Search Results for author:
Found 12 papers, 8 papers with code
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning
Language models struggle with handling numerical data and performing arithmetic operations.
Towards Multimodal In-Context Learning for Vision & Language Models
Inspired by the emergence of Large Language Models (LLMs) that can truly understand human language, significant progress has been made in aligning other, non-language, modalities to be `understandable' by an LLM, primarily via converting their samples into a sequence of embedded language-like tokens directly fed into the LLM (decoder) input stream.
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs).
Going Beyond Nouns With Vision & Language Models Using Synthetic Data
We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models.
Ranked #68 on
Visual Reasoning
on Winoground
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.
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.
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.
DEGAS: Differentiable Efficient Generator Search
In this work, we propose an alternative strategy for GAN search by using a method called DEGAS (Differentiable Efficient GenerAtor Search), which focuses on efficiently finding the generator in the GAN.
Ranked #15 on
Image Generation
on STL-10
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.
ASAP: Architecture Search, Anneal and Prune
In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.
