Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval

Liang, Xiaodan; Yang, Wei; Luo, Ping; Huang, Junshi; Yan, Shuicheng

doi:10.1109/tmm.2016.2542983

Cited by 150 publications

(61 citation statements)

References 44 publications

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“…Extensive studies have been conducted on fashion analysis due to its huge profit potentials. Most existing methods focus on clothing parsing [44,28], clothing recognition by attributes [31], matching clothing seen on the street to online products [30,14], fashion recommendation [19], visual compatibility learning [43,16], and fashion trend prediction [2]. Compared to these lines of work, we focus on virtual try-on with only 2D images as input.…”

Section: Related Workmentioning

confidence: 99%

VITON: An Image-Based Virtual Try-on Network

Han

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

423

507

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We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new clothing-agnostic yet descriptive person representation, our framework first generates a coarse synthesized image with the target clothing item overlaid on that same person in the same pose. We further enhance the initial blurry clothing area with a refinement network. The network is trained to learn how much detail to utilize from the target clothing item, and where to apply to the person in order to synthesize a photo-realistic image in which the target item deforms naturally with clear visual patterns. Experiments on our newly collected dataset demonstrate its promise in the image-based virtual try-on task over state-of-the-art generative models. 1

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Section: Related Workmentioning

confidence: 99%

VITON: An Image-Based Virtual Try-on Network

Han

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

423

507

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“…Recently, computer vision for fashion has attracted significant attention, with various applications typically built on top of deep convolutional networks. Clothing 'parsing' is one such application, which seeks to parse and categorize garments in a fashion image [49,23,50]. Since clothing has fine-graind style attributes (e.g.…”

Section: Related Workmentioning

confidence: 99%

Complete the Look: Scene-Based Complementary Product Recommendation

Kang

Kim²,

Leskovec

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Modeling fashion compatibility is challenging due to its complexity and subjectivity. Existing work focuses on predicting compatibility between product images (e.g. an image containing a t-shirt and an image containing a pair of jeans). However, these approaches ignore real-world 'scene' images (e.g. selfies); such images are hard to deal with due to their complexity, clutter, variations in lighting and pose (etc.) but on the other hand could potentially provide key context (e.g. the user's body type, or the season) for making more accurate recommendations. In this work, we propose a new task called 'Complete the Look', which seeks to recommend visually compatible products based on scene images. We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images. Our approach measures compatibility both globally and locally via CNNs and attention mechanisms. Extensive experiments show that our method achieves significant performance gains over alternative systems. Human evaluation and qualitative analysis are also conducted to further understand model behavior. We hope this work could lead to useful applications which link large corpora of real-world scenes with shoppable products.

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“…Cross-scenario clothing retrieval has widely applicability for commercial systems. There have been extensive efforts on similar clothing retrieval [1,7,8,16,13,17] and exactly same clothing retrieval [14,23].…”

Section: Cross-scenario Clothing Retrievalmentioning

confidence: 99%

“…For similar clothing retrieval, clothing recognition and segmentation techniques are used in [16,13] to retrieve similar clothing. In order to tackle the domain discrepancy between street photos and shop photos, sparse representations are utilized in [17].…”

Section: Cross-scenario Clothing Retrievalmentioning

confidence: 99%

Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

Cheng

Liu

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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In recent years, both online retail and video hosting service are exponentially growing. In this paper, we explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exact same items in online shops. A novel deep neural network, called AsymNet, is proposed to explore this problem. For the image side, wellestablished methods are used to detect and extract features for clothing patches with arbitrary sizes. For the video side, deep visual features are extracted from detected object regions in each frame, and further fed into a Long Short-Term Memory (LSTM) framework for sequence modeling, which captures the temporal dynamics in videos. To conduct exact matching between videos and online shopping images, LSTM hidden states, representing the video, and image features, which represent static object images, are jointly modeled under the similarity network with reconfigurable deep tree structure. Moreover, an approximate training method is proposed to achieve the efficiency when training. Extensive experiments conducted on a large cross-domain dataset have demonstrated the effectiveness and efficiency of the proposed AsymNet, which outperforms the state-of-the-art methods.

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Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval

Cited by 150 publications

References 44 publications

VITON: An Image-Based Virtual Try-on Network

VITON: An Image-Based Virtual Try-on Network

Complete the Look: Scene-Based Complementary Product Recommendation

Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

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