SOTAVerified

Novel Concepts

Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.

Source: BIG-bench

Papers

Showing 131140 of 158 papers

TitleStatusHype
Open-vocabulary object 6D pose estimation0
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of ImagesCode0
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Can Vision Language Models Learn from Visual Demonstrations of Ambiguous Spatial Reasoning?Code0
Revisit Systematic Generalization via Meaningful LearningCode0
Task-Aware Feature Generation for Zero-Shot Compositional LearningCode0
Task-Driven Modular Networks for Zero-Shot Compositional LearningCode0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
Neural Concept Formation in Knowledge GraphsCode0
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