STMVPA: Spatiotemporal multivariate pattern analysis permits 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...

Weakly Supervised Fine-Grained Image Categorization

In this paper, we categorize fine-grained images without using any object / part annotation neither in the training nor in the testing stage, a step towards making it suitable for deployments. Fine-grained image categorization aims to classify objects with subtle distinctions. Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using o...