Shypd.aiThe largest open index of AI toolsBottom-up-attention is an Open Source & Models tool that implements a bottom-up attention model for image captioning and VQA. It generates output features for salient image regions, improving performance in downstream tasks.
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Bottom-up-attention provides an open-source implementation of a bottom-up attention model, built upon multi-GPU training of Faster R-CNN with ResNet-101. It leverages object and attribute annotations from Visual Genome to generate output features corresponding to salient image regions. These features can serve as a direct replacement for traditional CNN features in attention-based image captioning and visual question answering (VQA) models. The approach has demonstrated state-of-the-art performance in image captioning on MSCOCO and won the 2017 VQA Challenge. The repository includes code for training the Faster R-CNN model and provides pretrained features for the MSCOCO dataset, making it a valuable resource for researchers and developers in computer vision.
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