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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International conference on image, Signal processing. and pattern recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing. cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing.systems

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Visual Tracking with Confidence Correction based on Log-polar Mapping

Visual Tracking with Confidence Correction based on Log-pola...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Liang, Qifeng Liu, Peiqiang Fu, Jingyi An, Zhiyong School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai264005 China

ISBN: (纸本)9781510666351

Transformer-based object tracking has become mainstream due to its high tracking performance at present. Most transformer-based trackers still classify the foreground and background and then calculate the bounding box by regression. However, we find that the classification confidence of transformer tracking is inaccurate. This leads to the bounding box with the highest confidence is not the closest to the ground truth. To address the problem, we propose a robust visual tracking approach with confidence correction using log-polar mapping. By correcting the confidence, the transformer tracking can obtain the best bounding box and improve the accuracy of the tracker. Extensive experiments on OTB2015, UAV123, and UAV20L show that our tracker is superior to the state-of-the-art trackers. © 2023 SPIE.

关键词： Deep learning

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Single-Shot End-to-end Road Graph Extraction

Single-Shot End-to-end Road Graph Extraction

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Bahl, Gaetan Bahri, Mehdi Lafarge, Florent IRT St Exupery Toulouse France Imperial Coll London London England Univ Cote Azur INRIA Nice France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources and incomplete outputs. By contrast, we propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest such as intersections, dead ends and turns, and a Graph Neural Network which predicts links between these points. Such a strategy is more efficient than iterative methods and allows us to streamline the training process by removing the need for generation of starting locations while keeping the training end-to-end. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results. We also benchmark the speed of our method and show that it outperforms existing approaches. Our method opens the possibility of in-flight processing.on embedded devices for applications such as real-time road network monitoring and alerts for disaster response.

关键词： Training Satellites Head Roads image edge detection Feature extraction Graph neural networks

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A proposed matrix-based Pythagorean fuzzy set distance metric and its usage in disease diagnosis

A proposed matrix-based Pythagorean fuzzy set distance metri...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Zhao, Rongcheng Academy of Computer and Information Science Software Academy Southwest University ChongQing China

ISBN: (纸本)9781510666351

It is currently unclear how to calculate the distance between two Pythagorean fuzzy sets. Several techniques have been proposed. However, not all available approaches can accurately exhibit differences between Pythagorean fuzzy sets and fulfill the axiom of measurement characteristic. And some other methods ignore the relation between the three variables of the Pythagorean fuzzy set. A new direction of estimating distance is presented to address the issue. Then some numerical examples are provided to demonstrate that the way of measuring distances avoids producing counter-intuitive and irrational conclusions and is more effective and reasonable than other similar methods. Furthermore, the suggested method of measuring distances between PFSs is evaluated in a real-world application scenario and compared to earlier approaches to illustrate its superiority and efficiency. At the same time, the viability of the suggested method in dealing with uncertainties in practice is demonstrated. © 2023 SPIE.

关键词： Fuzzy sets

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Multi-Stream Facial Adaptive Network for Expression recognition from a Single image 48

Multi-Stream Facial Adaptive Network for Expression Recognit...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Zhang, Baichuan Meng, Fanyang Ding, Runwei Liu, Mengyuan Peking University Shenzhen Graduate School Key Laboratory of Machine Perception China Peng Cheng Laboratory China

ISBN: (纸本)9781728163277

Facial expression recognition from a single image has potential applications in fields including human-computer interaction and medical diagnosis. Most recent methods use deep neural networks to directly learn from a roughly cropped facial image which is usually detected from a whole image by face detection algorithms. We observe that unrelated surrounding regions in the rough facial image prevent deep neural networks from learning facial-related discriminate features. To solve this problem, we present a Facial Adaptive Network (FAN) which is able to adaptively select an interest region from the given facial image, thus suffering less from the effect of unrelated regions. Based on the selected interest region, we further apply the self-attention mechanism to learn discriminate facial features. Moreover, we introduce a multi-stream FAN (ms-FAN) that learns richer facial features from multiple interest regions that are selected from pose-augmented facial images. Extensive experiments on Oulu-CASIA, CK+, and RAF-DB datasets consistently verify the effect of our proposed MS-FAN by achieving comparable results with state-of-the-art methods. Our code is available at https://***/zhangbc12/DAtt-ViT. © 2023 ieee.

关键词： Expression recognition self-attention

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Rethinking Implicit Neural Representations For Vision Learners 48

Rethinking Implicit Neural Representations For Vision Learne...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Song, Yiran Zhou, Qianyu Ma, Lizhuang Shanghai Jiao Tong University China

ISBN: (纸本)9781728163277

Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. The questions on how to explore INRs to high-level tasks and deep networks are still under-explored. Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks;2) lack of representation capabilities to deep networks. Motivated by above facts, we reformulate the definitions of INRs from a novel perspective, and propose an innovative Implicit Neural Representation Network (INRN), which is the first study of INRs to tackle both low-level and high-level tasks. Specifically, we present three key designs for basic blocks in INRN along with two different stacking ways and corresponding loss functions. Extensive experiments with analysis on both low-level task (image fitting) and high-level vision tasks (image classification, object detection, instance segmentation) demonstrate the effectiveness of the proposed method. © 2023 ieee.

