Due to DNNs' memorization effect, label noise lessens the performance of the web-
supervised fine-grained visual categorization task. Previous literature primarily relies on …

Labeling objects at a subordinate level typically requires expert knowledge, which is not
always available when using random annotators. As such, learning directly from web …

Manually labeling fine-grained datasetsis laborious and typically requires domain-specific
expert knowledge. Conversely, a vast amount of web data is relatively easy to obtain with …

Constructing fine-grained image datasets typically requires domain-specific expert
knowledge, which is not always available for crowd-sourcing platform annotators …

Learning mid-level representation for fine-grained recognition is easily dominated by a
limited number of highly discriminative patterns, degrading its robustness and generalization …

Learning from the web can ease the extreme dependence of deep learning on large-scale
manually labeled datasets. Especially for fine-grained recognition, which targets at …

The lack of sufficient training data has been one obstacle to fine-grained visual classification
research because labeling subcategories generally requires specialist knowledge. As one …

Fine-grained visual recognition tasks typically require training data with reliable acquisition
and annotation processes. Acquiring such datasets with precise fine-grained annotations is …

To distinguish subtle differences among fine-grained categories, a large amount of well-
labeled images are typically required. However, acquiring manual annotations for fine …

Webly supervised fine-grained recognition aims to distinguish subordinate categories (eg,
bird species) with freely available web data. It has significant research and application value …