Search Results for author:
Found 21 papers, 14 papers with code
Deep Phase Coded Image Prior
We propose a new method named "Deep Phase Coded Image Prior" (DPCIP) for jointly recovering the depth map and all-in-focus image from a coded-phase image using solely the captured image and the optical information of the imaging system.
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning
Language models struggle with handling numerical data and performing arithmetic operations.
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition.
Teaching Structured Vision & Language Concepts to Vision & Language Models
Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks.
PIP: Positional-encoding Image Prior
In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e. g. noisy) image but in the process learns to reconstruct the clean image.
MAEDAY: MAE for few and zero shot AnomalY-Detection
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD).
Teaching Structured Vision&Language Concepts to Vision&Language Models
Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks.
FETA: Towards Specializing Foundation Models for Expert Task Applications
However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.
Ranked #1 on
Image-to-Text Retrieval
on FETA Car-Manuals
Unsupervised Domain Generalization by Learning a Bridge Across Domains
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system.
Detector-Free Weakly Supervised Grounding by Separation
In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector.
Ranked #1 on
Phrase Grounding
on Visual Genome
ISP Distillation
We then train a model that is applied directly to the RAW data by using knowledge distillation such that the model predictions for RAW images will be aligned with the predictions of an off-the-shelf pre-trained model for processed RGB images.
Fine-grained Angular Contrastive Learning with Coarse Labels
A very practical example of C2FS is when the target classes are sub-classes of the training classes.
StarNet: towards Weakly Supervised Few-Shot Object Detection
Few-shot detection and classification have advanced significantly in recent years.
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
In this work, we propose to employ tools inspired by the Differentiable Neural Architecture Search (D-NAS) literature in order to optimize the architecture for FSL without over-fitting.
Baby steps towards few-shot learning with multiple semantics
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems.
Ranked #9 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
Beauty is in the eye of the beholder.
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few.
RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it.
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters.
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline.
