Cdul: Clip-driven unsupervised learning for multi-label image classification
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 …
label image classification, including three stages: initialization, training, and inference. At the …
Natural language-assisted sign language recognition
Sign languages are visual languages which convey information by signers' handshape,
facial expression, body movement, and so forth. Due to the inherent restriction of …
facial expression, body movement, and so forth. Due to the inherent restriction of …
Exploring structured semantic prior for multi label recognition with incomplete labels
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 …
to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to …
2D-3D interlaced transformer for point cloud segmentation with scene-level supervision
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 …
3D data for weakly supervised point cloud segmentation. Research studies have shown that …
TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images
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 …
related disease diagnosis. Although fully supervised deep learning models can segment …
Bridging the gap between model explanations in partially annotated multi-label classification
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 …
annotated multi-label classification has become an emerging field in computer vision. One …
Pefat: Boosting semi-supervised medical image classification via pseudo-loss estimation and feature adversarial training
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 …
(SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding …
Spatial consistency loss for training multi-label classifiers from single-label annotations
Multi-label image classification is more applicable'in the wild'than single-label classification,
as natural images usually contain multiple objects. However, exhaustively annotating …
as natural images usually contain multiple objects. However, exhaustively annotating …
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis
Abstract We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL
approach for conditional distribution estimation in regression settings where multiple targets …
approach for conditional distribution estimation in regression settings where multiple targets …
Class-distribution-aware pseudo-labeling for semi-supervised multi-label learning
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 …
data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional …