The integration of language descriptions in the gaze estimation process through CLIP-Gaze may have both positive and negative impacts on interpretability and explainability. On one hand, using language prompts can provide context and insights into why certain features are deemed relevant or irrelevant for estimating gaze direction. This linguistic input can enhance human understanding of the model's decision-making process by offering explanations in natural language terms.
However, relying on complex vision-language interactions might introduce challenges in interpreting how specific words or phrases influence the final output. It could make it harder to pinpoint exactly which aspects of an image contribute most significantly to the estimated gaze direction since this information is embedded within a multimodal representation learned by CLIP.
Overall, while integrating language descriptions enhances interpretability at a high level by providing contextual clues, it also introduces complexities that require careful consideration when aiming for full transparency and explainability in AI-driven systems like CLIP-Gaze.