A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
The emerging trends of multi-label learning
Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
General multi-label image classification with transformers
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 …
objects, attributes or other entities present in an image. In this work we propose the …
Contrastive embedding for generalized zero-shot learning
Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and
unseen classes, when only the labeled examples from seen classes are provided. Recent …
unseen classes, when only the labeled examples from seen classes are provided. Recent …
Multi-label image recognition with graph convolutional networks
The task of multi-label image recognition is to predict a set of object labels that present in an
image. As objects normally co-occur in an image, it is desirable to model the label …
image. As objects normally co-occur in an image, it is desirable to model the label …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
Dualcoop: Fast adaptation to multi-label recognition with limited annotations
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 …
task with many real-world applications. Recent work learns an alignment between textual …
Distribution-balanced loss for multi-label classification in long-tailed datasets
We present a new loss function called Distribution-Balanced Loss for the multi-label
recognition problems that exhibit long-tailed class distributions. Compared to conventional …
recognition problems that exhibit long-tailed class distributions. Compared to conventional …
Recent advances in open set recognition: A survey
In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …
difficult to collect training samples to exhaust all classes when training a recognizer or …