wangzheallen / vsad Goto Github PK

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:

Note: The encoding method based on our scene_patchenet feature surpass human performance on sun397(68.5%).

• mit_hybrid_vsad.mat
• mit_hybrid_vsad.mat, Google_drive

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

They both take 128 * 128 patches as input.

• 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

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

• Zhe Wang
• Limin Wang

Figure Plot for Reference