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

2026-01-30

AI Summary

This work addresses the degradation of cross-modal alignment in vision-language models under out-of-distribution (OOD) scenarios by proposing SynerNet, a novel framework that integrates four synergistic computational units—visual perception, linguistic context, named embeddings, and global coordination—to establish a structured message-passing mechanism for mitigating modality discrepancies. SynerNet innovatively combines multi-agent latent-space naming, semantic context exchange algorithms, and an adaptive dynamic balancing mechanism to enable collaborative optimization of cross-modal semantics. Evaluated on the VISTA-Beyond benchmark, the proposed method achieves accuracy improvements of 1.2% to 5.4% over existing approaches in both few-shot and zero-shot OOD settings, demonstrating its superior robustness and generalization capability.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Multimodal Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Out-of-Distribution
• Cross-modal Alignment
• Few-Shot Learning
• Zero-Shot Learning
• Vision-Language Models

Innovation

• SynerNet
• cross-modal alignment
• out-of-distribution (OOD)
• few-shot learning
• vision-language models