Previous research suggests that the right and left hemispheres dominate global and local perception of hierarchical patterns,respectively.The current work examined whether global perception of hierarchi-cal stimuli re...Previous research suggests that the right and left hemispheres dominate global and local perception of hierarchical patterns,respectively.The current work examined whether global perception of hierarchi-cal stimuli requires coherent work of bilateral visual cortices using transcranial magnetic stimulation(TMS).Subjects discriminated global or local properties of compound letters in Experiment 1.Reaction times were recorded when single-pulse real TMS or sham TMS was delivered over the left or right visual cortex.While a global precedence effect(i.e.,faster responses to global than local targets and stronger global-to-local interference than the reverse)was observed,TMS decreased global-to-local interference whereas increased local-to-global interference.Experiment 2 ruled out the possibility that the effects observed in Experiment 1 resulted from perceptual learning.Experiment 3 used compound shapes and observed TMS effect similar to that in Experiment 1.Moreover,TMS also slowed global RTs whereas speeded up local RTs in Experiment 3.Finally,the TMS effects observed in Experiments 1 and 3 did not differ between the conditions when TMS was applied over the left and right hemispheres.The results support a coherence hypothesis that global perception of compound stimuli depends upon the co-herent work of bilateral visual cortices.展开更多
Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the qu...Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the quality of clothing patterns is very high on conventional design,the input time and output amount ratio is relative low for conventional design.In order to break through the bottleneck of conventional clothing patterns design,this paper proposes a novel way based on generative adversarial network(GAN)model for automatic clothing patterns generation,which not only reduces the dependence of experienced designer,but also improve the input-output ratio.Design/methodology/approach-In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details,this paper improves the conventional GAN model from two aspects:a multi-scales discriminators strategy is introduced to deal with the local texture details;and the selfattention mechanism is introduced to improve the global artistic perception.Therefore,the improved GAN called multi-scales self-attention improved generative adversarial network(MS-SA-GAN)model,which is used for high resolution clothing patterns generation.Findings-To verify the feasibility and effectiveness of the proposed MS-SA-GAN model,a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures,and a comparative experiment is conducted on our designed clothing patterns dataset.In experiments,we have adjusted different parameters of the proposed MS-SA-GAN model,and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/value-Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GANmodel are superior to the conventional algorithms in some local texture detail indexes.In addition,a group of clothing design professionals is invited to evaluate the global artistic perception through a valencearousal scale.The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.展开更多
In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible...In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible for global topological perception, and for modulating the local feature processing in the classical ventral visual pathway. Inspired by this recent cognitive discovery,we developed a novel CogNet architecture to emulate the global-local dichotomy of human visual cognitive mechanisms. The thorough experimental results indicate that the proposed CogNet not only significantly improves image classification accuracies but also effectively addresses the texture bias problem observed in baseline CNN models. We have also conducted mathematical analysis for the generalization gap for general neural networks. Our theoretical derivations suggest that the Hurst parameter, a measure of the curvature of the loss landscape, can closely bind the generalization gap. A larger Hurst parameter corresponds to a better generalization ability. We found that our proposed CogNet achieves a lower test error and attains a larger Hurst parameter,strengthening its superiority over the baseline CNN models further.展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.30630025,30225026,and 30328016)the Ministry of Science and Technology of China(Grant No.2002CCA01000)
文摘Previous research suggests that the right and left hemispheres dominate global and local perception of hierarchical patterns,respectively.The current work examined whether global perception of hierarchi-cal stimuli requires coherent work of bilateral visual cortices using transcranial magnetic stimulation(TMS).Subjects discriminated global or local properties of compound letters in Experiment 1.Reaction times were recorded when single-pulse real TMS or sham TMS was delivered over the left or right visual cortex.While a global precedence effect(i.e.,faster responses to global than local targets and stronger global-to-local interference than the reverse)was observed,TMS decreased global-to-local interference whereas increased local-to-global interference.Experiment 2 ruled out the possibility that the effects observed in Experiment 1 resulted from perceptual learning.Experiment 3 used compound shapes and observed TMS effect similar to that in Experiment 1.Moreover,TMS also slowed global RTs whereas speeded up local RTs in Experiment 3.Finally,the TMS effects observed in Experiments 1 and 3 did not differ between the conditions when TMS was applied over the left and right hemispheres.The results support a coherence hypothesis that global perception of compound stimuli depends upon the co-herent work of bilateral visual cortices.
基金This paper is supported by university fund project of Hubei Institute of Fine Arts,named“The construction of blended teaching mode based on flipped classroom-Taking the Course of“Fashion Painting Illustration”as an Example.”(No.202028)。
文摘Purpose-Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art.Conventional clothing patterns design relies on experienced designers.Although the quality of clothing patterns is very high on conventional design,the input time and output amount ratio is relative low for conventional design.In order to break through the bottleneck of conventional clothing patterns design,this paper proposes a novel way based on generative adversarial network(GAN)model for automatic clothing patterns generation,which not only reduces the dependence of experienced designer,but also improve the input-output ratio.Design/methodology/approach-In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details,this paper improves the conventional GAN model from two aspects:a multi-scales discriminators strategy is introduced to deal with the local texture details;and the selfattention mechanism is introduced to improve the global artistic perception.Therefore,the improved GAN called multi-scales self-attention improved generative adversarial network(MS-SA-GAN)model,which is used for high resolution clothing patterns generation.Findings-To verify the feasibility and effectiveness of the proposed MS-SA-GAN model,a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures,and a comparative experiment is conducted on our designed clothing patterns dataset.In experiments,we have adjusted different parameters of the proposed MS-SA-GAN model,and compared the global artistic perception and local texture details of the generated clothing patterns.Originality/value-Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GANmodel are superior to the conventional algorithms in some local texture detail indexes.In addition,a group of clothing design professionals is invited to evaluate the global artistic perception through a valencearousal scale.The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.
基金supported by the National Key Research and Development Project of China (Grant No. 2020AAA0105600)the National Natural Science Foundation of China (Grant Nos. U21B2048 and 62276208)+1 种基金Shenzhen Key Technical Projects (Grant No. CJGJZD2022051714160501)the Chinese Academy of Sciences (Grant Nos. 2021091 and YSBR-068)。
文摘In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible for global topological perception, and for modulating the local feature processing in the classical ventral visual pathway. Inspired by this recent cognitive discovery,we developed a novel CogNet architecture to emulate the global-local dichotomy of human visual cognitive mechanisms. The thorough experimental results indicate that the proposed CogNet not only significantly improves image classification accuracies but also effectively addresses the texture bias problem observed in baseline CNN models. We have also conducted mathematical analysis for the generalization gap for general neural networks. Our theoretical derivations suggest that the Hurst parameter, a measure of the curvature of the loss landscape, can closely bind the generalization gap. A larger Hurst parameter corresponds to a better generalization ability. We found that our proposed CogNet achieves a lower test error and attains a larger Hurst parameter,strengthening its superiority over the baseline CNN models further.
