Few-shot image classification is a challenging problem that aims to achieve the human level
of recognition based only on a small number of training images. One main solution to few …

Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

Multi-label image classification is the task of predicting a set of labels corresponding to
objects, attributes or other entities present in an image. In this work we propose the …

Solving multi-label recognition (MLR) for images in the low-label regime is a challenging
task with many real-world applications. Recent work learns an alignment between textual …

The simultaneous recognition of multiple objects in one image remains a challenging task,
spanning multiple events in the recognition field such as various object scales, inconsistent …

This paper presents a CLIP-based unsupervised learning method for annotation-free multi-
label image classification, including three stages: initialization, training, and inference. At the …

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-
trained models (eg CLIP) to various downstream tasks in data-limited or label-limited …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Multi-label image recognition is a practical and challenging task compared to single-label
image classification. However, previous works may be suboptimal because of a great …

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