Search Results for author:
Found 19 papers, 7 papers with code
With Great Backbones Comes Great Adversarial Transferability
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} and \emph{ViT} models tuned with SSL methods such as \emph{SimCLR}.
How Aligned are Generative Models to Humans in High-Stakes Decision-Making?
This work considers how such models compare to humans and predictive AI models on a specific case of recidivism prediction.
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Key to advancing the reliability of vision systems is understanding whether existing methods can overcome multiple shortcuts or struggle in a Whac-A-Mole game, i. e., where mitigating one shortcut amplifies reliance on others.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Generating High Fidelity Data from Low-density Regions using Diffusion Models
We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold.
Large-Scale Attribute-Object Compositions
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.
Towards Measuring Fairness in AI: the Casual Conversations Dataset
The videos were recorded in multiple U. S. states with a diverse set of adults in various age, gender and apparent skin tone groups.
Attention-Based Query Expansion Learning
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, generally leading to increased recall and precision.
Using Hindsight to Anchor Past Knowledge in Continual Learning
In continual learning, the learner faces a stream of data whose distribution changes over time.
Decoupling Representation and Classifier for Long-Tailed Recognition
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.
Ranked #3 on
Long-tail learning with class descriptors
on CUB-LT
Rosetta: Large scale system for text detection and recognition in images
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.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Considerations When Learning Additive Explanations for Black-Box Models
In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations.
A deep architecture for unified aesthetic prediction
Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories.
Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval
Following this observation, we learn a visual embedding of the images where the similarity in the visual space is correlated with their semantic similarity surrogate.
End-to-end Learning of Deep Visual Representations for Image Retrieval
Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it.
Ranked #14 on
Image Retrieval
on ROxford (Medium)
Deep Image Retrieval: Learning global representations for image search
We propose a novel approach for instance-level image retrieval.
Ranked #3 on
Image Retrieval
on Oxf105k
What is the right way to represent document images?
In this article we study the problem of document image representation based on visual features.
LEWIS: Latent Embeddings for Word Images and their Semantics
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images.
Deep Fishing: Gradient Features from Deep Nets
Convolutional Networks (ConvNets) have recently improved image recognition performance thanks to end-to-end learning of deep feed-forward models from raw pixels.
Supervised mid-level features for word image representation
Machine learning techniques can then be supplied with these representations to produce models useful for word retrieval or recognition tasks.
