peteanderson80/bottom-up-attention

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1469 stars · 369 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A bottom-up attention model based on Faster R-CNN with ResNet-101 trained on Visual Genome, producing features for salient image regions. These features serve as drop-in replacements for CNN features in attention-based image captioning and visual question answering models.

Use cases

• generate bottom-up attention features for image captioning models
• extract salient region features for visual question answering
• train a Faster R-CNN model on Visual Genome object and attribute annotations
• download pretrained MSCOCO image features instead of building the model
• replicate state-of-the-art VQA challenge results from 2017
• improve captioning CIDEr and BLEU scores with object-level attention

When to choose

• you need object-level region features for captioning or VQA research
• you want pretrained MSCOCO features without building Caffe code
• you are reproducing the Bottom-Up Top-Down attention paper

When to avoid

• you need a modern maintained framework like PyTorch or Transformers
• you want an end-to-end captioning model (use the separate Up-Down-Captioner repo)
• your environment cannot support legacy Caffe-based training

Facets

library · maturity maintenance

machine-learning computer-vision image-processing deep-learning computer-vision machine-learning deep-learning python faster-rcnn visual-genome vqa image-captioning mscoco caffe attention-features object-detection natural-language-processing linux gpu

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