Fine-Grained Visual Categorization
24 papers with code • 0 benchmarks • 5 datasets
These leaderboards are used to track progress in Fine-Grained Visual Categorization
Most implemented papers
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
Therefore, our multi-branch and multi-scale learning network(MMAL-Net) has good classification ability and robustness for images of different scales.
iCassava 2019 Fine-Grained Visual Categorization Challenge
Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in Africa. At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava.
The Plant Pathology 2020 challenge dataset to classify foliar disease of apples
Appropriate and timely deployment of disease management depends on early disease detection.
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).
VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
While the existing datasets for FGVC are mainly focused on animal breeds or man-made objects with limited labelled data, VegFru is a larger dataset consisting of vegetables and fruits which are closely associated with the daily life of everyone.
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples.
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree.
Feathers dataset for Fine-Grained Visual Categorization
This paper introduces a novel dataset FeatherV1, containing 28, 272 images of feathers categorized by 595 bird species.
Fine-grained Visual-textual Representation Learning
As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas.
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.