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Drone-Based Car Counting via Density Map Learning

Drone-Based Car Counting via Density Map Learning

引用

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

作者： Huang, Jingxian Ding, Guanchen Guo, Yujia Yang, Daiqin Wang, Sihan Wang, Tao Zhang, Yunfei Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Inc Shenzhen 518052 Peoples R China

ISBN: (纸本)9781728180687

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

关键词： Car counting density map learning drone-based image

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FRIDAY: Mitigating Unintentional Facial Identity in Deepfake Detectors Guided by Facial Recognizers

FRIDAY: Mitigating Unintentional Facial Identity in Deepfake...

引用

2024 conference on visual communications and image processing

作者： Kim, Younghun Kwon, Myung-Joon Lee, Wonjun Kim, Changick Korea Adv Inst Sci & Technol Grad Sch Green Growth & Sustainabil Daejeon South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Previous Deepfake detection methods perform well within their training domains, but their effectiveness diminishes significantly with new synthesis techniques. Recent studies have revealed that detection models make decision boundaries based on facial identity instead of synthetic artifacts, leading to poor cross-domain performance. To address this issue, we propose FRIDAY, a novel training method that attenuates facial identity utilizing a face recognizer. To be specific, we first train a face recognizer using the same backbone as the Deepfake detector. We then freeze the recognizer and use it during the detector's training to mitigate facial identity information. This is achieved by feeding input images into both the recognizer and the detector, then minimizing the similarity of their feature embeddings using our Facial Identity Attenuating loss. This process encourages the detector to produce embeddings distinct from the recognizer, effectively attenuating facial identity. Comprehensive experiments demonstrate that our approach significantly improves detection performance on both in-domain and cross-domain datasets.

关键词： Deepfake Detection Unintentional Facial Identity Face Recognition image Forensics

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

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

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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Single image Super-Resolution Using ConvNeXt

Single Image Super-Resolution Using ConvNeXt

引用

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

作者： You, Chenghui Hong, Chaoqun Liu, Lijuan Lin, Xuehan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665475921

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 similar to 0.32dB in quantitative metrics with fewer parameters.

关键词： single image super-resolution convolutional neural network deep separable convolution

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Variable Bitrate Models For Learned image Compression with Multi-gain units and Weighted Probability Assignment

Variable Bitrate Models For Learned Image Compression with M...

引用

2024 conference on visual communications and image processing

作者： Wang, Ran Jiang, Wen Sun, Heming Katto, Jiro Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa Japan Waseda Univ Waseda Res Inst Sci & Engn Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

With the advancement of deep learning techniques, learned image compression (LIC) has surpassed traditional compression methods. However, these methods typically require training separate models to achieve optimal rate-distortion performance, leading to increased time and resource consumption. To tackle this challenge, we propose leveraging multi-gain and inverse multi-gain unit pairs to enable variable rate adaptation within a single model. Nevertheless, experiments have shown that rate-distortion performance may degrade at certain bitrates. Therefore, we introduce weighted probability assignment, where different selection probabilities are assigned during training based on lambda values, to increase the model's training frequency under specific bitrate conditions. To validate our approach, extensive experiments were conducted on Transformer-based and CNN-based models. The experimental results validate the efficiency of our proposed method.

关键词： Learned image compression variable rate multigain units weighted probability assignment

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A Multi-dimensional Aesthetic Quality Assessment Model for Mobile Game images

A Multi-dimensional Aesthetic Quality Assessment Model for M...

引用

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

作者： Wang, Tao Sun, Wei Min, Xiongkuo Lu, Wei Zhang, Zicheng Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

With the development of the game industry and the popularization of mobile devices, mobile games have played an important role in people's entertainment life. The aesthetic quality of mobile game images determines the users' Quality of Experience (QoE) to a certain extent. In this paper, we propose a multi-task deep learning based method to evaluate the aesthetic quality of mobile game images in multiple dimensions (i.e. the fineness, color harmony, colorfulness, and overall quality). Specifically, we first extract the quality-aware feature representation through integrating the features from all intermediate layers of the convolution neural network (CNN) and then map these quality-aware features into the quality score space in each dimension via the quality regressor module, which consists of three fully connected (FC) layers. The proposed model is trained through a multi-task learning manner, where the quality-aware features are shared by different quality dimension prediction tasks, and the multi-dimensional quality scores of each image are regressed by multiple quality regression modules respectively. We further introduce an uncertainty principle to balance the loss of each task in the training stage. The experimental results show that our proposed model achieves the best performance on the Multi-dimensional Aesthetic assessment for Mobile Game image database (MAMG) among state-of-the-art image quality assessment (IQA) algorithms and aesthetic quality assessment (AQA) algorithms.

关键词： mobile game image aesthetic quality assessment multi-task learning deep learning

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RGBD-based Real-time Volumetric Reconstruction System: Architecture Design and Implementation

RGBD-based Real-time Volumetric Reconstruction System: Archi...

引用

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

作者： Zhou, Kai Guo, Shuai Hu, Jingchuan Wang, Jionghao Wang, Qiuwen Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781665475921

With the increasing popularity of commercial depth cameras, 3D reconstruction of dynamic scenes has aroused widespread interest. Although many novel 3D applications have been unlocked, real-time performance is still a big problem. In this paper, a low-cost, real-time system: LiveRecon3D, is presented, with multiple RGB-D cameras connected to one single computer. The goal of the system is to provide an interactive frame rate for 3D content capture and rendering at a reduced cost. In the proposed system, we adopt a scalable volume structure and employ ray casting technique to extract the surface of 3D content. Based on a pipeline design, all the modules in the system run in parallel and are designed to minimize the latency to achieve an interactive frame rate of 30 FPS. At last, experimental results corresponding to implementation with three Kinect v2 cameras are presented to verify the system's effectiveness in terms of visual quality and real-time performance.

关键词： 3D reconstruction real-time multiview RGB-D cameras

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Quality of Experience Evaluation for Streaming Video Using CGNN

Quality of Experience Evaluation for Streaming Video Using C...

引用

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

作者： Zhou, Zhiming Dong, Yu Song, Li Xie, Rong Li, Lin Zhou, Bing Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China MIGU Co Ltd Beijing Peoples R China

ISBN: (纸本)9781728180687

One of the principal contradictions these days in the field of v ideo is l ying b etween t he b ooming d emand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.

关键词： Quality of Experience (QoE) adaptive video streaming Gate Recurrent Unit

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Swin Transformer based Siamese Network for Thermal and Optical image Registration 31

Swin Transformer based Siamese Network for Thermal and Optic...

引用

31st IEEE conference on Signal processing and communications Applications (SIU)

作者： Elsaeidy, Mohamed Yagmur, Ismail Can Ates, Hasan Fehmi Gunturk, Bahadir Kursat Istanbul Medipol Univ Dept Comp Engn Istanbul Turkiye Ozyegin Univ Dept Comp Sci Istanbul Turkiye

ISBN: (纸本)9798350343557

The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.

关键词： Multi-modal image registration Transformer network Keypoint

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DEN: Disentanglement and Enhancement Networks for Low Illumination images

DEN: Disentanglement and Enhancement Networks for Low Illumi...

引用

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

作者： Bow, Nelson Chong Ngee Vu-Hoang Tran Kerdsiri, Punchok Loh, Yuen Peng Huang, Ching-Chun Multimedia Univ Fac Comp & Informat Cyberjaya Selangor Malaysia Ho Chi Minh City Univ Technol & Educ Fac Elect & Elect Engn Ho Chi Minh City Vietnam PTT Explorat & Prod PCL DCOE Digital Ctr Excellence Bangkok Thailand Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728180687

Though learning-based low-light enhancement methods have achieved significant success, existing methods are still sensitive to noise and unnatural appearance. The problems may come from the lack of structural awareness and the confusion between noise and texture. Thus, we present a low-light image enhancement method that consists of an image disentanglement network and an illumination boosting network. The disentanglement network is first used to decompose the input image into image details and image illumination. The extracted illumination part then goes through a multi-branch enhancement network designed to improve the dynamic range of the image. The multi-branch network extracts multi-level image features and enhances them via numerous subnets. These enhanced features are then fused to generate the enhanced illumination part. Finally, the denoised image details and the enhanced illumination are entangled to produce the normal-light image. Experimental results show that our method can produce visually pleasing images in many public datasets

关键词： low-light enhancement image disentanglement multi-branch enhancement network

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