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23rd International Symposium on Computers in Education, SIIE 2021

作者： Stavroulia, Kalliopi Evangelia Aedo, Ignacio Diaz, Paloma Lanitis, Andreas Cyprus University of Technology Department of Multimedia and Graphic Arts Limassol Cyprus Universidad Carlos III de Madrid Computer Science Department Madrid Spain Cyprus University of Technology and CYENS Centre of Excellence Visual Media Computing Lab Department of Multimedia and Graphic Arts Limassol Cyprus

ISBN: (纸本)9781665440240

The coronavirus pandemic has created an educational crisis without precedent, posed new challenges, and highlighted the need for digital skills empowerment among teachers along with the use of technology-based approaches to support the delivery of education and ensure the future of learning. As a return to normality is far from over, the implementation of novel training approaches for teacher training is more imperative than ever. In this context, this paper presents the VRTEACHER project that aims to promote the use of digital technologies and innovative and open pedagogies as part of teachers' training methodology. The vision of the project is to empower teacher's skills (such as empathy, perspective-taking, self-efficacy, adaptability, etc.) and readiness related to class management in crisis situations, through Virtual Reality (VR) immersive and experiential training approaches that reflect real-life scenarios and situations. The aim and challenge of the project are not just to adapt the training provided to the digital era and connect it with new technologies but to build a holistic new approach to teacher training through the use of VR. © 2021 IEEE.

关键词： Virtual reality

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3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation

3D-MetaConNet: Meta-learning for 3D Shape Classification and...

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International Conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Hao Huang Xiang Li Lingjing Wang Yi Fang NYU Multimedia and Visual Computing Lab USA New York University Abu Dhabi UAE

ISBN: (纸本)9781665426893

Supervised learning on 3D shapes are extensively studied by prior literature, among which PointNet [29] and its variants PointNet++ [31] are representatives. However, these methods tackle 3D shape learning problems by training from scratch using a fixed learning algorithm over large amounts of labeled data, potentially challenged by data and computation bottlenecks. In the paper, we design a novel model, under the framework of meta-learning, to learn 3D shape representation. By training over multiple 3D tasks, each of which is defined as a supervised learning problem, our method can fast adapt to unseen tasks containing limited labeled data. Specifically, our model consists of a 3Dmeta-learner and a task-oriented 3D-learner, where the 3D-meta-learner produces parameter initialization for the 3D-learner after being trained over different tasks. With adaptively initialized parameters, the 3D-learner can be tuned rapidly in a few steps to achieve good performance on novel tasks with a small amount of training data. To further facilitate discriminative shape feature learning, we introduce a novel task-aware feature adaptation module under a contrastive learning scheme, in which all shapes in each task are considered as a whole and task-oriented compact features are learned. Therefore, we dub our model as 3DMetaConNet. Experiments on three public 3D datasets for few-shot shape classification and segmentation demonstrate that our method can learn compact and discriminative 3D shape features efficiently and robustly in a fast adaptation manner. Our method particularly outperforms the methods without a meta-learning framework and is also superior to existing meta-learning approaches.

关键词： Training Representation learning Adaptation models Solid modeling Three-dimensional displays Shape Computational modeling

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Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

Understanding the Impact of Image Quality and Distance of Ob...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yu Hao Haoyang Pei Yixuan Lyu Zhongzheng Yuan John-Ross Rizzo Yao Wang Yi Fang NYU Multimedia and Visual Computing Lab NYU Tandon and NYU Abu Dhabi NYU Tandon School of Engineering New York University USA NYU Langone Health USA NYUAD Center for Artificial Intelligence and Robotics NYU Abu Dhabi UAE

Object detection is a fundamental task for autonomous driving, which aim to identify and localize objects within an image. Deep learning has made great strides for object detection, with popular models including Faster R-CNN, YOLO, and SSD. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Furthermore, original images are often compressed and uploaded to a remote server for object detection. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. As existing models are optimized for uncompressed (or lightly compressed) images over a narrow range of spatial resolution, we develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the ‘tolerated’ image size (constrained by the available bandwidth or storage). We further assess the impact of the distance of an o

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Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance

arXiv

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arXiv 2022年

作者： Hao, Yu Pei, Haoyang Lyu, Yixuan Yuan, Zhongzheng Rizzo, John-Ross Wang, Yao Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon School of Engineering New York University USA New York University Abu Dhabi UAE NYU Langone Health USA

