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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...

引用

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...

引用

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...

引用

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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A New Model for Prototype-based Continual Learning in Hyperspherical Space

A New Model for Prototype-based Continual Learning in Hypers...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Ren, Yixin Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhou, Shuigeng School of Computer Science Fudan University Shanghai200438 China Alibaba Group Hangzhou311121 China

ISBN: (纸本)9798350368741

The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance degradation on old tasks. A good continual learning model should be robustly adapted to the upcoming tasks while effectively handling catastrophic forgetting. In this paper, we focus on the class-incremental learning (CIL) task, and propose a novel prototype-based continual learning model C-HPN that projects the visual features into a hypersphere geometric space, where continual learning is conducted. C-HPN features two-fold contributions. On the one hand, instead of using the popular cross-entropy loss, we develop an instance-prototype compact loss to obtain well-clustered hyperspherical embeddings and a prototype-prototype separability loss to boost the model's generalization by introducing large angle distance inductive bias between prototypes in the hyperspherical space. On the other hand, prototype construction and adaptation strategies are designed for effectively adapting new classes, and an instance-prototype relationship preservation distillation mechanism is introduced to overcome catastrophic forgetting. Extensive experiments on several image datasets validate the effectiveness of the proposed method. © 2025 ieee.

关键词： class-incremental learning continual learning hypersphere geometry prototype networks

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English Handwriting image Edge Detection Based on Fuzzy Logic 3

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

引用

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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Improving Deep Learning-based Automatic Checkout System Using image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Pham, Long Hoang Tran, Duong Nguyen-Ngoc Nguyen, Huy-Hung Jeon, Hyung-Joon Tran, Tai Huu-Phuong Jeon, Hyung-Min Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350302493

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing.step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest;(2) tracking objects in consecutive frames;and (3) counting objects using a track management pipeline. Several data generation techniques - including copy-and-paste, random placement, and augmentation - are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792. © 2023 ieee.

关键词： image enhancement

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Datta, Gourav Liu, Zeyu Li, Anni Beerel, Peter A. Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9798350368741

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and imageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10. © 2025 ieee.

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A new RCNN-RF for efficient and robust parking lot available detection 16

A new RCNN-RF for efficient and robust parking lot available...

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16th International conference on Digital image processing. ICDIP 2024

作者： Zhang, Qian Ran, Yaqin Luo, Di Xu, Kaili Shi, Jilaing School of Data Science and Information Engineering GuiZhou MinZu University Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

It's very meaningful to conduct the driver to the parking space available in the parking lot clearly and accurately by computer vision and computational intelligence. While it is an extremely difficult task, because of the scene complexity in the parking lot. In this paper, a new computational intelligence technology is proposed to detect parking spaces available in the parking lot outdoor based on fusing of Region Convolution Neural Network (RCNN) and random forests (RF), and we called it as RCNN-RF. Within the RCNN-RF, a new activation function is design. And with the help of random forests, RCNN-RF overcomes scene complexity includes illumination, weather, and slight occlusion successfully. Extensive experimental results on the benchmark dataset named PKLot and the self-built dataset named GZMU-LOT outperform those of the state-of-the-art, and it shows the superiority of our proposed RCNN-RF method. © 2024 SPIE.

关键词： Random forests

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