Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning
model pretrained on narrated videos which are readily-available at scale. The Vid2Seq …

Recent video and language pretraining frameworks lack the ability to generate sentences.
We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining …

Dense video captioning aims to generate multiple associated captions with their temporal
locations from the video. Previous methods follow a sophisticated" localize-then-describe" …

Audio Description (AD) is the task of generating descriptions of visual content, at suitable
time intervals, for the benefit of visually impaired audiences. For movies, this presents …

Large language models (LLMs) have shown remarkable text understanding capabilities
which have been extended as Video LLMs to handle video data for comprehending visual …

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 …

Recently, deepfakes have raised severe concerns about the authenticity of online media.
Prior works for deepfake detection have made many efforts to capture the intra-modal …

A generic video summary is an abridged version of a video that conveys the whole story and
features the most important scenes. Yet the importance of scenes in a video is often …

Due to the large memory footprint of untrimmed videos, current state-of-the-art video
localization methods operate atop precomputed video clip features. These features are …