Most weakly supervised fine-grained image recognition (WFGIR) approaches predominantly
focus on learning the discriminative details which contain the visual variances and position …

Existing deep learning based weakly supervised fine-grained image recognition (WFGIR)
methods usually pick out the discriminative regions from the high-level feature (HLF) maps …

Existing fine-grained image recognition works have attempted to dig into low-level details for
emphasizing subtle discrepancies among sub-categories. However, a potential limitation of …

Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick
out the discriminative regions from the high-level feature maps directly. We discover that due …

Fine-grained image recognition puts forward a special challenge due to the difficulties of
distinguishing subtle inter-class differences and large intra-class variances. Existing weakly …

We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …

The underlining task for fine-grained image recognition captures both the inter-class and
intra-class discriminate features. Existing methods generally use auxiliary data to guide the …

Attention-based learning for fine-grained image recognition remains a challenging task,
where most of the existing methods treat each object part in isolation, while neglecting the …

Discriminative region localization and feature learning are crucial for fine-grained visual
recognition. Existing approaches solve this issue by attention mechanism or part based …

Abstract The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to
pick out the discriminative regions and learn the discriminative features from them. However …