Papers Generalized Few-Shot Learning
“Generalized Few-Shot Learning” 태그가 달린 논문 14편 · 필터 해제
MetaChest: Generalized few-shot learning of pathologies from chest X-rays
The limited availability of annotated data presents a major challenge for applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a small number of label…
Generalized Few-Shot LearningMulti-Label ClassificationTransfer LearningImproving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning
As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well. If we have only observed a few examples of violations of a new safet…
Data AugmentationFew-Shot LearningGeneralized Few-Shot Learningparameter-efficient fine-tuningBetter Generalized Few-Shot Learning Even Without Base Data
This paper introduces and studies zero-base generalized few-shot learning (zero-base GFSL), which is an extreme yet practical version of few-shot learning problem. Motivated by the cases where base data is not available …
Few-Shot LearningGeneralized Few-Shot LearningImpossible Triangle: What's Next for Pre-trained Language Models?
Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-specific fine-tuning and zero-shot / few-s…
Data AugmentationFew-Shot LearningGeneralized Few-Shot LearningKnowledge Distillation+1Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
Intent Detection is one of the core tasks of dialog systems. Few-shot Intent Detection is challenging due to limited number of annotated utterances for novel classes. Generalized Few-shot intent detection is more realist…
Few-Shot LearningGeneralized Few-Shot LearningGoal-Oriented DialogIntent Detection+3Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from s…
Few-Shot LearningGeneralized Few-Shot LearningIntent DetectionGeneralized Few-Shot Video Classification with Video Retrieval and Feature Generation
Few-shot learning aims to recognize novel classes from a few examples. Although significant progress has been made in the image domain, few-shot video classification is relatively unexplored. We argue that previous metho…
Few-Shot Image ClassificationFew-Shot LearningGeneral ClassificationGeneralized Few-Shot Learning+6From Generalized zero-shot learning to long-tail with class descriptors
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompanied by side information like textual desc…
Few-Shot LearningGeneralized Few-Shot LearningGeneralized Zero-Shot LearningLong-tail Learning+2Relational Generalized Few-Shot Learning
Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most prop…
Few-Shot LearningGeneralized Few-Shot LearningLearning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
Object recognition in the real-world requires handling long-tailed or even open-ended data. An ideal visual system needs to recognize the populated head visual concepts reliably and meanwhile efficiently learn about emer…
Few-Shot LearningGeneralized Few-Shot LearningObject RecognitionTransfer LearningGeneralized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by gen…
Few-Shot LearningGeneralized Few-Shot LearningGeneralized Zero-Shot LearningGeneralized Zero-Shot Learning - Unseen+2Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances…
Few-Shot Image ClassificationFew-Shot LearningGeneral ClassificationGeneralized Few-Shot Learning+1Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space. As labeled images are expensive, one direction is to augment the dataset by gen…
Few-Shot LearningGeneralized Few-Shot LearningGeneralized Zero-Shot LearningZero-Shot Learning+1Learning Robust Visual-Semantic Embeddings
Many of the existing methods for learning joint embedding of images and text use only supervised information from paired images and its textual attributes. Taking advantage of the recent success of unsupervised learning …
Generalized Few-Shot LearningRepresentation LearningRetrieval