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 …

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive
to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to …

Due to the expensive costs of collecting labels in multi-label classification datasets, partially
annotated multi-label classification has become an emerging field in computer vision. One …

Multi-label image recognition in the low-label regime is a task of great challenge and
practical significance. Previous works have focused on learning the alignment between …

Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer
their relationships for a given video. It requires not only a comprehensive understanding of …

Ingredient prediction has received more and more attention with the help of image
processing for its diverse real-world applications, such as nutrition intake management and …

Partially annotated images are easy to obtain in multi-label classification. However,
unknown labels in partially annotated images exacerbate the positive-negative imbalance …

Abstract Generalized Zero-Shot Learning (GZSL) has become an important research due to
its powerful ability of recognizing unseen objects. Generative methods, converting …

Multi-label classification (MLC) suffers from the inevitable label noise in training data due to
the difficulty in annotating various semantic labels in each image. To mitigate the influence …