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Large-Scale Object Recognition with Weak Supervision. Weiqiang Ren , Chong Wang, Yanhua Cheng, Kaiqi Huang, Tieniu Tan. { wqren,cwang,yhcheng,kqhuang,tnt }@nlpr.ia.ac.cn. Task2 : Classification + Localization. Task 2b: Classification + localization with additional training data

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large scale object recognition with weak supervision

Large-Scale Object Recognition with Weak Supervision

WeiqiangRen, Chong Wang, Yanhua Cheng,

KaiqiHuang, TieniuTan

{wqren,cwang,yhcheng,kqhuang,tnt}@nlpr.ia.ac.cn

task2 classification localization
Task2 : Classification + Localization

Task 2b: Classification + localization with additional training data

— Ordered by classification error

Only classification labels are used

Full image as object location

outline
Outline
  • Motivation
  • Method
  • Results
why weakly supervised localization wsl
Why Weakly Supervised Localization (WSL)?

Knowing where to look, recognizing objects will be easier !

However, in the classification-only task, no annotations of object location are available.

Weakly Supervised Localization

slide7

13.9: Weakly supervised object detector learning with model drift detection, ICCV 2011

15.0: Object-centric spatial pooling for image classification, ECCV 2012

22.4: Multi-fold mil training for weakly supervised object localization, CVPR 2014

22.7: On learning to localize objects with minimal supervision, ICML 2014

26.2: Discovering Visual Objects in Large-scale Image Datasets with Weak Supervision, submitted to TPAMI

26.4: Weakly supervised object detection with posterior regularization, BMVC 2014

31.6: Weakly supervised object localization with latent category learning, ECCV 2014

Sep 11, Poster Session 4A, #34

our work
Our Work

Weakly Supervised Object Localization with Latent Category Learning

Discovering Visual Objects in Large-scale Image Datasets with Weak Supervision

ECCV 2014

Submitted to TPAMI

For the consideration of high efficiency in large-scale tasks, we use the second one.

framework
Framework

2

Det Prediction

3

Rescoring

4

Cls Prediction

Conv Layers

1

Input Images

FC Layers

1 st cnn architecture
1st: CNN Architecture

Chatfield et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets

slide13

MILinear : Region Proposal

  • Good region proposal algorithms
    • High recall
    • High overlap
    • Small number
    • Low computation cost
  • MCG pretrained on VOC 2012
    • Additional Data
    • Training: 128 windows/ image
    • Testing: 256 windows/image
    • Compared to Selective Search (~2000)
milinear feature representations
MILinear: Feature Representations
  • Low Level Features
    • SIFT, LBP, HOG
    • Shape context, Gabor, …
  • Mid-Level Features
    • Bag of Visual Words (BoVW)
  • Deep Hierarchical Features
    • Convolutional Networks
    • Deep Auto-Encoders
    • Deep Belief Nets
slide15

MILinear: Positive Window Mining

  • Clustering
    • KMeans
  • Topic Model
    • pLSA, LDA, gLDA
  • CRF
  • Multiple Instance Learning
    • DD, EMDD, APR
    • MI-NN,
    • MI-SVM, mi-SVM
    • MILBoost
milinear objective function and optimization
MILinear: Objective Function and Optimization
  • Multiple instance Linear SVM
  • Optimization: trust region Newton
    • A kind of Quasi Newton method
    • Working in the primal
    • Faster convergence
3 rd detection rescoring
3rd: Detection Rescoring
  • Rescoring with softmax

train

softmax

max

128 boxes

……

……

1000 dim

1000 dim

1000 classes

Softmax: consider all the categories simultaneously  at each minibatch of the optimization – Suppress the response of other appearance similar object categories

4 th classification rescoring
4th: Classification Rescoring
  • Linear Combination

1000 dim

1000 dim

1000 dim

One funny thing: We have tried some other strategies of score combination, but it seems not working !

2 nd milinear on ilsvrc 2013 detection
2nd: MILinear on ILSVRC 2013 detection

mAP: 9.63%! vs 8.99% (DPM5.0)

3 rd wsl rescoring softmax
3rd: WSL Rescoring (Softmax)

The Softmax based rescoring successfully suppresses the predictions of other appearance similar object categories !

4 th cls and wsl combinataion
4th: Cls and WSL Combinataion

WSL and Cls can be complementary to each other!

conclusion
Conclusion
  • WSL always helps classification
  • WSL has large potential: WSL data is cheap