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 …

Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …

Recent progress on fine-grained visual recognition and visual question answering has
featured Bilinear Pooling, which effectively models the 2nd order interactions across multi …

The click feature of an image, defined as the user click frequency vector of the image on a
predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained …

Delicate feature representation about object parts plays a critical role in fine-grained
recognition. For example, experts can even distinguish fine-grained objects relying only on …

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

Fine-grained classification is a challenging problem, due to subtle differences among highly-
confused categories. Most approaches address this difficulty by learning discriminative …

Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird
observation and protection but also a prevalent task for image classification in multimedia …

We address the problem of fine-grained generalized zero-shot recognition of visually similar
classes without training images for some classes. We propose a dense attribute-based …