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Improving image Classification using Triple-GAN: A Fusion of Generative Adversarial Networks and Transfer Learning

Improving Image Classification using Triple-GAN: A Fusion of...

引用

Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Danial Ebrahimzadeh Sarah Sharif Yaser Banad School of Electrical and Computer Engineering University of Oklahoma Oklahoma US

Generative neural networks, particularly Generative Adversarial Networks (GANs), have gained a significant attention for their ability to generate new data. Moreover, transfer learning, known for its efficient processing and promising research outcomes, has emerged as a popular approach for feature extraction. This paper explores the application of the Triple-GAN model, which combines the advantages of GANs with transfer learning, particularly in the context of image classification. The study focuses on utilizing the Triple-GAN model combined with a pre-trained ResNet101 model for image classification. The evaluation of the proposed approach is conducted on the CIFAR-100 dataset, employing specific metrics to assess the model’s efficacy. The results highlight the promising nature of the proposed model, showcasing its potential for accurate image classification. Our model exhibits a remarkable level of accuracy reaching above 71.52 % outperforming other conventional ANN models. The significance of this research lies in its potential to advance image classification techniques by employing the Triple-GAN model with transfer learning as a viable alternative to traditional classification methods.

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Explaining Face Recognition Through SHAP-Based Pixel-Level Face image Quality Assessment

Explaining Face Recognition Through SHAP-Based Pixel-Level F...

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ieee international Joint conference on Biometrics (IJCB)

作者： Clara Biagi Louis Rethfeld Arjan Kuijper Philipp Terhörst Paderborn University Germany Technical University of Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Germany

Biometric face recognition models are widely used in many different real-world applications. The output of these models can be used to make decisions that may strongly impact people. However, an explanation of how and why such outputs are derived is usually not given to humans. The lack of explainability of face recognition models leads to distrust in their decisions and does not encourage their use. The performance of face recognition models is influenced by the quality of the input image. In case the quality of a face image is too low, the face recognition system will reject it to avoid compromising its performance. The quality is evaluated by Face image Quality (FIQ) approaches, which assigned quality scores to the input images. Pixel-level face image quality (PLFIQ) increases the explainability of quality scores by explaining face image quality at the pixel level. This allows the users of face recognition systems to spot low-quality areas and allows them to make guided corrections. Previous works introduced the concept of PLFIQ and proposed evaluation procedures. This work proposes a new way of computing PLFIQ values depending on given FIQ methods using Shapley Values. They score the contribution of each pixel to the overall image quality evaluation. Therefore, Integrating Shapley Values increases the explainability of the FIQ models. Results show that using these methods leads to significantly better and more robust PLFIQ values estimates and thus provide better explainability.

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Twin Branch Transformers-Based Re-Ranking for image Retrieval

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

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ieee international conference on Big Data and Cloud Computing (BdCloud)

作者： Ziqi Wang Wei Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor; re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric); the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity.

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PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs

PlankAssembly: Robust 3D Reconstruction from Three Orthograp...

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international conference on computer Vision (ICCV)

作者： Wentao Hu Jia Zheng Zixin Zhang Xiaojun Yuan Jian Yin Zihan Zhou Sun Yat-Sen University Guangdong Key Laboratory of Big Data Analysis and Processing Manycore Tech Inc. University of Electronic Science and Technology of China

In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.

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Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation

Learning Foresightful Dense Visual Affordance for Deformable...

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international conference on computer Vision (ICCV)

作者： Ruihai Wu Chuanruo Ning Hao Dong CFCS School of CS PKU BAAI National Key Laboratory for Multimedia Information Processing School of CS PKU School of EECS PKU

