| peteanderson80/bottom-up-attention |
979 |
|
0 |
0 |
about 5 years ago |
0 |
|
56 |
mit |
Jupyter Notebook |
| Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome |
| KaiyuYue/cgnl-network.pytorch |
253 |
|
0 |
0 |
about 5 years ago |
0 |
|
1 |
mit |
Python |
| Compact Generalized Non-local Network (NIPS 2018) |
| BestSonny/SSTD |
222 |
|
0 |
0 |
about 8 years ago |
0 |
|
3 |
other |
C++ |
| Single Shot Text Detector with Regional Attention |
| peteanderson80/Up-Down-Captioner |
218 |
|
0 |
0 |
almost 7 years ago |
0 |
|
17 |
mit |
Jupyter Notebook |
| Automatic image captioning model based on Caffe, using features from bottom-up attention. |
| wenguanwang/AGS |
209 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
|
Python |
| Learning Unsupervised Video Object Segmentation through Visual Attention (CVPR19, PAMI20) |
| MILVLG/bottom-up-attention.pytorch |
207 |
|
0 |
0 |
about 4 years ago |
0 |
|
2 |
apache-2.0 |
Jupyter Notebook |
| A PyTorch reimplementation of bottom-up-attention models |
| PKU-ICST-MIPL/OPAM_TIP2018 |
80 |
|
0 |
0 |
about 7 years ago |
0 |
|
10 |
|
Jupyter Notebook |
| Source code of our TIP 2018 paper "Object-Part Attention Model for Fine-grained Image Classification" |
| developfeng/MGCAM |
79 |
|
0 |
0 |
over 7 years ago |
0 |
|
4 |
|
Python |
| Mask data and code for 'Mask-guided Contrastive Attention Model for Person Re-Identification' (CVPR-2018) |
| ZhiwenShao/JAANet |
76 |
|
0 |
0 |
about 6 years ago |
0 |
|
0 |
|
C++ |
| ECCV 2018 "Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment" |
| wenguanwang/deepattention |
55 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
|
MATLAB |
| Deep Visual Attention Prediction (TIP18) |
