DeepAI
Log In Sign Up

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

10/10/2022
by   Qingyi Si, et al.
0

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.

READ FULL TEXT

page 13

page 14

12/01/2017

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

A number of studies have found that today's Visual Question Answering (V...
06/24/2019

RUBi: Reducing Unimodal Biases in Visual Question Answering

Visual Question Answering (VQA) is the task of answering questions about...
09/18/2020

MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering

While progress has been made on the visual question answering leaderboar...
09/18/2022

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances

Despite the great progress of Visual Question Answering (VQA), current V...
04/05/2019

Actively Seeking and Learning from Live Data

One of the key limitations of traditional machine learning methods is th...
04/01/2021

An Investigation of Critical Issues in Bias Mitigation Techniques

A critical problem in deep learning is that systems learn inappropriate ...
12/01/2022

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

Visual Question Answering (VQA) models often perform poorly on out-of-di...

Code Repositories

VQA-VS

Code for our Findings of EMNLP-2022 paper: "Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA"


view repo