SeeBel: Seeing is Believing
Sourajit Saha Shubhashis Roy Dipta
Semantic Segmentation is a significant research field in Computer Vision.
Despite being a widely studied subject area, many visualization tools do not
exist that capture segmentation quality and dataset statistics such as a class
imbalance in the same view. While the significance of discovering and
introspecting the correlation between dataset statistics and AI model
performance for dense prediction computer vision tasks such as semantic
segmentation is well established in the computer vision literature, to the best
of our knowledge, no visualization tools have been proposed to view and analyze
the aforementioned tasks. Our project aims to bridge this gap by proposing
three visualizations that enable users to compare dataset statistics and AI
performance for segmenting all images, a single image in the dataset, explore
the AI model's attention on image regions once trained and browse the quality
of masks predicted by AI for any selected (by user) number of objects under the
same tool. Our project tries to further increase the interpretability of the
trained AI model for segmentation by visualizing its image attention weights.
For visualization, we use Scatterplot and Heatmap to encode correlation and
features, respectively. We further propose to conduct surveys on real users to
study the efficacy of our visualization tool in computer vision and AI domain.
The full system can be accessed at https://github.com/dipta007/SeeBel