b'Pietro Perona'

research

∙ 07/21/2022

The MABe22 Benchmarks for Representation Learning of Multi-Agent Behavior

Real-world behavior is often shaped by complex interactions between mult...

Jennifer J. Sun, et al. ∙

research

∙ 07/20/2022

On Label Granularity and Object Localization

Weakly supervised object localization (WSOL) aims to learn representatio...

Elijah Cole, et al. ∙

research

∙ 07/20/2022

Visual Knowledge Tracing

Each year, thousands of people learn new visual categorization tasks – r...

Neehar Kondapaneni, et al. ∙

research

∙ 07/19/2022

The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

We present the Caltech Fish Counting Dataset (CFC), a large-scale datase...

Justin Kay, et al. ∙

research

∙ 02/11/2022

Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

Text spotting end-to-end methods have recently gained attention in the l...

Yair Kittenplon, et al. ∙

research

∙ 01/25/2022

Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features

We present a method for finding paths in a deep generative model's laten...

Guha Balakrishnan, et al. ∙

research

∙ 12/09/2021

Self-Supervised Keypoint Discovery in Behavioral Videos

We propose a method for learning the posture and structure of agents fro...

Jennifer J. Sun, et al. ∙

research

∙ 09/28/2021

Weakly Supervised Keypoint Discovery

In this paper, we propose a method for keypoint discovery from a 2D imag...

Serim Ryou, et al. ∙

research

∙ 07/03/2021

Species Distribution Modeling for Machine Learning Practitioners: A Review

Conservation science depends on an accurate understanding of what's happ...

Sara Beery, et al. ∙

research

∙ 06/17/2021

Multi-Label Learning from Single Positive Labels

Predicting all applicable labels for a given image is known as multi-lab...

Elijah Cole, et al. ∙

research

∙ 04/06/2021

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions

Multi-agent behavior modeling aims to understand the interactions that o...

Jennifer J. Sun, et al. ∙

research

∙ 01/29/2021

A linearized framework and a new benchmark for model selection for fine-tuning

Fine-tuning from a collection of models pre-trained on different domains...

Aditya Deshpande, et al. ∙

research

∙ 12/08/2020

A Number Sense as an Emergent Property of the Manipulating Brain

The ability to understand and manipulate numbers and quantities emerges ...

Neehar Kondapaneni, et al. ∙

research

∙ 11/27/2020

Task Programming: Learning Data Efficient Behavior Representations

Specialized domain knowledge is often necessary to accurately annotate t...

Jennifer J. Sun, et al. ∙

research

∙ 07/13/2020

Towards causal benchmarking of bias in face analysis algorithms

Measuring algorithmic bias is crucial both to assess algorithmic fairnes...

Guha Balakrishnan, et al. ∙

research

∙ 03/03/2020

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

Trained on large datasets, deep learning (DL) can accurately classify vi...

Biagio Brattoli, et al. ∙

research

∙ 02/05/2020

Geocoding of trees from street addresses and street-level images

We introduce an approach for updating older tree inventories with geogra...

Daniel Laumer, et al. ∙

research

∙ 12/31/2019

HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a...

Bhairav Chidambaram, et al. ∙

research

∙ 11/27/2019

PanDA: Panoptic Data Augmentation

The recently proposed panoptic segmentation task presents a significant ...

Yang Liu, et al. ∙

research

∙ 10/07/2019

From Google Maps to a Fine-Grained Catalog of Street trees

Up-to-date catalogs of the urban tree population are important for munic...

Steve Branson, et al. ∙

research

∙ 09/24/2019

Anchor Loss: Modulating Loss Scale based on Prediction Difficulty

We propose a novel loss function that dynamically rescales the cross ent...

Serim Ryou, et al. ∙

research

∙ 07/15/2019

The iWildCam 2019 Challenge Dataset

Camera Traps (or Wild Cams) enable the automatic collection of large qua...

Sara Beery, et al. ∙

research

∙ 06/12/2019

Presence-Only Geographical Priors for Fine-Grained Image Classification

Appearance information alone is often not sufficient to accurately diffe...

Oisin Mac Aodha, et al. ∙

research

∙ 04/11/2019

The iWildCam 2018 Challenge Dataset

Camera traps are a valuable tool for studying biodiversity, but research...

