Search Results for author: Babak Ehteshami Bejnordi

Found 14 papers, 6 papers with code

MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers

1 code implementation5 Jul 2023 Jakob Drachmann Havtorn, Amelie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi

The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content.

Revisiting Single-gated Mixtures of Experts

no code implementations11 Apr 2023 Amelie Royer, Ilia Karmanov, Andrii Skliar, Babak Ehteshami Bejnordi, Tijmen Blankevoort

Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time.

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

no code implementations5 Apr 2022 Babak Ehteshami Bejnordi, Amirhossein Habibian, Fatih Porikli, Amir Ghodrati

In this paper, we propose SALISA, a novel non-uniform SALiency-based Input SAmpling technique for video object detection that allows for heavy down-sampling of unimportant background regions while preserving the fine-grained details of a high-resolution image.

object-detection Video Object Detection

Skip-Convolutions for Efficient Video Processing

1 code implementation CVPR 2021 Amirhossein Habibian, Davide Abati, Taco S. Cohen, Babak Ehteshami Bejnordi

We reformulate standard convolution to be efficiently computed on residual frames: each layer is coupled with a binary gate deciding whether a residual is important to the model prediction,~\eg foreground regions, or it can be safely skipped, e. g. background regions.

Model Compression

TimeGate: Conditional Gating of Segments in Long-range Activities

no code implementations3 Apr 2020 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.

Conditional Channel Gated Networks for Task-Aware Continual Learning

1 code implementation CVPR 2020 Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, Babak Ehteshami Bejnordi

Therefore, we additionally introduce a task classifier that predicts the task label of each example, to deal with settings in which a task oracle is not available.

Continual Learning

The importance of stain normalization in colorectal tissue classification with convolutional networks

1 code implementation20 Feb 2017 Francesco Ciompi, Oscar Geessink, Babak Ehteshami Bejnordi, Gabriel Silva de Souza, Alexi Baidoshvili, Geert Litjens, Bram van Ginneken, Iris Nagtegaal, Jeroen van der Laak

The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image.

Classification General Classification

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