Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches.
In a weakly supervised object localization task, we should find common features within a set of intra-class images, discriminate those intra-class features with each other, and define the most probable region in terms of target class. Transfer learning based on well pre-trained networks (preliminarily trained on different-but-similar datasets) helps to perform those challenging tasks, since pre-trained CNN properly defines ROIs based on discriminative convolutional filters already learned from semantically similar datasets @cite_25 @cite_2 @cite_27 .
@cite_27 @cite_25 @cite_2
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Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches. Most instance search systems are based on modeling local features. It remains a challenge to apply deep learning techniques into this task because of the asymmetrical similarity between the query region and dataset images. In this paper, we propose ALADDIN, A Locality Aligned Deep moDel for INstance search. This model deals with the asymmetrical similarity by searching query instances at the scale of aligned target regions instead of the whole image. Towards discriminative region representations, we utilize a deep convolutional network which captures both intra-class and inter-class distinctions of the regions. In addition, we propose a semi-supervised method to collect appropriate data to train the network. Extensive experiments confirm that our method is more suitable for generic instance search than most conventional methods, and outperforms the best CNNs-based method in both accuracy and efficiency. We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations. We present a unified knowledge transfer framework based on training a single neural network multi-class object detector over all source classes, organized in a semantic hierarchy. This generates proposals with scores at multiple levels in the hierarchy, which we use to explore knowledge transfer over a broad range of generality, ranging from class-specific (bycicle to motorbike) to class-generic (objectness to any class). Experiments on the 200 object classes in the ILSVRC 2013 detection dataset show that our technique (1) leads to much better performance on the target classes (70.3 CorLoc, 36.9 mAP) than a weakly supervised baseline which uses manually engineered objectness [11] (50.5 CorLoc, 25.4 mAP). (2) delivers target object detectors reaching 80 of the mAP of their fully supervised counterparts. (3) outperforms the best reported transfer learning results on this dataset (+41 CorLoc and +3 mAP over [18, 46], +16.2 mAP over [32]). Moreover, we also carry out several across-dataset knowledge transfer experiments [27, 24, 35] and find that (4) our technique outperforms the weakly supervised baseline in all dataset pairs by 1.5 A— - 1.9A—, establishing its general applicability. We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same object class, we formulate FCSS using local self-similarity (LSS) within a fully convolutional network. In contrast to existing CNN-based descriptors, FCSS is inherently insensitive to intra-class appearance variations because of its LSS-based structure, while maintaining the precise localization ability of deep neural networks. The sampling patterns of local structure and the self-similarity measure are jointly learned within the proposed network in an end-to-end and multi-scale manner. As training data for semantic correspondence is rather limited, we propose to leverage object candidate priors provided in existing image datasets and also correspondence consistency between object pairs to enable weakly-supervised learning. Experiments demonstrate that FCSS outperforms conventional handcrafted descriptors and CNN-based descriptors on various benchmarks.