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 2130 of 158 papers

TitleStatusHype
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class DiscoveryCode1
IFSeg: Image-free Semantic Segmentation via Vision-Language ModelCode1
LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object DetectionCode1
Few-Shot Class-Incremental Learning via Class-Aware Bilateral DistillationCode1
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual LearningCode1
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
XCon: Learning with Experts for Fine-grained Category DiscoveryCode1
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference TimeCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and IndexingCode1
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