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Found 7 papers, 7 papers with code
VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning
Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding.
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models
Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
In light of the advancements in current multi-modal large language models, we explore their effectiveness in counterfactual reasoning.
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on.
Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction.
Self-Supervised Multimodal Learning: A Survey
In this survey, we provide a comprehensive review of the state-of-the-art in SSML, in which we elucidate three major challenges intrinsic to self-supervised learning with multimodal data: (1) learning representations from multimodal data without labels, (2) fusion of different modalities, and (3) learning with unaligned data.
MEDFAIR: Benchmarking Fairness for Medical Imaging
In this work, we introduce MEDFAIR, a framework to benchmark the fairness of machine learning models for medical imaging.
