Existing image classification datasets used in computer vision tend to have a uniform
distribution of images across object categories. In contrast, the natural world is heavily …

Transferring the knowledge learned from large scale datasets (eg, ImageNet) via fine-tuning
offers an effective solution for domain-specific fine-grained visual categorization (FGVC) …

While neural networks have been successfully applied to many natural language processing
tasks, they come at the cost of interpretability. In this paper, we propose a general …

We propose bilinear models, a recognition architecture that consists of two feature extractors
whose outputs are multiplied using outer product at each location of the image and pooled …

In the context of fine-grained visual categorization, the ability to interpret models as human-
understandable visual manuals is sometimes as important as achieving high classification …

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the
computer vision community will need to face in order to realize its goal of recognizing all …

We address the problem of cross-domain image retrieval, considering the following practical
application: given a user photo depicting a clothing image, our goal is to retrieve the same or …

Current approaches for fine-grained recognition do the following: First, recruit experts to
annotate a dataset of images, optionally also collecting more structured data in the form of …

This paper proposes a joint multi-task learning algorithm to better predict attributes in
images using deep convolutional neural networks (CNN). We consider learning binary …

The world is long-tailed. What does this mean for computer vision and visual recognition?
The main two implications are (1) the number of categories we need to consider in …