Search Results for author:
Found 9 papers, 6 papers with code
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning
Language models struggle with handling numerical data and performing arithmetic operations.
CHARTER: heatmap-based multi-type chart data extraction
The digital conversion of information stored in documents is a great source of knowledge.
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
StarNet: towards Weakly Supervised Few-Shot Object Detection
Few-shot detection and classification have advanced significantly in recent years.
LaSO: Label-Set Operations networks for multi-label few-shot learning
We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning.
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.
Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training
We approach the problem of fast detection and recognition of a large number (thousands) of object categories while training on a very limited amount of examples, usually one per category.
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography.
