An Attribute-Enriched Dataset and Auto-Annotated Pipeline for Open Detection
This work addresses the problem of perceptual discrepancies in object detection for AI researchers and practitioners, but it is incremental as it builds on an existing dataset.
The authors tackled the challenge of detecting uncommon or complex objects through language by introducing the Objects365-Attr dataset, which extends Objects365 with 5.6M attribute annotations across 1.4M bounding boxes, and they validated it by evaluating YOLO-World models to show improved detection performance.
Detecting objects of interest through language often presents challenges, particularly with objects that are uncommon or complex to describe, due to perceptual discrepancies between automated models and human annotators. These challenges highlight the need for comprehensive datasets that go beyond standard object labels by incorporating detailed attribute descriptions. To address this need, we introduce the Objects365-Attr dataset, an extension of the existing Objects365 dataset, distinguished by its attribute annotations. This dataset reduces inconsistencies in object detection by integrating a broad spectrum of attributes, including color, material, state, texture and tone. It contains an extensive collection of 5.6M object-level attribute descriptions, meticulously annotated across 1.4M bounding boxes. Additionally, to validate the dataset's effectiveness, we conduct a rigorous evaluation of YOLO-World at different scales, measuring their detection performance and demonstrating the dataset's contribution to advancing object detection.