Detection & Segmentation

This post is a summary and paper skimming on detection and segmentation related research. So, this post will be keep updating by the time.

Paper List

Segmentation

• Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised semantic Segmentation, CVPR2018
  • Paper
• What’s the point: semantic segmentation with point supervision, ICCV2016
  • Paper

Detection

• Unsupervised Learning of Object Landmarks by Factorized Spatial embeddings, ICCV2016
  • Paper
• Scalable Deep Learning Logo Detection
  • Paper

Revisiting Dilated Convolution

• Title: Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised semantic Segmentation
• Conference: CVPR2018
• Institute: UIUC, NUS, IBM, Tencent

Summary

• Problem Statement
  • Time-consuming boudning box annotation is sidestepped in weakly supervised learning.
  • In this case, the supervised information is restricted to binary labels (object absence/presence) without their locations.
• Research Objective
  • To infer the object locations during weakly supervised learning
• Proposed Solution
  • Propose a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images
  • Window refinement method
• Contribution
  • Multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations
  • Window refinement method improves the localization accuracy by incorporating an objectness prior.

Figure: Multi-fold weakly supervised training

References

• Paper: Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised semantic Segmentation

Unsupervised Learning of Object Landmarks by Factorized Spatial embeddings

• Conference: ICCV2016
• Institute: University of Oxford

Summary

• Problem Statement
  • Learning automatically the structure of object categories is an oppen problem in computer vision.
• Research Objective
  • To learn landmarks of objects with unsupervised approach
• Proposed Solution
  • Propose a unsupervised approach that can discover and learn landmarks in object categories, thus characterizing their structure.
  • Approach is based on factorizing image deformations, as induced by a viewpoint change or an object deformation, by learning a deep neural network that detects landmarks consistently with such visual effects.
• Contribution
  • Learned-landmarks establish meaningful correspondences between different object instances in a category without having to impose this requirement explicitly.
  • Proposed unsupervised landmarks are highly predictive of manually-annotated landmarks in face benchmark datasets, and can be used to regree these with a high degree of accuracy.

Figure: Proposed method that cna learn view point invariant landmarks without any supervision.

References

• Paper: Unsupervised Learning of Object Landmarks by Factorized Spatial embeddings

Scalable Deep Learning Logo Detection

• Conference: Arxiv
• Institute: Queen Mary University of London, Vision Semantics Ltd.

Summary

• Problem Statement
  • Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations.
  • This is not scalable to real-world dynamic applications.
• Research Objective
  • To handle the problem by exploring the webly data learning principle without the need for exhaustive manual labelling.
  • To learn scalable logo detection method
• Proposed Solution
  • Propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL²)
  • It is capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner.
• Contribution
  • Introduce a very large (2,190,757 images of 194 logo classes) logo dataset “WebLogo-2M”
  • Proposed SL² method is superior over the state-of-the-art and weekly supervised detection and contemporary webly data learning approaches.

Figure: Logo detection performance on WebLogo-2M.

References

• Paper: Scalable Deep Learning Logo Detection

What’s the point

• Title: What’s the point: semantic segmentation with point supervision
• Conference: ICCV2016

Summary

• Problem Statement
  • Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain
  • Image-level class labels are an order of magnitude cheaper but result in less accurate models
• Research Objective
  • To take a natural step (point) from image-level annotation towards stronger supervision
• Proposed Solution
  • Annotators point to an object if one exists
  • Incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model.
• Contribution
  • Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9% mIOU over image-level supervision
  • Models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget

Figure:(Top): Overview of our semantic segmentation training framework. (Bottom): Different levels of training supervision

References

• Paper: What’s the point: semantic segmentation with point supervision