The Caltech-UCSD Birds-200-2011 Dataset
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Summary
This work introduces benchmarks and baseline experiments for multi-class categorization and part localization in CUB-200, a challenging dataset of 200 bird species and adds new part localization annotations.
- Type
- article
- Published
- 2011-07-01
- Cited by
- 5,218
- References
- 7
- Access
- Open access
- OpenAlex
- https://openalex.org/W1797268635
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16119123
Keywords
Bounding overwatch, Categorization, Class (philosophy), Computer science, Minimum bounding box
References
- Caltech-UCSD Birds 200
- Caltech-256 Object Category Dataset
- The Pascal Visual Object Classes (VOC) Challenge
- ImageNet: A large-scale hierarchical image database
- A discriminatively trained, multiscale, deformable part model
- Learning to detect unseen object classes by between-class attribute transfer
- The Multidimensional Wisdom of Crowds
- International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge
Cited by
- A Novel Visual Representation on Text Using Diverse Conditional GAN for Visual Recognition
- Object-centric Sampling for Fine-grained Image Classification
- Spatial, Temporal and Spatio-Temporal Correspondence for Computer Vision Problems
- Fine-grained Recognition Datasets for Biodiversity Analysis
- Collecting a Large-scale Dataset of Fine-grained Cars
- Deep Filter Banks for Texture Recognition, Description, and Segmentation
- Deep Learning for Semantic Part Segmentation with High-Level Guidance
- Orientational Spatial Part Modeling for Fine-Grained Visual Categorization
- Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
- Adaptive Task Assignment for Crowdsourced Classification
- Visual and acoustic identification of bird species
- Deep Image: Scaling up Image Recognition
- Bird Species Categorization Using Pose Normalized Deep Convolutional Nets
- Automatic classification of flying bird species using computer vision techniques
- Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
- Fine-Grained Visual Classification of Aircraft
- Fine-grained recognition without part annotations
- Three viewpoints toward exemplar SVM
- DeepBag: Recognizing Handbag Models
- Subset feature learning for fine-grained category classification
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