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

• URL: http://arxiv.org/abs/2406.09201v3
• Date: Fri, 21 Jun 2024 08:15:12 GMT
• Title: Enhanced Object Detection: A Study on Vast Vocabulary Object Detection Track for V3Det Challenge 2024
• Authors: Peixi Wu, Bosong Chai, Xuan Nie, Longquan Yan, Zeyu Wang, Qifan Zhou, Boning Wang, Yansong Peng, Hebei Li,
• Abstract summary: We present our findings from the research conducted on the Vast Vocabulary Visual Detection dataset for Supervised Vast Vocabulary Visual Detection task. 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.
• Score: 3.5043076887736198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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