Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Corbière, Charles; Ben-younes, Hedi; Ramé, Alexandre; Ollion, Charles

doi:10.1109/iccvw.2017.266

Cited by 91 publications

(70 citation statements)

References 16 publications

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“…Street2Shop [25] DARN [22] DeepFashion [ [22,25,34,24,44,9,8,58]. These methods usually follow a global similarity computation and matching pipeline, i.e.…”

Section: Datasetsmentioning

confidence: 99%

“…[22,34] explored attributes via multi-task learning to learn representations which are related to specific tags such as "Crew neck", "Short sleeves" and "Rectangle-shaped"; [25,29] investigated different network architectures which are adept at extracting global features for customer-to-shop clothes retrieval. Instead, [58,9] attempted to train models with weakly or noisy supervised signals to reduce the dependency of data annotation and increase the global feature learning efficiency. Recently, [24] utilized attribute labels to pay more attention to local discriminative regions.…”

Section: Datasetsmentioning

confidence: 99%

“…x = {x i } and y = {y i }, where x i ∈ R C×1 and y i ∈ R C×1 are local features of the customer clothes image and the shop one respectively. Previous customer-to-shop clothes retrieval approaches [22,25,34,24,44,9,13,8,58] adopt the following global similarity as:…”

Section: Motivationmentioning

confidence: 99%

“…More importantly, garments may differ in small local regions such as logos only. The task of customer-to-shop clothes retrieval has great progresses [22,25,34,24,44,9,13,8,58] by using convolutional neural networks (CNNs) [28,19,14,21,39]. Existing methods often employed the global similarity pipeline.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Kuang¹,

Gao

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Matching clothing images from customers and online shopping stores has rich applications in E-commerce. Existing algorithms encoded an image as a global feature vector and performed retrieval with the global representation. However, discriminative local information on clothes are submerged in this global representation, resulting in suboptimal performance. To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales. The similarity pyramid is represented by a Graph of similarity, where nodes represent similarities between clothing components at different scales, and the final matching score is obtained by message passing along edges. In GRNet, graph reasoning is solved by training a graph convolutional network, enabling to align salient clothing components to improve clothing retrieval. To facilitate future researches, we introduce a new benchmark FindFashion, containing rich annotations of bounding boxes, views, occlusions, and cropping. Extensive experiments show that GRNet obtains new state-of-the-art results on two challenging benchmarks, e.g. pushing the top-1, top-20, and top-50 accuracies on DeepFashion to 26%, 64%, and 75% (i.e. 4%, 10%, and 10% absolute improvements), outperforming competitors with large margins. On FindFashion, GRNet achieves considerable improvements on all empirical settings.

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“…Street2Shop [25] DARN [22] DeepFashion [ [22,25,34,24,44,9,8,58]. These methods usually follow a global similarity computation and matching pipeline, i.e.…”

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Kuang¹,

Gao

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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show abstract

“…Recently, fashion concept prediction has attracted increasing interests in various tasks such as clothing recognition [3,18], retrieval [7,11,15,17], parsing [30] and landmark detection [18,27]. Earlier methods [3,17] [5]), which was both time consuming and computationally intensive. In this paper, we also take advantage of weakly-labeled data to enhance our fashion concept prediction model.…”

Section: Related Workmentioning

confidence: 99%

Who, Where, and What to Wear?

Yang

Liao

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

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Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf tools due to their flexibility and satisfactory performance. For clothing recognition and occasion prediction, we unify the two tasks by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts. To alleviate the heavy burden of human annotations, we introduce a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data. In experiments, we contribute a benchmark dataset and conduct extensive experiments from both quantitative and qualitative perspectives. The results demonstrate the effectiveness of our model in fashion concept prediction, and the usefulness of extracted knowledge with comprehensive analysis. CCS CONCEPTS• Information systems → Specialized information retrieval.

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A novel approach of boundary preservative apparel detection and classification of fashion images using deep learning

Murtaza

Sharif

Yasmin

et al. 2022

Math Methods in App Sciences

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Visual analysis of fashion images gain much attention in the fashion industry due to its commercial and social importance. In recent years, deep learning techniques offer overwhelming progress in improving the accuracy of fine‐grained apparel segmentation with accurate bounding box prediction. The baseline pixel‐based masking techniques show excellent performance in object detection and segmentation but sometimes ignores the boundary of objects, resulting in uneven and complicated segmentation masks. Moreover, it is time taking to generate a multi‐scale feature map against each anchor box. To remedy this problem, a more accurate, faster, and suitable deep learning architecture is proposed that accurately detects, classify, and performs fine‐grained segmentation of cloth products in a single platform. In this paper, initially, an Object Class Head Detector model is proposed in which the baseline Mask‐RCNN model is used as a reference model. Here, we replace the Region Proposal Network with the proposed modified YoloV2 model to locate apparel products with its class prediction. The modified YoloV2 model has more capability to detect tiny objects because of local and high‐level feature fusion. The goal of this step is to accurately locate the objects in minimum time intervals. Furthermore, the predicted bounding box is converted to object shape offsets using deep snake architecture that tightly fits onto the apparel shape. It can improve the accuracy of cloth shape segmentation by preserving object contours. The proposed architecture is empirically validated on various existing fashion image datasets. The experimental results illustrate that the proposed architecture performs better on the Deepfashion2 dataset with mAP of 86.86%, as compared to other state‐of‐the‐art deep learning models.

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Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

Cited by 91 publications

References 16 publications

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid

Who, Where, and What to Wear?

A novel approach of boundary preservative apparel detection and classification of fashion images using deep learning

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