Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675441
Rijun Liao, Zhu Li, S. Bhattacharyya, George York
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.
{"title":"Aerial Image Classification with Label Splitting and Optimized Triplet Loss Learning","authors":"Rijun Liao, Zhu Li, S. Bhattacharyya, George York","doi":"10.1109/VCIP53242.2021.9675441","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675441","url":null,"abstract":"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.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675329
D. Cozzolino, Diego Gragnaniello, G. Poggi, L. Verdoliva
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.
{"title":"Towards Universal GAN Image Detection","authors":"D. Cozzolino, Diego Gragnaniello, G. Poggi, L. Verdoliva","doi":"10.1109/VCIP53242.2021.9675329","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675329","url":null,"abstract":"The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116346835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675406
Dong-Jae Lee, Kang-Kyu Lee, Jong-Ok Kim
After the invention of electric bulbs, most of lights surrounding our worlds are powered by alternative current (AC). This intensity variation can be captured with a high-speed camera, and we can utilize the intensity difference between consecutive video frames for various vision tasks. For color constancy, conventional methods usually focus on exploiting only the spatial feature. To overcome the limitations of conventional methods, a couple of methods to utilize AC flickering have been proposed. The previous work employed temporal correlation between high-speed video frames. To further enhance the previous work, we propose a deep spatio-temporal color constancy method using spatial and temporal correlations. To extract temporal features for illuminant estimation, we calculate the temporal correlation between feature maps where global features as well as local are learned. By learning global features through spatio-temporal correlation, the proposed method can estimate illumination more accurately, and is particularly robust to noisy practical environments. The experimental results demonstrate that the performance of the proposed method is superior to that of existing methods.
{"title":"Deep Color Constancy Using Spatio-Temporal Correlation of High-Speed Video","authors":"Dong-Jae Lee, Kang-Kyu Lee, Jong-Ok Kim","doi":"10.1109/VCIP53242.2021.9675406","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675406","url":null,"abstract":"After the invention of electric bulbs, most of lights surrounding our worlds are powered by alternative current (AC). This intensity variation can be captured with a high-speed camera, and we can utilize the intensity difference between consecutive video frames for various vision tasks. For color constancy, conventional methods usually focus on exploiting only the spatial feature. To overcome the limitations of conventional methods, a couple of methods to utilize AC flickering have been proposed. The previous work employed temporal correlation between high-speed video frames. To further enhance the previous work, we propose a deep spatio-temporal color constancy method using spatial and temporal correlations. To extract temporal features for illuminant estimation, we calculate the temporal correlation between feature maps where global features as well as local are learned. By learning global features through spatio-temporal correlation, the proposed method can estimate illumination more accurately, and is particularly robust to noisy practical environments. The experimental results demonstrate that the performance of the proposed method is superior to that of existing methods.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114591126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675396
Zizheng Liu, Zhenzhong Chen, Shan Liu
In this paper, we propose a two stage optimal bit allocation scheme for HEVC hierarchical coding structure. The two stage, i.e., the frame-level and the CTU-level bit allocation, are separately conducted in the traditional rate control methods. In our proposed method, the optimal allocation in the second stage is firstly considered, and then the allocation strategy in the second stage is deemed as a foreknowledge in the first stage and applied to guide the frame-level bit allocation. With the formulation, the two stage bit allocation problem can be converted to a joint optimization problem. By solving the formulated optimization problem, the two stage optimal bit allocation scheme is established, in which more appropriate number of bits can be allocated to each frame and each CTU. The experimental results show that our proposed method can bring higher coding efficiency while satisfying the constraint of bit rate precisely.
{"title":"Two Stage Optimal Bit Allocation for HEVC Hierarchical Coding Structure","authors":"Zizheng Liu, Zhenzhong Chen, Shan Liu","doi":"10.1109/VCIP53242.2021.9675396","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675396","url":null,"abstract":"In this paper, we propose a two stage optimal bit allocation scheme for HEVC hierarchical coding structure. The two stage, i.e., the frame-level and the CTU-level bit allocation, are separately conducted in the traditional rate control methods. In our proposed method, the optimal allocation in the second stage is firstly considered, and then the allocation strategy in the second stage is deemed as a foreknowledge in the first stage and applied to guide the frame-level bit allocation. With the formulation, the two stage bit allocation problem can be converted to a joint optimization problem. By solving the formulated optimization problem, the two stage optimal bit allocation scheme is established, in which more appropriate number of bits can be allocated to each frame and each CTU. The experimental results show that our proposed method can bring higher coding efficiency while satisfying the constraint of bit rate precisely.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115179157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675315
S. Alam, Moid Ul Huda, Muhammad Farhan
In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.
