arXiv subjects

Jiaxin Guo

Publications and source records attributed to Jiaxin Guo.

61 records · Page 4Linked to original sources

arXiv2021

The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation

This paper describes our work in participation of the IWSLT-2021 offline speech translation task. Our system was built in a cascade form, including a speaker diarization module, an Automatic Speech Recognition (ASR) module and a Machine Translation (MT) module. We directly use the LIUM SpkDiarization tool as the diarization module. The ASR module is trained with three ASR datasets from different sources, by multi-source training, using a modified Transformer encoder. The MT module is pretrained on the large-scale WMT news translation dataset and fine-tuned on the TED corpus. Our method achieves 24.6 BLEU score on the 2021 test set.

cs.CL↗

arXiv2021

Priority prediction of Asian Hornet sighting report using machine learning methods

As infamous invaders to the North American ecosystem, the Asian giant hornet (Vespa mandarinia) is devastating not only to native bee colonies, but also to local apiculture. One of the most effective way to combat the harmful species is to locate and destroy their nests. By mobilizing the public to actively report possible sightings of the Asian giant hornet, the governmentcould timely send inspectors to confirm and possibly destroy the nests. However, such confirmation requires lab expertise, where manually checking the reports one by one is extremely consuming of human resources. Further given the limited knowledge of the public about the Asian giant hornet and the randomness of report submission, only few of the numerous reports proved positive, i.e. existing nests. How to classify or prioritize the reports efficiently and automatically, so as to determine the dispatch of personnel, is of great significance to the control of the Asian giant hornet. In this paper, we propose a method to predict the priority of sighting reports based on machine learning. We model the problem of optimal prioritization of sighting reports as a problem of classification and prediction. We extracted a variety of rich features in the report: location, time, image(s), and textual description. Based on these characteristics, we propose a classification model based on logistic regression to predict the credibility of a certain report. Furthermore, our model quantifies the impact between reports to get the priority ranking of the reports. Extensive experiments on the public dataset from the WSDA (the Washington State Department of Agriculture) have proved the effectiveness of our method.

cs.CL↗

arXiv2021

PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation

Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the image style from another environment to the one on which models are trained. In this paper, we propose a weakly-paired setting for the style translation, where the content in the two images is aligned with errors in poses. These images could be acquired by different sensors in different conditions that share an overlapping region, e.g. with LiDAR or stereo cameras, from sunny days or foggy nights. We consider this setting to be more practical with: (i) easier labeling than the paired data; (ii) better interpretability and detail retrieval than the unpaired data. To translate across such images, we propose PREGAN to train a style translator by intentionally transforming the two images with a random pose, and to estimate the given random pose by differentiable non-trainable pose estimator given that the more aligned in style, the better the estimated result is. Such adversarial training enforces the network to learn the style translation, avoiding being entangled with other variations. Finally, PREGAN is validated on both simulated and real-world collected data to show the effectiveness. Results on down-stream tasks, classification, road segmentation, object detection, and feature matching show its potential for real applications. https://github.com/wrld/PRoGAN

cs.CV↗

arXiv2020

3D Point-to-Keypoint Voting Network for 6D Pose Estimation

Object 6D pose estimation is an important research topic in the field of computer vision due to its wide application requirements and the challenges brought by complexity and changes in the real-world. We think fully exploring the characteristics of spatial relationship between points will help to improve the pose estimation performance, especially in the scenes of background clutter and partial occlusion. But this information was usually ignored in previous work using RGB image or RGB-D data. In this paper, we propose a framework for 6D pose estimation from RGB-D data based on spatial structure characteristics of 3D keypoints. We adopt point-wise dense feature embedding to vote for 3D keypoints, which makes full use of the structure information of the rigid body. After the direction vectors pointing to the keypoints are predicted by CNN, we use RANSAC voting to calculate the coordinate of the 3D keypoints, then the pose transformation can be easily obtained by the least square method. In addition, a spatial dimension sampling strategy for points is employed, which makes the method achieve excellent performance on small training sets. The proposed method is verified on two benchmark datasets, LINEMOD and OCCLUSION LINEMOD. The experimental results show that our method outperforms the state-of-the-art approaches, achieves ADD(-S) accuracy of 98.7\% on LINEMOD dataset and 52.6\% on OCCLUSION LINEMOD dataset in real-time.

cs.CV↗

arXiv2020

Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map

With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems that have large scale variance of viewpoints of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software "Metashape", map matching with a proposed template matching algorithm, weighted normalized cross-correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with the feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset.

cs.RO↗

arXiv2020

Fast and Robust Bin-picking System for Densely Piled Industrial Objects

Objects grasping, also known as the bin-picking, is one of the most common tasks faced by industrial robots. While much work has been done in related topics, grasping randomly piled objects still remains a challenge because much of the existing work either lack robustness or costs too much resource. In this paper, we develop a fast and robust bin-picking system for grasping densely piled objects adaptively and safely. The proposed system starts with point cloud segmentation using improved density-based spatial clustering of application with noise (DBSCAN) algorithm, which is improved by combining the region growing algorithm and using Octree to speed up the calculation. The system then uses principle component analysis (PCA) for coarse registration and iterative closest point (ICP) for fine registration. We propose a grasp risk score (GRS) to evaluate each object by the collision probability, the stability of the object, and the whole pile's stability. Through real tests with the Anno robot, our method is verified to be advanced in speed and robustness.

cs.RO↗

arXiv2018

SEA: A Combined Model for Heat Demand Prediction

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

cs.LG↗