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 110 of 158 papers

TitleStatusHype
Training Compute-Optimal Large Language ModelsCode6
Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing CommunityCode3
Is CLIP the main roadblock for fine-grained open-world perception?Code2
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary ConstraintsCode2
Attention Calibration for Disentangled Text-to-Image PersonalizationCode2
PaLM: Scaling Language Modeling with PathwaysCode2
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
A Language Model's Guide Through Latent SpaceCode1
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic SegmentationCode1
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
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