The key concept extraction approach may have limitations such as:
Ambiguity in Questions: Key concept extraction may struggle with ambiguous or vague questions that do not clearly specify the objects of interest.
Complex Scenes: In highly complex scenes with overlapping objects or intricate relationships, the key concept extraction may miss important details.
Limited Context Understanding: The approach may not fully capture the context of the scene, leading to inaccuracies in identifying key concepts.
To improve the key concept extraction approach and handle a wider range of question types, enhancements can be made:
Contextual Understanding: Incorporate contextual information from the entire scene to better identify key concepts and their relationships.
Semantic Parsing: Use semantic parsing techniques to break down questions into structured representations, aiding in more accurate key concept extraction.
Machine Learning Models: Employ advanced machine learning models that can learn from diverse datasets to improve the accuracy of key concept extraction.
Feedback Mechanisms: Implement feedback mechanisms where the system learns from its mistakes and refines the key concept extraction process over time.