Sara Beery, et al. ∙

research

∙ 04/11/2019

Synthetic Examples Improve Generalization for Rare Classes

The ability to detect and classify rare occurrences in images has import...

Sara Beery, et al. ∙

research

∙ 02/10/2019

Task2Vec: Task Embedding for Meta-Learning

We introduce a method to provide vectorial representations of visual cla...

Alessandro Achille, et al. ∙

research

∙ 07/13/2018

Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generaliz...

Sara Beery, et al. ∙

research

∙ 05/21/2018

Teaching Multiple Concepts to Forgetful Learners

How can we help a forgetful learner learn multiple concepts within a lim...

Anette Hunziker, et al. ∙

research

∙ 05/17/2018

It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data

We address the problem of 3D human pose estimation from 2D input images ...

Matteo Ruggero Ronchi, et al. ∙

research

∙ 04/08/2018

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes

We present and evaluate the Fast (conditional) Independence Test (FIT) -...

Krzysztof Chalupka, et al. ∙

research

∙ 02/20/2018

Teaching Categories to Human Learners with Visual Explanations

We study the problem of computer-assisted teaching with explanations. Co...

Oisin Mac Aodha, et al. ∙

research

∙ 02/14/2018

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

In real-world applications of education and human teaching, an effective...

Yuxin Chen, et al. ∙

research

∙ 09/22/2017

Context Embedding Networks

Low dimensional embeddings that capture the main variations of interest ...

Kun ho Kim, et al. ∙

research

∙ 09/05/2017

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

The world is long-tailed. What does this mean for computer vision and vi...

Grant Van Horn, et al. ∙

research

∙ 07/20/2017

The iNaturalist Challenge 2017 Dataset

Existing image classification datasets used in computer vision tend to h...

Grant Van Horn, et al. ∙

research

∙ 07/17/2017

Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation

We propose a new method to analyze the impact of errors in algorithms fo...

Matteo Ruggero Ronchi, et al. ∙

research

∙ 03/17/2017

Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

We propose and systematically evaluate three strategies for training dyn...

Mason McGill, et al. ∙

research

∙ 11/04/2016

Estimating Causal Direction and Confounding of Two Discrete Variables

We propose a method to classify the causal relationship between two disc...

Krzysztof Chalupka, et al. ∙

research

∙ 11/01/2016

Learning recurrent representations for hierarchical behavior modeling

We propose a framework for detecting action patterns from motion sequenc...

Eyrun Eyjolfsdottir, et al. ∙

research

∙ 10/03/2016

Seeing into Darkness: Scotopic Visual Recognition

Images are formed by counting how many photons traveling from a given se...

Bo Chen, et al. ∙

research

∙ 07/12/2016

Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class

We present a simple unified framework for multi-class cost-sensitive boo...

Ron Appel, et al. ∙

research

∙ 05/30/2016

Unsupervised Discovery of El Nino Using Causal Feature Learning on Microlevel Climate Data

We show that the climate phenomena of El Nino and La Nina arise naturall...

Krzysztof Chalupka, et al. ∙

research

∙ 05/20/2016

Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art

A video dataset that is designed to study fine-grained categorisation of...

David Hall, et al. ∙

research

∙ 12/25/2015

Multi-Level Cause-Effect Systems

We present a domain-general account of causation that applies to setting...

Krzysztof Chalupka, et al. ∙

research

∙ 06/07/2015

Describing Common Human Visual Actions in Images

Which common human actions and interactions are recognizable in monocula...

Matteo Ruggero Ronchi, et al. ∙

research

∙ 12/07/2014

Visual Causal Feature Learning

We provide a rigorous definition of the visual cause of a behavior that ...

Krzysztof Chalupka, et al. ∙

research

∙ 10/16/2014

Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling

We frame the task of predicting a semantic labeling as a sparse reconstr...

Michael Maire, et al. ∙

research

∙ 06/11/2014

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

We propose an architecture for fine-grained visual categorization that a...

Steve Branson, et al. ∙

research

∙ 05/01/2014

Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art...

Tsung-Yi Lin, et al. ∙

research

∙ 10/08/2012

Semisupervised Classifier Evaluation and Recalibration

How many labeled examples are needed to estimate a classifier's performa...

Peter Welinder, et al. ∙

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