RegionDet: A Benchmark for Region Detection Beyond Object Instances

2026-08-07

AI Summary

This work addresses a critical gap in existing object detection benchmarks, which primarily focus on isolated object instances and fail to evaluate semantic regions defined by visual states, scene context, or human activities—such as construction zones or queuing crowds. The paper formally introduces the task of “region detection” and presents RegionDet, a novel benchmark comprising eight categories of such semantic regions, annotated and evaluated using COCO-style protocols. Through systematic evaluation of both closed-set and zero-shot/open-vocabulary detectors on non-instance region localization, the study reveals that while closed-set models can partially capture regional patterns, vision-language models exhibit significant performance degradation in zero-shot settings. This highlights their object-centric bias and insufficient contextual modeling, underscoring the unique challenges posed by region detection, including boundary ambiguity, strong contextual dependence, and the need for relational understanding.

Abstract

Object detection is a fundamental task in computer vision and has achieved remarkable progress on standard benchmarks by localizing discrete and well-bounded object instances. However, many visual targets in real-world scenarios are not individual objects, but regions defined by visual states, scene context, object relations, and human activities, such as construction areas, damaged road regions, queues, group conversations, and vendor regions. Existing detection benchmarks are mainly built around object instances, providing limited support for systematically evaluating such region targets. To address this gap, we introduce Region Detection, a task that extends conventional object detection beyond object instances, and construct RegionDet, a benchmark for region target localization. RegionDet contains eight region categories, including Construction, Crossing, Damage, Queuing, Talking, Vendor, Waiting, and Walking, with COCO-style bounding-box annotations and evaluation protocols. We systematically evaluate representative closed-set and zero-shot/open-vocabulary detectors on RegionDet. Results show that closed-set detectors can partially learn region-level patterns under supervision, while zero-shot/open-vocabulary detectors struggle severely, revealing the strong object-centric bias of current vision-language detectors. Further analyses highlight key challenges in Region Detection, including weak boundary cues, strong context dependency, and insufficient relation-level region understanding. The RegionDet will be released.

Problem

• region detection
• object detection
• visual regions
• context dependency
• non-instance targets

Innovation

• Region Detection
• Beyond Object Instances
• Context-dependent Regions
• Open-vocabulary Detection
• Vision-Language Bias