Understanding and manipulating deformable objects (e.g., ropes and fabrics) is an essential yet challenging task with broad applications. Difficulties come from complex states and dynamics, diverse configurations and high-dimensional action space of deformable objects. Besides, the manipulation tasks usually require multiple steps to accomplish, and greedy policies may easily lead to local optimal states. Existing studies usually tackle this problem using reinforcement learning or imitating expert demonstrations, with limitations in modeling complex states or requiring hand-crafted expert policies. In this paper, we study deformable object manipulation using dense visual affordance, with generalization towards diverse states, and propose a novel kind of foresightful dense affordance, which avoids local optima by estimating states’ values for long-term manipulation. We propose a framework for learning this representation, with novel designs such as multi-stage stable learning and efficient self-supervised data collection without experts. Experiments demonstrate the superiority of our proposed foresightful dense affordance. Project page: https://***/DeformableAffordance

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Multimodal Fusion of Transformer with Attention Mechanism for Improved Contextual image Captioning

Multimodal Fusion of Transformer with Attention Mechanism fo...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： V Singampalli Suma Latha Shriya Dindi Vannelaganti Sai Prabhu Bejjam Praveen Poddu Abhishek R. Cristin Department of Computer Science & Engineering GMR Institute of Technology Rajam Vizianagaram

image captioning can provide automatic annotations or labels for images, describing the objects, scenes or activities present in the image. This can be beneficial in applications that require large-scale image analysis such as image recognition, recommendation systems and content classification. Traditional methods for generating image descriptions rely on rule-based systems, which are time consuming and lack the ability to capture the contextual details of complex images. The proposed method combines the power of two state-of the-art models: attention mechanism and Transformer. Attention mechanism is a powerful model for multimodal understanding that combines the domains of vision and language processing. Attention mechanism leverages pertaining on large-scale datasets to capture semantic relationships between images and text. By combining the multimodal understanding of attention mechanism with the sequence modelling capabilities of Transformer, superior performance in image captioning task can be achieved. The model can better capture complicated context dependence in input captions by adding more transformer layers. The suggested system's performance is assessed using BLEU-1,2,3,4 and other objective evaluation criteria.

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Complex-valued SAR image Compression: A Novel Approach for Amplitude and Phase Recovery

Complex-valued SAR Image Compression: A Novel Approach for A...

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ieee Visual Communications and image processing (VCIP)

作者： Paras Maharjan Zhu Li University of Missouri Kansas City

Synthetic Aperture Radar (SAR) is an active and coherent imaging system that utilizes radio waves to illuminate the Earth’s surface, generating complex-valued images of the ground. It is widely employed in environmental monitoring and surveillance due to its ability to function effectively in diverse weather and lighting conditions, including day and night. SAR achieves this through the emission of coherent radio frequency signals and the capture of slant range and azimuth information. These details are stored as a complex signal in the form of Inphase (I) and Quadrature (Q) components, known as complex-valued SAR. The complex values (I/Q) can be transformed into amplitude and phase information. SAR images have a substantial size and a wide dynamic range, which pose challenges for storage and transmission. Thus, the adoption of compression techniques becomes essential to manage the large size of SAR images. In contrast to existing methods that primarily compress amplitude information, our study presents a novel pipeline to compress complex-valued SAR images using a prediction-recovery framework that utilizes a conventional image compression algorithm, High-Efficiency Video Codec (HEVC), to encode the original image to a bitstream. Subsequently, the framework decodes a prediction of the I/Q channels and then recovers the phase and amplitude via SARRecoveryNet, a deep-learning-based network, to effectively remove compression artifacts and recover information that suits different applications. By directly compressing the I/Q values as a predictor, and recovery via a deep learning module, our method preserves both the amplitude and phase information of the SAR image as part of the loss function in the training. Experimental results demonstrate that our proposed approach significantly enhances complex-valued SAR image compression in terms of rate-distortion (RD) performance and visual perception. The code will be made available on GitHub after the paper is accepted.

