Results 301 to 310 of about 2,484,152 (338)

Enhancing Glaucoma Diagnosis Through Multi-Layer Transformer and Multi-Modal Feature Fusion. [PDF]

Transl Vis Sci Technol
Zhao D, Fang H, Gao Q, Shi Y, Duan L, Xu Y.   +5 more
europepmc   +1 more source

A multi-modal parcellation of human cerebral cortex

Nature, 2016
Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience.
Matthew F Glasser, Timothy S Coalson, Carl D Hacker   +2 more
exaly   +2 more sources

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ShareGPT4V: Improving Large Multi-Modal Models with Better Captions

European Conference on Computer Vision, 2023
In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data.
Lin Chen, Jinsong Li, Xiao-wen Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, Dahua Lin   +7 more
semanticscholar   +1 more source

Otter: A Multi-Modal Model With In-Context Instruction Tuning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Recent advances in Large Multimodal Models (LMMs) have unveiled great potential as visual assistants. However, most existing works focus on responding to individual instructions or using previous dialogues for contextual understanding.
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, Ziwei Liu   +5 more
semanticscholar   +1 more source

mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Computer Vision and Pattern Recognition, 2023
Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily fo-cus on enhancing multi-modal capabilities.
Qinghao Ye, Haiyang Xu, Jiabo Ye, Mingshi Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, Fei Huang, Jingren Zhou   +9 more
semanticscholar   +1 more source

PointAugmenting: Cross-Modal Augmentation for 3D Object Detection

Computer Vision and Pattern Recognition, 2021
Camera and LiDAR are two complementary sensors for 3D object detection in the autonomous driving context. Camera provides rich texture and color cues while LiDAR specializes in relative distance sensing.
Chunwei Wang, Chao Ma, Ming Zhu, Xiaokang Yang   +3 more
semanticscholar   +1 more source

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

Computer Vision and Pattern Recognition
In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding.
Chaoyou Fu, Yuhan Dai, Yondong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong Ji, Xing Sun   +19 more
semanticscholar   +1 more source