12 citations · 19 across the 7 of their papers we have counts for
7 papers
A Survey on Benchmarks of Multimodal Large Language Models
Jian Li, Weiheng Lu, Hao Fei +11
Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual…
VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models
Chenyu Zhou, Mengdan Zhang, Peixian Chen +5
The swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language. Yet, most current models and benchmarks…
Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models
Yi-Fan Zhang, Qingsong Wen, Chaoyou Fu +4
Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning. Existing works usually…
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM
Timin Gao, Peixian Chen, Mengdan Zhang +8
With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, visual reasoning problem is usually decomposed into manageable sub-tasks and tackl…
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
Xiangyang Zhu, Renrui Zhang, Bowei He +6
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'see…
CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes
Yulei Qin, Xingyu Chen, Yunhang Shen +5
Webly supervised learning has attracted increasing attention for its effectiveness in exploring publicly accessible data at scale without manual annotation. However, most existing…