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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： He, Xuanlin Yang, Jie Kasabov, Nikola Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9783030614003

Object detection is an important and fundamental task in computer vision. Recently, the emergence of deep neural network has made considerable progress in object detection. Deep neural network object detectors can be grouped in two broad categories: the two-stage detector and the one-stage detector. One-stage detectors are faster than two-stage detectors. However, they suffer from a severe foreground-backg-round class imbalance during training that causes a low accuracy performance. RetinaNet is a one-stage detector with a novel loss function named Focal Loss which can reduce the class imbalance effect. Thereby RetinaNet outperforms all the two-stage and one-stage detectors in term of accuracy. The main idea of focal loss is to add a modulating factor to rectify the cross-entropy loss, which down-weights the loss of easy examples during training and thus focuses on the hard examples. However, cross-entropy loss only focuses on the loss of the ground-truth classes and thus it can’t gain the loss feedback from the false classes. Thereby cross-entropy loss does not achieve the best convergence. In this paper, we proposed a new loss function named Dual Cross-Entropy Focal Loss, which improves on the focal loss. Dual cross-entropy focal loss adds a modulating factor to rectify the dual cross-entropy loss towards focusing on the hard samples. Dual cross-entropy loss is an improved variant of cross-entropy loss, which gains the loss feedback from both the ground-truth classes and the false classes. We changed the loss function of RetinaNet from focal loss to our dual cross-entropy focal loss and performed some experiments on a small vehicle dataset. The experimental results show that our new loss function improves the vehicle detection performance. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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Supervised Classification of Plant image Based on Attention Mechanism

Supervised Classification of Plant Image Based on Attention ...

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International Conference on Systems and Informatics (ICSAI)

作者： Jie Li Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665426251

In view of the wide variety of plants on the earth, the plant species identification is particularly necessary to protect and preserve biodiversity. In this work, we propose a plant image classification method based on the encoder-decoder model with additive attention mechanism to extract plant image features and convert them into text descriptions related to plant features. In a well-trained network, it can successfully classify on the species of the generated plant texts. We show that, the proposed method not only equalizes the results of deep convolutional neural network on classification task, but also uses of the prior information of botanists in classification, and thus provide a significant prediction result.

关键词： Earth image recognition Text recognition Biological system modeling Feature extraction Convolutional neural networks Biodiversity

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Optimal Initialization Conditions Discovery to Improve Clustering Based image Segmentation

SSRN

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SSRN 2022年

作者： Khan, Zubair Khan, Tehreem Yang, Jie Tu, Enmei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China University of Central Punjab Bahawalpur Pakistan

A B S T R A C TIn this paper, we propose a solution to the most important factor that deteriorates the performance of the clustering algorithms. We propose an efficient approach for discovering optimal initialization conditions for clustering-driven high-quality image segmentation (EAIS). The proposed approach solves the key clustering issue by uniquely utilizing image histograms to determine the optimal initialization conditions for pixel clusters (segmentation). The proposed approach comprises five modules, deep image reconstruction, intra-histogram peaks determination, inter-histogram peaks association, mutual consensus-oriented cluster seeds merging, and morphological reconstruction-driven spatial post-processing. Diverse experimental results on the BSDS500 benchmark validate that our proposed approach outperforms state-of-the-art (SOTA) methods regarding segmentation quality and computational efficiency. © 2022, The Authors. All rights reserved.

关键词： Merging

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Towards unbiased random features with lower variance for stationary indefinite kernels

arXiv

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arXiv 2021年

作者： Luo, Qin Fang, Kun Yang, Jie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Random Fourier Features (RFF) demonstrate well-appreciated performance in kernel approximation for large-scale situations but restrict kernels to be stationary and positive definite. And for non-stationary kernels, the corresponding RFF could be converted to that for stationary indefinite kernels when the inputs are restricted to the unit sphere. Numerous methods provide accessible ways to approximate stationary but indefinite kernels. However, they are either biased or possess large variance. In this article, we propose the generalized orthogonal random features, an unbiased estimation with lower variance. Experimental results on various datasets and kernels verify that our algorithm achieves lower variance and approximation error compared with the existing kernel approximation methods. With better approximation to the originally selected kernels, improved classification accuracy and regression ability is obtained with our approximation algorithm in the framework of support vector machine and regression. © 2021, CC BY.

关键词： Approximation algorithms

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Translatotron-V(ison): An End-to-End Model for In-image Machine Translation

arXiv

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arXiv 2024年

作者： Lan, Zhibin Niu, Liqiang Meng, Fandong Zhou, Jie Zhang, Min Su, Jinsong School of Informatics Xiamen University China Pattern Recognition Center WeChat AI Tencent Inc China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism China Institute of Computer Science and Technology Soochow University China

In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, conventional cascaded methods suffer from issues such as error propagation, massive parameters, and difficulties in deployment and retaining visual characteristics of the input image. Thus, constructing end-to-end models has become an option, which, however, faces two main challenges: 1) the huge modeling burden, as it is required to simultaneously learn alignment across languages and preserve the visual characteristics of the input image;2) the difficulties of directly predicting excessively lengthy pixel sequences. In this paper, we propose Translatotron-V(ision), an end-to-end IIMT model consisting of four modules. In addition to an image encoder, and an image decoder, our model contains a target text decoder and an image tokenizer. Among them, the target text decoder is used to alleviate the language alignment burden, and the image tokenizer converts long sequences of pixels into shorter sequences of visual tokens, preventing the model from focusing on low-level visual features. Besides, we present a two-stage training framework for our model to assist the model in learning alignment across modalities and languages. Finally, we propose a location-aware evaluation metric called Structure-BLEU to assess the translation quality of the generated images. Experimental results demonstrate that our model achieves competitive performance compared to cascaded models with only 70.9% of parameters, and significantly outperforms the pixel-level end-to-end IIMT model. Copyright © 2024, The Authors. All rights reserved.

