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SAM struggles in concealed scenes—empirical study on “Segment Anything”

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Science China(Information Sciences) 2023年第12期66卷 278-280页

作者： ge-peng ji Deng-Ping FAN peng XU Bowen ZHOU Ming-Ming CHENG Luc VAN GOOL School of Computing Australian National University Computer Vision Lab(CVL) ETH Zurich Department of Electronic Engineering Tsinghua University College of Computer Science Nankai University

Large models open up new opportunities for artificial intelligence. In the past few months, there has been a boom in training foundation models on the vast linguistic corpus to produce amazing applications, e.g., Chat GPT, *** natural language processing and multimodal learning communities have been revolutionized. Large models’ capacity for generalization and emergent makes it easy for users to believe that large models can solve anything.

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Full-duplex strategy for video object segmentation

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Computational Visual Media 2023年第1期9卷 155-175页

作者： ge-peng ji Deng-Ping Fan Keren Fu Zhe Wu jianbing Shen Ling Shao School of Computer Science Wuhan UniversityWuhanChina Computer Vision Lab ETH ZurichETF C113.2Sternwartstrasse 78092 ZurichSwitzerland College of Computer Science Sichuan UniversityChengduChina peng Cheng Laboratory ShenzhenChina School of Computer Science Beijing Institute of TechnologyBeijingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two *** this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding ***,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding *** improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the *** experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object *** project is publicly available at https://***/gewelsji/FSNet.

关键词： video object segmentation(VOS) video salient object detection(V-SOD) visual attention

Light field salient object detection:A review and benchmark

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Computational Visual Media 2022年第4期8卷 509-534页

作者： Keren Fu Yao jiang ge-peng ji Tao Zhou Qijun Zhao Deng-Ping Fan College of Computer Science Sichuan Universityand National Key Laboratory of Fundamental Science on Synthetic VisionSichuan UniversityChengdu 610065China School of Computer Science Wuhan UniversityWuhan 430072China PCA Lab School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Computer Vision Lab ETH ZürichZürichSwitzerland

Salient object detection(SOD)is a long-standing research topic in computer vision with increasing interest in the past *** light fields record comprehensive information of natural scenes that benefit SOD in a number of ways,using light field inputs to improve saliency detection over conventional RGB inputs is an emerging *** paper provides the first comprehensive review and a benchmark for light field SOD,which has long been lacking in the saliency ***,we introduce light fields,including theory and data forms,and then review existing studies on light field SOD,covering ten traditional models,seven deep learning-based models,a comparative study,and a brief *** datasets for light field SOD are also ***,we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets,providing insightful discussions and analyses,including a comparison between light field SOD and RGB-D SOD *** to the inconsistency of current datasets,we further generate complete data and supplement focal stacks,depth maps,and multi-view images for them,making them consistent and *** supplemental data make a universal benchmark ***,light field SOD is a specialised problem,because of its diverse data representations and high dependency on acquisition hardware,so it differs greatly from other saliency detection *** provide nine observations on challenges and future directions,and outline several open *** the materials including models,datasets,benchmarking results,and supplemented light field datasets are publicly available at https://***/kerenfu/LFSOD-Survey.

关键词： light field salient object detection(SOD) deep learning benchmarking

Masked Vision-language Transformer in Fashion

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Machine Intelligence Research 2023年第3期20卷 421-434页

作者： ge-peng ji Mingchen Zhuge Dehong Gao Deng-Ping Fan Christos Sakaridis Luc Van Gool International Core Business Unit Alibaba GroupHangzhou 310051China Computer Vision Lab ETH ZürichZürich 8092Switzerland

We present a masked vision-language transformer(MVLT)for fashion-specific multi-modal ***,we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers(BERT)in the pre-training model,making MVLT the first end-to-end framework for the fashion ***,we designed masked image reconstruction(MIR)for a fine-grained understanding of *** is an extensible and convenient architecture that admits raw multimodal inputs without extra pre-processing models(e.g.,ResNet),implicitly modeling the vision-language *** importantly,MVLT can easily generalize to various matching and generative *** results show obvious improvements in retrieval(rank@5:17%)and recognition(accuracy:3%)tasks over the Fashion-gen 2018 winner,*** code is available at https://***/gewelsji/MVLT.

关键词： Vision-language masked image reconstruction transformer fashion e-commercial

Deep Gradient Learning for Efficient Camouflaged Object Detection

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Machine Intelligence Research 2023年第1期20卷 92-108页

作者： ge-peng ji Deng-Ping Fan Yu-Cheng Chou Dengxin Dai Alexander Liniger Luc Van Gool School of Computer Science Wuhan UniversityWuhan 430072China Computer Vision Laboratory ETH ZürichZürich 8092Switzerland

This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a context and a texture *** es-sential connection is the gradient-induced transition,representing a soft grouping between context and texture *** from the simple but efficient framework,DGNet outperforms existing state-of-the-art COD models by a large ***,our efficient version,DGNet-S,runs in real-time(80 fps)and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82%*** application results also show that the proposed DGNet performs well in the polyp segmentation,defect detec-tion,and transparent object segmentation *** code will be made available at https://***/gewelsji/DGNet.

