Comparison of Multimodal Semantic Similarity Prediction Errors Under Synthetic and Natural Distribution Shifts

Abstract

Abstract: Deep learning (DL) models are widely used in real-world applications but remain vulnerable to distribution shifts, especially due to weather and lighting changes. Collecting diverse real-world data for testing the robustness of DL models is resource-intensive, making synthetic corruptions an attractive alternative for robustness testing. However, are synthetic corruptions a reliable proxy for real-world corruptions? To answer this, we conduct the largest benchmarking study on semantic segmentation models, comparing performance on real-world corruptions and synthetic corruptions datasets. Our r

Research Question

How does the mean squared error of multimodal semantic similarity predictions on the SICK-R entailment subset compare between synthetic image corruptions and natural distribution shifts in lighting and weather conditions?

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