Multiple Instance Detection Network with Online Instance Classifier Refinement
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1 1 Multiple Instance Detection Network with Online Instance Classifier Refinement Peng Tang
2 Weakly-supervised visual learning (WSVL) 2 Weakly-supervised visual learning is a new trend in CVPR Search keyword weakly supervised (14 papers), weakly-supervised (5 papers), multi-instance (1 paper), and multiple instance (3 papers), 23/783 papers in total
3 WSVL avoids the expensive human annotations 3 Tasks Training info Testing output Weakly-supervised object detection (WSOD) Weakly-supervised sematic segmentation Weakly-supervised instance segmentation Semi-supervised object detection/segmentation Image-level Image-level Imagelevel/bounding box Partial of fullylabeled data Bounding box Pixel-level Instance pixel-level Bounding box/pixellevel
4 WSVL avoids the expensive human annotations 4 Tasks Training info Testing output Weakly-supervised object detection (WSOD) Weakly-supervised sematic segmentation Weakly-supervised instance segmentation Semi-supervised object detection/segmentation Image-level Image-level Imagelevel/bounding box Partial of fullylabeled data Bounding box Pixel-level Instance pixel-level Bounding box/pixellevel
5 How to do WSOD 5
6 How to do WSOD 6 Possible solutions to this problem, clustering based, matching based, co-segmentation based, topic model based, multi-instance learning based method.
7 How to do WSOD 7 Possible solutions to this problem, clustering based, matching based, co-segmentation based, topic model based, multi-instance learning based method. Solving this problem by multiple instance learning Image as bag, since image label is given Proposals (Selective Search, EdgeBox, Bing) as instances Proposal descriptors: Deep CNN Features, Fisher Vectors Number of proposals: ~2k (SS), ~4k (EB)
8 What is the core problem in WSOD 8 Is it a bird? The answers are YES!
9 What is the core problem in WSOD 9 Is it a bird? The answers are YES! However, only some of them are correct detection results (IoU>0.5)
10 What is the core problem in WSOD 10 Is it a bird? The answers are YES! However, only some of them are correct detection results (IoU>0.5)
11 What is the core problem in WSOD 11 Is it a bird? ambiguity The answers are YES! However, only some of them are correct detection results (IoU>0.5)
12 What is the core problem in WSOD 12 Previous methods tend to localize parts of objects instead of whole objects. Result of MIDN/WSDDN [4]
13 13 Multiple Instance Detection Network with Online Instance Classifier Refinement
14 Motivation 14 Proposals having high spatial overlaps with detected parts may cover the whole object, or at least contain larger portion of the object. Result of MIDN/WSDDN [4]
15 Motivation 15 Propagating the scores to the highly overlapped proposals to alleviate the problem cased by ambiguity Result of MIDN/WSDDN [4] Result of OICR
16 The multi-instance detection network (MIDN) 16
17 The multi-instance detection network (MIDN) 17 The basic network of WSDDN [4] by H. Bilen and A. Vedaldi Single network, end to end training
18 The multi-instance detection network (MIDN) 18
19 The multi-instance detection network (MIDN) 19
20 The network for OICR 20
21 The network for OICR 21 Additional blocks (instance classifiers) for score propagation In-network supervision
22 Effective online training/refinement 22
23 Effective online training/refinement 23 The top scoring proposal can always detect at least parts of objects.
24 Effective online training/refinement 24 The top scoring proposal can always detect at least parts of objects. Proposals having high spatial overlaps with detected parts may cover larger portion of the object.
25 Effective online training/refinement 25 The top scoring proposal can always detect at least parts of objects. Proposals having high spatial overlaps with detected parts may cover larger portion of the object. Proposals with high spatial overlap could share similar label information.
26 Effective online training/refinement 26
27 Effective online training/refinement 27 The loss weight controls the learning process.
28 Experimental Results 28
29 Experimental Results 29 The influence of refinement times and different refinement strategies
30 Experimental Results 30 Detection results (map) on VOC 2007 test set Detection results (map) on VOC 2012 test set
31 Experimental Results 31
32 Conclusion 32 Advantages Our method can improve the detection results a lot through our OICR strategy, especially for rigid objects. The network can be trained in a very efficiently (online) way.
33 Conclusion 33 Advantages Our method can improve the detection results a lot through our OICR strategy, especially for rigid objects. The network can be trained in a very efficiently (online) way. Limitations The performance is poor for non-rigid objects, as these objects are always with great deformation and their representative parts are with much less deformation.
34 Thank you! 34 Preprint is available at Codes are available at
arxiv: v1 [cs.cv] 1 Apr 2017
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Multiple Instance Detection Network with Online Instance Classifier Refinement Peng Tang Xinggang Wang Xiang Bai Wenyu Liu School of EIC, Huazhong University of Science and Technology {pengtang,xgwang,xbai,liuwy}@hust.edu.cn
Multiple Instance Detection Network with Online Instance Classifier Refinement
Multiple Instance Detection Network with Online Instance Classifier Refinement Peng Tang Xinggang Wang Xiang Bai Wenyu Liu School of EIC, Huazhong University of Science and Technology {pengtang,xgwang,xbai,liuwy}@hust.edu.cn
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