The interpretation harmonization scheme, while effective in aligning saliency maps with human attention, may have limitations and potential failure cases. One limitation could be the reliance on the assumption that the gaze maps accurately represent human attention, which may not always be the case. Human attention is complex and can vary based on individual preferences, context, and task requirements. If the gaze maps do not capture the relevant features or if they introduce biases, the harmonization process may lead to misaligned saliency maps.
To improve the interpretation harmonization scheme, several strategies can be considered:
Incorporating Uncertainty: Introduce measures of uncertainty in the gaze maps to account for variability in human attention. By incorporating uncertainty estimates, the harmonization process can adapt to different levels of confidence in the gaze annotations.
Multi-Modal Fusion: Combine gaze maps with other modalities, such as eye-tracking data, verbal descriptions, or behavioral cues, to capture a more comprehensive view of human attention. Integrating multiple sources of information can enhance the robustness and accuracy of the harmonization process.
Adaptive Weighting: Dynamically adjust the weights assigned to the gaze map during the harmonization process based on the model's confidence in the alignment. By adaptively weighting the gaze map, the scheme can better handle cases where the gaze map may not fully capture the relevant features.
By addressing these limitations and incorporating adaptive strategies, the interpretation harmonization scheme can be further improved to provide more accurate and reliable alignment between saliency maps and human attention.