This is my reading note for VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling. This paper proposes a method to pre-train video-text model. The paper has two major innovations 1) use video SWIN transformer to extract the temporal features; 2) uses VQVAE to extract visual tokens and apply mask recovery on the tokens.

This is my reading note for Flamingo: a Visual Language Model for Few-Shot Learning. This paper proposes to formulate vision language model vs text prediction task given existing text and visual. The model utilizes frozen visual encoder and LLM, and only fine tune the visual adapter (perceiver). The ablation study strongly against fine tune/retrain those components.

This is my reading note for MM-VID: Advancing Video Understanding with GPT-4V(ision). The paper proposes a system of understanding long video based on GPT 4V. To this end it first converts long video to short clips and pass every frames of clips to GPT 4V to generate text description. This description, together with audio transcription, is then ted to GPT 4U for final video understand. The analyst is based user ratings between normal vision subjects and vision impaired subjects.

This is my reading note for Florence: A New Foundation Model for Computer Vision. This paper proposes a foundation model for vision (image/video) and text based on UniCL loss. It uses Swin-transformer and Roberta for the encoder.

This is my reading note for Unified Contrastive Learning in Image-Text-Label Space. This paper proposes to combine label in image-text contrast loss. It treats the image or text from the same labels are from the same class and thus is required to have higher similarity; in contrast loss of CLIP, image/text is required to be similar if they are from the same pair.

This is my reading note for OmniVL:One Foundation Model for Image-Language and Video-Language Tasks. The paper proposes a vision language pre-training method optimized to linear probe for classification problem. To this end, it modifies the contrast loss by creating positive. samples from the images of same label class.

This is my reading note for VPA: Fully Test-Time Visual Prompt Adaptation. VPA introduces a small number of learnable tokens, enabling fully test-time and storage-efficient adaptation without necessitating source-domain information.

This is my reading note for Video Language Planning. This paper proposes to combine a video-language model and text to video generation model for visual planning: video-language models creates a execution plan given an image as current state and a text as the goal; text-to-video generation model generates a video given the plan; finally video-language models validated the plan via the generated videos.

This is my reading note for Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training. This paper proposes several methods to improve image-text model pre-training: 1) filtering the dataset according complexity, action and text spotting (CAT); 2) concept distillation (object category and attributes); 3) hard negative mining for contrast pairs.

This is my reading note for In-Context Pretraining: Language Modeling Beyond Document Boundaries. This paper proposes to group relevant instead of random documents in each batch to improve Long text learning. The relevant docs are found by performs a traveling salesmen problem in a graph of documents. The edges of two documents define whether the two documents are in the top k nearest neighbors.