Transformer models have shown great success handling long-range interactions, making
them a promising tool for modeling video. However, they lack inductive biases and scale …

Abstract ecent progress in video anomaly detection suggests that the features of
appearance and motion play crucial roles in distinguishing abnormal patterns from normal …

Streaming video clips with large-scale video tokens impede vision transformers (ViTs) for
efficient recognition, especially in video action detection where sufficient spatiotemporal …

While several long-form VideoQA datasets have been introduced, the length of both videos
used to curate questions and sub-clips of clues leveraged to answer those questions have …

Visual interactivity understanding within visual scenes presents a significant challenge in
computer vision. Existing methods focus on complex interactivities while leveraging a simple …

Multimedia content, such as advertisements and story videos, exhibit a rich blend of
creativity and multiple modalities. They incorporate elements like text, visuals, audio, and …

Learning computer vision models from (and for) movies has a long-standing history. While
great progress has been attained, there is still a need for a pretrained multimodal model that …

Dense video understanding requires answering several questions such as who is doing
what to whom, with what, how, why, and where. Recently, Video Situation Recognition …

Video event extraction (VEE) aims to extract key events and generate the event arguments
for their semantic roles from the video. Despite promising results have been achieved by …

Situation Recognition is the task of generating a structured summary of what is happening in
an image using an activity verb and the semantic roles played by actors and objects. In this …