1. Wenchi MA CV Group EECS,KU 03/20/2017
Rethinking architectures of DCNN
and object detection in scene recognition
Wenchi MA CV Group EECS,KU 03/20/2017

2. Current work and related consideration
Task: Object Detection
Algorithm: You Only Look Once(YOLO)
Architecture: Googlenet based
Parameters:　>=97M (relatively small)
Techniques: Inception V3 (construction series)
Efficient Grid Size Reduction (channels in parallel)
Feature fusion by multi-resolution feature maps
Problems: relatively high training loss and non-ideal mAP
3*3
Thinking:
Structure of the model impacts the detection accuracy so much (Reasonable loss? Small gap between training loss and test loss? Is the model too large?(easy to overfit and make the model out of control) )! Keep searching for the balance between accuracy and model size. Standard for construction?
Relationship between objects and scenes. Dose scene classification benefits objective detection?　Merge scene information in object detection.

3. Wide–Residual-Inception Networks for Real-time Object Detection
Computer Vision Laboratory, Inha University
Wide–Residual-Inception Networks for Real-time Object Detection
Scale down the size of the model furtherly
Wide-Residual-Inception Networks for Real-time Object Detecciton——Youngwan Lee[2017]

4. Wide–Residual-Inception Networks for Real-time Object Detection
Computer Vision Laboratory, Inha University
Wide–Residual-Inception Networks for Real-time Object Detection
inception
Resnet
Feature extractor
Wide-Residual-Inception Networks for Real-time Object Detecciton——Youngwan Lee[2017]

5. Wide–Residual-Inception Networks for Real-time Object Detection
Computer Vision Laboratory, Inha University
Wide–Residual-Inception Networks for Real-time Object Detection
SSD
WR-Inception network
Wide-Residual-Inception Networks for Real-time Object Detecciton——Youngwan Lee[2017]

6. Wide–Residual-Inception Networks for Real-time Object Detection
Computer Vision Laboratory, Inha University
Wide–Residual-Inception Networks for Real-time Object Detection
Model
Car
Pedestrian
Cyclist
mAP
mAR AP AR
VGG-16 74 75 50 56 52 71 58 69
ResNet-101
76.04
74.82
47.74
56.07
53.61
75.26
58.9
70.06
WR-Inception
77.2
76.18
52.51
63.01
54.63
76.17
61.18
73.51
WR-Inception-12
78.24
80.24
51.08
64.29
59.28
63.03
75.14
Dataset: KITTI: obtained through stereo cameras and lidar scannerss in urban, rural, and highway driving environments, and has 10 categories in total, which are small cars, vans, trucks pedestrains, sitting people, cyclists, traims, miscellaneous, and “do not care”
Wide-Residual-Inception Networks for Real-time Object Detecciton——Youngwan Lee[2017]

7. Wide–Residual-Inception Networks for Real-time Object Detection
Computer Vision Laboratory, Inha University
Wide–Residual-Inception Networks for Real-time Object Detection
Contribution: Propose the model that requires less memory and
fewer computations but shows better performance
Ensure the real-time performance of object detector
Query: KITTI is still a relatively small dataset. And its categories are
limited. The performance of this model should be tested on
more common and large dataset like Imagenet.

8. Proper model for a specific dataset
Cambridge
Proper model for a specific dataset
The somewhat unanswered question in deep learning:
Is the selected CNN optimal for the dataset in terms of accuracy and model size?
There needs some certain standard, but base what?
Given a pre-trained CNN for a specific dataset, refine the architecture in order to potentially increase the accuracy while possibly reducing the model size.
Standard: the feature extraction ability of a CNN for a specific dataset.
Intuition: separation enhancement
To best separate the classes of a dataset, assuming a constant depth of the network.
Refining Architectures of Deep Convolutional Neural Networks——Machine Intelligence Lab, University of Cambridge , UK and Microsoft Research Cambridge, UK[CVPR 2016]

9. Separation enhancement and deterioration capacity of a layer
Cambridge
Correlation Matrices for 8 Convolutional Layers of VGG-11 trained on SAD and CAMIT-NSAD
Dark blue: minimum correlation between classes
Bright yellow: maximum correlation
Correlation Matrics give an indication of the separation between classes for a given convolutional layer.
Top Row(SAD):The lower layers can separate the class better as compared to deeper layers
Bottom Row(CAMIT-NSAD):The classes are separated lesser in lower layers and more prominently in deeper layers

10. Separation enhancement and deterioration capacity of a layer
Cambridge
Comparing Cl and Cl+1, which class pairs, the separation increased and which deteriorated
The number of class pairs where the separation increased compared between layer l and l-1
The number of class pairs where the separation decreased compared between layer l and l-1
Finding the inner-class separation
Separation situation varies through layers for different dataset

11. Separation enhancement and deterioration capacity of a layer
Cambridge
t: 22084
V: 3056
T: 5618
t: V: 3056 T: 5618
DR=Deep Refined Architecture (proposed approach)
DR-1=Deep Refined Architecture with only the Stretch network
DR-2=Deep Refined Architecture with only the Symmetric Split
Sp-1=L1 Sparsified network
Sp-2=L2 Sparsified network

12. Separation enhancement and deterioration capacity of a layer
Cambridge
Contribution: Provide quantified refining network architecture
Realize the balance between precision and model size
Query: SAD and CAMIT-NSAD are relatively small dataset and they
are only scene data. What about big object dataset like
ImageNet?
Generalization problem has been avoided in this paper. When
we do not know the source of the test data, how to transfer
transfer learning the model and refine the better model?

13. MIT: Object Detectors Emerge in Deep Scene CNNs
The same network can do both object localization and scene recognition in a single forward-pass
The deep features from Places-CNN tend to perform better on scene-related recognition tasks compared to the features from ImageNet-CNN
Scene recognition and classification
Published as a conference paper at ICLR 2015
Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS)
Mix scene data and object data together in the training process
CVPR 2016

14. What needs to be taken into consideration?
Cambridge
What needs to be taken into consideration?
Scale down the size of our model and make it more easy to controlled while improving feature extraction ability.
How to carry out training with both object dataset and scene dataset with one single feature detector and how to merge the abstracted scene information with the features of objects?

15. Thank you!