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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

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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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Soft coarse-grained particle model for particle-fluid systems

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Particuology 2024年第1期84卷 178-193页

作者： Yanhao Ye ji Xu Wei ge State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing100190China

By modeling a group of neighboring real particles as a single coarse-grained particle(CGP),discrete particle method(DPM)is now capable of simulating industrial-scale particle-fluid ***,a systematic approach to determine the CGP properties and develop their interaction models is still lacking,which casts uncertainty on the predictivity of the *** this study,collisions between predefined particle groups are analyzed to construct kernel functions for modeling the CGPs and then the model parameters are determined by equating the statistical properties of the CGPs and the real particles in the physical process *** approach is implemented for homogeneous cooling of granular gas,then demonstrated effective in simulating experimental fluidized beds.

关键词： Coarse-grain(CG) Discrete particle method(DPM) Fluidized bed Multi-scale simulation

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

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Chinese Journal of Chemical Engineering 2024年第2期66卷 263-272页

作者： Kai ge Yiping Huang Yuanhui ji jiangsu Province Hi-Tech Key Laboratory for Biomedical Research School of Chemistry and Chemical EngineeringSoutheast UniversityNanjing 211189China

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical *** this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular *** the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'*** results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

关键词： Multi-task machine learning Density functional theory Hydrogen bond interaction Miscibility Solubility

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