338 citations · 771 across the 16 of their papers we have counts for
18 papers
With Great Backbones Comes Great Adversarial Transferability
Erik Arakelyan, Karen Hambardzumyan, Davit Papikyan +4
Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet}…
Generative Models, Humans, Predictive Models: Who Is Worse at High-Stakes Decision Making?
Keri Mallari, Julius Adebayo, Kori Inkpen +3
Despite strong advisory against it, large generative models (LMs) are already being used for decision making tasks that were previously done by predictive models or humans. We put…
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo +5
Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address thi…
Generating High Fidelity Data from Low-density Regions using Diffusion Models
Vikash Sehwag, Caner Hazirbas, Albert Gordo +2
Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthe…
Large-Scale Attribute-Object Compositions
Filip Radenovic, Animesh Sinha, Albert Gordo +2
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best…
Towards Measuring Fairness in AI: the Casual Conversations Dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky +3
This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and…
Attention-Based Query Expansion Learning
Albert Gordo, Filip Radenovic, Tamara Berg
Query expansion is a technique widely used in image search consisting in combining highly ranked images from an original query into an expanded query that is then reissued, general…
Using Hindsight to Anchor Past Knowledge in Continual Learning
Arslan Chaudhry, Albert Gordo, Puneet K. Dokania +2
In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forge…
Rosetta: Large scale system for text detection and recognition in images
Fedor Borisyuk, Albert Gordo, Viswanath Sivakumar
In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharin…
Decoupling Representation and Classifier for Long-Tailed Recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach +4
The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions…
Considerations When Learning Additive Explanations for Black-Box Models
Sarah Tan, Giles Hooker, Paul Koch +2
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four expl…
A deep architecture for unified aesthetic prediction
Naila Murray, Albert Gordo
Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in…
End-to-end Learning of Deep Visual Representations for Image Retrieval
Albert Gordo, Jon Almazan, Jerome Revaud +1
While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level i…
Deep Image Retrieval: Learning global representations for image search
Albert Gordo, Jon Almazan, Jerome Revaud +1
We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descript…
What is the right way to represent document images?
Gabriela Csurka, Diane Larlus, Albert Gordo +1
In this article we study the problem of document image representation based on visual features. We propose a comprehensive experimental study that compares three types of visual do…
LEWIS: Latent Embeddings for Word Images and their Semantics
Albert Gordo, Jon Almazan, Naila Murray +1
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images. Although text recognition and retrieval have received a lot of atten…
Deep Fishing: Gradient Features from Deep Nets
Albert Gordo, Adrien Gaidon, Florent Perronnin
Convolutional Networks (ConvNets) have recently improved image recognition performance thanks to end-to-end learning of deep feed-forward models from raw pixels. Deep learning is a…
Supervised mid-level features for word image representation
Albert Gordo
This paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-le…