DeepMark: One-Shot Clothing Detection

10/02/2019

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The one-shot approach, DeepMark, for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in the paper. The state-of-the-art accuracy of 0.723 mAP for bounding box detection task and 0.532 mAP for landmark detection task on the DeepFashion2 Challenge dataset were achieved. The proposed architecture can be used effectively on the low-power devices.

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DeepMark: One-Shot Clothing Detection

DeepMark++: CenterNet-based Clothing Detection

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Decoupling Object Detection from Human-Object Interaction Recognition

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PartSLAM: Unsupervised Part-based Scene Modeling for Fast Succinct Map Matching

Relative distance matters for one-shot landmark detection

DeepMark: One-Shot Clothing Detection

Related Research

DeepMark++: CenterNet-based Clothing Detection

Scalable Object Detection for Stylized Objects

Decoupling Object Detection from Human-Object Interaction Recognition

Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation

TransNet V2: An effective deep network architecture for fast shot transition detection

PartSLAM: Unsupervised Part-based Scene Modeling for Fast Succinct Map Matching

Relative distance matters for one-shot landmark detection