I would like to cite 西方哲学史思维导图+脉络图（完整版）

This is my reading note on LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models. The paper proposes a method to fine tune a pretrained LLM to handle long context. To this end, it divide the data into different groups and performed attention within group; for half of heads, it shift the groups by half to enable attention across the groups.

This is my reading note for ideo-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models. The paper extends chatGPT to understand the video. It’s based on LLAVA and CLIP. One of the key contribution is that is spatially and temporal pool the per frame visual feature from the clip visual encoder and finally concatenate them as features a video.

This is my reading note for VideoChat: Chat-Centric Video Understanding. The papers extends chatGPT to understand the video. To this end.it develops a video backbone based on BLIP2

This is my reading note for MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks. The paper proposes an efficient multi modality model. it proposes to unify generative loss (masked language modeling) and contrast loss via a two pass training process. One pass is for generate loss which utilizes casual attention model in text decoder and the other pass is bidirectional text decoding. The order of two passes are shuffled during the training.

This is my reading note for Scaling Vision Transformers. This paper provides a detailed comparison and study of designing vision transformer.

This is my reading note for Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone. This papers propose a two-stage pre-training strategy: (i) coarse-grained pre-training based on image-text data; followed by (ii) fine-grained pre-training based on image-text-box data.

This is my reading note for An Empirical Study of Training End-to-End Vision-and-Language Transformers. This paper provides a good review and comparison of multi modality (video and text) model’s design choice.

This is my reading note for FreeU: Free Lunch in Diffusion U-Net. The paper analyzed the cause of artifact from diffusion model. The paper should that the backbone (U-Net) captures the global or low frequency information and skip connection capture the fine detail or high frequency information.it also shows that the high frequency information causes artifacts. As a results, this paper proposes increasing weight of half channel of U-Net and suppress the low frequency information from the skip connection

This is my reading note for 360 Reconstruction From a Single Image Using Space Carved Outpainting. This paper proposes a method of 3D reconstruction from a single image. To the it represents the 3D object by NERF and iteratively update the NERF by rendering new view using Dream booth.