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 …

From the beginning of zero-shot learning research, visual attributes have been shown to
play an important role. In order to better transfer attribute-based knowledge from known to …

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 …

When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …

Recognizing fine-grained categories (eg, bird species) is difficult due to the challenges of
discriminative region localization and fine-grained feature learning. Existing approaches …

Recognizing fine-grained categories (eg, bird species) highly relies on discriminative part
localization and part-based fine-grained feature learning. Existing approaches …

Vehicle re-identification (Re-ID) has been attracting more interests in computer vision owing
to its great contributions in urban surveillance and intelligent transportation. With 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 …

Fine-grained recognition poses the unique challenge of capturing subtle inter-class
differences under considerable intra-class variances (eg, beaks for bird species) …

Fine-grained visual recognition is challenging because it highly relies on the modeling of
various semantic parts and fine-grained feature learning. Bilinear pooling based models …