In this work I contribute two solutions towards increasing the utility and performance of fine-grained categorization. First, I present a software infrastructure designed to ease the burden of collecting and managing a fine-grained datasets. Second, I present a technique that significantly advances state-of-the-art performance on bird species categorization. These contributions provide the groundwork for expanding to other fine-grained domain
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Understanding the patterns and processe...
In many real data science problems, it is common to encounter a domain mismatch between the training...
Understanding the patterns and processes underlying the uneven distribution of biodiversity across s...
We address the problem of large-scale fine-grained vi-sual categorization, describing new methods we...
We propose an architecture for fine-grained visual categorization that approaches expert human perfo...
We propose an architecture for fine-grained visual categorization that approaches expert human perfo...
Fine-grained categorization has emerged in recent years as a problem of great interest to the comput...
We introduce tools and methodologies to collect high quality, large scale fine-grained computer visi...
The objective of this work is to improve performance in fine-grained visual categorization (FGVC). I...
The main purpose of fine-grained classification is to distinguish among many subcategories of a sing...
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large...
CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The e...
We study fine-grained categorization, the task of distin-guishing among (sub)categories of the same ...
A bird recognition system identifies bird species by combining computer vision and machine learning ...
Understanding the patterns and processes underlying the uneven distribution of biodiversity across s...
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Understanding the patterns and processe...
In many real data science problems, it is common to encounter a domain mismatch between the training...
Understanding the patterns and processes underlying the uneven distribution of biodiversity across s...
We address the problem of large-scale fine-grained vi-sual categorization, describing new methods we...
We propose an architecture for fine-grained visual categorization that approaches expert human perfo...
We propose an architecture for fine-grained visual categorization that approaches expert human perfo...
Fine-grained categorization has emerged in recent years as a problem of great interest to the comput...
We introduce tools and methodologies to collect high quality, large scale fine-grained computer visi...
The objective of this work is to improve performance in fine-grained visual categorization (FGVC). I...
The main purpose of fine-grained classification is to distinguish among many subcategories of a sing...
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large...
CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The e...
We study fine-grained categorization, the task of distin-guishing among (sub)categories of the same ...
A bird recognition system identifies bird species by combining computer vision and machine learning ...
Understanding the patterns and processes underlying the uneven distribution of biodiversity across s...
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Understanding the patterns and processe...
In many real data science problems, it is common to encounter a domain mismatch between the training...
Understanding the patterns and processes underlying the uneven distribution of biodiversity across s...