Summary of Vfm-det: Towards High-performance Vehicle Detection Via Large Foundation Models, by Wentao Wu et al.
VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models
by Wentao Wu, Fanghua Hong, Xiao Wang, Chenglong Li, Jin Tang
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel vehicle detection paradigm, VFM-Det, which leverages pre-trained foundation models for vehicles (VehicleMAE) and large language models (T5). The approach combines region proposal-based detection with VehicleMAE-enhanced features and a new VAtt2Vec module that predicts semantic attributes of proposals. This enhances vision features via contrastive learning. Experiments on three benchmark datasets demonstrate the effectiveness, improving the baseline by +5.1% and +6.2% in AP0.5 and AP0.75 metrics respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to detect vehicles using AI models. It combines two types of AI models: one that’s good at recognizing vehicle features (VehicleMAE) and another that’s good with words (T5). The approach uses these models together to improve the accuracy of detecting vehicles in images. The results show that this method is better than previous methods, which only used visual features. |
Keywords
» Artificial intelligence » Region proposal » T5
