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

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Showing 51–100 of 158 papers

TitleStatusHype
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of ImagesCode0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
Neural Concept Formation in Knowledge GraphsCode0
Variational Prototype Replays for Continual LearningCode0
A Robust, Efficient Predictive Safety FilterCode0
Simple and Lightweight Human Pose EstimationCode0
SketchEmbedNet: Learning Novel Concepts by Imitating DrawingsCode0
Subspace Distillation for Continual LearningCode0
Revisit Systematic Generalization via Meaningful LearningCode0
Task-Aware Feature Generation for Zero-Shot Compositional LearningCode0
Task-Driven Modular Networks for Zero-Shot Compositional LearningCode0
Understanding MCMC Dynamics as Flows on the Wasserstein SpaceCode0
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant AnalysisCode0
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel ConceptsCode0
Few-Shot Novel Concept Learning for Semantic Parsing—0
Few-Shot Task Learning through Inverse Generative Modeling—0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers—0
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what—0
From Data to Modeling: Fully Open-vocabulary Scene Graph Generation—0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts—0
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration—0
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models—0
Assessing the Variety of a Concept Space Using an Unbiased Estimate of Rao's Quadratic Index—0
A Closer Look at Rehearsal-Free Continual Learning—0
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models—0
Hyperbolic Learning with Synthetic Captions for Open-World Detection—0
Decision Making Using Rough Set based Spanning Sets for a Decision System—0
Image Inpainting by Patch Propagation Using Patch Sparsity—0
Influence zones for continuous beam systems—0
Integrated Planning of Multi-energy Grids: Concepts and Challenges—0
Interactive Visual Task Learning for Robots—0
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models—0
Theoretical and Empirical Analysis of Adaptive Entry Point Selection for Graph-based Approximate Nearest Neighbor Search—0
Knowledge Transfer Across Modalities with Natural Language Supervision—0
The Price of Cognition and Replicator Equations in Parallel Neural Networks—0
Contrastive Visual Data Augmentation—0
Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Experiments, and Challenges—0
Contextual Blocking Bandits—0
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review—0
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks—0
Coherent Temporal Synthesis for Incremental Action Segmentation—0
Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery—0
Learning to Learn with Compound HD Models—0
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks—0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering—0
ChatAnything: Facetime Chat with LLM-Enhanced Personas—0
Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers—0
Malware Detection and Prevention using Artificial Intelligence Techniques—0
Memorizing Complementation Network for Few-Shot Class-Incremental Learning—0
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