2 citations · 4 across the 9 of their papers we have counts for
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Elevating Visual Perception in Multimodal LLMs with Visual Embedding Distillation
Jitesh Jain, Zhengyuan Yang, Humphrey Shi +2
In recent times, the standard practice for developing MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. This approach often…
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
Min Shi, Fuxiao Liu, Shihao Wang +13
The ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs). Recent work indicates that enhanced visual perception…
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
Jiachen Li, Xinyao Wang, Sijie Zhu +6
Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on mul…
Benchmarking Object Detectors with COCO: A New Path Forward
Shweta Singh, Aayan Yadav, Jitesh Jain +3
The Common Objects in Context (COCO) dataset has been instrumental in benchmarking object detectors over the past decade. Like every dataset, COCO contains subtle errors and imperf…