Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization

By Kevin J. Shih, Arun Mallya, Saurabh Singh, Derek Hoiem. 2015

43 citations recorded by the source corpus.

We present a simple deep learning framework to simultaneously predict keypoint locations and their respective visibilities and use those to achieve state-of-the-art performance for fine-grained classification. We show that by conditioning the predictions on object proposals with sufficient image support, our method can do well without complicated spatial reasoning. Instead, inference methods with robustness to outliers, yield state-of-the-art for keypoint localization. We demonstrate the effectiveness of our accurate keypoint localization and visibility prediction on the fine-grained bird recognition task with and without ground truth bird bounding boxes, and outperform existing state-of-the-art methods by over 2%.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “The majority of research in fine-grained recognition has focused on developing improved models for classification [2, 5, 7, 15, 17, 18, 24, 30, 37, 44, 45, 58, 59, 60, 63, 64].”
Single Image 3D Interpreter Network (2016, methodology): “Our method is comparable to Mdshift [30] (better in AE but worse in PCP), and better than all other algorithms.”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Most ﬁne-grained research papers present a novel model for classiﬁcation [4, 6, 7, 12, 15, 17, 25, 31, 40, 45, 46, 57, 58, 59, 61, 62].”
Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings (2017, background): “Beyond faces, there is work on humans [65, 58], birds [55, 41, 69] and furniture [63].”
A survey on deep learning-based fine-grained object classification and semantic segmentation (2017, methodology): “Part-based R-CNN [19] AlexNet BBox + Parts BBox 76.4 Part-based R-CNN [19] AlexNet BBox + Parts − 73.9 Multi-proposal consensus [22] AlexNet BBox BBox 80.3 PoseNorm [24] Alexnet BBox + Parts − 75.7 PS-CNN [26] AlexNet BBox + Parts BBox 76.2 Deep LAC [28] AlexNet BBox BBox 80.3”
Part-Stacked CNN for Fine-Grained Visual Categorization (2015, methodology): “The complete PS-CNN model with a bounding-box and 15 object parts achieves 76% accuracy, which is comparable with part-based R-CNN [51], while being slightly lower than several most recent state-of-the-art methods [22, 21, 35] due to the effectiveness-efﬁciency tradeoff.”