The iNaturalist Species Classification and Detection Dataset
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- Type
- article
- Published
- 2017-07-20
- Cited by
- 2,152
- References
- 53
- Access
- Open access
- OpenAlex
- https://openalex.org/W2797977484
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:29156801
Keywords
Computer science, Artificial intelligence, Feature (linguistics), Variety (cybernetics), Pattern recognition (psychology)
References
- How Many Species Are There on Earth and in the Ocean?
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
- The Caltech-UCSD Birds-200-2011 Dataset
- Fine-Grained Visual Classification of Aircraft
- Becoming the expert - interactive multi-class machine teaching
- Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
- A large-scale car dataset for fine-grained categorization and verification
- Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds
- The Pascal Visual Object Classes (VOC) Challenge
- Care of aged doctors
- Understanding Objects in Detail with Fine-Grained Attributes
- Fine-Grained Visual Comparisons with Local Learning
- FaceNet: A unified embedding for face recognition and clustering
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
- 3D Object Representations for Fine-Grained Categorization
- DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- A Visual Vocabulary for Flower Classification
- Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
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- Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
- Teaching Multiple Concepts to a Forgetful Learner
- Applying Domain Randomization to Synthetic Data for Object Category Detection
- Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
- Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
- Using Pre-Training Can Improve Model Robustness and Uncertainty
- CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
- Graph-RISE: Graph-Regularized Image Semantic Embedding
- Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition
- Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity
- A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
- Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning
- Task2Vec: Task Embedding for Meta-Learning
- Few-Shot Learning With Localization in Realistic Settings
- How Convolutional Neural Networks Diagnose Plant Disease
- Differential Privacy Has Disparate Impact on Model Accuracy
- LVIS: A Dataset for Large Vocabulary Instance Segmentation
- Presence-Only Geographical Priors for Fine-Grained Image Classification
- Does learning require memorization? a short tale about a long tail
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