Causal inference has shown potential in enhancing the predictive accuracy, fairness,
robustness, and explainability of Natural Language Processing (NLP) models by capturing …

Abstract In Multimodal Large Language Models (MLLMs) a visual projector plays a crucial
role in bridging pre-trained vision encoders with LLMs enabling profound visual …

We present a modern formulation of Embodied Question Answering (EQA) as the task of
understanding an environment well enough to answer questions about it in natural …

We study visually grounded VideoQA in response to the emerging trends of utilizing
pretraining techniques for video-language understanding. Specifically by forcing vision …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with
large language models (LLMs) and computer vision (CV) systems driving advancements in …

Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …

What makes good representations for video understanding, such as anticipating future
activities, or answering video-conditioned questions? While earlier approaches focus on …

Existing research of video understanding still struggles to achieve in-depth comprehension
and reasoning in complex videos, primarily due to the under-exploration of two key …

Despite impressive advancements in multimodal compositional reasoning approaches, they
are still limited in their flexibility and efficiency by processing fixed modality inputs while …