Latest Articles for Zero-shot Learning

RevCD enhances zero-shot learning by linking visual and semantic information for unseen categories.

Open-source models that efficiently classify political texts without extensive training.

This article examines how relative representations improve AI communication and task adaptability.

Evaluating VLMs on spatial tasks using visual and unclear text.

This study evaluates zero-shot and few-shot learning in clinical applications.

Exploring how AI-generated images evoke emotions and reveal negativity.

A method allowing models to learn new concepts using only text descriptions.

Using Freq-Synth to improve predictions with limited data.

Boosting robot accuracy in recognizing new images using clever word techniques.

A new approach for faster computer learning in various tasks.

New method pairs CLIP and DINO to classify images without labels.

Machines are taking a lead in spotting product defects for better quality.

A new method automates news classification, saving time and resources for organizations.

Discover how AI can engage in conversations with multiple speakers.

SyncFlow merges audio and video generation for seamless content creation.

A new method enhances how models understand images and text.

Discover how zero-shot learning changes the game in environmental audio recognition.

ConfigX simplifies configuring evolutionary algorithms for diverse problem-solving tasks.

Discover how large language models are reshaping financial predictions.

A new approach improves video analysis with dynamic token systems.

DAAN improves how machines learn from audio-visual data in zero-shot scenarios.

Researchers enhance AI’s ability to interpret images through better training data.

Discover how audio-language models are changing sound recognition technology.

TimeRAF enhances predictions using past data and external knowledge.