Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition

TL;DR

This paper introduces STAN, a novel spatial-temporal attention network inspired by brain mechanisms, designed to improve fine-grained image recognition, especially in open-set scenarios, by aggregating features over multiple moments.

Contribution

The paper proposes a new spatial-temporal attention network (STAN) that enhances fine-grained recognition by integrating multiple attention modules and a Long Short-Term Memory network.

Findings

STAN-OSFGR outperforms 9 state-of-the-art methods on multiple datasets.

The proposed model effectively learns accurate attention maps for fine-grained images.

Experimental results demonstrate significant improvements in open-set recognition accuracy.

Abstract

Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision community. However, we empirically found that a typical vision transformer with the spatial self-attention mechanism could not learn accurate attention maps for distinguishing different categories of fine-grained images. To address this problem, motivated by the temporal attention mechanism in brains, we propose a spatial-temporal attention network for learning fine-grained feature representations, called STAN, where the features learnt by implementing a sequence of spatial self-attention operations corresponding to multiple moments are aggregated progressively. The proposed STAN consists of four modules: a self-attention backbone module for learning a sequence of features with self-attention operations, a spatial feature self-organizing module for facilitating the model training, a spatial-temporal feature learning module for aggregating the re-organized features via a Long Short-Term Memory network, and a context-aware module that is implemented as the forget block of the spatial-temporal feature learning module for preserving/forgetting the long-term memory by utilizing contextual information. Then, we propose a STAN-based method for open-set fine-grained recognition by integrating the proposed STAN network with a linear classifier, called STAN-OSFGR. Extensive experimental results on 3 fine-grained datasets and 2 coarse-grained datasets demonstrate that the proposed STAN-OSFGR outperforms 9 state-of-the-art open-set recognition methods significantly in most cases.