Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection
This addresses the problem of scene understanding in computer vision for applications requiring adaptation to new, complex environments, though it appears incremental as it builds on existing VLM frameworks.
The paper tackles the challenge of adapting Vision-Language Models (VLMs) to unseen complex wide-area scenes by proposing a Hierarchical Coresets Selection (HCS) mechanism, which enables VLMs to achieve rapid understanding without fine-tuning and demonstrates superior performance in experiments.
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.