Search Results for author:
Found 2 papers, 2 papers with code
VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives
In this paper, we show that model FI supervision can meaningfully improve VQA model accuracy as well as performance on several Right-for-the-Right-Reason (RRR) metrics by optimizing for four key model objectives: (1) accurate predictions given limited but sufficient information (Sufficiency); (2) max-entropy predictions given no important information (Uncertainty); (3) invariance of predictions to changes in unimportant features (Invariance); and (4) alignment between model FI explanations and human FI explanations (Plausibility).
Can Deep Learning Recognize Subtle Human Activities?
Deep Learning has driven recent and exciting progress in computer vision, instilling the belief that these algorithms could solve any visual task.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
