The development of multimodal large language models (MLLMs) represents a significant leap forward. These advanced systems, which integrate language and visual processing, have broad applications, from image captioning to visible question answering. However, a major challenge has been the high computational resources these models typically require. Existing models, while powerful, necessitate substantial resources for training…

The conventional NeRF and its variations demand considerable computational resources, often surpassing the typical availability in constrained settings. Additionally, client devices’ limited video memory capacity imposes significant constraints on processing and rendering extensive assets concurrently in real-time. The considerable demand for resources poses a crucial challenge in rendering expansive scenes in real-time, requiring rapid loading…

Integrating multimodal data such as text, images, audio, and video is a burgeoning field in AI, propelling advancements far beyond traditional single-mode models. Traditional AI has thrived in unimodal contexts, yet the complexity of real-world data often intertwines these modes, presenting a substantial challenge. This complexity demands a model capable of processing and seamlessly integrating…