Person

Beril Besbinar

This person is no longer with EPFL

Related publications (5)

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Guillermo Ortiz Jimenez, Gizem Yüce, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

IEEE COMPUTER SOC2022

Self-Supervision By Prediction For Object Discovery In Videos

Pascal Frossard, Beril Besbinar

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data, and unsupervised settings pose many challenges, such as finding the right inductive bias in diverse scenarios. In this paper, we propose an object-centric mo ...

IEEE2021

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Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, ...

IEEE2019

Unsupervised Change Detection in Satellite Images using Oversegmentation and Mutual Information

Bahar Taskesen, Beril Besbinar

In this paper, a novel solution to the problem of unsupervised change detection in bitemporal satellite images is presented. Information measures, which are well-known and commonly-used in the change detection literature, result in unsharp change maps and ...

Ieee2017

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