414 citations · 1.1k across the 47 of their papers we have counts for
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Context Embedding Networks
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. One way to construct these embeddings is to acqu…
The Devil is in the Tails: Fine-grained Classification in the Wild
Grant Van Horn, Pietro Perona
The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applicat…
Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation
Matteo Ruggero Ronchi, Pietro Perona
We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and…
The iNaturalist Species Classification and Detection Dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song +6
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imb…
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
Mason McGill, Pietro Perona
We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input…