions on average for Y, Cb and Cr components, respectively, with only 2% decoding time increasing under Random Access configuration, compared with ECM-4.0, which is a reference software developed by JVET, targeting at future video coding technologies beyond VVC. Owing to the good trade-off between coding performance and complexity, the proposed method has been adopted in ECM.

关键词： Video coding Interpolation image coding visual communication Simulation Motion compensation Software

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Object Detection Model Based on Dual visual Pathway

Object Detection Model Based on Dual Visual Pathway

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International conference on Wireless communications and Signal processing (WCSP)

作者： Ruixuan Chen Xiao Yao Yifeng Zeng The College of IoT Engineering Hohai University Changzhou Jiangsu China

ISBN: (纸本)9781665450867

Object detection has made great progress with the development of deep learning. However, existing object detection algorithms still have limitations compared with human brain. In addition, the interpretability of the model becomes more and more inefficient with the increasing accuracy of the model. Most of the object detection models are considered as “black box” that cannot be explained. Considering that the visual information processing system is composed of two pathways spatially distributed in the ventral and dorsal sides of the cerebral cortex when the brain detects objects, this paper proposes object detection model based on dual visual pathway by simulating the process of detecting objects in the brain. Intra layer recursion and remote connections of different modules are added to the feedforward network to achieve feature extraction for image, since the ventral flow pathway is a recurrent network composed of loops and feedback. Experimental results carried out on PASCAL VOC show that the performance of object detection has been further improved, and the interpretability of the model has been enhanced.

关键词： Wireless communication Deep learning visualization Object detection Information processing visual systems Signal processing

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Clothing Retrieval from Channel Attention to KN Loss Learning

Clothing Retrieval from Channel Attention to KN Loss Learnin...

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IEEE visual communications and image processing (VCIP)

作者： Kuan-Hsien Liu Yu-Hsiang Wu Tsung-Jung Liu National Taichung University of Science and Technology Taichung Taiwan National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9781665475938

Due to the high diversity of clothing types and names, using text to search may not find the desired clothes. Clothing retrieval via images can help users find their favorite clothes more conveniently. So, it is widely used in many online stores and platforms, and gradually replaces text search. Clothing retrieval performance is often affected due to factors such as shooting angle, occlusion, and lighting. Therefore, we propose a low-parameter clothing retrieval model without using any additional annotations or labels to aid learning. In the model, we use the classification capability of the Capsule Network with multiple residual convolution modules to learn features, and the VGG16 network is used as a clothing feature extractor to help the model learn features such as clothing color and texture. We add different attention mechanisms to the model for feature enhancement and design a new embedding method. We also adjust the loss function to sample multiple negative classes to improve the similarity clustering effect, and add weights to strengthen it. From the experimental results, one can find that our proposed method achieves better retrieval accuracy than several state-of-the-art models.

关键词： Weight measurement Training image color analysis Annotations visual communication Convolution Clothing

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