LAVIS
CLIP-Caption-Reward
| LAVIS | CLIP-Caption-Reward | |
|---|---|---|
| 18 | 2 | |
| 8,781 | 225 | |
| 2.9% | - | |
| 6.3 | 0.0 | |
| 18 days ago | over 1 year ago | |
| Jupyter Notebook | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LAVIS
- FLaNK AI for 11 March 2024
- FLaNK 04 March 2024
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[D] Why is most Open Source AI happening outside the USA?
For multimodal, there's China (*many), then Salesforce.
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Need help for a colab notebook running Lavis blip2_instruct_vicuna13b?
Been trying for all day to get a working inference for this example: https://github.com/salesforce/LAVIS/tree/main/projects/instructblip
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most sane web3 job listing
There's also been big breakthroughs in computer vision. Not that long ago it was hard to recognize if a photo contained a bird; that's solved now by models like CLIP, Yolo, or Segment Anything. Now research has moved on to generating 3D scenes from images or interactively answering questions about images.
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I work at a non-tech company and have been asked to make software that is impossible. How do I explain it to my boss?
The new hotness is multimodal vision-language models like InstructBLIP that can interactively answer questions about images. Check out the examples in the github repo, I would not have thought this was possible a few years ago.
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Two-minute Daily AI Update (Date: 5/15/2023)
Salesforce’s BLIP family has a new member– InstructBLIP, a vision-language instruction-tuning framework using BLIP-2 models. It has achieved state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks, substantially outperforming BLIP-2 and Flamingo. (Source)
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InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Github
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Can I use my own art as a training set?
Most of my workflows are self-made. For captioning I used Blip-2 in a custom script I made that automates the process by going into directories and their sub-directories and creates a .txt file beside each image. This way I can keep my images organized in their proper directories, without having to put dump them all in a single place.
- FLiP Stack Weekly for 13-Feb-2023
CLIP-Caption-Reward
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is there any "image to text" ai?
Look for 'image captioning'. Here's an on-line example: https://vision-explorer.allenai.org/image_captioning . Here's a recent one that was open sourced: https://github.com/j-min/CLIP-Caption-Reward
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Adobe AI Researchers Open-Source Image Captioning AI CLIP-S: An Image-Captioning AI Model That Produces Fine-Grained Descriptions of Images
Continue reading | Checkout the paper, github
What are some alternatives?
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Oscar - Oscar and VinVL
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
VLDet - [ICLR 2023] PyTorch implementation of VLDet （https://arxiv.org/abs/2211.14843）
robo-vln - Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"
MAGIC - Language Models Can See: Plugging Visual Controls in Text Generation
DeepViewAgg - [CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"
prismer - The implementation of "Prismer: A Vision-Language Model with Multi-Task Experts".
linkis - Apache Linkis builds a computation middleware layer to facilitate connection, governance and orchestration between the upper applications and the underlying data engines.
multimodal - A collection of multimodal datasets, and visual features for VQA and captionning in pytorch. Just run "pip install multimodal"
clipseg - This repository contains the code of the CVPR 2022 paper "Image Segmentation Using Text and Image Prompts".