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9th IEEE International Conference on Data science in Cyberspace, DSC 2024

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

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Medical image registration and its application in retinal images:a review

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visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization

arXiv

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arXiv 2024年

作者： Zhang, Qi Zhang, Kaiyi Chan, Antoni B. Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen China China Department of Computer Science City University of Hong Kong Hong Kong

Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, the performance of existing methods is limited by the ambiguity of the density maps in crowded areas, where local peaks can be smoothed away. To mitigate the weakness of density map supervision, optimal transport-based point supervision methods have been proposed in the single-image crowd localization tasks, but have not been explored for multi-view crowd localization yet. Thus, in this paper, we propose a novel Mahalanobis distance-based multi-view optimal transport (M-MVOT) loss specifically designed for multi-view crowd localization. First, we replace the Euclidean-based transport cost with the Mahalanobis distance, which defines elliptical iso-contours in the cost function whose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propose a strategy to consider all the input camera views in the model loss (M-MVOT) by computing the optimal transport cost for each ground-truth point based on its closest camera. Experiments demonstrate the advantage of the proposed method over density map-based or common Euclidean distance-based optimal transport loss on several multi-view crowd localization datasets. Project page: MVOT Project. © 2024, CC BY.

关键词： Cost functions

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Learning to Rank Patches for Unbiased Image Redundancy Reduction

Learning to Rank Patches for Unbiased Image Redundancy Reduc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang Luo Zhineng Chen Peng Zhou Zuxuan Wu Xieping Gao Yu-Gang Jiang Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University University of Maryland College of Information Science and Engineering Hunan Normal University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue, we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g., 90%). Building upon it, we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch, and learning to rank the patches with the pseudo score. The entire process is self-supervised, thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content. Code is available at https://***/irsLu/1trp.

关键词： visualization Accuracy Semantic segmentation Redundancy Semantics Self-supervised learning Object detection

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Improving Text-guided Object Inpainting with Semantic Pre-inpainting

arXiv

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arXiv 2024年

作者： Chen, Yifu Chen, Jingwen Pan, Yingwei Li, Yehao Yao, Ting Chen, Zhineng Mei, Tao School of Computer Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China HiDream.ai Inc.

Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the editability of images has sparked significant interest in the downstream task of inpainting a novel object described by a text prompt within a designated region in the image. Nevertheless, the problem is not trivial from two aspects: 1) Solely relying on one single U-Net to align text prompt and visual object across all the denoising timesteps is insufficient to generate desired objects;2) The controllability of object generation is not guaranteed in the intricate sampling space of diffusion model. In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space;2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features. To achieve this, we cascade a Transformer-based semantic inpainter and an object inpainting diffusion model, leading to a novel CAscaded Transformer-Diffusion (CAT-Diffusion) framework for text-guided object inpainting. Technically, the semantic inpainter is trained to predict the semantic features of the target object conditioning on unmasked context and text prompt. The outputs of the semantic inpainter then act as the informative visual prompts to guide high-fieldity object generation through a reference adapter layer, leading to controllable object inpainting. Extensive evaluations on OpenImages-V6 and MSCOCO validate the superiority of CAT-Diffusion against the state-of-the-art methods. Code is available at https://***/Nnn-s/CATdiffusion. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds

arXiv

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arXiv 2025年

作者： Jensen, Simon B. Oehmcke, Stefan Møgelmose, Andreas Madadi, Meysam Igel, Christian Escalera, Sergio Moeslund, Thomas B. Visual Analysis and Perception Laboratory Aalborg University Denmark Pioneer Centre for Artificial Intelligence Denmark Department of Computer Science Copenhagen University Denmark Institute for Visual & Analytic Computing Rostock University Germany University of Barcelona and Computer Vision Center Spain

Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (12.5 cm resolution) and 3D airborne laser scanning (ALS) point clouds (8 points/m2) can enhance biodiversity assessment. We introduce the BioVista dataset, comprising 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving mean accuracies of 69.4% and 72.8%, respectively. We explore two fusion approaches: a confidence-based ensemble method and a feature-level concatenation strategy, with the latter achieving a mean accuracy of 75.5%. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment. © 2025, CC BY.

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Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems

Trace-based Multi-Dimensional Root Cause Localization of Per...

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International Conference on Software Engineering (ICSE)

作者： Chenxi Zhang Zhen Dong Xin Peng Bicheng Zhang Miao Chen Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multidimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multidimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multidimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution.

关键词： Location awareness Runtime environment Accuracy Microservice architectures Benchmark testing Data mining Spectral analysis

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Data-Free Network Debiasing for Long-Tailed visual Recognition

Data-Free Network Debiasing for Long-Tailed Visual Recogniti...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Cai, Jinmian Wang, Zheng Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Biren Technology IHPC A-STAR

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Real-world data is often unbalanced and exhibits long-tailed distribution over classes. Vanilla classification models trained on imbalanced datasets inherently exhibit bias towards dominant classes. Existing debiasing methods mostly balance the data or the loss during training. Nevertheless, these data-acquiring methods are not suitable for situations where training data are unavailable. In this paper, we appeal to solutions without access to training data and propose a datafree debiasing (Free-D) method that serves as a plug-and-play module for any standard classification model. Specifically, our method adjusts both the feature representation via feature representation shifting and the classifier weight via class prior compensation in a data-free manner. We evaluate and compare our methods on four long-tailed visual recognition datasets, i.e., long-tailed CIFAR-10/-100, ImageNet-LT, and Places-LT. Extensive experiments demonstrate that the proposed data-free method achieves comparable results of other data-acquired methods. © 2022 IEEE.

关键词： Classification (of information)

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference 41

Causal-IQA: Towards the Generalization of Image Quality Asse...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhong, Yan Wu, Xingyu Zhang, Li Yang, Chenxi Jiang, Tingting School of Mathematical Sciences Peking University Beijing China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China National Biomedical Imaging Center Peking University Beijing China

Due to the high cost of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA *** address this, this paper proposes a novel end-to-end blind IQA method: ***, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ***, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization *** optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor *** experiments illustrate the superiority of Causal-IQA. Copyright 2024 by the author(s)

关键词： Image correlation

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