{"title":"Alpha-trimmed Mean Filter and XOR based Image Enhancement for Embedding Data in Image","authors":"S. Alam, Moid Ul Huda, Muhammad Farhan","doi":"10.1109/VCIP53242.2021.9675315","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675315","url":null,"abstract":"In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116199601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675399
J. Schneider, Johannes Sauer, M. Wien
RDPlot is an open source GUI application for plotting Rate-Distortion (RD)-curves and calculating Bjøntegaard Delta (BD) statistics [1]. It supports parsing the output of commonly used reference software packages, parsing *.csv-formatted files, and *.xml-formatted files. Once parsed, RDPlot offers the ability to evaluate video coding results interactively. Conceptually, several measures can be plotted over the bitrate and BD measurements can be conducted accordingly. Moreover, plots and corresponding BD statistics can be exported, and directly integrated into LaTeX documents.
{"title":"RDPlot – An Evaluation Tool for Video Coding Simulations","authors":"J. Schneider, Johannes Sauer, M. Wien","doi":"10.1109/VCIP53242.2021.9675399","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675399","url":null,"abstract":"RDPlot is an open source GUI application for plotting Rate-Distortion (RD)-curves and calculating Bjøntegaard Delta (BD) statistics [1]. It supports parsing the output of commonly used reference software packages, parsing *.csv-formatted files, and *.xml-formatted files. Once parsed, RDPlot offers the ability to evaluate video coding results interactively. Conceptually, several measures can be plotted over the bitrate and BD measurements can be conducted accordingly. Moreover, plots and corresponding BD statistics can be exported, and directly integrated into LaTeX documents.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126903550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675326
Hannah Och, T. Strutz, A. Kaup
Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.
{"title":"Optimization of Probability Distributions for Residual Coding of Screen Content","authors":"Hannah Och, T. Strutz, A. Kaup","doi":"10.1109/VCIP53242.2021.9675326","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675326","url":null,"abstract":"Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128064230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675328
Fang-Tsung Hsiao, Yi Lin, Yi-Chang Lu
In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.
{"title":"Using Regularity Unit As Guidance For Summarization-Based Image Resizing","authors":"Fang-Tsung Hsiao, Yi Lin, Yi-Chang Lu","doi":"10.1109/VCIP53242.2021.9675328","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675328","url":null,"abstract":"In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125898417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675398
Jinhui Feng, Sumei Li, Yongli Chang
In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.
{"title":"No-Reference Stereoscopic Image Quality Assessment Considering Binocular Disparity and Fusion Compensation","authors":"Jinhui Feng, Sumei Li, Yongli Chang","doi":"10.1109/VCIP53242.2021.9675398","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675398","url":null,"abstract":"In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124477881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-05DOI: 10.1109/VCIP53242.2021.9675390
Zhiyuan Huang, Zhaohui Hou, Pingyu Wang, Fei Su, Zhicheng Zhao
Rotating object detection is more challenging than horizontal object detection because of the multi-orientation of the objects involved. In the recent anchor-based rotating object detector, the IoU-based matching mechanism has some mismatching and wrong-matching problems. Moreover, the encoding mechanism does not correctly reflect the location relationships between anchors and objects. In this paper, RBox-Diff-based matching (RDM) mechanism and angle-first encoding (AE) method are proposed to solve these problems. RDM optimizes the anchor-object matching by replacing IoU (Intersection-over-Union) with a new concept called RBox-Diff, while AE optimizes the encoding mechanism to make the encoding results consistent with the relative position between objects and anchors more. The proposed methods can be easily applied to most of the anchor-based rotating object detectors without introducing extra parameters. The extensive experiments on DOTA-v1.0 dataset show the effectiveness of the proposed methods over other advanced methods.
{"title":"Rethinking Anchor-Object Matching and Encoding in Rotating Object Detection","authors":"Zhiyuan Huang, Zhaohui Hou, Pingyu Wang, Fei Su, Zhicheng Zhao","doi":"10.1109/VCIP53242.2021.9675390","DOIUrl":"https://doi.org/10.1109/VCIP53242.2021.9675390","url":null,"abstract":"Rotating object detection is more challenging than horizontal object detection because of the multi-orientation of the objects involved. In the recent anchor-based rotating object detector, the IoU-based matching mechanism has some mismatching and wrong-matching problems. Moreover, the encoding mechanism does not correctly reflect the location relationships between anchors and objects. In this paper, RBox-Diff-based matching (RDM) mechanism and angle-first encoding (AE) method are proposed to solve these problems. RDM optimizes the anchor-object matching by replacing IoU (Intersection-over-Union) with a new concept called RBox-Diff, while AE optimizes the encoding mechanism to make the encoding results consistent with the relative position between objects and anchors more. The proposed methods can be easily applied to most of the anchor-based rotating object detectors without introducing extra parameters. The extensive experiments on DOTA-v1.0 dataset show the effectiveness of the proposed methods over other advanced methods.","PeriodicalId":114062,"journal":{"name":"2021 International Conference on Visual Communications and Image Processing (VCIP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121490280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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