2020/10/20 by Sameer Dharur, Dharur, Sameer, Purva Tendulkar +7
Computer Science · #Advanced Image and Video Retrieval Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #cs.AI #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2010.10038
Accepted to the NeurIPS 2020 workshop on Interpretable Inductive Biases and Physically Structured Learning
openalex publication_date 2020/10/20 · arxiv created 2020/12/01 · arxiv updated 2020/12/02 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.