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(IJCAI 2018) Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image Classification

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Keyword [Multi-Scale]

Cheng G, Gao D, Liu Y, et al. Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image Classification[C]//IJCAI. 2018: 649-655.


1. Overview

1.1. Motivation

  • global CNN feature lack geometric invariance for addressing the problem of intra-class variation

In this paper, it proposes Multi-scale and Discriminative Part Detectors (MsDPD)

  • task-driven feature pooling

1.2. Architecture





1.3. Formulation



  • S = softmax(); sigma = sigmoid()
  • xij. a patch of origin image
  • phi. pooled DPD-based feature
  • O(xij). a point of DPD-based feature map (1x1xK)
  • P. prediction

1.4. Loss Function



  • classification loss. BCE



  • generalized max pooling regularization term



  1. enforce the dot product similarity between O(xij) and the pooled feature phi(Xi) to be a constant one
  • object part-level classification loss term


  1. y_l∈R^K. stands for object part label vector of object part instance xl