7 citations · 14 across the 4 of their papers we have counts for
9 papers
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…
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…
Skip-Convolutions for Efficient Video Processing
Amirhossein Habibian, Davide Abati, Taco S. Cohen +1
We propose Skip-Convolutions to leverage the large amount of redundancies in video streams and save computations. Each video is represented as a series of changes across frames and…
TimeGate: Conditional Gating of Segments in Long-range Activities
Noureldien Hussein, Mihir Jain, Babak Ehteshami Bejnordi
When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes. Thus, it is of great importance…
Conditional Channel Gated Networks for Task-Aware Continual Learning
Davide Abati, Jakub Tomczak, Tijmen Blankevoort +3
Convolutional Neural Networks experience catastrophic forgetting when optimized on a sequence of learning problems: as they meet the objective of the current training examples, the…
Batch-Shaping for Learning Conditional Channel Gated Networks
Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling
We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost. We achieve this by gating the deep-learnin…