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 …

Sign languages are visual languages which convey information by signers' handshape,
facial expression, body movement, and so forth. Due to the inherent restriction of …

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 …

Abstract We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and
3D data for weakly supervised point cloud segmentation. Research studies have shown that …

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 …

The localization and segmentation of biomarkers in OCT images are critical steps in retina-
related disease diagnosis. Although fully supervised deep learning models can segment …

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning
(SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding …

Abstract We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL
approach for conditional distribution estimation in regression settings where multiple targets …

Multi-label image classification is more applicable'in the wild'than single-label classification,
as natural images usually contain multiple objects. However, exhaustively annotating …

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled
data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional …