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Joint clothes image detection and search via anchor free framework.

Mingbo Zhao1, Shanchuan Gao1, Jianghong Ma2

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PubMed
Summary

This study introduces an efficient anchor-free framework for joint clothes detection and search, improving fashion analysis by accurately identifying clothing items from images.

Keywords:
Anchor-based and anchor-free detectorsClothes image searchEnd-to-end learningFashion analysis

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Fashion Analysis

Background:

  • Clothes image search is crucial for fashion analysis, but existing two-step methods are inefficient.
  • One-step methods offer efficiency but often rely on anchor-based detectors with limitations like high complexity and hyperparameter sensitivity.

Purpose of the Study:

  • To develop an efficient and effective anchor-free framework for integrated clothes detection and search.
  • To overcome the limitations of existing two-step and anchor-based one-step approaches in fashion image retrieval.

Main Methods:

  • Proposed an anchor-free framework integrating clothes detection and similarity learning.
  • Incorporated a mask prediction branch for precise clothing segmentation.
  • Utilized a Re-ID embedding branch with mask pooling for rich feature extraction.
  • Introduced a match loss to refine embedding features for enhanced retrieval.

Main Results:

  • The proposed anchor-free framework effectively performs joint clothes detection and search.
  • The mask prediction and Re-ID embedding branches successfully extract relevant clothing features.
  • The match loss significantly improves retrieval performance.

Conclusions:

  • The developed anchor-free framework offers an efficient and effective solution for clothes image search.
  • This approach enhances fashion analysis by improving the accuracy and speed of clothing retrieval.