Overcoming language priors for visual question answering via loss rebalancing label and global context
R Cao, Z Li - Uncertainty in Artificial Intelligence, 2023 - proceedings.mlr.press
Despite the advances in Visual Question Answering (VQA), many VQA models currently
suffer from language priors (ie generating answers directly from questions without using …
suffer from language priors (ie generating answers directly from questions without using …
Overcoming language priors with self-supervised learning for visual question answering
Most Visual Question Answering (VQA) models suffer from the language prior problem,
which is caused by inherent data biases. Specifically, VQA models tend to answer questions …
which is caused by inherent data biases. Specifically, VQA models tend to answer questions …
Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA
To increase the generalization capability of VQA systems, many recent studies have tried to
de-bias spurious language or vision associations that shortcut the question or image to the …
de-bias spurious language or vision associations that shortcut the question or image to the …
Overcoming language priors via shuffling language bias for robust visual question answering
J Zhao, Z Yu, X Zhang, Y Yang - IEEE Access, 2023 - ieeexplore.ieee.org
Recent research has revealed the notorious language prior problem in visual question
answering (VQA) tasks based on visual-textual interaction, which indicates that well …
answering (VQA) tasks based on visual-textual interaction, which indicates that well …
[HTML][HTML] Robust visual question answering via semantic cross modal augmentation
Recent advances in vision-language models have resulted in improved accuracy in visual
question answering (VQA) tasks. However, their robustness remains limited when faced with …
question answering (VQA) tasks. However, their robustness remains limited when faced with …
Multi-stage reasoning on introspecting and revising bias for visual question answering
Visual Question Answering (VQA) is a task that involves predicting an answer to a question
depending on the content of an image. However, recent VQA methods have relied more on …
depending on the content of an image. However, recent VQA methods have relied more on …
Towards robust visual question answering: Making the most of biased samples via contrastive learning
Models for Visual Question Answering (VQA) often rely on the spurious correlations, ie, the
language priors, that appear in the biased samples of training set, which make them brittle …
language priors, that appear in the biased samples of training set, which make them brittle …
Vqa-bc: Robust visual question answering via bidirectional chaining
Current VQA models are suffering from the problem of overdependence on language bias,
which severely reduces their robustness in real-world scenarios. In this paper, we analyze …
which severely reduces their robustness in real-world scenarios. In this paper, we analyze …
Suppressing biased samples for robust VQA
Most existing visual question answering (VQA) models strongly rely on language bias to
answer questions, ie, they always tend to fit question-answer pairs on the train split and …
answer questions, ie, they always tend to fit question-answer pairs on the train split and …
Debiased Visual Question Answering via the perspective of question types
Abstract Visual Question Answering (VQA) aims to answer questions according to the given
image. However, current VQA models tend to rely solely on textual information from the …
image. However, current VQA models tend to rely solely on textual information from the …