paper-conference

Though the state-of-the architectures for semantic segmentation, such as HRNet, demonstrate impressive accuracy, the complexity arising …

Humans do not perceive all parts of a scene with the same resolution, but rather focus on few regions of interest (ROIs). Traditional …

In this paper, we propose a conditional early exiting framework for efficient video recognition. While existing works focus on …

We propose Skip-Convolutions to leverage the large amount of redundancies in video streams and save computations. Each video is …

Video action classification and temporal localization are two key components of video understanding where we witnessed significant …

We tackle the task of efficient video super resolution. Motivated by our study on the quality vs. efficiency trade-off on a wide range …

In this paper, we present a novel adversarial lossy video compression model. At extremely low bit-rates, standard video coding schemes …

Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing …

This paper studies the effect of quality degradation, caused by lossy video compression, on video recognition. We investigate how the …

In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder …