To enhance the imagination module for generating diverse and realistic reference candidates, especially for challenging cases like images with numerous identical instances, the following strategies can be considered:
Instance-aware Colorization: Implement instance-aware colorization techniques to differentiate between identical instances and assign unique colors based on contextual information and semantic understanding.
Semantic Segmentation Refinement: Enhance the semantic segmentation process to accurately identify and segment identical instances, enabling the imagination module to generate distinct and realistic color references for each instance.
Multi-Modal Inputs: Incorporate multi-modal inputs, such as text descriptions or additional reference images, to provide more comprehensive guidance for generating diverse and realistic colorization references.
Adversarial Training: Explore adversarial training methods to encourage the imagination module to generate diverse and high-quality reference candidates by learning from the distribution of plausible colorizations.
Data Augmentation: Augment the training data with a wide range of images containing various instances to expose the imagination module to diverse colorization scenarios and improve its ability to handle challenging cases.
By implementing these enhancements, the imagination module can be further improved to generate more diverse and realistic reference candidates, even for challenging cases with numerous identical instances, leading to more accurate and visually appealing colorization results.