This is my reading note on Key-Locked Rank One Editing for Text-to-Image Personalization. This paper proposes a personalized image generation method base on controlling attention module of the diffusion model. Especially key captures the layout of concept and value captures the identity of the new concept. A rank one update is applied to the attention weight to this purpose.

This is my reading note on DiffBIR Towards Blind Image Restoration with Generative Diffusion Prior. This paper proposes a two stage method for restore degraded images: stage 1 is trained neural network to recover image degradation; stage 2 is a pretrained diffusion model to restore the details in the image recovered from stage 1.

This is my reading note 2/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-data for the other part.

This is my reading note 1/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-model for the other part.

This is my reading note on Neuralangelo: High-Fidelity Neural Surface Reconstruction. This paper proposes a method to reconstruct 3D surface at very high details. The proposed method is based on two improvements: 1) use numerical gradient instead of analytical one to remove non locality 2) use multi resolution instant NGP improve details from coarse to fine.

This is my reading note on Multimodal Learning with Transformers A Survey. This a paper provides a very nice overview of the transformer based multimodality learning techniques.

This is my reading note on DreamFusion: Text-to-3D using 2D Diffusion. This paper proposes a method (score distillation sampling or SDS) to distill a pre-trained text to image diffusion model to a 3D model. The 3D model, which is based on NERF, is trained per text prompt.

This is my reading note on DreamBooth. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model (Imagen, although our method is not limited to a specific model) such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, our technique enables synthesizing the subject in diverse scenes, poses, views, and lighting conditions that do not appear in the reference images.

This is my reading note on MovieChat: From Dense Token to Sparse Memory for Long Video Understanding. This paper proposes a method for long video understands it utilizes existing image encoder to extract tokens form the video via sliding window. A short term memory is a FIFO of those tokens, a long term memory is to merge the similar tokens. Those short term memory and long term memory are then appended after the question and feed to the LLM. The alignment of visual features to LLM purely depends on the existing image encoder.

This is my reading note on TokenFlow Consistent Diffusion Features for Consistent Video Editing, which is diffusion based on video editing method. This paper proposes a method to edit a video given text prompt. To do this, the paper relies on two things. First, it extracts bey lames from video and perform image on those key frames jointly. In addition, the paper found that the feature in diffusion has strong correspondence to the pixels. As a results it propose to propagate the features of edited key frames to other frames, accord to the correspondence in the original video.