This is my reading note for InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning. The paper proposes an extension of blip 2 with institution tuning. This has dramatically improved the performance to unseen tasks. The method is based on query transformer, but adding the tokens from the instruction to guide the feature extraction.

This is my reading note for GIT: A Generative Image-to-text Transformer for Vision and Language. This paper proposes a image-text pre-training model. The model contains visual encoder and text decoder; the text decoder is based on self-attention, which takes concatenated text tokens and visual tokens as input.

This is reading note for PaLI-3 Vision Language Models: Smaller, Faster, Stronger. This paper proposes to use image-text-matching to replace contrast loss. The experiment indicates this method is especially effective in relatively small models.

This is my reading note for Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation. This paper proposes a system on how to use GPT4V to generate images from idea by calling an image generation tool. Especially.it generates text prompt based on idea, given the images generated from the prompt, it ranks and selects the best image; it then generate a new promote to guide image generation process.

This is my reading note for Word-As-Image for Semantic Typography. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text. Check my note for related paper in # VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models

This is my reading note for VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text.

This is my reading note for Align before Fuse: Vision and Language Representation Learning with Momentum Distillation. The paper proposes a multi modality model which is trained base on contrast loss, mask language modeling and image-text match. To handle noisy pairs of text and image, it track moving average of model and distill to the final model.

This is my reading note for Small-scale proxies for large-scale Transformer training instabilities. This paper discusses the method to improve model training stability related to hyper parameter.

This is my reading note for A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions. This paper provides a review for multimodality recommendation system. However, it doesn’t cover the method based on transformer. It still provides a good review on the metric of recommendation system.

This is my reading note for RoBERTa: A Robustly Optimized BERT Pretraining Approach. This paper revisits the design choice of BERT. It provides that 1) adding more data; 2) using larger batch size; 3) training for more iterations could significantly improves the performance. In addition, using longer sentence/context could also improve performance and next sentence prediction is no longer useful.