25,458 research outputs found
Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation
We propose an approach to discover class-specific pixels for the
weakly-supervised semantic segmentation task. We show that properly combining
saliency and attention maps allows us to obtain reliable cues capable of
significantly boosting the performance. First, we propose a simple yet powerful
hierarchical approach to discover the class-agnostic salient regions, obtained
using a salient object detector, which otherwise would be ignored. Second, we
use fully convolutional attention maps to reliably localize the class-specific
regions in a given image. We combine these two cues to discover class-specific
pixels which are then used as an approximate ground truth for training a CNN.
While solving the weakly supervised semantic segmentation task, we ensure that
the image-level classification task is also solved in order to enforce the CNN
to assign at least one pixel to each object present in the image.
Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of
60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to
the published state-of-the-art results. The code is made publicly available
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on initially unlabeled data with existing tools and to use them for
pre-training. For the subsequent fine-tuning of the network with manually
labeled data, we introduce error corrective boosting (ECB), which emphasizes
parameter updates on classes with lower accuracy. Furthermore, we introduce
SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that
combines skip connections with the unpooling strategy for upsampling. The
SD-Net addresses challenges of severe class imbalance and errors along
boundaries. With application to whole-brain MRI T1 scan segmentation, we
generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on
two datasets with manual annotations. Our results show that the inclusion of
auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D
scan in 7 secs in comparison to 30 hours for the closest multi-atlas
segmentation method, while reaching similar performance. It also outperforms
the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
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Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.
The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks
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