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Found 9 papers, 1 papers with code
SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
In this way, SemPLeS can perform better semantic alignment between object regions and the associated class labels, resulting in desired pseudo masks for training the segmentation model.
Language-Guided Transformer for Federated Multi-Label Classification
Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification.
Frequency-Aware Self-Supervised Long-Tailed Learning
Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples.
Bias-Eliminating Augmentation Learning for Debiased Federated Learning
Learning models trained on biased datasets tend to observe correlations between categorical and undesirable features, which result in degraded performances.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest.
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities.
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.
Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data.
Domain Generalization
Generalizable Person Re-identification
+1
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #16 on
Unsupervised Domain Adaptation
on Market to Duke
