Importance Weighted Adversarial Nets for Partial Domain Adaptation

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains

Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos

Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks

It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e.g., category-level 6D object pose and size estimation. However, the easy annotations in synthetic domains bring the downside effect of synthetic-to-real (Sim2Real) domain gap. In this work, we aim to address this issue in the task setting of Sim2Real, unsupervised domain adaptation for category-level 6D object pose and size estimation. We propose a method that is built upon a novel Deep Prior Deformation Network, shortened as DPDN. DPDN learns to deform features of categorical shape priors to match those of object observations, and is thus able to establish deep correspondence in the feature space for direct regression of object poses and sizes. To reduce the Sim2Real domain gap, we formulate a novel self-supervised objective upon DPDN via consistency learning; more specifically, we apply two rigid transformations to each object observation in parallel, and feed them into DPDN respectively to yield dual sets of predictions; on top of the parallel learning, an inter-consistency term is employed to keep cross consistency between dual predictions for improving the sensitivity of DPDN to pose changes, while individual intra-consistency ones are used to enforce self-adaptation within each learning itself. We train DPDN on both training sets of the synthetic CAMERA25 and real-world REAL275 datasets; our results outperform the existing methods on REAL275 test set under both the unsupervised and supervised settings. Ablation studies also verify the efficacy of our designs. Our code is released publicly at https://github.com/JiehongLin/Self-DPDN.Comment: Accepted by ECCV202

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

Importance Weighted Adversarial Nets for Partia Domain Adaptation

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains

Effects of Biochar Amendment on CO₂ Emissions from Paddy Fields under Water-Saving Irrigation

The role of carbon pool of biochar as a method of long-term C sequestration in global warming mitigation is unclear. A two-year field study was conducted to investigate the seasonal variations of CO2 emissions from water-saving irrigation paddy fields in response to biochar amendment and irrigation patterns. Three biochar treatments under water-saving irrigation and one biochar treatment under flooding irrigation were studied, and the application rates were 0, 20, 40, and 40 t ha−1 and labeled as CI + NB (controlled irrigation and none biochar added), CI + MB (controlled irrigation and medium biochar added), CI + HB (controlled irrigation and high biochar added), and FI + HB (flood irrigation and high biochar added), respectively. Results showed that biochar application at medium rates (20 t ha−1) decreased CO2 emissions by 1.64⁻8.83% in rice paddy fields under water-saving irrigation, compared with the non-amendment treatment. However, the CO2 emissions from paddy fields increased by 4.39⁻5.43% in the CI + HB treatment, compared with CI + NB. Furthermore, the mean CO2 emissions from paddy fields under water-saving irrigation decreased by 2.22% compared with flood irrigation under the same amount of biochar application (40 t ha−1). Biochar amendment increased rice yield and water use efficiency by 9.35⁻36.30% and 15.1⁻42.5%, respectively, when combined with water-saving irrigation. The CO2 emissions were reduced in the CI + MB treatment, which then increased rice yield. The CO2 emissions from paddy fields were positively correlated with temperature. The highest value of the temperature sensitivity coefficient (Q10) was derived for the CI + MB treatment. The Q10 was higher under water-saving irrigation compared with flooding irrigation

An attention-based CNN for automatic whole-body postural assessment

Fully automatic postural assessment is highly useful, but has been challenging. Conventional methods either require manual assessment by ergonomists or depend on special devices that are intrusive, thus being hardly feasible in daily activities and workplaces. In this work, an attention-based convolutional neural network (CNN) is developed for automatic whole-body postural assessment. The proposed network learns to identify highly relevant regions (or body parts) and extract features automatically. Risk of the posture is estimated from the extracted features accordingly. To evaluate the proposed method, a postural dataset, referred to as pH36M, is created by re-targeting Human3.6M, one of the largest publicly available datasets for pose estimation using the Rapid Entire Body Assessment (REBA) criteria. Experimental results on pH36M demonstrate that proposed method achieves promising performance in comparison to baselines and the average assessment scores are substantially aligned with human assessment with a Kappa value of 0.73

Effects of Biochar Addition on Rice Growth and Yield under Water-Saving Irrigation

To reveal the effect of biochar addition on rice growth and yield under water-saving irrigation, a 2-year field experiment was carried out to clarify the variations of rice tiller number, plant height, yield components, and irrigation water use efficiency with different biochar application amounts (0, 20, 40 t/ha) and irrigation management (flooding irrigation and water-saving irrigation). The results showed that the rice yield with biochar addition (20 and 40 t/ha) was 15.53% and 24.43% higher than that of non-biochar addition paddy fields under water-saving irrigation. The addition of biochar promoted the growth of tillers and plant height, improved the filled grain number, productive panicle number, and seed setting rate, thus affecting rice yield. Rice yield was raised with the increase in the biochar application amount. Under the condition of water-saving irrigation, water deficit had a certain negative effect on the rice growth indexes, resulting in a slight decrease in yield. However, irrigation water input was significantly decreased with water-saving irrigation compare to flooding irrigation. Under the comprehensive effect of water-saving irrigation and biochar application, the irrigation water use efficiency of a rice paddy field with high biochar application (40 t/ha) under water-saving irrigation was the highest, with an average increase of 91.05% compared to a paddy field with flooding irrigation. Therefore, the application of biochar in paddy fields with water-saving irrigation can substantially save irrigation water input, stably increase rice yield, and ultimately improve irrigation water productive efficiency

Effects of Biochar Addition on Rice Growth and Yield under Water-Saving Irrigation

To reveal the effect of biochar addition on rice growth and yield under water-saving irrigation, a 2-year field experiment was carried out to clarify the variations of rice tiller number, plant height, yield components, and irrigation water use efficiency with different biochar application amounts (0, 20, 40 t/ha) and irrigation management (flooding irrigation and water-saving irrigation). The results showed that the rice yield with biochar addition (20 and 40 t/ha) was 15.53% and 24.43% higher than that of non-biochar addition paddy fields under water-saving irrigation. The addition of biochar promoted the growth of tillers and plant height, improved the filled grain number, productive panicle number, and seed setting rate, thus affecting rice yield. Rice yield was raised with the increase in the biochar application amount. Under the condition of water-saving irrigation, water deficit had a certain negative effect on the rice growth indexes, resulting in a slight decrease in yield. However, irrigation water input was significantly decreased with water-saving irrigation compare to flooding irrigation. Under the comprehensive effect of water-saving irrigation and biochar application, the irrigation water use efficiency of a rice paddy field with high biochar application (40 t/ha) under water-saving irrigation was the highest, with an average increase of 91.05% compared to a paddy field with flooding irrigation. Therefore, the application of biochar in paddy fields with water-saving irrigation can substantially save irrigation water input, stably increase rice yield, and ultimately improve irrigation water productive efficiency