research
∙
05/27/2022
Neural Basis Models for Interpretability
Due to the widespread use of complex machine learning models in real-wor...
share
research
∙
05/27/2022
Scalable Interpretability via Polynomials
Generalized Additive Models (GAMs) have quickly become the leading choic...
research
∙
03/24/2022
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals
A visual counterfactual explanation replaces image regions in a query im...
research
∙
01/20/2022
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
Model pre-training is a cornerstone of modern visual recognition systems...
research
∙
12/09/2021
Adaptive Methods for Aggregated Domain Generalization
Domain generalization involves learning a classifier from a heterogeneou...
research
∙
05/24/2021
Large-Scale Attribute-Object Compositions
We study the problem of learning how to predict attribute-object composi...
research
∙
03/29/2021
Adaptive Methods for Real-World Domain Generalization
Invariant approaches have been remarkably successful in tackling the pro...
research
∙
03/23/2021
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Weakly supervised instance segmentation reduces the cost of annotations ...
research
∙
08/13/2020
What leads to generalization of object proposals?
Object proposal generation is often the first step in many detection mod...
research
∙
01/09/2020
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Existing models often leverage co-occurrences between objects and their ...
research
∙
12/21/2019
Measuring Dataset Granularity
Despite the increasing visibility of fine-grained recognition in our fie...
research
∙
12/20/2019
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Blind or no-reference (NR) perceptual picture quality prediction is a di...
research
∙
12/06/2019
ClusterFit: Improving Generalization of Visual Representations
Pre-training convolutional neural networks with weakly-supervised and se...
research
∙
11/28/2019
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
The visual and audio modalities are highly correlated yet they contain d...
research
∙
05/03/2019
Scaling and Benchmarking Self-Supervised Visual Representation Learning
Self-supervised learning aims to learn representations from the data its...
research
∙
05/02/2019
Large-scale weakly-supervised pre-training for video action recognition
Current fully-supervised video datasets consist of only a few hundred th...
research
∙
05/02/2019
Billion-scale semi-supervised learning for image classification
This paper presents a study of semi-supervised learning with large convo...
research
∙
04/02/2019
Activity Driven Weakly Supervised Object Detection
Weakly supervised object detection aims at reducing the amount of superv...
research
∙
03/05/2019
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search
A plethora of recent work has shown that convolutional networks are not ...
research
∙
05/02/2018
Exploring the Limits of Weakly Supervised Pretraining
State-of-the-art visual perception models for a wide range of tasks rely...
research
∙
02/01/2018
Distributed Newton Methods for Deep Neural Networks
Deep learning involves a difficult non-convex optimization problem with ...
research
∙
11/15/2017
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
For many applications, an ensemble of base classifiers is an effective s...
research
∙
03/30/2016