Deep learning has made great strides for object detection in images, with popular models including Faster R-CNN, YOLO, and SSD. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Furthermore, original images are often compressed and uploaded to a remote server for object detection. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. As existing models are optimized for uncompressed (or lightly compressed) images over a narrow range of spatial resolution, we develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU [1] and Eurocity [2] datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the'tolerated' image size (constrained by the available bandwidth or storage). We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater de

关键词： Object detection

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Meta-Learning 3D Shape Segmentation Functions

arXiv

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arXiv 2021年

作者： Hao, Yu Huang, Hao Yuan, Shuaihang Fang, Yi NYU Multimedia and Visual Computing Lab New York University Abu Dhabi Abu Dhabi United Arab Emirates

Learning robust 3D shape segmentation functions with deep neural networks has emerged as a powerful paradigm, offering promising performance in producing a consistent part segmentation of each 3D shape. Generalizing across 3D shape segmentation functions requires robust learning of priors over the respective function space and enables consistent part segmentation of shapes in presence of significant 3D structure variations. Existing generalization methods rely on extensive training of 3D shape segmentation functions on large-scale labeled datasets. In this paper, we proposed to formalize the learning of a 3D shape segmentation function space as a meta-learning problem, aiming to predict a 3D segmentation model that can be quickly adapted to new shapes with no or limited training data. More specifically, we define each task as unsupervised learning of shape-conditioned 3D segmentation function which takes as input points in 3D space and predicts the part-segment labels. The 3D segmentation function is trained by a self-supervised 3D shape reconstruction loss without the need for part labels. Also, we introduce an auxiliary deep neural network as a meta-learner which takes as input a 3D shape and predicts the prior over the respective 3D segmentation function space. We show in experiments that our meta-learning approach, denoted as Meta-3DSeg, leads to improvements on unsupervised 3D shape segmentation over the conventional designs of deep neural networks for 3D shape segmentation functions. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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ROSS: Robust Learning of One-Shot 3D Shape Segmentation

ROSS: Robust Learning of One-Shot 3D Shape Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shuaihang Yuan Yi Fang NYU Multimedia and Visual Computing Lab USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

3D shape segmentation is a fundamental computer vision task that partitions the object into labeled semantic parts. Recent approaches to 3D shape segmentation learning heavily rely on high-quality labeled training datasets. This limits their use in applications to handle the large scale unannotated datasets. In this paper, we proposed a novel semi-supervised approach, named Robust Learning of OneShot 3D Shape Segmentation (ROSS), which only requires one single exemplar labeled shape for training. The proposed ROSS can generalize its ability from a one-shot training process to predict the segmentation for previously unseen 3D shape models. The proposed ROSS is composed of three major modules for 3D shape segmentation as follows. The global shape descriptor generator is the first module that utilizes the proposed reference weighted convolution to learn a 3D shape descriptor. The second module is a part-aware shape descriptor constructor that can generate weighted descriptors from a learned 3D shape descriptor according to semantic parts without supervision. The shape morphing with label transferring works as the last module. It morphs the exemplar shape and then transfers labels from the transformed exemplar shape to the target shape. The extensive experimental results on 3D mesh datasets demonstrate the ROSS is robust to noise and incomplete shapes and it can be applied to unannotated datasets. The experiment shows the proposed ROSS can achieve comparable performance with the supervised method.

关键词： Shape Three-dimensional displays Task analysis Convolution Learning systems Robustness Training

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Pyramid Learnable Tokens for 3D LiDAR Place Recognition

Pyramid Learnable Tokens for 3D LiDAR Place Recognition

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Congcong Wen Hao Huang Yu-Shen Liu Yi Fang NYUAD Center for Artificial Intelligence and Robotics New York University Abu Dhabi UAE NYU Multimedia and Visual Computing Lab New York University USA New York University Abu Dhabi UAE School of Software Tsinghua University Beijing P. R. China

3D LiDAR place recognition plays a vital role in various robot applications' including robotic navigation, autonomous driving, and simultaneous localization and mapping. However, most previous studies evaluated their models on accumulated 2D scans instead of real-world 3D LiDAR scans with a larger number of points, which limits the application in real scenarios. To address this limitation, we propose a point transformer network with pyramid learnable tokens (PTNet-PLT) to learn global descriptors for an actual scanned 3D LiDAR place recognition. Specifically, we first present a novel shifted cube attention module that consists of a self-attention module for local feature extraction and a cross-attention module for regional feature aggregation. The self-attention module constrains attention computation on a locally partitioned cube and builds connections across cubes based on the shifted cube scheme. In addition, the cross-attention module introduces several learnable tokens to separately aggregate features of points with similar features but spatially distant into an arbitrarily shaped region, which enables the model to capture long-term dependencies of the points. Next, we build a pyramid architecture network to learn multi-scale features and involve a decreasing number of tokens at each layer to aggregate features over a larger region. Finally, we obtain the global descriptor by concatenating learned region tokens of all layers. Experiments on three datasets, including USyd Campus, Oxford Robot-Car, and KITTI, demonstrate the effectiveness and generalization of the proposed model for large-scale 3D LiDAR place recognition.

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Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision

arXiv

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arXiv 2022年

作者： Hao, Yu Feng, Junchi Rizzo, John-Ross Wang, Yao Fang, Yi Nyu Multimedia and Visual Computing Lab Nyu Tandon School of Engineering New York University United States New York University Abu Dhabi United Arab Emirates Nyu Langone Health United States

People with blindness and low vision (pBLV) experience significant challenges when locating final destinations or targeting specific objects in unfamiliar environments. Furthermore, besides initially locating and orienting oneself to a target object, approaching the final target from one's present position is often frustrating and challenging, especially when one drifts away from the initial planned path to avoid obstacles. In this paper, we develop a novel wearable navigation solution to provide real-time guidance for a user to approach a target object of interest efficiently and effectively in unfamiliar environments. Our system contains two key visual computing functions: initial target object localization in 3D and continuous estimation of the user's trajectory, both based on the 2D video captured by a low-cost monocular camera mounted on in front of the chest of the user. These functions enable the system to suggest an initial navigation path, continuously update the path as the user moves, and offer timely recommendation about the correction of the user's path. Our experiments demonstrate that our system is able to operate with an error of less than 0.5 meter both outdoor and indoor. The system is entirely vision-based and does not need other sensors for navigation, and the computation can be run with the Jetson processor in the wearable system to facilitate real-time navigation assistance. © 2022, CC BY.

关键词： Object detection

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MDA-Net: Memorable Domain Adaptation Network for Monocular Depth Estimation 31

MDA-Net: Memorable Domain Adaptation Network for Monocular D...

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31st British Machine Vision Conference, BMVC 2020

作者： Zhu, Jing Shi, Yunxiao Ren, Mengwei Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Monocular depth estimation is a challenging task that aims to predict a corresponding depth map from a given single RGB image. Recent deep learning models have been proposed to predict the depth from the image by learning the alignment of deep features between the RGB image and the depth domains. In this paper, we present a novel approach, named Memorable Domain Adaptation Network (MDA-Net), to more effectively transfer domain features for monocular depth estimation by taking into account the common structure regularities (e.g., repetitive structure patterns, planar surfaces, symmetries) in domain adaptation. To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain. More specifically, in the SOM module, we develop a Memorable Bank of Filters (MBF) unit to learn a set of filters that memorize the structure-aware image-depth residual pattern, and also an Attention Guided Controller (AGC) unit to control the filter selection in the MBF given image features queries. Given the query image feature, the trained SOM module is able to adaptively select the best customized filters for cross-domain feature transferring with an optimal structural disparity between image and depth. In summary, we focus on addressing this structure-specific domain adaption challenge by proposing a novel end-to-end multi-scale memorable network for monocular depth estimation. The experiments show that our MDA-Net demonstrates the superior performance compared to the existing supervised monocular depth estimation approaches on the challenging KITTI and NYU Depth V2 benchmarks. © 2020. The copyright of this document resides with its authors.

关键词： Benchmarking

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Robust Image Matching By Dynamic Feature Selection 31

Robust Image Matching By Dynamic Feature Selection

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31st British Machine Vision Conference, BMVC 2020

作者： Huang, Hao Chen, Jianchun Li, Xiang Wang, Linging Fang, Yi NYU Multimedia and Visual Computing Lab United States New York University United States New York University Abu Dhabi United Arab Emirates

Estimating dense correspondences between images is a long-standing image understanding task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching. However, high-level feature maps are in low spatial resolution and therefore insufficient to provide accurate and fine-grained features to distinguish intra-class variations for correspondence matching. To address this problem, we generate robust features by dynamically selecting features at different scales. To resolve two critical issues in feature selection, i.e., how many and which scales of features to be selected, we frame the feature selection process as a sequential Markov decision-making process (MDP) and introduce an optimal selection strategy using reinforcement learning (RL). We define an RL environment for image matching in which each individual action either requires new features or terminates the selection episode by referring a matching score. Deep neural networks are incorporated into our method and trained for decision making. Experimental results show that our method achieves comparable/superior performance with state-of-the-art methods on three benchmarks, demonstrating the effectiveness of our feature selection strategy. © 2020. The copyright of this document resides with its authors.

关键词： Reinforcement learning

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