Tag: instruct-blip

• mPLUG-Owl2 Revolutionizing Multi-modal Large Language Model with Modality Collaboration (11 Nov 2023)
  

  This is my reading note for mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration. This paper proposes a method to unify visual and text data for multi modal model. To this end, it uses QFormer to extract visual information and concatenate to text and feed to LLM. However, it separates the projection layer and layer norm for visual and text. This paper is similar to COGVLM.

• CogVLM Visual Expert for Pretrained Language Models (10 Nov 2023)
  

  This is my reading note for CogVLM: Visual Expert for Pretrained Language Models. This paper proposes a vision language model similarly to mPLUG-OWL2. To avoid impacting the performance of LLM, it proposes a visual adapter which adds visual specific projection layer to each attention and feed forward layer.

• Aligning Large Multimodal Models with Factually Augmented RLHF (02 Oct 2023)
  

  This is my reading note for Aligning Large Multimodal Models with Factually Augmented RLHF. This paper discusses how to mitigate hallucination for large multimodal model.it proposes two methods, 1) add additional human labeled data to train a reward model to guide the fine tune of the final model: 2) add additional factual data to the reward model besides model’s response.

• Improved Baselines with Visual Instruction Tuning (22 Jul 2023)
  

  This is my reading note for Improved Baselines with Visual Instruction Tuning. This paper shows how to improve the performance of LLAVA with simple methods.

• SEED-Bench Benchmarking Multimodal LLMs with Generative Comprehension (23 Jun 2023)
  

  This is my reading note for SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension. This paper proposes a benchmark suite of modality LLM. It introduces how is the data created and how is the task derived. For evaluation, it utilizes the model’s output of likelihood of answers instead of directly on text answers.