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Bag of Tricks with Quantized Convolutional Neural Networks for image Classification

Bag of Tricks with Quantized Convolutional Neural Networks f...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jie Hu Mengze Zeng Enhua Wu State Key Laboratory of Computer Science ISCAS University of Chinese Academy of Sciences ByteDance Inc University of Macau

Deep neural networks have been proven effective in a wide range of tasks. However, their high computational and memory costs make them impractical to deploy on resource-constrained devices. To address this issue, quantization schemes have been proposed to reduce the memory footprint and improve inference speed. While numerous quantization methods have been proposed, they lack systematic analysis for their effectiveness. To bridge this gap, we collect and improve existing quantization methods and propose a gold guideline for post-training quantization. We evaluate the effectiveness of our proposed method with two popular models, ResNet50 and MobileNetV2, on the imageNet dataset. By following our guidelines, no accuracy degradation occurs even after directly quantizing the model to 8-bits without additional training. A quantization-aware training based on the guidelines can further improve the accuracy in lower-bits quantization. Moreover, we have integrated a multi-stage fine-tuning strategy that works harmoniously with existing pruning techniques to reduce cost even further. Remarkably, our results reveal that a quantized MobileNetV2 with 30% sparsity actually surpasses the performance of the equivalent full-precision model, underscoring the effectiveness and resilience of our proposed scheme.

关键词： Training Deep learning Gold Quantization (signal) Costs Systematics Neural networks

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Multiinstitutional Lung image Classification Using Privacy-Preserving Horizontal Federated Learning with Homomorphic Encryption

Multiinstitutional Lung Image Classification Using Privacy-P...

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international conference on e-health Networking, applications and Services (HealthCom)

作者： Desen Cao Can Wang Hao Sun Chaolun Cao Ming Kang Hailin Zheng Shaosong Zhou Xingze Guan Yuanyang Cao Qing Tong Department of Biomedical Engineering Chinese PLA General Hospital Beijing China Medical Business Department Wonders Information Co. Ltd. Shanghai China Technology Research Department Wonders Information Co. Ltd. Shanghai China

Nowadays, deep learning is widely used for medical image analysis and computer-aided diagnosis. However, the process of training them requires a large amount of data and accurate annotations. Medical data is scarce, sensitive, and heterogeneous across multiple institutions, which poses challenges for building accurate models. To address this, we develop a hor-izontal Federated Learning system with Homomorphic Encryption (FLHE) and a deep convolutional neural network ResNet-50 for multiinstitutional lung image classification. The proposed system enables multiple medical institutions to collaboratively train a model without sharing sensitive data and uses encryption to ensure privacy. Experimental results demonstrate that our system achieves high accuracy while preserving privacy, and has the potential to improve diagnosis and treatment efficiency. Our work highlights the potential of federated learning in addressing the challenges of medical image classification, facilitating collaborative research, and promoting medical data sharing in healthcare and medical domains.

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Herbal Medicinal Plant Identification Using Leaf Vein Through image processing and Convolutional Neural Network

Herbal Medicinal Plant Identification Using Leaf Vein Throug...

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ieee Region 10 international conference TENCON

作者： Rayellee Myrtle Laire Ang Noel B. Linsangan School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

The utilization of herbal medicinal plants dates to antiquity, and as human civilization has progressed and technology has advanced, a significant proportion of contemporary medicines have originated from herbal sources. The Philippines is renowned for its extensive utilization of herbal medicinal plants, exemplified by the Department of Health's recognition of ten prominent herbal medicines under the “Traditional and Alternative Medicine Act (TAMA)”, or the “Republic Act No. 8423”. This legislative measure not only enhances the healthcare system within the country but also underscores the nation's commitment to incorporating traditional healing methods. Many herbal medicinal plants possess valuable therapeutic properties; however, the lack of comprehensive research and clinical trials has resulted in limited knowledge regarding the specific benefits associated with each of these plants. This research endeavor culminated in developing a device capable of identifying the herbal medicinal plant name, scientific name, and medicinal purposes by capturing a leaf image. This study's initial phase entails pre-processing the captured image and extracting the leaf vein characteristics using the Histogram of Oriented Gradient (HOG) feature extraction algorithm. Subsequently, the Convolutional Neural Network (CNN) algorithm is employed to identify the leaf based on these extracted features. Following the execution of 25 trials for each of the five selected herbal medicinal plants, namely Banaba, Bayabas, Bignay, Sambong, and Serpentina, in addition to 25 tests for two untrained plants, the research findings demonstrated a commendable accuracy rate of 95%.

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