meshed-memory-transformer VS a-PyTorch-Tutorial-to-Image-Captioning

Compare meshed-memory-transformer vs a-PyTorch-Tutorial-to-Image-Captioning and see what are their differences.

meshed-memory-transformer

Meshed-Memory Transformer for Image Captioning. CVPR 2020 (by aimagelab)
image-captioning Transformer captioning-images caption-generation visual-semantic Pytorch cvpr2020
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a-PyTorch-Tutorial-to-Image-Captioning

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning (by sgrvinod)
Pytorch pytorch-tutorial show-attend-and-tell image-captioning encoder-decoder attention-mechanism Computer Vision mscoco
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meshed-memory-transformer a-PyTorch-Tutorial-to-Image-Captioning
2 1
497 2,657
0.0% -
0.0 0.0
over 1 year ago almost 2 years ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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meshed-memory-transformer

Posts with mentions or reviews of meshed-memory-transformer. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-03.
  • [D] Data transfer(image features) between different models in separate docker containers
    2 projects | /r/MachineLearning | 3 Jun 2021
  • [R] end-to-end image captioning
    3 projects | /r/MachineLearning | 25 Feb 2021
    I could use some up-to-date models (e.g, this one: https://github.com/aimagelab/meshed-memory-transformer), but all those I looked into require pre-processing step of features/bounding-boxes generation. The problem is that I can't use an off-the shelf bounding-box extraction model as it would not perform well on the dataset I have (images are not like COCO at all). So I was wondering if there is a relatively up-to-date architecture that I can use that will not require this processing step. That is, an implementation that requires only inputs (images) and outputs (sentences).

a-PyTorch-Tutorial-to-Image-Captioning

Posts with mentions or reviews of a-PyTorch-Tutorial-to-Image-Captioning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-25.
  • [R] end-to-end image captioning
    3 projects | /r/MachineLearning | 25 Feb 2021
    I have found this repository: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning that, seemingly, requires only images and captions, but this is quite old (3 years ago), and is based on LSTMs. I was hoping there are transformers-based implementations that I could use.

What are some alternatives?

When comparing meshed-memory-transformer and a-PyTorch-Tutorial-to-Image-Captioning you can also consider the following projects:

clip-glass - Repository for "Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search"

BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

stargan-v2 - StarGAN v2 - Official PyTorch Implementation (CVPR 2020)

image-to-latex - Convert images of LaTex math equations into LaTex code.

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

catr - Image Captioning Using Transformer

py-bottom-up-attention - PyTorch bottom-up attention with Detectron2

blip - A tool for seeing your Internet latency. Try it at http://gfblip.appspot.com/

efficient-attention - An implementation of the efficient attention module.

meshed-memory-transformer vs clip-glass a-PyTorch-Tutorial-to-Image-Captioning vs BLIP meshed-memory-transformer vs stargan-v2 a-PyTorch-Tutorial-to-Image-Captioning vs image-to-latex meshed-memory-transformer vs BLIP a-PyTorch-Tutorial-to-Image-Captioning vs pytorch-tutorial meshed-memory-transformer vs catr a-PyTorch-Tutorial-to-Image-Captioning vs catr meshed-memory-transformer vs py-bottom-up-attention a-PyTorch-Tutorial-to-Image-Captioning vs clip-glass a-PyTorch-Tutorial-to-Image-Captioning vs blip a-PyTorch-Tutorial-to-Image-Captioning vs efficient-attention