Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition

Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. In particular, we form ensemble methods based on deep representations and handcrafted 3D shape descriptors. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios. For the evaluation purpose, in addition to real object datasets, we generate a large synthetic household objects dataset consisting of 27000 views of 90 objects. Experimental results demonstrate the effectiveness of the proposed method on online few-shot 3D object recognition tasks, as well as its superior performance over the state-of-the-art open-ended learning approaches. Furthermore, our results show that while ensemble learning is modestly beneficial in offline settings, it is significantly beneficial in lifelong few-shot learning situations. Additionally, we demonstrated the effectiveness of our approach in both simulated and real-robot settings, where the robot rapidly learned new categories from limited examples

Chlorine and Bromine Isotope Fractionation of Halogenated Organic Pollutants on Gas Chromatography Columns

Compound-specific chlorine/bromine isotope analysis (CSIA-Cl/Br) has become a useful approach for degradation pathway investigation and source appointment of halogenated organic pollutants (HOPs). CSIA-Cl/Br is usually conducted by gas chromatography-mass spectrometry (GC-MS), which could be negatively impacted by chlorine and bromine isotope fractionation of HOPs on GC columns. In this study, 31 organochlorines and 4 organobromines were systematically investigated in terms of Cl/Br isotope fractionation on GC columns using GC-double focus magnetic-sector high resolution MS (GC-DFS-HRMS). On-column chlorine/bromine isotope fractionation behaviors of the HOPs were explored, presenting various isotope fractionation modes and extents. Twenty-nine HOPs exhibited inverse isotope fractionation, and only polychlorinated biphenyl-138 (PCB-138) and PCB-153 presented normal isotope fractionation. And no observable isotope fractionation was found for the rest four HOPs, i.e., PCB-101, 1,2,3,7,8-pentachlorodibenzofuran, PCB-180 and 2,3,7,8-tetrachlorodibenzofuran. The isotope fractionation extents of different HOPs varied from below the observable threshold (0.50%) to 7.31% (PCB-18). The mechanisms of the on-column chlorine/bromine isotope fractionation were tentatively interpreted with the Craig-Gordon model and a modified two-film model. Inverse isotope effects and normal isotope effects might contribute to the total isotope effects together and thus determine the isotope fractionation directions and extents. Proposals derived from the main results of this study for CSIA-Cl/Br research were provided for improving the precision and accuracy of CSIA-Cl/Br results. The findings of this study will shed light on the development of CSIA-Cl/Br methods using GC-MS techniques, and help to implement the research using CSIA-Cl/Br to investigate the environmental behaviors and pollution sources of HOPs.Comment: 30 pages, 5 figure

Lifelong ensemble learning based on multiple representations for few-shot object recognition

Service robots are increasingly integrating into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. In particular, we form ensemble methods based on deep representations and handcrafted 3D shape descriptors. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios. For evaluation purposes, in addition to real object datasets, we generate a large synthetic household objects dataset consisting of 27000 views of 90 objects. Experimental results demonstrate the effectiveness of the proposed method on online few-shot 3D object recognition tasks, as well as its superior performance over the state-of-the-art open-ended learning approaches. Furthermore, our results show that while ensemble learning is modestly beneficial in offline settings, it is significantly beneficial in lifelong few-shot learning situations. Additionally, we demonstrated the effectiveness of our approach in both simulated and real-robot settings, where the robot rapidly learned new categories from limited examples. A video of our experiments is available online at: https://youtu.be/nxVrQCuYGdI.</p

Fine-grained Object Categorization for Service Robots

A robot working in a human-centered environment is frequently confronted with fine-grained objects that must be distinguished from one another. Fine-grained visual classification (FGVC) still remains a challenging problem due to large intra-category dissimilarity and small inter-category dissimilarity. Furthermore, flaws such as the influence of illumination and information inadequacy persist in fine-grained RGB datasets. We propose a novel deep mixed multi-modality approach based on Vision Transformer (ViT) and Convolutional Neural Network (CNN) to improve the performance of FGVC. Furthermore, we generate two synthetic fine-grained RGB-D datasets consisting of 13 car objects with 720 views and 120 shoes with 7200 sample views. Finally, to assess the performance of the proposed approach, we conducted several experiments using fine-grained RGB-D datasets. Experimental results show that our method outperformed other baselines in terms of recognition accuracy, and achieved 93.40 $\%$ and 91.67 $\%$ recognition accuracy on shoe and car dataset respectively. We made the fine-grained RGB-D datasets publicly available for the benefit of research communities

Research on the Cascade Vehicle Detection Method Based on CNN

This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness