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Visual commonsense reasoning with directional visual connections 被引量:2
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作者 Yahong HAN Arning WU +1 位作者 Linchao ZHU Yi YANG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2021年第5期625-637,共13页
To boost research into cognition-level visual understanding,i.e.,making an accurate inference based on a thorough understanding of visual details,visual commonsense reasoning(VCR)has been proposed.Compared with tradit... To boost research into cognition-level visual understanding,i.e.,making an accurate inference based on a thorough understanding of visual details,visual commonsense reasoning(VCR)has been proposed.Compared with traditional visual question answering which requires models to select correct answers,VCR requires models to select not only the correct answers,but also the correct rationales.Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity,which is helpful in solving specific cognition tasks.Inspired by this idea,we propose a directional connective network to achieve VCR by dynamically reorganizing the visual neuron connectivity that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability.Specifically,we first develop a GraphVLAD module to capture visual neuron connectivity to fully model visual content correlations.Then,a contextualization process is proposed to fuse sentence representations with visual neuron representations.Finally,based on the output of contextualized connectivity,we propose directional connectivity to infer answers and rationales,which includes a ReasonVLAD module.Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method. 展开更多
关键词 visual commonsense reasoning Directional connective network visual neuron connectivity Contextualized connectivity Directional connectivity
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Altered intrinsic functional connectivity of the primary visual cortex in youth patients with comitant exotropia: a resting state fMRI study 被引量:13
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作者 Pei-Wen Zhu Xin Huang +5 位作者 Lei Ye Nan Jiang Yu-Lin Zhong Qing Yuan Fu-Qing Zhou Yi Shao 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2018年第4期668-673,共6页
AIM: To evaluate the differences in the functional connectivity(FC) of the primary visual cortex(V1) between the youth comitant exotropia(CE) patients and health subjects using resting functional magnetic reson... AIM: To evaluate the differences in the functional connectivity(FC) of the primary visual cortex(V1) between the youth comitant exotropia(CE) patients and health subjects using resting functional magnetic resonance imaging(f MRI) data.METHODS: Totally, 32 CEs(25 males and 7 females) and 32 healthy control subjects(HCs)(25 males and 7 females) were enrolled in the study and underwent the MRI scanning. Two-sample t-test was used to examine differences in FC maps between the CE patients and HCs. RESULTS: The CE patients showed significantly less FC between the left brodmann area(BA17) and left lingual gyrus/cerebellum posterior lobe, right middle occipital gyrus, left precentral gyrus/postcentral gyrus and right inferior parietal lobule/postcentral gyrus. Meanwhile, CE patients showed significantly less FC between right BA17 and right middle occipital gyrus(BA19, 37).CONCLUSION: Our findings show that CE involves abnormal FC in primary visual cortex in many regions, which may underlie the pathologic mechanism of impaired fusion and stereoscopic vision in CEs. 展开更多
关键词 comitant exotropia functional connectivity primary visual cortex spontaneous activity
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基于联结主义的视听语音识别方法
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作者 车娜 朱奕明 +3 位作者 赵剑 孙磊 史丽娟 曾现伟 《吉林大学学报(工学版)》 EI CAS CSCD 北大核心 2024年第10期2984-2993,共10页
针对视听语音识别技术存在的数据需求量大、音视频数据对齐、噪声鲁棒性等问题,深入分析了联结主义时序分类器、长短期记忆神经网络、Transformer、Conformer四类核心模型的特点与优势,归纳了各模型的适用场景,并提出了优化模型性能的... 针对视听语音识别技术存在的数据需求量大、音视频数据对齐、噪声鲁棒性等问题,深入分析了联结主义时序分类器、长短期记忆神经网络、Transformer、Conformer四类核心模型的特点与优势,归纳了各模型的适用场景,并提出了优化模型性能的思路和方法。基于主流数据集和常用评价标准,对模型性能进行了量化分析。结果表明:CTC在噪声条件下性能波动较大,LSTM能有效捕捉长时序依赖,Transformer和Conformer在跨模态任务中可显著降低识别错误率。最后,从自监督训练和噪声鲁棒性两个层面,展望了未来的研究方向。 展开更多
关键词 计算机应用技术 视听语音识别 深度学习 联结主义
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