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11/25/2022
MAEDAY: MAE for few and zero shot AnomalY-Detection
The goal of Anomaly-Detection (AD) is to identify outliers, or outlying ...
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11/25/2022
PIP: Positional-encoding Image Prior
In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitte...
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11/21/2022
Teaching Structured Vision Language Concepts to Vision Language Models
Vision and Language (VL) models have demonstrated remarkable zero-shot p...
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09/08/2022
FETA: Towards Specializing Foundation Models for Expert Task Applications
Foundation Models (FMs) have demonstrated unprecedented capabilities inc...
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12/04/2021
Unsupervised Domain Generalization by Learning a Bridge Across Domains
The ability to generalize learned representations across significantly d...
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04/20/2021
Detector-Free Weakly Supervised Grounding by Separation
Nowadays, there is an abundance of data involving images and surrounding...
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01/25/2021
ISP Distillation
Nowadays, many of the images captured are "observed" by machines only an...
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12/07/2020
Fine-grained Angular Contrastive Learning with Coarse Labels
Few-shot learning methods offer pre-training techniques optimized for ea...
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03/15/2020
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
In this paper, we propose a new few-shot learning method called StarNet,...
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12/01/2019
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typicall...
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06/05/2019
Baby steps towards few-shot learning with multiple semantics
Learning from one or few visual examples is one of the key capabilities ...
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02/07/2019
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
Beauty is in the eye of the beholder. This maxim, emphasizing the subjec...
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09/29/2018
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields incl...
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06/12/2018
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Learning to classify new categories based on just one or a few examples ...
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06/12/2018
RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object c...
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04/29/2018
UNIQ: Uniform Noise Injection for non-uniform Quantization of neural networks
We present a novel method for training a neural network amenable to infe...
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04/29/2018
UNIQ: Uniform Noise Injection for the Quantization of Neural Networks
We present a novel method for training deep neural network amenable to i...
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01/20/2018
