HCP: A Flexible CNN Framework for Multi-Label Image Classification

By Yunchao Wei, Wei Xia, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, Shuicheng Yan. 2014

708 citations recorded by the source corpus.

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [12] based on hand-crafted features on the VOC 2012 dataset.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, methodology): “Approaches in the deep learning realm have also attempted to decompose the multi-label classification problem into single-label classification problems, by independently classifying features extracted from object proposals [69, 64, 35, 14] or by considering global image features and finetuning pre-t”
Proposal-Free Network for Instance-Level Object Segmentation (2015, neutral): “Deep convolutional neural networks (DCNN) have achieved great success in object classification [33] [17] [31] [37], object detection [28] [26] [29] and object segmentation [23] [4] [14] [22].”
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning (2016, background): “State-of-the-art image features are generally extracted by deep Convolutional Neural Networks (CNNs) [8, 25, 32].”
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet (2019, background): “A set of superficially similar but unrelated approaches are region proposal models (Wei et al., 2016; Tang et al., 2017; 2016; Arandjelovic et al., 2015).”
Adversarial Complementary Learning for Weakly Supervised Object Localization (2018, methodology): “[42] adopted a similar strategy to learn multi-label classification networks with max-pooling MIL.”
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition (2017, methodology): “An alternative way (Yang et al. 2016; Wei et al. 2016) is to introduce object proposals that are assumed to contain all possible foreground objects in the image, and aggregate features extracted from all these proposals to incorporate local information for multi-label image recognition.”
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers (2019, methodology): “We compare the performance of the proposed method against the following state-of-the-art approaches: HCP-2000 [Wei et al., 2014], HCP-VGG [Wei et al., 2016], RLSD+ft-RPN [Zhang et al., 2018], CNN+RNN [Wang et al., 2016], CNN+LSTM [Wang et al., 2017], PF-DLDL [Gao et al., 2017a] and the method of…”
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation (2015, background): “In recent years, deep convolutional neural networks (DCNNs) have demonstrated an outstanding capability in various computer vision tasks, such as image classification [1], [2], [3], [4], object detection [5], [6] and semantic segmentation [7], [8], [9], [10], [11], [12], [13].”
Multi-Label Image Recognition With Graph Convolutional Networks (2019, neutral): “6 HCP [31] 98.”