# SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency

arXiv: https://arxiv.org/abs/2010.10038 · HTML: https://mlsanity.com/paper/2010.10038/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models](https://mlsanity.com/paper/2303.16133/) (2023)
- [Measuring and Improving Complex-Atomic Answer Consistency in Endoscopic VQA](https://mlsanity.com/paper/2607.17834/) (2026)

## Abstract (arXiv)

Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world -- they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the higher level question correctly. To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. Next, we propose a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an <image, reasoning-question> pair. We show that SOrT improves model consistency by upto 6.5% points over existing baselines, while also improving visual grounding.