Search Results for author: Grant van Horn

Found 19 papers, 10 papers with code

Align and Distill: Unifying and Improving Domain Adaptive Object Detection

1 code implementation18 Mar 2024 Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, Grant van Horn

We address these problems by introducing: (1) A unified benchmarking and implementation framework, Align and Distill (ALDI), enabling comparison of DAOD methods and supporting future development, (2) A fair and modern training and evaluation protocol for DAOD that addresses benchmarking pitfalls, (3) A new DAOD benchmark dataset, CFC-DAOD, enabling evaluation on diverse real-world data, and (4) A new method, ALDI++, that achieves state-of-the-art results by a large margin.

Benchmarking object-detection +2

Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions

1 code implementation CVPR 2024 Oindrila Saha, Grant van Horn, Subhransu Maji

By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories.

Fine-Grained Image Classification Zero-Shot Learning

Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling

no code implementations8 Dec 2023 Gustavo Perez, Daniel Sheldon, Grant van Horn, Subhransu Maji

Human feedback on the pairwise similarity can be used to improve the clustering, but existing approaches do not guarantee accurate count estimates.

Clustering Fine-Grained Image Classification

Active Learning-Based Species Range Estimation

1 code implementation NeurIPS 2023 Christian Lange, Elijah Cole, Grant van Horn, Oisin Mac Aodha

Our results demonstrate that our method outperforms alternative active learning methods and approaches the performance of end-to-end trained models, even when only using a fraction of the data.

Active Learning

Spatial Implicit Neural Representations for Global-Scale Species Mapping

2 code implementations5 Jun 2023 Elijah Cole, Grant van Horn, Christian Lange, Alexander Shepard, Patrick Leary, Pietro Perona, Scott Loarie, Oisin Mac Aodha

Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem.

Representation Learning

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.

Object 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

3 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

19 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 Clustering +2

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.

Attribute Fine-Grained Visual Categorization +3

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