Vision-Language Integration for Zero-Shot Scene Understanding in Real-World Environments

• URL: http://arxiv.org/abs/2510.25070v1
• Date: Wed, 29 Oct 2025 01:16:21 GMT
• Title: Vision-Language Integration for Zero-Shot Scene Understanding in Real-World Environments
• Authors: Manjunath Prasad Holenarasipura Rajiv, B. M. Vidyavathi,
• Abstract summary: This work proposes a vision-language integration framework that unifies pre-trained visual encoders and large language models.<n>The proposed system achieves up to 18% improvement in top-1 accuracy and notable gains in semantic coherence metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot scene understanding in real-world settings presents major challenges due to the complexity and variability of natural scenes, where models must recognize new objects, actions, and contexts without prior labeled examples. This work proposes a vision-language integration framework that unifies pre-trained visual encoders (e.g., CLIP, ViT) and large language models (e.g., GPT-based architectures) to achieve semantic alignment between visual and textual modalities. The goal is to enable robust zero-shot comprehension of scenes by leveraging natural language as a bridge to generalize over unseen categories and contexts. Our approach develops a unified model that embeds visual inputs and textual prompts into a shared space, followed by multimodal fusion and reasoning layers for contextual interpretation. Experiments on Visual Genome, COCO, ADE20K, and custom real-world datasets demonstrate significant gains over state-of-the-art zero-shot models in object recognition, activity detection, and scene captioning. The proposed system achieves up to 18% improvement in top-1 accuracy and notable gains in semantic coherence metrics, highlighting the effectiveness of cross-modal alignment and language grounding in enhancing generalization for real-world scene understanding.

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