Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification
This work addresses the problem of distinguishing subtle differences in sub-classes (e.g., bird species) for computer vision researchers, representing an incremental improvement by integrating low-level information into existing feature fusion methods.
The paper tackled fine-grained visual classification by proposing a cross-layer navigation convolutional neural network that fuses high-level semantic and low-level texture features using a convolutional LSTM and attention mechanisms, achieving superior results on three standard datasets (CUB-200-2011, Stanford-Cars, and FGVC-Aircraft).
Fine-grained visual classification (FGVC) aims to classify sub-classes of objects in the same super-class (e.g., species of birds, models of cars). For the FGVC tasks, the essential solution is to find discriminative subtle information of the target from local regions. TraditionalFGVC models preferred to use the refined features,i.e., high-level semantic information for recognition and rarely use low-level in-formation. However, it turns out that low-level information which contains rich detail information also has effect on improving performance. Therefore, in this paper, we propose cross-layer navigation convolutional neural network for feature fusion. First, the feature maps extracted by the backbone network are fed into a convolutional long short-term memory model sequentially from high-level to low-level to perform feature aggregation. Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC. In the experiments, three commonly used FGVC datasets, including CUB-200-2011, Stanford-Cars, andFGVC-Aircraft datasets, are used for evaluation and we demonstrate the superiority of the proposed method by comparing it with other referred FGVC methods to show that this method achieves superior results.