The proposed Parameter-efficient Fine-Tuning (PEFT) and Bayesian inference framework can be effectively extended to other computer vision tasks, such as object detection and semantic segmentation, by leveraging the modularity and adaptability of the underlying architecture. Here are several strategies for this extension:
Task-Specific Adaptation: Similar to how the framework was tailored for monocular depth estimation (MDE), it can be adapted for object detection by integrating PEFT methods like LoRA or CoLoRA into the backbone networks of popular object detection architectures (e.g., Faster R-CNN, YOLO). This would involve fine-tuning the model on annotated datasets while maintaining the efficiency of parameter updates.
Uncertainty Quantification: The Bayesian inference component can be utilized to quantify uncertainty in predictions for object detection and semantic segmentation. By applying methods like SWAG or checkpoint ensembles, the model can provide uncertainty estimates for bounding box predictions or pixel-wise classifications, which is crucial for safety-critical applications.
Multi-Task Learning: The framework can be extended to a multi-task learning setting where a single model is trained to perform both depth estimation and object detection. By sharing the backbone and applying PEFT methods to task-specific heads, the model can learn complementary features, improving overall performance and efficiency.
Data Augmentation and Transfer Learning: The framework can incorporate data augmentation techniques and transfer learning from large-scale datasets to enhance performance on smaller datasets typical in object detection and segmentation tasks. This would allow the model to generalize better across different domains.
Evaluation Metrics: Adapting the evaluation metrics to include precision, recall, and mean Intersection over Union (IoU) for object detection and segmentation tasks will help in assessing the effectiveness of the PEFT-Bayesian framework in these domains.
By leveraging these strategies, the PEFT-Bayesian inference framework can be effectively adapted to enhance performance and reliability in various computer vision tasks beyond depth estimation.