Code to run a live demo with a webcam Evaluation code for dense captioning Instructions for training the model If you find this code useful in your research, please cite: @inproceedings{densecap, title={DenseCap: Fully Convolutional Localization Networks for Dense Captioning}, author={Johnson, Jus...
An image captioning model is utilized to describe the cropped image patch corresponding to each mask. Nouns or phrases are then extracted as candidate open-vocabulary categories. This process provides more diverse category labels. (III) Final decision module (orange). The SSA-engine uses a Class ...
It will generate html pages for each image visualizing the results under folder output/dense_cap/${TEST_IMDB}/vis. Contact If you have any questions regarding the repo, please send email to Linjie Yang (yljatthu@gmail.com).About Dense captioning with joint inference and visual context ...
You can check if the projections make sense by projecting the semantic labels from image to the target point cloud by: python scripts/project_multiview_labels.py --scene_id scene0000_00 --maxpool Usage End-to-End training for 3D dense captioning ...
Dense image captioning in Torch. Contribute to jcjohnson/densecap development by creating an account on GitHub.
(II) Open-vocabulary classifier (blue).An image captioning model is utilized to describe the cropped image patch corresponding to each mask. Nouns or phrases are then extracted as candidate open-vocabulary categories. This process provides more diverse category labels. ...
Dense Captioning with Joint Inference and Visual Context This repo is the released code of dense image captioning models described in the CVPR 2017 paper: @InProceedings{CVPR17, author = "Linjie Yang and Kevin Tang and Jianchao Yang and Li-Jia Li", title = "Dense Captioning with Joint Inferen...