# Enhanced Object Detection: A Study on Vast Vocabulary Object Detection Track for V3Det Challenge 2024

Source: [Enhanced Object Detection: A Study on Vast Vocabulary Object Detection Track for V3Det Challenge 2024](<https://www.sota2.com/research/paper/68fa73c21e209131cea5e4c0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:56:12.281Z

Authors: Peixi Wu, Bosong Chai, Xuan Nie, Longquan Yan, Zeyu Wang, Qifan Zhou, Boning Wang, Yansong Peng, Hebei Li

Published: 2024-06-13T14:59:45.000Z

## Abstract

In this technical report, we present our findings from the research conducted on the Vast Vocabulary Visual Detection (V3Det) dataset for Supervised Vast Vocabulary Visual Detection task. How to deal with complex categories and detection boxes has become a difficulty in this track. The original supervised detector is not suitable for this task. We have designed a series of improvements, including adjustments to the network structure, changes to the loss function, and design of training strategies. Our model has shown improvement over the baseline and achieved excellent rankings on the Leaderboard for both the Vast Vocabulary Object Detection (Supervised) track and the Open Vocabulary Object Detection (OVD) track of the V3Det Challenge 2024.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2406.09201>)

[PDF](<https://arxiv.org/pdf/2406.09201.pdf>)