Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in
a variety of tasks. However, some aspects of complex language understanding still remain a …

Many machine learning algorithms are trained and evaluated by splitting data from a single
source into training and test sets. While such focus on in-distribution learning scenarios has …

With the recent development of deep learning technology comes the wide use of artificial
intelligence (AI) models in various domains. AI shows good performance for definite …

Spatial understanding is a fundamental aspect of computer vision and integral for human-
level reasoning about images, making it an important component for grounded language …

Deep neural networks often make decisions based on the spurious correlations inherent in
the dataset, failing to generalize in an unbiased data distribution. Although previous …

While progress has been made on the visual question answering leaderboards, models
often utilize spurious correlations and priors in datasets under the iid setting. As such …

While large pre-trained language models are powerful, their predictions often lack logical
consistency across test inputs. For example, a state-of-the-art Macaw question-answering …

Object hallucination poses a significant challenge in vision-language (VL) models, often
leading to the generation of nonsensical or unfaithful responses with non-existent objects …

Medical visual question answering (VQA) aims to correctly answer a clinical question related
to a given medical image. Nevertheless, owing to the expensive manual annotations of …