In this work, we discuss building performant Multimodal Large Language Models (MLLMs).
In particular, we study the importance of various architecture components and data choices …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

The remarkable advancements in Multimodal Large Language Models (MLLMs) have not
rendered them immune to challenges, particularly in the context of handling deceptive …

We introduce Groma, a Multimodal Large Language Model (MLLM) with grounded and fine-
grained visual perception ability. Beyond holistic image understanding, Groma is adept at …

Despite significant progress in 3D point cloud segmentation, existing methods primarily
address specific tasks and depend on explicit instructions to identify targets, lacking the …

This study delves into the realm of multi-modality (ie, video and motion modalities) human
behavior understanding by leveraging the powerful capabilities of Large Language Models …

Multi-modal large language models (MLLMs) have made significant strides in various visual
understanding tasks. However, the majority of these models are constrained to process low …

Prior studies on 3D scene understanding have primarily developed specialized models for
specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D …

While Ferret seamlessly integrates regional understanding into the Large Language Model
(LLM) to facilitate its referring and grounding capability, it poses certain limitations …

Recent advancements in multimodal large language models (MLLMs) have been
noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend …