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DDIN: Enhancing Food Ingredient Recognition with Region and Category Discovery Modules

DDIN: Enhancing Food Ingredient Recognition with Region and ...

引用

IEEE visual communications and image processing (VCIP)

作者： Yiheng Ru Huaiyan Jiang Hang Song Bo Wei Yu Liu School of Microelectronics Tianjin University China School of Environment and Society Tokyo Institute of Technology Tokyo Japan School of Engineering The University of Tokyo Saitama Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Food ingredient recognition has received numerous attention for its importance for health-related applications. But there are still some challenges due to food dishes complexity such as detecting ingredients of varying sizes and identifying multiple ingredients within a single image. Addressing this challenge, we propose a novel ingredient recognition network named Dual Discovery Integration Network (DDIN) which consists of two modules: the Region Discovery (RD) module using deconvolution to get probability distribution map for find-grained region discover, the Category Discovery (CD) module using an ingredient dictionary to capture multiple ingredient category. Finally, the output from RD and CD modules are fused to obtain the final prediction results. The experimental results demonstrate that our model achieves state-of-the-art results in ingredient recognition on the Chinese Food dataset Vireo Food-172, and it also outperform existing methods for less parameters and lower computational complexity. Further visualization of discovered ingredient regions also shows the superiority of our method.

关键词： visualization image recognition Accuracy Graphical models Dictionaries Deconvolution visual communication Computational modeling Probability distribution Distribution functions

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image Captioning Algorithm Based on Sufficient visual Information and Text Information 27th

Image Captioning Algorithm Based on Sufficient Visual Inform...

引用

27th International conference on Neural Information processing

作者： Zhao, Yongqiang Rao, Yuan Wu, Lianwei Feng, Cong Xi An Jiao Tong Univ Xian 710049 Peoples R China

ISBN: (纸本)9783030638221;9783030638238

Most existing attention-based methods on image captioning focus on the current visual information and text information at each step to generate the next word, without considering the coherence between the visual information and the text information itself. We propose sufficient visual information (SVI) module to supplement the existing visual information contained in the network, and propose sufficient text information (STI) module to predict more text Words to supplement the text information contained in the network. Sufficient visual information module embed the attention value from the past two steps into the current attention to adapt to human visual coherence. Sufficient text information module can predict the next three words in one step, and jointly use their probabilities for inference. Finally, this paper combines these two modules to form an image captioning algorithm based on sufficient visual information and text information model (SVITI) to further integrate existing visual information and future text information in the network, thereby improving the image captioning performance of the model. These three methods are used in the classic image captioning algorithm, and have achieved achieve significant performance improvement compared to the latest method on the MS COCO dataset.

关键词： image captioning Sufficient visual information Sufficient text information

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Hybrid Implicit Neural image Compression with Subpixel Context Model and Iterative Pruner

Hybrid Implicit Neural Image Compression with Subpixel Conte...

引用

IEEE visual communications and image processing (VCIP)

作者： Hangyu Li Wei Jiang Litian Li Yongqi Zhai Ronggang Wang Shenzhen Graduate School Peking University Shenzhen China Pengcheng Laboratory Shenzhen China

Recently, image compression based on Implicit Neural Representation (INR) has gained interest. However, the performance of INR-based methods is poor because MLP can not fit high-frequency information well. Recently, a kind of hybrid INR method was proposed, which transmits not only the parameters of MLP but also some external latents to help to overfit at the decoder. Despite its low MACs (Multiply–Accumulate Operations per pixel), the decoding time is still slow due to the serial autoregressive context model for entropy coding. Hence, we propose two methods to improve the hybrid INR method’s decoding speed and performance. First, the Subpixel Context Model (SAM) is aimed to speed up the process of entropy coding. The proposed SAM is latent by latent instead of pixel by pixel. Second, we add an iterative pruner to the pipeline of hybrid INR compression, to further reduce the bit rate. The performance and MACs of our approach are on par with the SOTA of the hybrid INR methods. The decoding speed is about eight times faster on CPU, and three times faster on GPU.

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Learned Progressive image Compression With Spatial Autoregression

Learned Progressive Image Compression With Spatial Autoregre...

引用

IEEE visual communications and image processing (VCIP)

作者： Wenxin Tian Shaohui Li Wenrui Dai Cewu Lu Weisheng Hu Lin Zhang Junfeng Du Hongkai Xiong Shanghai Jiao Tong University Tsinghua Shenzhen International Graduate School Shanghai Cogent Technologies Co. Ltd. Caton Technology (Shanghai) Limited

Entropy modeling plays an important role in estimating the rates of latent representations and optimizing the rate-distortion performance for learned image compression. Autoregression modules are demonstrated to eliminate spatial/channel-wise redundancy of latent representations in fixed-rate learned image compression. However, it cannot be efficiently achieved in progressive coding due to the high computational complexity raised by element-wise probability prediction. In this paper, we propose a learned progressive image compression method that enables spatial autoregression for entropy modeling. Specifically, we develop a novel codeword alignment scheme to prevent coding redundancy and achieve efficient autoregression of latent representations in different quality layers. Consequently, conditional probability estimation for the latent prediction can be achieved based on spatial autoregression in a layer-wise manner. We further extend the proposed method by dead-zone quantizers to obtain promoted rate-distortion performance. The proposed method is a successful attempt to enable spatial autoregression in learned progressive coding and further bridge the performance gap with fixed-rate models. Experimental results show that it outperforms traditional methods such as JPEG and BPG, as well as recent fine-grained learned progressive coding models DPICT and PLONQ in terms of rate-distortion performance.

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High-Quality image Generation from Scene Graphs with Transformer

High-Quality Image Generation from Scene Graphs with Transfo...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Zhao, Xin Wu, Lei Chen, Xu Gong, Bin School of Software Shandong University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

image generation from scene graphs has traditionally focused on predicting layout from the scene graph using graph convolutional networks firstly, then converting the layout to an image. These methods might involve complex architectures, auxiliary losses, or side information such as object location or image crops during training. We propose a new method based on transformer for end-to-end image generation from scene graphs. To convert the scene graphs to the input format required by the transformer, we propose a novel method to tokenize the scene graphs. Furthermore, a discrete autoencoder is used to map the image to abstract space. Finally we train the transformer predicting image tokens conditioned on the scene graphs in an autoregressive way. Experiments on visual Genome and Coco-Stuff datasets show that our method significantly outperforms the state-of-the-art methods in terms of image quality. © 2022 IEEE.

关键词： Graphic methods

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visual Information Analysis Based on Viewing Aangle Distance Similarity and Velocity Measurement Model of Longitudinal Plane Translation

Visual Information Analysis Based on Viewing Aangle Distance...

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Asia-Pacific conference on communications Technology and Computer Science (ACCTCS)

作者： Wang Zhen Chang Xinlong Zhang Youhong Qi Chunbao Res Inst Hitechnol Xian Peoples R China

ISBN: (纸本)9781728176499

In order to obtain more accurate information in the analysis of visual information, this paper combines spatial relationships and uses two image and video processing tools, PS and AE, to establish "scale model", "cross-ratio theorem model", "similarity model of viewing angle distance", "velocity measurement model of longitudinal plane translation", and designed "viewing angle solution algorithm". The results show: through the above models and algorithms, mathematical tools such as MATLAB and Python3 can be used to efficiently, accurately and quickly calculate the height, floor area and road length of the building in the 3D video information, and obtain the horizontal view of the drone lens, longitudinal viewing angle and its flying height and speed.

关键词： image processing cross-ratio theorem model similarity model of viewing angle distance velocity measurement model of longitudinal plane translation

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Deep Learning Application of image Recognition Based on Self-driving Vehicle 5th

Deep Learning Application of Image Recognition Based on Self...

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5th International conference on Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, ICETCE 2022

作者： Bhujade, Stuti Kamaleshwar, T. Jaiswal, Sushma Babu, D. Vijendra Analyst in Artificial Intelligence UT Administration of DNH and Daman & Diu Daman396210 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Koni Chhattisgarh Bilaspur495009 India Department of Electronics and Communication Engineering Aarupadai Veedu Institute of Technology Vinayaka Mission’s Research Foundation Tamil Nadu Paiyanoor603104 India

ISBN: (纸本)9783031070112

A CNN (Convolutional Neural Network) is an artificial neural network used to evaluate visual pictures. It is used for visual image processing and is categorised as a deep neural network in deep learning. So, using real-time image processing, an AI autonomous driving model was built using a road crossing picture as an impediment. Based on the CNN model, we created a low-cost approach that can realistically perform autonomous driving. An end-to-end model is applied to the most widely used deep neural network technology for autonomous driving. It was shown that viable lane identification and maintaining techniques may be used to train and self-drive on a virtual road. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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A Study of visual Question Answering Techniques Based on Collaborative Multi-Head Attention

A Study of Visual Question Answering Techniques Based on Col...

引用

communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Yingli Yang Jingxuan Jin De Li Inst. of Intelligent Information Processing Yanbian University Yanji China Dept. of Compter Science and Technology Yanbian University Yanji China

In visual question answering task, the dominant approach recently has been to use a unified model for pre- training and fine tuning it. This unified model typically uses a transformer to fuse image and text information. In order to optimize the performance of the model on visual question answering task, this paper proposes a transformer architecture based on a collaborative multi-head attention mechanism to address the key/value projection redundancy problem in the multi-head attention mechanism of the transformer. In addition, this paper uses the Swin transformer model as the image feature extractor to extract multi-scale image information. Validation experiments are conducted on the VQA v2 dataset in this paper, and the experimental results show that applying the collaborative multi-head attention approach and the Swin transformer backbone to the visual question answering model can effectively improve the correct rate of the visual question answering task.

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Quantum Fourier Transform in image processing 1

引用

3rd International conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Mukhamedieva, D.T. Sobirov, R.A. Turgunova, N.M. Samijonov, B.N. Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University 39 Kari Niyaziy Street Tashkent100000 Uzbekistan Sejong University 209 Neungdong-ro Gwangjin-gu Seoul Korea Republic of

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

This paper presents an approach to apply quantum Fourier transform (QFT) to image processing using quantum computing. The use of quantum computing for image analysis and processing is becoming increasingly relevant in modern science and technology. A quantum QFT circuit is presented, implemented using the Qiskit framework, which is a tool for programming quantum computers. The paper presents the basic steps of QFT and their application to a state vector representing the pixel intensities of an image. The influence of quantum transformation on the image structure is studied and the results are presented in the form of graphs and visualizations. In addition, we have introduced QFT quantum circuit inference capabilities for a more visual representation of the algorithm. The results highlight the potential of quantum computing in the field of image processing and open new prospects for the use of quantum technologies in the field of computer vision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Circuit simulation

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High-Fidelity Free-View Talking Head Synthesis for Low-Bandwidth Video conference

High-Fidelity Free-View Talking Head Synthesis for Low-Bandw...

引用

IEEE visual communications and image processing (VCIP)

作者： Zhiyu Zhang Anni Tang Chen Zhu Guo Lu Rong Xie Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

As video conferencing becomes an indispensable part of human’s daliy life, how to achieve a high-fidelity calling experience under low bandwidth has been a popular and challenging issue. Deep generative models have great potential in low-bandwidth facial video compression due to the excellent generation capability based on abridged information. Nevertheless, exsiting deep generation-based compression methods tend to handle motion information in pure 2D or pseudo 3D space, causing facial distortion when large head poses are encountered. In this paper, we propose a 3D-aware high-fidelity facial video conferencing system based on a parameterized NeRF-based face model. Through the compression of the parameterized face model and the transmisstion of extracted facial parameters, we implement high-fidelity talking head synthesis for video conferencing at an ultra-low bitrate. Additionally, the 3D perception capability of the system allows for viewpoint control over the head, achieving higher interactivity and practicability. Extensive experiments verify the effectiveness of the proposed 3D-aware high-fidelity free-view facial video conferencing system.

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