Deformable Part Models with CNN Features

By Pierre-André Savalle, Stavros Tsogkas, George Papandreou, Iasonas Kokkinos. 2014

50 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Object Detection Networks on Convolutional Feature Maps (2015, methodology): “The mAP on PASCAL VOC 2007 is 45-47% [8], [9], [10], [11] for hybrid methods, and is 47% for R-CNN that just uses SVM on the last convolutional layer.”
Deformable part models are convolutional neural networks (2014, neutral): “C-DPM [34] 3 8 39.”
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization (2015, background): “In addition, several researchers (Savalle et al., 2014; Wan et al., 2015; Girshick et al., 2015) have married deep CNNs with part-based deformable models, where the parts are not semantic and the part locations are hidden/latent variables.”
Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection (2015, methodology): “Aspect ratio variability and search Sliding window detectors have typically substantially lower performance than region-based systems, even when trained with CNN features [13,33,44]; e.”
Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition (2016, background): “tion [39], [40], [41], [42], [43] and recognition [44], [45], [46], [47], [48] tasks to account for occlusion, pose variations, and non-rigid object deformations.”
Adaptive region pooling for object detection (2015, background): “However, large appearance variations of training examples lead to less desirable clustering results, where exemplars that are less common can be easily absorbed by the dominant clusters.”
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016, methodology): “Since the groundbreaking success of deep convolutional neural networks (CNN) [1] in image classification task [2] on the ImageNet large scale visual recognition challenge (ILSVRC) [3, 4], CNN-based object detection methods have recently attracted a great deal of research interest and have achieved s”
Convolutional Channel Features (2015, background): “There have already been some works attempting to connect CNN with popular shallow models, like DPM [22, 44] and Regionlet [57].”
Object Detection in 20 Years: A Survey (2019, background): “To answer this question, some researchers have tried to weaken the role of the proposals [133] or simply perform sliding window detection on CNN features [134–138], but none of them obtained satisfactory results.”
Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2019, unknown): “The deformable part model (DPM) [38] and its variants [24], [25], [39]–[42] have been the well-known methods.”