This is my reading note for Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond. This paper proposes a vision-language model capable of vision grounding and image text reading. To do that, it considers visual grounding and OCR tasks in pre-training. In architecture, the paper uses Qformer from BLIP2.

This is my reading note ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. This paper proposes to replace masked language modeling with the discriminator task of whether the token is from the authentic data distribution or fixed by the generator model. Especially the model contains a generator that’s trained with masked language modeling objects and discriminator to classify whether a token is filled by the generator or not.

This is my reading note for PaLI: A Jointly-Scaled Multilingual Language-Image Model. This paper formulates all the image-text pretraining tasks as visual question answering. The major contributions of this paper includes 1) shows balanced size of vision model and language model improves performances; 2) training with mixture of 8 tasks is important.

This is my reading note for Segment Anything Meets Point Tracking. This paper combines SAM with point tracker to perform object segment and tracking in video. To to that it use point tracker to track points through the frames.for points of each frame SAM generate masks from the points promote. After every 8 frames, new points will be sampled from the mask.for best performance, 8 positive points and l negative points is recommended.

This is my reading note for Blended Latent Diffusion. The major innovation of the paper is to apply mask in latent space instead of image space to reduce boundary inconsistency, as the foreground is generated from the VAE but the background is not. in addition to handle the thin detail of mask got lost due to downs sample step, it dilate the mask first.

This is my reading note for Otter: A Multi-Modal Model with In-Context Instruction Tuning. It is a replication of Flamingo model trained on MIMIC-IT: Multi-Modal In-Context Instruction Tuning.

This is my reading note for Blended Latent Diffusion. The major innovation of the paper is to apply mask in latent space instead of image space to reduce boundary inconsistency, as the foreground is generated from the VAE but the background is not. in addition to handle the thin detail of mask got lost due to downs sample step, it dilate the mask first.

This is my reading note for X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. This paper proposes a method on extending clip to video data. it mostly studied how to aggregate the similarity score from the frame level to video level.

This is my reading note for AutoMix: Automatically Mixing Language Models. Thy paper posses a verifier to verity the correctness of answer of small model and decide whether need to redirect the question to a larger model.

This is my reading note for MeshDiffusion: Score-based Generative 3D Mesh Modeling. This paper represents the 3D mesh as a reformed tetrahedral which is defined on a regular 3D grid with 4 channel features: 3D positional deformation of the vertex and signed distance function values to define the surface.