Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

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 …

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 …

To understand a scene in depth not only involves locating/recognizing individual objects, but
also requires to infer the relationships and interactions among them. However, since the …

Recognizing multiple labels of images is a practical and challenging task, and significant
progress has been made by searching semantic-aware regions and modeling label …

Fine-grained visual categorization (FGVC) is an important but challenging task due to high
intra-class variances and low inter-class variances caused by deformation, occlusion …

We present an interpretable deep model for fine-grained visual recognition. At the core of
our method lies the integration of region-based part discovery and attribution within a deep …

Fine-grained object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …

Facial expression recognition (FER) has received significant attention in the past decade
with witnessed progress, but data inconsistencies among different FER datasets greatly …

Multi-label image recognition with partial labels (MLR-PL), in which some labels are known
while others are unknown for each image, may greatly reduce the cost of annotation and …