关键词： computer Vision Implicit Neural Representations pattern recognition Representation Learning

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Medical image Segmentation of Systemic Lupus Erythematosus related Cerebral Small Vessel Disease

Medical Image Segmentation of Systemic Lupus Erythematosus r...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Yao, Zhiyuan Xie, Zhiheng Cao, Lei College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510666351

image segmentation is an important and difficult task in many medical applications. The segmentation results can be used to help doctors diagnose diseases, observe the lesion areas, and make surgical plans. Traditional medical image segmentation methods rely on experienced radiologists, which makes medical image segmentation time-consuming and costly. With the help of computer technology, the use of deep learning neural network for medical image segmentation has been applied in multiple disease lesions or organ segmentation tasks. Cerebral small vessel disease is a manifestation of systemic lupus erythematosus. In this paper, we apply Unet, Nested Unet and Recurrent Residual Unet to segment the collected medical images of systemic lupus erythematosus patients. The results of Unet and Nested Unet is better than Recurrent Residual Unet. All of the three networks can segment parts of lesion areas, but small areas are missed. The results of our experiments showed the performance of the existing segmentation neural networks on our dataset still need to be further improved to meet the requirements of practical applications. © 2023 SPIE.

关键词： Medical applications

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Understanding Adversarial Robustness on the Hypersphere

Understanding Adversarial Robustness on the Hypersphere

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Zhao, Zitian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510666351

Adversarial examples have raised public concern about the robustness of deep neural networks (DNNs). One universal approach to enhance the robustness is adversarial training which essentially augments the training data. However, adversarial training succeeded only to a very limited extent. This limited progress is partly due to the lack of interpretation and understanding of the robustness of DNNs. In this context, we try to explain the adversarial robustness by embedding the sample points onto a hypersphere, which naturally provides an interpretable metric for the distance between sample points. Different from the empirical intuition, it is observed that adversarial trained models show complex patterns when facing different datasets and training configurations. Observations and explanations about robustness and model behavior are made from the aspect of the distances between samples points. Lastly, we discuss the degradation of standard accuracy in the adversarial trained models and provide possible solutions. © 2023 SPIE.

关键词： Deep neural networks

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English Handwriting image Edge Detection Based on Fuzzy Logic 3

English Handwriting Image Edge Detection Based on Fuzzy Logi...

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3rd International conference on computer Vision and pattern Analysis, ICCPA 2023

作者： Wang, Xueying Liu, Hongwei Wang, Yuanshen School of Mathematics and Big Data Chongqing University of Arts and Sciences Chongqing China

ISBN: (纸本)9781510667563

This paper mainly applies fuzzy logic to the edge detection of handwritten images in English. image edge detection is an important part of image processing. Through image edge detection, the amount of data can be greatly reduced, and the information that can be considered irrelevant can be eliminated, and the important structural attributes of the image are retained. It uses six traditional operators to detect the edge of English handwritten images: canny, log, sobel, sprewitt, zerocross, and roberts. By comparing the edge detection results of the two kinds of experiments, we can find that the canny operator has the best edge detection effect among the traditional operators. In the fuzzy logic experiment, with the growth of Sx and Sy, the edge detection effect gradually weakened. In this paper, the experiment with Sx = 0. 05 and Sy = 0. 05 has the best edge detection results. In conclusion, the edge detection effect of English handwritten images based on fuzzy logic is better than that of English handwritten images based on traditional methods. © 2023 SPIE.

关键词： Fuzzy logic

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Preliminary Results of Laser Ablation during In Vivo Experiments: Comparison of Thermal Effects Obtained with Bare and Diffuser Tip Applicators

Preliminary Results of Laser Ablation during In Vivo Experim...

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45th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-society (EMBC)

作者： Korganbayev, Sanzhar De Landro, Martina Bianchi, Leonardo Verde, Juan Saccomandi, Paola Politecn Milan Dept Mech Engn I-20156 Milan Italy Inst Image Guided Surg Strasbourg F-67000 Strasbourg France

ISBN: (纸本)9798350324471

This work is a step towards the analysis of the effect of different laser applicator tips used for laser ablation of liver for in vivo experiments. As the thermal outcome of this minimally invasive treatment for tumors depends upon the interaction between the tissue and the light, the emission pattern of the laser applicator has a key role in the shape and size of the final treated region. Hence, we have compared two different laser applicators: a bare tip fiber (emitting light from the tip and forward) and a diffuser tip fiber (emitting light at 360 degrees circumferentially from the side of the fiber). The experiments have been carried out percutaneously in a preclinical scenario (anesthetized pigs), under computed tomography (CT) guidance. The thermal effects of the two applicators have been assessed in terms of real-time temperature distribution, by means of an array of 40 fiber Bragg grating (FBG) sensors, and in terms of cavitation and ablation volumes, measured through CT post-processing. The measurement error on the FBG-measured temperature due to breathing motion has been analyzed and filtered out. Results show that the maximum temperature reached 50.5 degrees C for the bare tip fiber experiment (measured at 6.24 mm distance from the applicator) and 60.9 degrees C for the diffuser tip fiber experiment (measured at 5.23 mm distance from the applicator). The diffuser tip fiber allowed to achieve a more symmetrical heat distribution than the bare tip fiber, and without cavitation volume.

关键词： Mammals

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