Bridging the Semantic Chasm: Synergistic Conceptual Anchoring for Generalized Few-Shot and Zero-Shot OOD Perception

TL;DR

This paper introduces SynerNet, a novel multi-agent framework that improves vision-language models' ability to recognize out-of-distribution concepts in few-shot and zero-shot settings by enhancing cross-modal alignment.

Contribution

The paper proposes a multi-agent latent space framework, a semantic context-interchange algorithm, and an adaptive equilibrium mechanism to address modality disparities in VLMs.

Findings

Achieves 1.2% to 5.4% accuracy improvements on VISTA-Beyond benchmark.

Enhances few-shot and zero-shot OOD perception performance.

Demonstrates robustness across diverse domains.

Abstract

This manuscript presents a pioneering Synergistic Neural Agents Network (SynerNet) framework designed to mitigate the phenomenon of cross-modal alignment degeneration in Vision-Language Models (VLMs) when encountering Out-of-Distribution (OOD) concepts. Specifically, four specialized computational units - visual perception, linguistic context, nominal embedding, and global coordination - collaboratively rectify modality disparities via a structured message-propagation protocol. The principal contributions encompass a multi-agent latent space nomenclature acquisition framework, a semantic context-interchange algorithm for enhanced few-shot adaptation, and an adaptive dynamic equilibrium mechanism. Empirical evaluations conducted on the VISTA-Beyond benchmark demonstrate that SynerNet yields substantial performance augmentations in both few-shot and zero-shot scenarios, exhibiting precision improvements ranging from 1.2% to 5.4% across a diverse array of domains.