Preference Optimization for Vision Language Models

Vision Language Models (Better, faster, stronger)

A Dive into Vision-Language Models

Vision Language Models Explained

Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models

A Dataset for Dynamic Human Preferences for Vision Language Models

arXiv:2606. 07653v1 Announce Type: cross Abstract: Given the increased adoption of Vision Language Models (VLMs) in human-interactive settings, it is important that we evaluate how well these models can adapt to real-time preferences for different users.

PaliGemma – Google's Cutting-Edge Open Vision Language Model

Welcome PaliGemma 2 – New vision language models by Google

SmolVLM - small yet mighty Vision Language Model

Accelerating vision-language models with LFM2.5-VL-DSpark

Accelerating Vision-Language Models: BridgeTower on Habana Gaudi2

Vision-driven Preference Synthesis for Mitigating Hallucinations in VLMs

arXiv:2606. 28401v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have shown strong performance in visual understanding, yet they still suffer from hallucinations, generating content that is not grounded in the image.

P\textsuperscript{2}-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization

arXiv:2606. 03376v1 Announce Type: cross Abstract: Hallucination has recently garnered significant research attention in Large Vision-Language Models (LVLMs).