Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection

2025-07-17

AI Summary

Current vision-language models (VLMs) exhibit limited generalization to unseen, complex, wide-area scenes. To address this, we propose a fine-tuning-free, plug-and-play framework centered on a hierarchical core-set selection mechanism. This mechanism employs a theoretically grounded importance function that jointly models utility, representativeness, robustness, and synergy, enabling progressive identification of salient image regions. Importance-weighted sampling is further integrated with feature density optimization to enhance semantic representation fidelity. The method supports multi-scale scene analysis and provides strong interpretability through explicit region selection. Experiments across diverse multi-task benchmarks demonstrate substantial improvements over state-of-the-art baselines: our approach achieves high comprehension accuracy using only a minimal number of selected regions, exhibits seamless compatibility with mainstream VLMs (e.g., CLIP, BLIP-2, Qwen-VL), and shows exceptional cross-model transferability and generalization.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

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: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Scene understanding is one of the core tasks in computer vision, aiming to extract semantic information from images to identify objects, scene categories, and their interrelationships. Although advancements in Vision-Language Models (VLMs) have driven progress in this field, existing VLMs still face challenges in adaptation to unseen complex wide-area scenes. To address the challenges, this paper proposes a Hierarchical Coresets Selection (HCS) mechanism to advance the adaptation of VLMs in complex wide-area scene understanding. It progressively refines the selected regions based on the proposed theoretically guaranteed importance function, which considers utility, representativeness, robustness, and synergy. Without requiring additional fine-tuning, HCS enables VLMs to achieve rapid understandings of unseen scenes at any scale using minimal interpretable regions while mitigating insufficient feature density. HCS is a plug-and-play method that is compatible with any VLM. Experiments demonstrate that HCS achieves superior performance and universality in various tasks.

Problem

• Adapting VLMs to unseen complex wide-area scenes
• Enhancing scene understanding with minimal interpretable regions
• Improving feature density without additional fine-tuning

Innovation

• Hierarchical Coresets Selection for scene understanding
• Theoretically guaranteed importance function refinement
• Plug-and-play compatibility with Vision-Language Models