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Found 11 papers, 5 papers with code
Training-Free Semantic Segmentation via LLM-Supervision
Additionally, we propose an assembly that merges the segmentation maps from the various subclass descriptors to ensure a more comprehensive representation of the different aspects in the test images.
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation
This paper investigates the problem of scene graph generation in videos with the aim of capturing semantic relations between subjects and objects in the form of $\langle$subject, predicate, object$\rangle$ triplets.
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning
By learning to retain and recall the learning process of past training tasks, EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative.
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks
To account for the uncertainty caused by the limited training tasks, we propose a variational MetaModulation where the modulation parameters are treated as latent variables.
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail
Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances.
Hierarchical Variational Memory for Few-shot Learning Across Domains
To explore and exploit the importance of different semantic levels, we further propose to learn the weights associated with the prototype at each level in a data-driven way, which enables the model to adaptively choose the most generalizable features.
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation
A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses.
MetaKernel: Learning Variational Random Features with Limited Labels
Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Batch normalization plays a crucial role when training deep neural networks.
Learning to Learn Variational Semantic Memory
The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework.
