This document provides a brief introduction to recent image recognition methods and ChainerCV. It first introduces the presenter Shunta Saito and their background and research interests. It then outlines several major image recognition problems including image classification, object detection, semantic segmentation, instance-aware segmentation, image captioning, and visual question answering. For each problem, it lists some popular datasets and example methods that have been proposed. It also provides an overview and link to ChainerCV, an open source framework for computer vision research. Finally, it mentions some datasets and methods for computer vision applications in fashion.

1. Brief Introduction to
Recent Image Recognition Methods
and ChainerCV
Shunta Saito
Researcher at Preferred Networks, Inc.

2. Self-introduction
● Shunta Saito, Ph. D. in Engineering (@mitmul)
● Background: Keio Univ. → UC Berkeley → Keio Leading Edge Lab.
→ Facebook, Inc. → Preferred Networks, Inc.
● Research interest: Computer Vision, semantic segmentation, etc.
● Job: Research on computer vision applications, Development of
Chainer, Driving global alliance with Microsoft, Intel, etc...

3. Related work to fashion…?
Virtual fitting demo using Kinect (2011) at Geis, Inc.

4. Major Image Recognition Problems
● Image Classification
● Object Detection
● Semantic Segmentation
● Instance-aware Segmentation
● Image Captioning
● Visual Question Answering

5. Image Classification
● MNIST, CIFAR-10/101, ImageNet, Places205, etc…
● Various methods based on ConvNets have been proposed
● ILSVRC 2017: the last ImageNet challenge
– 1.28 million images, 1000 classes

6. Object Detection
● Dataset: Pascal VOC, MS COCO, KITTI, Cityscapes, etc…
● Methods: Faster R-CNN, SSD, R-FCN, etc...
Complicated...

7. Semantic Segmentation
● Dataset: Pascal VOC, MS COCO, Cityscapes, KITTI, CamVid,
LabelMe, SUN RGB-D, Mapillaly, etc...
● Methods: FCN, Deconv Net, U-Net, SegNet, Dilated, RefineNet,
PSPNet, etc...
See details here:
http://bit.ly/seg-slide
Semantic Segmentation result
on Cityscapes dataset ->

8. Instance-aware Segmentation
● Dataset: MS COCO, Cityscapes, Mapillary, etc...
● Methods: DeepMask, SharpMask, Multi-task Network
Cascades, Mask R-CNN, etc...
Instance-aware: Differentiate each instance of the same class

9. Image Captioning
● Dataset: MS COCO, etc...
● Methods: Show and Tell, Show,
Attend and Tell, Review Networks,
Knowing When to Look,
Hierarchical Recurrent Network,
etc...

10. Visual Question Answering
● Dataset: VQA dataset ( http://visualqa.org ) ...
● Methods: MCB for VQA, Dual Attention Networks, etc...

11. ChainerCV
Want to
compare
your own
method on
public
datasets...
Datasets
Pascal VOC,
Caltech-UCSD
Birds-200-2011,
Stanford Online
Products,
CamVid, etc.
Models
Faster R-CNN,
SSD, SegNet
(will add more
models!)
Training
scripts
Evaluation
tools
Dataset
abstraction
Want to
apply to
new data...
Make image recognition research / develpment much easier
https://github.com/chainer/chainercv

12. Computer Vision for Fashion
● Dataset: Etsy dataset, Wear dataset, Fashion144k, DeepFashion,
● Methods: Algorithmic clothing, Pose Guided Person Image Generation, etc...
↑ Kota
Yamaguchi
←Edgar
Simo-Serra
https://sites.google.com/zalando.de/cvf-iccv2017