3 citations · 8 across the 5 of their papers we have counts for
5 papers
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs
Erik Daxberger, Nina Wenzel, David Griffiths +8
Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-q…
Multimodal Autoregressive Pre-training of Large Vision Encoders
Enrico Fini, Mustafa Shukor, Xiujun Li +13
We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framewo…
Contrastive Localized Language-Image Pre-Training
Hong-You Chen, Zhengfeng Lai, Haotian Zhang +7
Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. R…
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Erik Daxberger, Floris Weers, Bowen Zhang +7
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of t…