关键词： Camouflaged object detection(COD) object gradient soft grouping efficient model image segmentation

How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges

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Machine Intelligence Research 2023年第5期20卷 605-613页

作者： Haotong Qin ge-peng ji Salman Khan Deng-Ping Fan Fahad Shahbaz Khan Luc Van Gool Computer Vision Lab(CVL) ETH ZurichZurich 8001Switzerland College of Engineering Computing&CyberneticsAustralian National UniversityCanberra 8105Australia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi 999041UAE

Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational ***,Bard has recently been updated to handle visual inputs alongside text prompts during *** Bard's impressive track record in handling textual inputs,we explore its capabilities in understanding and interpreting visual data(images)conditioned by text *** exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal generative models,especially in addressing complex computer vision problems that demand accurate visual and language ***,in this study,we focus on 15 diverse task scenarios encompassing regular,camouflaged,medical,under-water and remote sensing data to comprehensively evaluate Bard's *** primary finding indicates that Bard still struggles in these vision scenarios,highlighting the significant gap in vision-based understanding that needs to be bridged in future *** expect that this empirical study will prove valuable in advancing future models,leading to enhanced capabilities in comprehending and interpreting finegrained visual *** project is released on https://***/htqin/GoogleBard-VisUnderstand.

关键词： Google Bard multi-modal understanding visual comprehension large language models conversational AI chatbot.

Video Polyp Segmentation: A Deep Learning Perspective

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Machine Intelligence Research 2022年第6期19卷 531-549页

作者： ge-peng ji Guobao Xiao Yu-Cheng Chou Deng-Ping Fan Kai Zhao geng Chen Luc Van Gool Research School of Engineering Australian National UniversityCanberra 2601Australia College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China Department of Computer Science Johns Hopkins UniversityBaltimore 21218USA Computer Vision Laboratory ETH ZürichZürich 8092Switzerland Department of Radiological Sciences University of CaliforniaLos Angeles 90095USA School of Computer Science and Engineering Northwestern Polytechnical UniversityXi′an 710072China

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning *** the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation *** address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known *** provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and ***,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)*** global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS *** experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS ***,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based ***,we discuss several open issues and suggest possible research directions for the VPS *** project and dataset are publicly available at https://***/gewelsji/VPS.

关键词： Video polyp segmentation(VPS) dataset self-attention colonoscopy abdomen

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认知规律启发的物体分割评价标准及损失函数

中国科学：信息科学 2021年第9期51卷 1475-1489页

作者：范登平季葛鹏秦雪彬程明明南开大学计算机学院天津300350中国武汉大学计算机学院武汉430070中国 Department of Computing Science Faculty of ScienceUniversity of AlbertaEdmonton T6G 2R3Canada

物体分割技术是计算机视觉中的研究热点,在多个领域都有广泛的应用.本文从人类视觉系统对场景中的全局信息和局部细节非常敏感的角度出发,设计了一种新颖、高效且易于计算的增强匹配标准(Eξ)来评估物体分割模型的性能.Eξ将局部像素值... 详细信息

物体分割技术是计算机视觉中的研究热点,在多个领域都有广泛的应用.本文从人类视觉系统对场景中的全局信息和局部细节非常敏感的角度出发,设计了一种新颖、高效且易于计算的增强匹配标准(Eξ)来评估物体分割模型的性能.Eξ将局部像素值与全局平均值有机结合,以便评估分割结果与标准结果在图像级和像素级的相似度.在国际主流的4个公开数据集上的大量实验表明,Eξ在多个方面,如应用关联度、随机偏好度、噪声偏好度、感知度上相比现有广泛采纳的评价标准(IoU和Fβ)均有大幅相对提升.通过利用加权二值交叉熵损失函数、本文的增强匹配损失函数以及加权交并比损失函数,本文进一步设计了一套组合损失函数(Hybrid-Eloss)来促进网络学习到像素级、对象级和图像级的分割特征.定性和定量的结果表明,在3个不同领域的分割任务中使用这一组合损失函数能够进一步提高物体分割的精度.

关键词：物体分割技术评价标准视觉感知增强匹配标准损失函数

Brain age estimation:premise,promise,and problems

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Neural Regeneration Research 2025年第8期20卷 2313-2314页

作者： Jarrad Perron ji Hyun Ko Graduate Program in Biomedical Engineering Price Faculty of EngineeringUniversity of ManitobaWinnipegMBCanada PrairieNeuro Research Centre Kleysen Institute for Advanced MedicineHealth Sciences CentreWinnipegMBCanada Department of Human Anatomy and Cell Science Rady Faculty of Health SciencesUniversity of ManitobaWinnipegMBCanada

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North *** old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

关键词： estimation providing birth