SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation

The remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training. In contrast, feature-based learning methods, such as the Random Forest, require little training data, but never reach the segmentation accuracy of CNNs. This work bridges the two approaches in a transfer learning setting. We show that a CNN can be trained to correct the errors of the Random Forest in the source domain and then be applied to correct such errors in the target domain without retraining, as the domain shift between the Random Forest predictions is much smaller than between the raw data. By leveraging a few brushstrokes as annotations in the target domain, the method can deliver segmentations that are sufficiently accurate to act as pseudo-labels for target-domain CNN training. We demonstrate the performance of the method on several datasets with the challenging tasks of mitochondria, membrane and nuclear segmentation. It yields excellent performance compared to microscopy domain adaptation baselines, especially when a significant domain shift is involved.

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Domain Adaptation for Semantic Segmentation of Historical Panchromatic Orthomosaics in Central Africa

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080523 ◽

2021 ◽

Vol 10 (8) ◽

pp. 523

Author(s):

Nicholus Mboga ◽

Stefano D’Aronco ◽

Tais Grippa ◽

Charlotte Pelletier ◽

Stefanos Georganos ◽

...

Keyword(s):

Land Cover ◽

Domain Adaptation ◽

Central Africa ◽

Semantic Segmentation ◽

Target Domain ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

The Cost ◽

Performance Gains

Multitemporal environmental and urban studies are essential to guide policy making to ultimately improve human wellbeing in the Global South. Land-cover products derived from historical aerial orthomosaics acquired decades ago can provide important evidence to inform long-term studies. To reduce the manual labelling effort by human experts and to scale to large, meaningful regions, we investigate in this study how domain adaptation techniques and deep learning can help to efficiently map land cover in Central Africa. We propose and evaluate a methodology that is based on unsupervised adaptation to reduce the cost of generating reference data for several cities and across different dates. We present the first application of domain adaptation based on fully convolutional networks for semantic segmentation of a dataset of historical panchromatic orthomosaics for land-cover generation for two focus cities Goma-Gisenyi and Bukavu. Our experimental evaluation shows that the domain adaptation methods can reach an overall accuracy between 60% and 70% for different regions. If we add a small amount of labelled data from the target domain, too, further performance gains can be achieved.

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Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽

10.3390/e23070898 ◽

2021 ◽

Vol 23 (7) ◽

pp. 898

Author(s):

Marta Saiz-Vivó ◽

Adrián Colomer ◽

Carles Fonfría ◽

Luis Martí-Bonmatí ◽

Valery Naranjo

Keyword(s):

High Performance ◽

Computational Models ◽

Domain Adaptation ◽

Semantic Segmentation ◽

Patient Specific ◽

Mr Images ◽

Training Samples ◽

Volumetric Images ◽

Acquisition Costs ◽

Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

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Elimination of Unnecessary Feature Points by Semantic Segmentation Aimed at Improving the Accuracy of Feature-Based SLAM

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2021.1p2-c06 ◽

2021 ◽

Vol 2021 (0) ◽

pp. 1P2-C06

Author(s):

Tomoki HAKOTANI ◽

Ryota SAKUMA ◽

Masanao KOEDA ◽

Akihiro HAMADA ◽

Atsuro SAWADA ◽

...

Keyword(s):

Semantic Segmentation ◽

Feature Points ◽

Feature Based

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Combining Semantic Self-Supervision and Self-Training for Domain Adaptation in Semantic Segmentation

10.1109/ivworkshops54471.2021.9669255 ◽

2021 ◽

Author(s):

Joshua Niemeijer ◽

Jorg P. Schafer

Keyword(s):

Domain Adaptation ◽

Semantic Segmentation

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Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

Remote Sensing ◽

10.3390/rs10060964 ◽

2018 ◽

Vol 10 (6) ◽

pp. 964 ◽

Cited By ~ 34

Author(s):

Zhenfeng Shao ◽

Ke Yang ◽

Weixun Zhou

Keyword(s):

Remote Sensing ◽

Performance Evaluation ◽

Deep Learning ◽

Image Retrieval ◽

Semantic Segmentation ◽

Semantic Content ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Benchmark Datasets ◽

Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

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USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-483-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 483-492

Author(s):

D. Gritzner ◽

J. Ostermann

Keyword(s):

Time Window ◽

Domain Adaptation ◽

Geographical Area ◽

Model Performance ◽

Ground Truth ◽

Semantic Segmentation ◽

Training Data ◽

Aerial Images ◽

Target Domain ◽

Training Examples

Abstract. Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases.

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