Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

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 …

This paper presents OmniVL, a new foundation model to support both image-language and
video-language tasks using one universal architecture. It adopts a unified transformer-based …

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot
video and text understanding, without using any labels on downstream tasks. VideoCLIP …

Abstract Video-Language Pretraining (VLP), which aims to learn transferable representation
to advance a wide range of video-text downstream tasks, has recently received increasing …

The canonical approach to video captioning dictates a caption generation model to learn
from offline-extracted dense video features. These feature extractors usually operate on …

We present in this paper a new architecture, named Convolutional vision Transformer (CvT),
that improves Vision Transformer (ViT) in performance and efficiency by introducing …

Our objective in this work is video-text retrieval-in particular a joint embedding that enables
efficient text-to-video retrieval. The challenges in this area include the design of the visual …