Learning Discriminative Part Detectors for Image Classification and Cosegmentation

By Jian Sun, Jean Ponce. 2013

142 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Unsupervised Visual Representation Learning by Context Prediction (2015, background): “Finally, our work is related to a line of research on discriminative patch mining [13, 47, 26, 34, 49, 11], which has emphasized weak supervision as a means of object discovery.”
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition (2014, background): “26.05 DPM[28] 30.40 Object Bank[23] 37.60 RBow[29] 37.93 BoP[20] 46.10 miSVM[24] 46.40 D-Parts[38] 51.40 IFV[20] 60.77 MLrep[10] 64.03 CNN-SVM 58.44 points are annotated with their ground truth and estimated labels.”
Unsupervised Representation Learning by Sorting Sequences (2017, methodology): “Before the resurgence of CNNs, hand-craft features such as SIFT and HOG have been used to discover semantic classes using clustering [32, 35], or mining discriminative mid-level features [34, 5, 38].”
Context as Supervisory Signal: Discovering Objects with Predictable Context (2014, background): “A number of recent works have argued that weak supervision can be an effective way to get more visually meaningful clusters [14,15,16,17,18,19,20,21,22,23].”
Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals (2015, background): “Recent work on discriminative patch discovery [15, 44, 50] learns mid-level visual representations in a weakly-supervised mode, and use them for object recognition [15, 44] and discovery [13, 50].”
Picking Deep Filter Responses for Fine-Grained Image Recognition (2016, methodology): “Most previous works employ some form of unsupervised clustering, such as k-means [23], [24], or template matching [30], to initialize a part model.”
Food-101 - Mining Discriminative Components with Random Forests (2014, methodology): “For classifying an image, we only need to score all of its superpixels using the previously trained component models, instead of applying multi scale sliding window detectors [7,35,38,25,8,19,34,40].”
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks (2016, methodology): “In computer vision, the dominant approach for WSL is the Multiple Instance Learning (MIL) paradigm [9]: an image is considered as a bag of regions, and the model seeks the max scoring instance in each bag [35, 41, 37, 5, 47, 44, 11].”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, unknown): “[133] solved cosegmentation by learning discriminative part”
Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017, methodology): “Mid-level Image Representation: Since the seminal work on discriminative image patch discovery [53], midlevel visual elements based image representation has been explored intensively and found being effective on boosting the performance of many visual computing tasks, particularly scene recognition ”
Mid-level Visual Element Discovery as Discriminative Mode Seeking (2013, background): “Recently, several approaches [5, 6, 11, 12, 15, 23, 26, 27] have proposed mining visual data for discriminative mid-level visual elements, i.”
Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification (2015, background): “Comparing to current existing feature learning methods, they can generally get better local descriptors, and extra information (e.g. discriminative information) can be better expressed by manually inserting prior knowledge.”
Top-k Multiclass SVM (2015, unknown): “4 - SP [28] 51.”
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation (2017, unknown): “The seminal DPM model [16] including several template regions for decision has been extensively studied [54, 46], optionally incorporating priors, e.g. sparsity or diversity, in order to learn sensible models [30, 59].”
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation (2015, background): “, [7, 16, 29, 3]), recent studies attempt to train multiple convolutional neural networks on aligned parts.”