activity
20162022
most citedSALISA: Saliency-based Input Sampling for Efficient Video Object Detection

4 citations · 4 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV20224 cited

SALISA: Saliency-based Input Sampling for Efficient Video Object Detection

Babak Ehteshami Bejnordi, Amirhossein Habibian, Fatih Porikli +1

High-resolution images are widely adopted for high-performance object detection in videos. However, processing high-resolution inputs comes with high computation costs, and naive d…

cs.CV2021

FrameExit: Conditional Early Exiting for Efficient Video Recognition

Amir Ghodrati, Babak Ehteshami Bejnordi, Amirhossein Habibian

In this paper, we propose a conditional early exiting framework for efficient video recognition. While existing works focus on selecting a subset of salient frames to reduce the co…

cs.CV2018

Video Time: Properties, Encoders and Evaluation

Amir Ghodrati, Efstratios Gavves, Cees G. M. Snoek

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying vi…

cs.CV2018

Actor and Action Video Segmentation from a Sentence

Kirill Gavrilyuk, Amir Ghodrati, Zhenyang Li +1

This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of a…

cs.CV2016

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

Amir Ghodrati, Ali Diba, Marco Pedersoli +2

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained…