Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification

TL;DR

This paper introduces a new video dataset and systematically evaluates deep learning approaches for fine-grained object classification in videos, demonstrating significant accuracy improvements by exploiting temporal information.

Contribution

The work presents the first study of video-based fine-grained classification, introduces a new dataset, and adapts multiple DCNN architectures to leverage temporal cues.

Findings

Classification accuracy improved from 23.1% to 41.1% with Spatio-Temporal Co-occurrence.

Further accuracy increase to 53.6% with automatic bounding box detection.

Systematic comparison of 3D, two-stream, and bilinear DCNN approaches.

Abstract

Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification. In this work we present the novel task of video-based fine-grained object classification, propose a corresponding new video dataset, and perform a systematic study of several recent deep convolutional neural network (DCNN) based approaches, which we specifically adapt to the task. We evaluate three-dimensional DCNNs, two-stream DCNNs, and bilinear DCNNs. Two forms of the two-stream approach are used, where spatial and temporal data from two independent DCNNs are fused either via early fusion (combination of the fully-connected layers) and late fusion (concatenation of the softmax outputs of the DCNNs). For bilinear DCNNs, information from the convolutional layers of the spatial and temporal DCNNs is combined via local co-occurrences. We then fuse the bilinear DCNN and early fusion of the two-stream approach to combine the spatial and temporal information at the local and global level (Spatio-Temporal Co-occurrence). Using the new and challenging video dataset of birds, classification performance is improved from 23.1% (using single images) to 41.1% when using the Spatio-Temporal Co-occurrence system. Incorporating automatically detected bounding box location further improves the classification accuracy to 53.6%.