Task-Oriented Pre-Training for Drivable Area Detection

• URL: http://arxiv.org/abs/2409.20166v1
• Date: Mon, 30 Sep 2024 10:25:47 GMT
• Title: Task-Oriented Pre-Training for Drivable Area Detection
• Authors: Fulong Ma, Guoyang Zhao, Weiqing Qi, Ming Liu, Jun Ma,
• Abstract summary: We propose a task-oriented pre-training method that begins with generating redundant segmentation proposals. We then introduce a Specific Category Enhancement Fine-tuning (SCEF) strategy for fine-tuning the Contrastive Language-Image Pre-training (CLIP) model. This approach can generate a lot of coarse training data for pre-training models, which are further fine-tuned using manually annotated data.
• Score: 5.57325257338134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-training techniques play a crucial role in deep learning, enhancing models' performance across a variety of tasks. By initially training on large datasets and subsequently fine-tuning on task-specific data, pre-training provides a solid foundation for models, improving generalization abilities and accelerating convergence rates. This approach has seen significant success in the fields of natural language processing and computer vision. However, traditional pre-training methods necessitate large datasets and substantial computational resources, and they can only learn shared features through prolonged training and struggle to capture deeper, task-specific features. In this paper, we propose a task-oriented pre-training method that begins with generating redundant segmentation proposals using the Segment Anything (SAM) model. We then introduce a Specific Category Enhancement Fine-tuning (SCEF) strategy for fine-tuning the Contrastive Language-Image Pre-training (CLIP) model to select proposals most closely related to the drivable area from those generated by SAM. This approach can generate a lot of coarse training data for pre-training models, which are further fine-tuned using manually annotated data, thereby improving model's performance. Comprehensive experiments conducted on the KITTI road dataset demonstrate that our task-oriented pre-training method achieves an all-around performance improvement compared to models without pre-training. Moreover, our pre-training method not only surpasses traditional pre-training approach but also achieves the best performance compared to state-of-the-art self-training methods.

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