Two-Phase Learning for Weakly Supervised Object Localization

Kim

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

et al. 2019

435

359

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations. Most methods based on image-level annotations use localization maps obtained from the classifier, but these only focus on the small discriminative parts of objects and do not capture precise boundaries. FickleNet explores diverse combinations of locations on feature maps created by generic deep neural networks. It selects hidden units randomly and then uses them to obtain activation scores for image classification. Fick-leNet implicitly learns the coherence of each location in the feature maps, resulting in a localization map which identifies both discriminative and other parts of objects. The ensemble effects are obtained from a single network by selecting random hidden unit pairs, which means that a variety of localization maps are generated from a single image. Our approach does not require any additional training steps and only adds a simple layer to a standard convolutional neural network; nevertheless it outperforms recent comparable techniques on the Pascal VOC 2012 benchmark in both weakly and semi-supervised settings.

Section: Feature-level Processingmentioning

confidence: 99%

FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference

Kim

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

et al. 2019

435

359

“…Our method achieves mIoU values of 63.9 [45] 49.8 51.2 TransferNet CVPR '16 [11] 52.1 51.2 AISI ECCV '18 [16] 61.3 62. [33] 52.8 53.7 TPL ICCV '17 [22] 53.1 53.8 AE_PSL CVPR '17 [44] 55.0 55.7 DCSP BMVC '17 [2] 58.6 59.2 MEFF CVPR '18 [9] -55.6 GAIN CVPR '18 [26] 55.3 56.8 MCOF CVPR '18 [43] 56.2 57.6 AffinityNet CVPR '18 [1] 58.4 60.5 DSRG CVPR '18 [17] 59.0 60.4 MDC CVPR '18 [46] 60.4 60.8 SeeNet NIPS '18 [15] 61.1 60.7 FickleNet CVPR '19 [24] 61.2 61.9 Ours 63.9 65.0 and 65.0 for PASCAL VOC 2012 validation and test images respectively, which is 94.4% of that of DeepLab [3], trained with fully annotated data, which achieved an mIoU of 67.6 on validation images. Our method is 3.1% better on test images than the best method which uses only image-level annotations for supervision.…”

Section: Results On Image Segmentationmentioning

confidence: 99%

Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

Kim

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

et al. 2019

When a deep neural network is trained on data with only image-level labeling, the regions activated in each image tend to identify only a small region of the target object. We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image. The temporal variations in a video allow different regions of the target object to be activated. We obtain an activated region in each frame of a video, and then aggregate the regions from successive frames into a single image, using a warping technique based on optical flow. The resulting localization maps cover more of the target object, and can then be used as proxy ground-truth to train a segmentation network. This simple approach outperforms existing methods under the same level of supervision, and even approaches relying on extra annotations. Based on VGG-16 and ResNet 101 backbones, our method achieves the mIoU of 65.0 and 67.4, respectively, on PASCAL VOC 2012 test images, which represents a new state-of-the-art.

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

“…That means, there is no parameter overheads even when applied to multiple feature maps at the same time. Furthermore, with ADL, the most discriminative region can be identified and erased efficiently, without auxiliary classifiers [17,59], re-training [49], or additional forward-backward propagation [20].…”

Section: Adl: Attention-based Dropout Layermentioning

confidence: 99%

Attention-Based Dropout Layer for Weakly Supervised Object Localization

Choe

Shim

2019

328

313

Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniques is that they cover only the most discriminative part of the object, not the entire object. To address this problem, we propose an Attention-based Dropout Layer (ADL), which utilizes the self-attention mechanism to process the feature maps of the model. The proposed method is composed of two key components: 1) hiding the most discriminative part from the model for capturing the integral extent of object, and 2) highlighting the informative region for improving the recognition power of the model. Based on extensive experiments, we demonstrate that the proposed method is effective to improve the accuracy of WSOL, achieving a new state-of-the-art localization accuracy in CUB-200-2011 dataset. We also show that the proposed method is much more efficient in terms of both parameter and computation overheads than existing techniques.