Multimodal LLMs contain an encoder, LLM, and a “connector” between the multiple modalities. The LLM is typically pre-trained. For instance, LLaVA uses the CLIP ViT-L/14 for an image encoder and Vicuna ...

A vision encoder is a necessary component for allowing many leading LLMs to be able to work with images uploaded by users.

With a groundbreaking fine-tuning approach, researchers bridge text and vision models to set a new standard for cross-lingual and long-caption retrieval in multimodal AI. LLM2CLIP Overview. After ...

A Solution: Encoder-Decoder Separation The key to addressing these challenges lies in separating the encoder and decoder components of multimodal machine learning models.

Patronus AI today announced the launch of the industry's first Multimodal LLM-as-a-Judge (MLLM-as-a-Judge), a groundbreaking evaluation capability that enables developers to score and optimize ...

AnyGPT , a multimodal large-scale language model (LLM) that can process multiple types of data at once, including audio, text, images, and music, was announced. AnyGPT https://junzhan2000.github ...

CAVG is structured around an Encoder-Decoder framework, comprising encoders for Text, Emotion, Vision, and Context, alongside a Cross-Modal encoder and a Multimodal decoder. Recently, the team led ...

NVIDIA’s latest AI model, NVLM 1.0, pushes the boundaries of multimodal learning by mastering both visual and textual data, introducing powerful hybrid architectures, and setting a new standard ...

Apple has revealed its latest development in artificial intelligence (AI) large language model (LLM), introducing the MM1 family of multimodal models capable of interpreting both images and text data.