Abstract Large Vision-Language Models (LVLMs) have advanced considerably intertwining
visual recognition and language understanding to generate content that is not only coherent …

Abstract Visual Question Answering (VQA) models often perform poorly on out-of-distribution
data and struggle on domain generalization. Due to the multi-modal nature of this task …

This study investigates unsupervised anomaly action recognition, which identifies video-
level abnormal-human-behavior events in an unsupervised manner without abnormal …

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 …

Existing work on VQA explores data augmentation to achieve better generalization by
perturbing the images in the dataset or modifying the existing questions and answers. While …

Deep neural networks often learn unintended bias during training, which might have harmful
effects when deployed in realworld settings. This work surveys 214 papers related to …

Despite rapid progress in Visual question answering (\textit {VQA}), existing datasets and
models mainly focus on testing reasoning in 2D. However, it is important that VQA models …

Deep learning models are challenged by the distribution shift between the training data and
test data. Recently, the large models pre-trained on diverse data have demonstrated …

Diverse visual stimuli can evoke various human affective states, which are usually
manifested in an individual's muscular actions and facial expressions. In lab-controlled …

Image-Text Matching (ITM) aims to establish the correspondence between images and
sentences. ITM is fundamental to various vision and language understanding tasks …