this is the release code for Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition:
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition
Zhe Wang, Limin Wang, Yali Wang, Bowen Zhang, and Yu Qiao
The performance is as below:
| acc | MIT_indoor | SUN397 |
|---|---|---|
| mean: | 78.5 | 63.5 |
| VLAD: | 83.9 | 70.1 |
| FV | 83.6 | 69.0 |
| VSAD | 84.9 | 71.7 |
Note: The encoding method based on our scene_patchenet feature surpass human performance on sun397(68.5%).
Feature
we released the concise and effective feature for MIT indoor feature, it is notated as hybrid_PatchNet+VSAD in the paper which obtains 86.1 accuracy. You can use it as baseline or as complementary feature for further study.| acc on MIT | dimension | storage |
|---|---|---|
| 86.1 | 100*256*2*2 | 1.9G |
Model
Our trained scene_patchnet and object_patchenet, the model is based on cudnn_v4, if your system is based on cudnn_v5, you can use the code below cudnn_v4 to cudnn_v5: https://github.com/yjxiong/caffe/blob/action_recog/python/bn_convert_style.py
| acc | Top5 |
|---|---|
| Object_patchnet_on_ImageNet: | 85.3 |
| Scene_patchnet_on_Places205: | 82.7 |
They both take 128 * 128 patches as input.
Code
- mit_hybrid_vsad.mat -- you can use this feature as your baseline or to concatenate for further study, it is only 100*256*2*2 dimensions while performs 86.1 acc on MIT indoor, you can download from mit_hybrid_vsad.mat
- extracting_feature_exmaple.m -- you can use this code as template to extract scene_patchnet_feature or object_patchnet_probability, for scene_patchnet_feature it is global average pool feature and for for object_patchnet_probability it is fully connnect feature with softmax function
- for_encoder_scene67.mat -- serve as assist to your handle on MIT_indoor dataset, from vl_feat
- for_encoder_sun397.mat -- serve as assist to your handle on sun397 dataset
- mit_pca.mat -- our generated scene_patchnet_feature pca matrix for mit indoor, used in vsad_encoding_example.m
- mit_vsad_codebook.mat -- our generated semantical codebook for mit_indoor, used in vsad_encoding_example.m
- multi_crop.m -- dense crop as 10 * 10 grid, used in extracting_feature_example.m
- object_selection_256.mat -- 256 objects selected from 1000(in ImageNet), applied to both MIT_indoor and SUN397
- sun_pca.mat -- our generated scene_patchnet_feature pca matrix for sun397, used in vsad_encoding_example.m
- sun_vsad_codebook.mat -- our generated semantical codebookfor sun397, used in vsad_encoding_example.m
- vsad_encoding_example.m -- an example for VSAD encoding algorithm
- vsad_encoding.m -- our developed VSAD encoding function
- plot_mit_sun.m -- Plot the figure in the below of this page
- xticklabel_rotate.m -- Serve for plot_mit_sun and rotate the text in the figure
Usage
1. Download code and model
2. Extract scene_net_feature and object_net_probability (extracting_feature_example.m, multi_crop.m)
3. VSAD encoding (vsad_encoding.m, vsad_encoding_example.m, mit_pca.mat, mit_vsad_codebook.mat, object_selection_256.mat)
Contact
Figure Plot for Reference
