Learning Visual Spatial Planning from Symbolic State via Modality-Gap-Aware Self-Distillation

1. 1Implement a vision-language model to understand general multimodal concepts
2. 2Identify the modality gap in visual spatial planning and its impact on model performance
3. 3Apply modality-gap-aware self-distillation to bridge the gap and improve reasoning capabilities
4. 4Test the model on visual spatial planning tasks to evaluate its effectiveness
5. 5Refine the model by fine-tuning its parameters and adjusting the self-distillation process

AI engineers and researchers working on multimodal understanding and visual spatial planning can benefit from this technique to improve model performance and reasoning capabilities

💡 Self-distillation can help bridge the modality gap in visual spatial planning by enabling models to infer latent state structures from pixels and reason over them effectively

💡 Bridging the modality gap in visual spatial planning with self-distillation techniques to improve vision-language models #AI #MultimodalUnderstanding