perturb-predict-paraphrase
LAVIS
| perturb-predict-paraphrase | LAVIS | |
|---|---|---|
| 1 | 18 | |
| 5 | 8,838 | |
| - | 3.5% | |
| 0.0 | 6.3 | |
| over 2 years ago | 28 days ago | |
| Python | Jupyter Notebook | |
| - | BSD 3-clause "New" or "Revised" License |
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perturb-predict-paraphrase
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
What are some alternatives?
catr - Image Captioning Using Transformer
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Parrot_Paraphraser - A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
CLIP-Caption-Reward - PyTorch code for "Fine-grained Image Captioning with CLIP Reward" (Findings of NAACL 2022)
paraphraser - Sentence paraphrase generation at the sentence level
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
InternGPT - InternGPT (iGPT) is an open source demo platform where you can easily showcase your AI models. Now it supports DragGAN, ChatGPT, ImageBind, multimodal chat like GPT-4, SAM, interactive image editing, etc. Try it at igpt.opengvlab.com (支持DragGAN、ChatGPT、ImageBind、SAM的在线Demo系统)
robo-vln - Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"
quotera - Text paraphrasing tool
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"
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"