Skip to main content

DeepFashion2: A Versatile Benchmark for Fashion Image Understanding


-By Yuying Ge1, Ruimao Zhang
, Lingyun Wu2, Xiaogang Wang
, Xiaoou Tang1, and Ping Luo
The Chinese University of Hong Kong
2SenseTime Research


Even as fashion image analysis gets more traction from today’s image recognition researchers, understanding fashion images remains challenging for real-world applications due to large deformations, occlusions, and discrepancies in clothing across domains and between consumer and commercial images.
DeepFashion is a large-scale clothes database introduced last year by a research team from the Chinese University of Hong Kong (CUHK). The dataset contains over 800k diverse fashion images, each labeled with 50 categories, 1,000 descriptive attributes, bounding boxes and clothing landmarks.
DeepFashion was a solid foundation, but it left a number of areas for improvement. It was limited to a single clothing-item per image, sparse landmarks (4~8 only), and had no per-pixel masks. CUHK researchers recently teamed up with Chinese AI giant SenseTime to develop a greatly improved iteration in DeepFashion2, a large-scale benchmark with comprehensive tasks and annotations of fashion image understanding.
DeepFashion2 contains 491K images of 13 popular clothing categories. A full spectrum of tasks are defined, including clothes detection and recognition, landmark and pose estimation, segmentation, as well as verification and retrieval. All these tasks are supported by rich annotations.
The dataset also includes a total of 801K images of pieces of clothing. Each item is labeled with scale, occlusion, zooming, viewpoint, bounding box, dense landmarks, and per-pixel mask. These items can be categorized as 43.8k clothing identities, where a clothing identity represents a class of apparel with nearly identical cuts, patterns, and designs. Images of the same clothing identities are taken from buyers and sellers, where an item from the buyer and an item from the seller forms a pair.
Researchers say the work makes three main contributions:
  1. Compared with other clothes datasets, DeepFashion2 annotations are at least 3.5× those of DeepFashion, 6.7× of ModaNet, and 8× of FashionAI.
  2. A full spectrum of tasks is carefully defined on the proposed dataset.
  3. Researchers extensively evaluated Mask R-CNN with DeepFashion2. A novel Match R-CNN is also proposed to aggregate all the learned features from clothes categories, poses, and masks to solve clothing image retrieval in an end-to-end manner.
333The research team believes the rich data and labels of DeepFashion2 will accelerate the development of future algorithms to understand fashion images. The paper DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images is on arXiv

Comments

Post a Comment

Popular posts from this blog

ABOD and its PyOD python module

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...
Post a Comment
Read more

DEEP LEARNING FOR ANOMALY DETECTION: A SURVEY

-By  Raghavendra Chalapathy  University of Sydney,  Capital Markets Co-operative Research Centre (CMCRC)  Sanjay Chawla  Qatar Computing Research Institute (QCRI),  HBKU  Paper Link Anomaly detection also known as outlier detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions Hawkins defines an outlier as an observation that deviates so significantly from other observations as to arouse suspicion that it was generated by a different mechanism. Aim of this paper is two-fold, First is a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Further...
1 comment
Read more

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems

  Ranwa Al Mallah , Godwin Badu-Marfo , Bilal Farooq image Courtesy: Comparitech Abstract Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified informat...
Post a Comment
Read more