4 citations · 7 across the 9 of their papers we have counts for
8 papers · 1 filter
EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents
Zhili Cheng, Yuge Tu, Ran Li +9
Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily u…
Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions
Jiarui Zhang, Ollie Liu, Tianyu Yu +2
Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception (LLVP) -- particularly the ability to a…
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
Jinyi Hu, Shengding Hu, Yuxuan Song +6
Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Con…
AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation
Zanlin Ni, Yulin Wang, Renping Zhou +6
Recent studies have demonstrated the effectiveness of token-based methods for visual content generation. As a representative work, non-autoregressive Transformers (NATs) are able t…
Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis
Zanlin Ni, Yulin Wang, Renping Zhou +6
The field of image synthesis is currently flourishing due to the advancements in diffusion models. While diffusion models have been successful, their computational intensity has pr…
Exploring Perceptual Limitation of Multimodal Large Language Models
Jiarui Zhang, Jinyi Hu, Mahyar Khayatkhoei +2
Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perc…