Search Results for author: Oisin Mac Aodha

Found 21 papers, 8 papers with code

Multi-Label Learning from Single Positive Labels

no code implementations CVPR 2021 Elijah Cole, Oisin Mac Aodha, Titouan Lorieul, Pietro Perona, Dan Morris, Nebojsa Jojic

When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image.

Classification Multi-Label Classification +2

When Does Contrastive Visual Representation Learning Work?

no code implementations12 May 2021 Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie

Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.

Contrastive Learning Fine-Grained Image Classification +2

ViewNet: Unsupervised Viewpoint Estimation From Conditional Generation

no code implementations ICCV 2021 Octave Mariotti, Oisin Mac Aodha, Hakan Bilen

Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale.

Image Reconstruction Self-Supervised Learning +1

Learning Stereo from Single Images

2 code implementations ECCV 2020 Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel J. Brostow, Michael Firman

We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.

Monocular Depth Estimation Stereo Matching

Geocoding of trees from street addresses and street-level images

no code implementations5 Feb 2020 Daniel Laumer, Nico Lang, Natalie van Doorn, Oisin Mac Aodha, Pietro Perona, Jan Dirk Wegner

We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching.

Global Optimization

The iWildCam 2018 Challenge Dataset

no code implementations11 Apr 2019 Sara Beery, Grant van Horn, Oisin Mac Aodha, Pietro Perona

Camera traps are a valuable tool for studying biodiversity, but research using this data is limited by the speed of human annotation.

Teaching Multiple Concepts to a Forgetful Learner

no code implementations NeurIPS 2019 Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla

Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.

Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

no code implementations NeurIPS 2018 Yuxin Chen, Adish Singla, Oisin Mac Aodha, Pietro Perona, Yisong Yue

We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference).

Context Embedding Networks

no code implementations CVPR 2018 Kun Ho Kim, Oisin Mac Aodha, Pietro Perona

Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications.

The iNaturalist Species Classification and Detection Dataset

3 code implementations CVPR 2018 Grant Van Horn, Oisin Mac Aodha, Yang song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.

Classification General Classification +1

Hierarchical Subquery Evaluation for Active Learning on a Graph

no code implementations CVPR 2014 Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz, Gabriel J. Brostow

Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning.

Active Learning graph construction

Becoming the Expert - Interactive Multi-Class Machine Teaching

no code implementations CVPR 2015 Edward Johns, Oisin Mac Aodha, Gabriel J. Brostow

However, image-importance is individual-specific, i. e. a teaching image is important to a student if it changes their overall ability to discriminate between classes.

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