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Fast Fine-Grained Image Classification via Weakly Supervised Discriminative Localization
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Visualizing and Understanding Convolutional Networks
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Part-Based R-CNNs for Fine-Grained Category Detection
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Edge Boxes: Locating Object Proposals from Edges
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Selective Search for Object Recognition
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The Pascal Visual Object Classes Challenge: A Retrospective
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Modeling visual attention via selective tuning
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Distinctive Image Features from Scale-Invariant Keypoints
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Emergence of complex cell properties by learning to generalize in natural scenes
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Histograms of Oriented Gradients for Human Detection
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ImageNet: A large-scale hierarchical image database
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Locality-constrained Linear Coding for image classification
inferredcites →https://doi.org/10.1109/cvpr.2013.128openalex-snapshot
POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation
inferredcites →https://doi.org/10.1109/cvpr.2014.222openalex-snapshot
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298668openalex-snapshot
Is object localization for free? - Weakly-supervised learning with convolutional neural networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298685openalex-snapshot
The application of two-level attention models in deep convolutional neural network for fine-grained image classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298775openalex-snapshot
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298880openalex-snapshot
Hyper-class augmented and regularized deep learning for fine-grained image classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7299059openalex-snapshot
Action recognition with trajectory-pooled deep-convolutional descriptors
inferredcites →https://doi.org/10.1109/cvpr.2015.7299194openalex-snapshot
Fine-grained recognition without part annotations
inferredcites →https://doi.org/10.1109/cvpr.2016.127openalex-snapshot
Fine-Grained Image Classification by Exploring Bipartite-Graph Labels
inferredcites →https://doi.org/10.1109/cvpr.2016.128openalex-snapshot
Picking Deep Filter Responses for Fine-Grained Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.129openalex-snapshot
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.130openalex-snapshot
Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop
inferredcites →https://doi.org/10.1109/cvpr.2016.131openalex-snapshot
Mining Discriminative Triplets of Patches for Fine-Grained Classification
inferredcites →https://doi.org/10.1109/cvpr.2016.132openalex-snapshot
Part-Stacked CNN for Fine-Grained Visual Categorization
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2016.36openalex-snapshot
InterActive: Inter-Layer Activeness Propagation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00436openalex-snapshot
Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition
inferredcites →https://doi.org/10.1109/iccv.1999.790410openalex-snapshot
Object recognition from local scale-invariant features
inferredcites →https://doi.org/10.1109/iccv.2013.206openalex-snapshot
Hierarchical Part Matching for Fine-Grained Visual Categorization
inferredcites →https://doi.org/10.1109/iccv.2015.136openalex-snapshot
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2015.170openalex-snapshot
Bilinear CNN Models for Fine-Grained Visual Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.276openalex-snapshot
Multiple Granularity Descriptors for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-snapshot
3D Object Representations for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/icpr.2014.15openalex-snapshot
Learning Features and Parts for Fine-Grained Recognition
inferredcites →https://doi.org/10.1109/tcsvt.2011.2133210openalex-snapshot
A Self-Configurable Systolic Architecture for Face Recognition System Based on Principal Component Neural Network
inferredcites →https://doi.org/10.1109/tcsvt.2016.2610078openalex-snapshot
A Hybrid Approach for Facial Performance Analysis and Editing
inferredcites →https://doi.org/10.1109/tip.2013.2290593openalex-snapshot
Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization
inferredcites →https://doi.org/10.1109/tip.2014.2310117openalex-snapshot
Spatial Pooling of Heterogeneous Features for Image Classification
inferredcites →https://doi.org/10.1109/tip.2015.2509425openalex-snapshot
Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization
inferredcites →https://doi.org/10.1109/tip.2016.2531289openalex-snapshot
Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation
inferredcites →https://doi.org/10.1109/tip.2016.2599102openalex-snapshot
Coarse-to-Fine Description for Fine-Grained Visual Categorization
inferredcites →https://doi.org/10.1109/tip.2016.2621661openalex-snapshot
Friend or Foe: Fine-Grained Categorization With Weak Supervision
inferredcites →https://doi.org/10.1109/tits.2017.2679114openalex-snapshot
Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2016.2637331openalex-snapshot
Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1145/2647868.2654937openalex-snapshot
Fused one-vs-all mid-level features for fine-grained visual categorization
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3123266.3123319openalex-snapshot
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
inferredcites →https://doi.org/10.1176/appi.psychotherapy.1987.41.1.146openalex-snapshot
Cognitive Psychology and Its Implications
inferredcites →https://doi.org/10.1609/aaai.v31i1.11202openalex-snapshot
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
inferredcites →https://doi.org/10.1609/aaai.v31i1.11223openalex-snapshot
Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification
inferredcites →https://doi.org/10.48550/arxiv.1406.2952openalex-snapshot
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot
Caffe: Convolutional Architecture for Fast Feature Embedding
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1612.03928openalex-snapshot
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets
inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot
Caltech-UCSD Birds-200-2011 Dataset
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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