To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

This paper presents X3D, a family of efficient video networks that progressively expand a
tiny 2D image classification architecture along multiple network axes, in space, time, width …

Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

Robustness is key to engineering, automation, and science as a whole. However, the
property of robustness is often underpinned by costly requirements such as over …

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have
demonstrated impressive transferability on various visual tasks. Transferring knowledge …

The prolific performances of Vision Transformers (ViTs) in image tasks have prompted
research into adapting the image ViTs for video tasks. However, the substantial gap …

In the face of the video data deluge, today's expensive clip-level classifiers are increasingly
impractical. We propose a framework for efficient action recognition in untrimmed video that …

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is
an important topic in computer vision research. Along with the growth of computational …

Video classification is computationally expensive. In this paper, we address theproblem of
frame selection to reduce the computational cost of video classification. Recent work has …