Papers On Object Detection
One-StageGong Qiqi

Paper List
• 5. You Only Look Once: Unified, Real-Time Object Detection (YOLO)• 6. SSD: Single Shot MultiBox Detector• 7. Focal Loss for Dense Object Detection

Yo u O n l y L o o k O n c e : U n i f i e d , R e a l - Ti m e O b j e c t
Detection (YOLO)
Author: Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi

YOLO• Basic Info.:• 2016 CVPR

• Motivation:• Two-stage methods are slow and hard to optimize• Contribution:• First to put forward one-stage detection method• Deal with detection problem as a regression problem• Bridge domain gap• Drawbacks:• Localization errors (but less likely to mistake categoery)YOLO• Overview:• Divides the input image into an S × S grid
• Each grid cell predicts B bounding boxes andconfidence scores for those boxes.
• Each bounding box consists of 5 predictions: x,y, w, h, and confidence.
• x,y: center of the box relative to the bounds ofgrid cell
• w, h : w i d t h a n d h e i g h t r e l a t i v e t o t h e w h o l eimage• Each grid cell also predicts C conditional classprobabilities

These predictions are encoded as an S × S × (B ∗5 + C) tensor.
VOC: S=7, B=2, C=20

YOLO
• Network:

YOLO• Training:• Pretrain convolutional layers on ImageNet (20conv + avepooling + 2fc)
• Increase resolution from 224² to 448²
• Final layer predicts class posibilities and b-box position• Loss:locationIoUcat.

YOLO• Limitations:• Impose strong spatial constraints on bounding box predictions• Unsensitive to new aspect ratios• Treat small objects the same with big objects

SSD: Single Shot MultiBox Detector
Author: Wei Liu, Dragomir Anguelov, Dumitru Erhan,
Christian Szegedy, Scott Reed

SSD
• Basic Info.:• 2016 ECCV• Motivation:• Two-stage methods are slow and hard to optimize• One-stage methods do not perform well

• Contribution:• Faster than Faster R-CNN and more accurate than YOLO

SSD
• Trainging Method• n priors(b-boxes with different aspect ratios) in total• Match with gt boxes first and asign lables to the rest of them according to IoU• Loss:multi-class logistic loss

SSD
• Trainging Method• Loss:• Location Loss:• Confidence Loss:• Hyper param: α=0.06

SSD• Fully Convolutional priors• Similar to RPN, but use 1*1 kernels to predict offsets and confidence• Suppose feat. map: m*m, k priors per location• (4+c)km² variables in total• Combine predictions from multiple feat. maps
• Suppose m feat. maps in total, f k is the k-th feat. map• scale (relative size) of priors:• aspect ratios:

SSD

Focal Loss for Dense Object DetectionAuthor: Tsung-Yi Lin， Priya Goyal， Ross Girshick
Kaiming He， Piotr Dollar

Focal Loss
• Basic Info.:• 2017 ICCV• FAIR• Motivation:• Accuracy-Speed tradoff is due to the imbalance between P&N example• Contribution:• Put forward Focal Loss
• A new detector: RetinaNet

Focal Loss
• Background:• In two-stage method, class imbalance is addressed (by set ratios manually orOHEM)• In one-stage method, easy negative examples are overwhelming• Cross Entropy Loss:• Take binary classification as eample

Focal Loss
• Balanced CE:• Focal Loss:focusing param.

modulating factor• Combine (3) and (4)Focal Loss
• RetinaNet:Focal Loss
• RetinaNet:• Anchors:• Aspect ratios:{1:2, 1:1, 2:1}
• Scale: {2 0 , 2 1 / 3 , 2 2 / 3 }• IoU Threshold: 0.5; IoU BG:[0,0.4); Else: Ignored

Focal Loss
• RetinaNet:• Class subnet:• Small FCN attached to each pyramidlevel• Share weights• Output of FPN level: C-channel; fourC*3*3 conv layers• Anchors: A

Focal Loss
• RetinaNet:• Regression subnet:• Regress offects to a nearby GT b-box4• Same as design of C-subnet (but endwith 4A channels)• Class-agnostic * bounding box regressor(fewer params)• Separate params. from C-subnet*class-agnostic: only differentiate BG & FG

Thanks for Listening~Gong Qiqi

Reference List
• 1. https://zhuanlan.zhihu.com/p/25236464/, YOLO详解
• 2. https://zhuanlan.zhihu.com/p/49981234, Focal loss论文详解