219 citations · 243 across the 8 of their papers we have counts for
4 papers · 1 filter
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
Ismail Elezi, Zhiding Yu, Anima Anandkumar +2
Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learn…
The 2021 Image Similarity Dataset and Challenge
Matthijs Douze, Giorgos Tolias, Ed Pizzi +9
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to…
Active Learning for Deep Object Detection via Probabilistic Modeling
Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee +2
Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. M…
Learning Intra-Batch Connections for Deep Metric Learning
Jenny Seidenschwarz, Ismail Elezi, Laura Leal-Taixé
The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric l…