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Found 11 papers, 6 papers with code
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
In this work, we introduce Fair Retrieval Augmented Generation (FairRAG), a novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human generation.
BloomVQA: Assessing Hierarchical Multi-modal Comprehension
We propose a novel VQA dataset, BloomVQA, to facilitate comprehensive evaluation of large vision-language models on comprehension tasks.
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
We propose a new direction: modifying the network architecture to impose inductive biases that make the network robust to dataset bias.
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Are Bias Mitigation Techniques for Deep Learning Effective?
We introduce a new dataset called Biased MNIST that enables assessment of robustness to multiple bias sources.
Detecting Spurious Correlations with Sanity Tests for Artificial Intelligence Guided Radiology Systems
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception.
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Out-of-distribution (OOD) testing is increasingly popular for evaluating a machine learning system's ability to generalize beyond the biases of a training set.
Visual Grounding Methods for VQA are Working for the Wrong Reasons!
Existing Visual Question Answering (VQA) methods tend to exploit dataset biases and spurious statistical correlations, instead of producing right answers for the right reasons.
REMIND Your Neural Network to Prevent Catastrophic Forgetting
While there is neuroscientific evidence that the brain replays compressed memories, existing methods for convolutional networks replay raw images.
Answering Questions about Data Visualizations using Efficient Bimodal Fusion
Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e. g. bar charts, pie charts, and line graphs.
Challenges and Prospects in Vision and Language Research
Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence.
Answer Them All! Toward Universal Visual Question Answering Models
Visual Question Answering (VQA) research is split into two camps: the first focuses on VQA datasets that require natural image understanding and the second focuses on synthetic datasets that test reasoning.
