Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing me...Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.展开更多
Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items ...Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items in a set to form collocational and compatible outfits”.Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task.In detail,the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network(Mask-RCNN),respectively,so that the low-level texture information and high-level semantic information can be both preserved.Then,the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory(Bi-LSTM)for the prediction.Extensive simulations are conducted based on DeepFashion2 datasets.Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.展开更多
With the development of the society,people's requirements for clothing matching are constantly increasing when developing clothing recommendation system.This requires that the algorithm for understanding the cloth...With the development of the society,people's requirements for clothing matching are constantly increasing when developing clothing recommendation system.This requires that the algorithm for understanding the clothing images should be sufficiently efficient and robust.Therefore,we detect the keypoints in clothing accurately to capture the details of clothing images.Since the joint points of the garment are similar to those of the human body,this paper utilizes a kind of deep neural network called cascaded pyramid network(CPN)about estimating the posture of human body to solve the problem of keypoints detection in clothing.In this paper,we first introduce the structure and characteristic of this neural network when detecting keypoints.Then we evaluate the results of the experiments and verify effectiveness of detecting keypoints of clothing with CPN,with normalized error about 5%7%.Finally,we analyze the influence of different backbones when detecting keypoints in this network.展开更多
Wavelength division multiplexing （WDM） has been becoming a promising solution to meet the rapidly growing demands on bandwidth. Multicast in WDM networks by employing free wavelength is an efficient approach to savi...Wavelength division multiplexing （WDM） has been becoming a promising solution to meet the rapidly growing demands on bandwidth. Multicast in WDM networks by employing free wavelength is an efficient approach to saving bandwidth and cost. However, the free wavelength may not identical between different hops in a multicast light-path, particularly in heavy load optical WDM networks. In order to implement multicast applications efficiently, a network coding （NC） technique was introduced into all-optical WDM multicast networks to solve wavelength collision problem between the multicast request and the unicast request. Compared with the wavelength conversion based optical multicast, the network coding based optical multicast can achieve better multicast performance with paying lower cost.展开更多
基金National Natural Science Foundation of China(No.61971121)。
文摘Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.
基金National Natural Science Foundation of China(No.61971121)。
文摘Clothing plays an important role in humans’social life as it can enhance people’s personal quality,and it is a practical problem by answering the question“which item should be chosen to match current fashion items in a set to form collocational and compatible outfits”.Motivated by this target an end-to-end clothing collocation learning framework is developed for handling the above task.In detail,the proposed framework firstly conducts feature extraction by fusing the features of deep layer from Inception-V3 and classification branch of mask regional convolutional neural network(Mask-RCNN),respectively,so that the low-level texture information and high-level semantic information can be both preserved.Then,the proposed framework treats the collocation outfits as a set of sequences and adopts bidirectional long short-term memory(Bi-LSTM)for the prediction.Extensive simulations are conducted based on DeepFashion2 datasets.Simulation results verify the effectiveness of the proposed method compared with other state-of-the-art clothing collocation methods.
基金National Key Research and Development Program,China(No.2019YFC1521300)。
文摘With the development of the society,people's requirements for clothing matching are constantly increasing when developing clothing recommendation system.This requires that the algorithm for understanding the clothing images should be sufficiently efficient and robust.Therefore,we detect the keypoints in clothing accurately to capture the details of clothing images.Since the joint points of the garment are similar to those of the human body,this paper utilizes a kind of deep neural network called cascaded pyramid network(CPN)about estimating the posture of human body to solve the problem of keypoints detection in clothing.In this paper,we first introduce the structure and characteristic of this neural network when detecting keypoints.Then we evaluate the results of the experiments and verify effectiveness of detecting keypoints of clothing with CPN,with normalized error about 5%7%.Finally,we analyze the influence of different backbones when detecting keypoints in this network.
基金supported by the Doctor Foundation of Shandong Province (BS2013DX032)the Youth Scholars Development Program of Shandong University of Technology
文摘Wavelength division multiplexing （WDM） has been becoming a promising solution to meet the rapidly growing demands on bandwidth. Multicast in WDM networks by employing free wavelength is an efficient approach to saving bandwidth and cost. However, the free wavelength may not identical between different hops in a multicast light-path, particularly in heavy load optical WDM networks. In order to implement multicast applications efficiently, a network coding （NC） technique was introduced into all-optical WDM multicast networks to solve wavelength collision problem between the multicast request and the unicast request. Compared with the wavelength conversion based optical multicast, the network coding based optical multicast can achieve better multicast performance with paying lower cost.
