Adaptive Triplet Model for Fine-Grained Visual Categorization

Co-Segmentation for Fine Grained Visual Categorization

In this extended abstract we review our works [1, 2] on fine-grained visual classification (FGVC) and present the most recent results of our classification pipeline. In particular, we focus on the importance of the foreground segmentation, and show that accurate segmentation of training images is highly beneficial for the accuracy of classification at test time. We demonstrate the merit of rela...

Visual Representations for Fine-grained Categorization

Visual Representations for Fine-grained Categorization

Advances in fine-grained visual categorization

Attention for Fine-Grained Categorization

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, beginning with fine-grained categorization on the Stanford Dogs data set. In this work we use an RNN of the same structure but substitute a more powerful visual network and perform large-scale pre-training of the visual network outside of the...

Fine-Grained Categorization for 3D Scene Understanding

Fine-grained categorization of object classes is receiving increased attention, since it promises to automate classification tasks that are difficult even for humans, such as the distinction between different animal species. In this paper, we consider fine-grained categorization for a different reason: following the intuition that fine-grained categories encode metric information, we aim to gen...