With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas su...With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, sev- eral methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media sum- marization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.展开更多
Face recognition has been rapidly developed and widely used.However,there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face re...Face recognition has been rapidly developed and widely used.However,there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face recognition are resulted from information loss.This study aims to tackle these challenges with a broad learning system(BLS).We integrated two models,IR3C with BLS and IR3C with a triplet loss,to control the learning process.In our experiments,we used different strategies to generate more challenging datasets and analyzed the competitiveness,sensitivity,and practicability of the proposed two models.In the model of IR3C with BLS,the recognition rates for the four challenging strategies are all 100%.In the model of IR3C with a triplet loss,the recognition rates are 94.61%,94.61%,96.95%,96.23%,respectively.The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.展开更多
This paper reviews our recent fMRI and psychophysical finding on： 1） perceived size represen- tation in V1; 2） border ownership representation in V2; and 3） neural processing of partially occluded face. These find...This paper reviews our recent fMRI and psychophysical finding on： 1） perceived size represen- tation in V1; 2） border ownership representation in V2; and 3） neural processing of partially occluded face. These findings demonstrate that the human early vi- sual cortex not only performs local feature analyses, but also contributes significantly to high-level visual computation with assistance of attention-enabled cortical feed- back. Moreover, by taking advantage of recent findings on early visual cortex from neuroscience and cognitive science, we build a biologically plausible attention model that can well predict human scanpaths on natural images.展开更多
Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal a...Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal anatomy,detect disease at early stages,plan treatments,and monitor therapeutic outcomes.This broad field encompasses a diverse array of imaging modalities such as fluorescence,magnetic resonance imaging(MRI),and positron emission tomography(PET),each relying on distinct physical principles to extract specific biological information.Among them,Raman imaging has emerged as a molecularly specific and powerful technique capable of providing detailed chemical information and generating high-resolution two-or three-dimensional maps that visualize the multiscale distribution of specific molecular components within a sample.展开更多
Computational biomedical imaging lies at the intersection of physics,computer science,and biomedicine,aiming to produce visual representations of biological or physiological phenomena that may be otherwise imperceptib...Computational biomedical imaging lies at the intersection of physics,computer science,and biomedicine,aiming to produce visual representations of biological or physiological phenomena that may be otherwise imperceptible to measuring instruments.Over the last few decades,breakthroughs in imaging physics-as evidenced by modalities like magnetic resonance imaging(MRI),computed tomography(CT),ultrasound,optical microscopy,and endoscopy-have profoundly impacted the way clinicians visualize and understand living systems.展开更多
The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems t...The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.展开更多
基金This work was partially supported by the National Basic Research Program of China （973 Program） （2014CB347600）, the National Natural Science Foundation of China （Grant Nos. 61522203 and U1611461）, the Natural Science Foundation of Jiangsu Province （Bird0140058）, and the National Ten Thousand Talent Program of China （Young Top-Notch Talent）.
文摘With the rapid increase in social websites that has dramatically increased the volume of social media, which includes the use of images and videos, visual understanding has attracted great interest in several areas such as multimedia, computer vision, and pattern recognition. Valuable auxiliary resources available on social websites, such as user-provided tags, aid in the tasks of visual understanding. Therefore, sev- eral methods have been proposed for exploring the auxiliary resources for tag refinement, image retrieval, and media sum- marization. This work conducts a comprehensive survey of recent advances in visual understanding by mining social media in order to discuss their merits and limitations. We then analyze the difficulties and challenges of visual understanding followed by several possible future research directions.
基金funded by the Shanghai High-Level Base-Building Project for Industrial Technology Innovation(1021GN204005-A06)the National Natural Science Foundation of China(41571299)the Ningbo Natural Science Foundation(2019A610106).
文摘Face recognition has been rapidly developed and widely used.However,there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face recognition are resulted from information loss.This study aims to tackle these challenges with a broad learning system(BLS).We integrated two models,IR3C with BLS and IR3C with a triplet loss,to control the learning process.In our experiments,we used different strategies to generate more challenging datasets and analyzed the competitiveness,sensitivity,and practicability of the proposed two models.In the model of IR3C with BLS,the recognition rates for the four challenging strategies are all 100%.In the model of IR3C with a triplet loss,the recognition rates are 94.61%,94.61%,96.95%,96.23%,respectively.The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.
文摘This paper reviews our recent fMRI and psychophysical finding on： 1） perceived size represen- tation in V1; 2） border ownership representation in V2; and 3） neural processing of partially occluded face. These findings demonstrate that the human early vi- sual cortex not only performs local feature analyses, but also contributes significantly to high-level visual computation with assistance of attention-enabled cortical feed- back. Moreover, by taking advantage of recent findings on early visual cortex from neuroscience and cognitive science, we build a biologically plausible attention model that can well predict human scanpaths on natural images.
文摘Biological imaging plays a pivotal role in visualizing and understanding biological structures and processes from molecular to macroscopic levels,enabling clinicians and researchers to noninvasively observe internal anatomy,detect disease at early stages,plan treatments,and monitor therapeutic outcomes.This broad field encompasses a diverse array of imaging modalities such as fluorescence,magnetic resonance imaging(MRI),and positron emission tomography(PET),each relying on distinct physical principles to extract specific biological information.Among them,Raman imaging has emerged as a molecularly specific and powerful technique capable of providing detailed chemical information and generating high-resolution two-or three-dimensional maps that visualize the multiscale distribution of specific molecular components within a sample.
基金supported by the National Key Research and Development Program of China(2022YFC3401100,2024YFC3406402,and 2024YFF0507400)the National Natural Science Foundation of China(62371007 and 6220071694)the Beijing Natural Science Foundation(Z240010).
文摘Computational biomedical imaging lies at the intersection of physics,computer science,and biomedicine,aiming to produce visual representations of biological or physiological phenomena that may be otherwise imperceptible to measuring instruments.Over the last few decades,breakthroughs in imaging physics-as evidenced by modalities like magnetic resonance imaging(MRI),computed tomography(CT),ultrasound,optical microscopy,and endoscopy-have profoundly impacted the way clinicians visualize and understand living systems.
基金Project supported by the Chinese Academy of Engi- neering, the National Natural Science Foundation of China （No. L1522023）, the National Basic Research Program （973） of China （No. 2015CB351703）, and the National Key Research and Development Plan （Nos. 2016YFB1001004 and 2016YFB1000903）
文摘The long-term goal of artificial intelligence （AI） is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models： one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.
