Zero-Shot-Detection

Introduction

A Keras implementation of Zero-Shot Detection Model based on YOLOv3 (Tensorflow backend), referring to keras-yolo3.

• Object Detection

  Object detection is a computer vision task that deals with detecting instances of semantic objects of a certain class (such as humans, buildings or cars) in digital images and videos, if present, to return the spatial location and extent of each object instance, e.g. via a bounding box.

• Zero-Shot Learning

  Zero-shot learning (ZSL) aims to minimize the annotation requirements by enabling recognition of unseen classes, i.e. those with no training examples. This is achieved by transferring knowledge from seen to unseen classes by means of auxiliary data, typically obtained easily from textual sources.

• Zero-Shot Detection

  The existing ZSL approaches predominantly focus on classification problems. While zero-shot object detection (ZSD) task aims to recognize and localize instances of object classes with no training examples, purely based on auxiliary information that describes the class characteristics. We apply the mainstream ZSL approach on the famous YOLO detection model to achieve the transfer ability. As follows, the classes dog, sofa and train are not seen during training stage, but we are still able to detect them with our model.

  
  

Results on PASCAL VOC

• Settings

  1. We split 16 / 4 of total 20 VOC classes as seen/unseen classes. We guarantee that there must be at least one category in seen classes that are semantically similar to any unseen classes. Unseen classes are as follows.

  2. Proved by experiments, we choose glove embedding to best represent each class semantics. VOC class GloVe embedding are extracted in data/glove_embedding.npy. (Later, we try class attribute vectors provided by aPascal and receive better performance. See attributes in data directory.)

  3. We apply basic ZSL method in the YOLO model, referring from the 2015 NIPS paper "An embarrassingly simple approach to zero-shot learning".

  4. We provide some self-made modules in the objectness proposal brance, to better detect unseen object and improve the localization and recognition results.

• MAP of Unseen Classes

  U2U mAP for VOC (embedding):

  car	dog	sofa	train	mAP
  12.84%	93.48%	55.93%	53.28%	53.88%

  U2U mAP for VOC (attribute):

  car	dog	sofa	train	mAP
  20.84%	94.83%	57.54%	61.13%	58.60%

• Feature Exaction

  From current detection mAP of ZSD model, it has not achieved the most desirable result as we expected. It is possible that nnsatisfactory feature extraction of one-stage model contributes to the problem. For further research, we would like to employ the two-stage models, such as Faster RCNN, which contains a Region Proposal Network (RPN) to generate coarse regions and re-sample from the feature map.

  For the sake of simplicity, we cluster object feature map extracted by resnet50 in Faster RCNN and the grid feature used in YOLO, with T-SNE tools.
  The visualization is as shown below:

  Left is FRCNN features and right is YOLO features. Apparently the feature are more clear for classification in two-stage models. We consider methods in the two-stage models in the future for better performance.

• Feature Resampling

  We finetune the zero-shot yolo model to obtain better embedding prediction by adding a feature resampling layer, RoIAlign, proposed by Mask R-CNN. We call it Zero-Shot YOLO+. Actually, the model is no longer a standard one-stage detector. It got reasonable results and stably increase the MAP on VOC dataset. The improvement work of model architecture and parameters will be continued.

Train and Evaluate

1. Generate your own annotation file and class names file.
   One row for one image.
   Row format: image_file_path box1 box2 ... boxN;
   Box format: x_min,y_min,x_max,y_max,class_id (no space).
   For VOC dataset, try python voc_annotation.py.
   Here is an example:

   path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
   path/to/img2.jpg 120,300,250,600,2
   ...

2. Download YOLOv3 weights from YOLO website. The file model_data/yolo_weights.h5 is used to load pretrained weights.

3. Modify train.py and start training python train.py. Use your trained weights or checkpoint weights in yolo.py. Remember to modify class path or anchor path.

4. Test ZSD model and evaluate the results using mAP, or run the visualization demo.
   python test.py OR python demo.py.
   Test file is in the form: path/to/img, one row for one image.

Issues to know

1. The train and test environment is

   • Python 3.5.4
   • Keras 2.2.0
   • tensorflow 1.6.0

2. Default yolo anchors are used. If you use your own anchors, probably some changes are needed.

3. The inference result is not totally the same as Darknet but the difference is small.

4. The speed is slower than Darknet. Replacing PIL with OpenCV may help a little.

5. Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.

6. The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.

7. For data and results analysis, you are recommended to run our scripts in the anaylsis directory as we provide.

8. Better semantic description can improve the results, like attributes.