关键词： Pixels

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Eliminating Contextual Prior Bias for Semantic image Editing via Dual-Cycle Diffusion

arXiv

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arXiv 2023年

作者： Yang, Zuopeng Chu, Tianshu Lin, Xin Gao, Erdun Liu, Daqing Yang, Jie Wang, Chaoyue Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China the Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China the School of Mathematics and Statistics The University of Melbourne MelbourneVIC3010 Australia the JD Explore Academy Beijing100176 China

The recent success of text-to-image generation diffusion models has also revolutionized semantic image editing, enabling the manipulation of images based on query/target texts. Despite these advancements, a significant challenge lies in the potential introduction of contextual prior bias in pre-trained models during image editing, e.g., making unexpected modifications to inappropriate regions. To address this issue, we present a novel approach called Dual-Cycle Diffusion, which generates an unbiased mask to guide image editing. The proposed model incorporates a Bias Elimination Cycle that consists of both a forward path and an inverted path, each featuring a Structural Consistency Cycle to ensure the preservation of image content during the editing process. The forward path utilizes the pre-trained model to produce the edited image, while the inverted path converts the result back to the source image. The unbiased mask is generated by comparing differences between the processed source image and the edited image to ensure that both conform to the same distribution. Our experiments demonstrate the effectiveness of the proposed method, as it significantly improves the D-CLIP score from 0.272 to 0.283. The code will be available at https://***/JohnDreamer/DualCycleDiffsion. © 2023, CC BY-NC-SA.

关键词： Diffusion

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FastFlowNet: A lightweight network for fast optical flow estimation

arXiv

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arXiv 2021年

作者： Kong, Lingtong Shen, Chunhua Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The University of Adelaide Australia

Dense optical flow estimation plays a key role in many robotic vision tasks. It has been predicted with satisfying accuracy than traditional methods with advent of deep learning. However, current networks often occupy large number of parameters and require heavy computation costs. These drawbacks have hindered applications on power- or memory-constrained mobile devices. To deal with these challenges, in this paper, we dive into designing efficient structure for fast and accurate optical flow prediction. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid feature while reducing parameters. Second, we introduce a novel center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to acclerate flow estimation with marginal drops in accuracy. Experiments on both synthetic Sintel and real-world KITTI datasets demonstrate the effectiveness of proposed approaches, which consumes only 1/10 computation of comparable networks to get 90% of their performance. In particular, FastFlowNet only contains 1.37 M parameters and runs at 90 or 5.7 fps with one desktop NVIDIA GTX 1080 Ti or embedded Jetson TX2 GPU on Sintel resolution images. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Depth Prior-Based Stable Tensor Decomposition for Video Snow Removal

SSRN

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SSRN 2023年

作者： Li, Yongji Wang, Luping Jia, Zhenhong Yang, Jie Kasabov, Nikola School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland1020 New Zealand

Heavy snow seriously degrades the performance of outdoor computer vision systems. Near- and far-field snowflakes in heavy snow videos exhibit distinctly disparate physical properties. To address this issue, this research proposes a video desnowing model that utilizes stable tensor decomposition with snow depth prior information. Initially, the depth of snowflakes is transformed by their speeds calculated from the optical flow field of snowfall. Next, the noise level in backgrounds is available by dense snow. In contrast to previous tensor decomposition (TD), the introduction of a noise term enhances the stability of the depth prior-based stable tensor decomposition (DP-STD) in addressing heavy snowfall. This leads to more efficient and accurate restoration of underlying nonsnow structure. Inspired by biomimicry, the snow on moving objects (MOs) is removed by the adaptive mimesis region of interest (AM-ROI). Both synthetic and real snowfall experimental results show that our proposed desnowing model is more effective than the current SOTA algorithm in removing heavy snow. © 2023, The Authors. All rights reserved.

关键词： Snow

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FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

FastFlowNet: A Lightweight Network for Fast Optical Flow Est...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lingtong Kong Chunhua Shen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The University of Adelaide Australia

Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current networks often consist of a large number of parameters and require heavy computation costs, largely hindering its application on low power-consumption devices such as mobile phones. In this paper, we tackle this challenge and design a lightweight model for fast and accurate optical flow prediction. Our proposed FastFlowNet follows the widely-used coarse-to-fine paradigm with following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid features while reducing parameters. Second, we introduce a new center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to accelerate flow estimation with marginal drops in accuracy. Experiments on both synthetic Sintel data and real-world KITTI datasets demonstrate the effectiveness of the proposed approach, which needs only ¹/10 computation of comparable networks to achieve on par accuracy. In particular, FastFlowNet only contains 1.37M parameters; and can execute at 90 FPS (with a single GTX 1080Ti) or 5.7 FPS (embedded Jetson TX2 GPU) on a pair of Sintel images of resolution 1024 × *** is available at: https://***/fastflow

关键词： Technological innovation Costs Head Correlation Estimation Predictive models Feature extraction

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Unsupervised Difference Learning for Noisy Rigid image Alignment

arXiv

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arXiv 2022年

作者： Chen, Yu-Xuan Feng, Dagan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method. Copyright © 2022, The Authors. All rights reserved.

关键词： image enhancement

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