Search Results for author: Grant van Horn

Found 14 papers, 6 papers with code

Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset

1 code implementation21 Jul 2022 Grant van Horn, Rui Qian, Kimberly Wilber, Hartwig Adam, Oisin Mac Aodha, Serge Belongie

We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods.

Fine-Grained Visual Categorization Video Classification

On Label Granularity and Object Localization

1 code implementation20 Jul 2022 Elijah Cole, Kimberly Wilber, Grant van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge Belongie, Andrew Howard, Oisin Mac Aodha

Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.

Weakly-Supervised Object Localization

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.

Recognition in Terra Incognita

2 code implementations ECCV 2018 Sara Beery, Grant van Horn, Pietro Perona

The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available.

Classification General Classification

Lean Multiclass Crowdsourcing

no code implementations CVPR 2018 Grant Van Horn, Steve Branson, Scott Loarie, Serge Belongie, Pietro Perona

We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets.

The Devil is in the Tails: Fine-grained Classification in the Wild

no code implementations5 Sep 2017 Grant Van Horn, Pietro Perona

We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods.

Classification General Classification +1

The iNaturalist Species Classification and Detection Dataset

11 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.

General Classification Image Classification

Lean Crowdsourcing: Combining Humans and Machines in an Online System

no code implementations CVPR 2017 Steve Branson, Grant van Horn, Pietro Perona

We develop specialized models and algorithms for binary annotation, part keypoint annotation, and sets of bounding box annotations.

Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection

no code implementations CVPR 2015 Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie

We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

no code implementations11 Jun 2014 Steve Branson, Grant van Horn, Serge Belongie, Pietro Perona

We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification.

Classification Fine-Grained Visual Categorization +1

Similarity Comparisons for Interactive Fine-Grained Categorization

no code implementations CVPR 2014 Catherine Wah, Grant van Horn, Steve Branson, Subhransu Maji, Pietro Perona, Serge Belongie

Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts.

Fine-Grained Visual Categorization General Classification +2

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