Detecting objects in real-world scenes is a complex task due to various
challenges, including the vast range of object categories, and potential
encounters with previously unknown or unseen objects. The challenges
necessitate the development of public benchmarks and challenges to advance the
field of object detection. Inspired by the success of previous COCO and LVIS
Challenges, we organize the V3Det Challenge 2024 in conjunction with the 4th
Open World Vision Workshop: Visual Perception via Learning in an Open World
(VPLOW) at CVPR 2024, Seattle, US. This challenge aims to push the boundaries
of object detection research and encourage innovation in this field. The V3Det
Challenge 2024 consists of two tracks: 1) Vast Vocabulary Object Detection:
This track focuses on detecting objects from a large set of 13204 categories,
testing the detection algorithm's ability to recognize and locate diverse
objects. 2) Open Vocabulary Object Detection: This track goes a step further,
requiring algorithms to detect objects from an open set of categories,
including unknown objects. In the following sections, we will provide a
comprehensive summary and analysis of the solutions submitted by participants.
By analyzing the methods and solutions presented, we aim to inspire future
research directions in vast vocabulary and open-vocabulary object detection,
driving progress in this field. Challenge homepage:
this https URL

通过分析参与者提交的方法和解决方案，我们旨在激励巨大词汇和开放词汇目标检测领域的未来研究方向，推动该领域的进展。

2024 年 V3Det 挑战赛：广泛词汇和开放词汇目标检测的方法和结果

V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object  Detection: Methods and Results

Recent advances in detecting arbitrary objects in the real world are trained
and evaluated on object detection datasets with a relatively restricted
vocabulary. To facilitate the development of more general visual object
detection, we propose V3Det, a vast vocabulary visual detection dataset with
precisely annotated bounding boxes on massive images. V3Det has several
appealing properties: 1) Vast Vocabulary: It contains bounding boxes of objects
from 13,029 categories on real-world images, which is 10 times larger than the
existing large vocabulary object detection dataset, e.g., LVIS. 2) Hierarchical
Category Organization: The vast vocabulary of V3Det is organized by a
hierarchical category tree which annotates the inclusion relationship among
categories, encouraging the exploration of category relationships in vast and
open vocabulary object detection. 3) Rich Annotations: V3Det comprises
precisely annotated objects in 245k images and professional descriptions of
each category written by human experts and a powerful chatbot. By offering a
vast exploration space, V3Det enables extensive benchmarks on both vast and
open vocabulary object detection, leading to new observations, practices, and
insights for future research. It has the potential to serve as a cornerstone
dataset for developing more general visual perception systems.

提出了一个新的物体检测数据集 V3Det，包含着丰富的边界框和类别层次结构，可以用于 vast 和 open vocabulary 的物体检测任务。