Improve Vision Language Model Chain-of-thought Reasoning Paper • 2410.16198 • Published Oct 21, 2024 • 22
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models Paper • 2410.02740 • Published Oct 3, 2024 • 52
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning Paper • 2409.20566 • Published Sep 30, 2024 • 55
MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines Paper • 2409.12959 • Published Sep 19, 2024 • 37
SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners Paper • 2408.16768 • Published Aug 29, 2024 • 26
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models Paper • 2407.07895 • Published Jul 10, 2024 • 40
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding Paper • 2406.09411 • Published Jun 13, 2024 • 18
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models Paper • 2404.07973 • Published Apr 11, 2024 • 30
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs Paper • 2404.05719 • Published Apr 8, 2024 • 83
GLIPv2: Unifying Localization and Vision-Language Understanding Paper • 2206.05836 • Published Jun 12, 2022 • 1
Ferret: Refer and Ground Anything Anywhere at Any Granularity Paper • 2310.07704 • Published Oct 11, 2023 • 11
From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions Paper • 2310.07699 • Published Oct 11, 2023 • 2
How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts Paper • 2402.13220 • Published Feb 20, 2024 • 13