52 citations · 114 across the 14 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.CV2024
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Kaixiong Gong, Kaituo Feng, Bohao Li +8
Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While th…
cs.CV2024
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
Sijie Cheng, Kechen Fang, Yangyang Yu +6
Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoTh…
cs.CV2024★ 3 cited
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
Bohao Li, Yuying Ge, Yi Chen +3
Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real…