Search Results for author:
Found 17 papers, 11 papers with code
Augmenting In-Context-Learning in LLMs via Automatic Data Labeling and Refinement
In this work, we propose Automatic Data Labeling and Refinement (ADLR), a method to automatically generate and filter demonstrations which include the above intermediate steps, starting from a small seed of manually crafted examples.
LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content
Moreover, we introduce an efficient evaluation approach that estimates the performance of all models on the evolving benchmark using evaluations of only a subset of models.
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of text prompts preferred by the downstream VLM.
Comparison Visual Instruction Tuning
Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation.
ConMe: Rethinking Evaluation of Compositional Reasoning for Modern VLMs
Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order.
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
State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality primarily via projecting the vision tokens from the encoder to language-like tokens, which are directly fed to the Large Language Model (LLM) decoder.
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 #19 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.
