Fine-grained zero-shot learning: Advances, challenges, and prospects
Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, ie, fine-
grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned …
grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned …
Improving zero-shot generalization for clip with variational adapter
The excellent generalization capability of pre-trained Vision-Language Models (VLMs)
makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite …
makes fine-tuning VLMs for downstream zero-shot tasks a popular choice. Despite …
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for
unseen classes which is an effective way to advance ZSL. However existing generative …
unseen classes which is an effective way to advance ZSL. However existing generative …
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic
interactions to transfer semantic knowledge from seen classes to unseen ones supported by …
interactions to transfer semantic knowledge from seen classes to unseen ones supported by …
Fine-grained side information guided dual-prompts for zero-shot skeleton action recognition
Skeleton-based zero-shot action recognition aims to recognize unknown human actions
based on the learned priors of the known skeleton-based actions and a semantic descriptor …
based on the learned priors of the known skeleton-based actions and a semantic descriptor …
An in-Depth Investigation Into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day …
N-shot learning has emerge in recent year as poten-tial learning approach to solve the
problem of data scarcity by learning underlying pattern from a few training sample. Despite …
problem of data scarcity by learning underlying pattern from a few training sample. Despite …
Towards an in-depth evaluation of the performance, suitability and plausibility of few-shot meta transfer learning on an unknown out-of-distribution cyber-attack …
The emergence of few-shot learning as a potential approach to address the problem of data
scarcity by learning underlying pattern from a few training sample had so far given a mix …
scarcity by learning underlying pattern from a few training sample had so far given a mix …
Causal Visual-semantic Correlation for Zero-shot Learning
Zero-Shot learning (ZSL) correlates visual samples and shared semantic information to
transfer knowledge from seen classes to unseen classes. Existing methods typically …
transfer knowledge from seen classes to unseen classes. Existing methods typically …
On the element-wise representation and reasoning in zero-shot image recognition: A systematic survey
Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in
unseen domains via learning generalized knowledge from limited data in the seen domain …
unseen domains via learning generalized knowledge from limited data in the seen domain …
[PDF][PDF] Parsnets: A parsimonious composition of orthogonal and low-rank linear networks for zero-shot learning
This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL),
dubbed ParsNets, in which we are interested in learning a composition of on-device friendly …
dubbed ParsNets, in which we are interested in learning a composition of on-device friendly …