Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels
usually results in inferior model performance. Existing state-of-the-art methods primarily …

Label noise has been a practical challenge in deep learning due to the strong capability of
deep neural networks in fitting all training data. Prior literature primarily resorts to sample …

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 …

Fine-grained visual classification (FGVC) is an important task in the field of computer vision
(CV), which aims to classify sub-categories that are hard to distinguish (eg, identifying …

One-shot semantic image segmentation aims to segment the object regions for the novel
class with only one annotated image. Recent works adopt the episodic training strategy to …

Due to the existence of label noise in web images and the high memorization capacity of
deep neural networks, training deep fine-grained (FG) models directly through web images …

Performances of deep neural networks are prone to be degraded by label noise due to their
powerful capability in fitting training data. Deeming low-loss instances as clean data is 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 …

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 …