mlsanityData to 7 Oct 2026

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

arXiv 2406.11739 ↗First posted 17 Jun 2024cs.CVcited by 1

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Abstract (arXiv)

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: https://v3det.openxlab.org.cn/challenge

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 43 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

36 of 43 references.

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24 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025CQ-DINO: Mitigating Gradient Dilution via Category Queries for Vast Vocabulary Object Detectioncited by 1