While Deep Neural Network (DNN) models have provided remarkable advances in machine vision capabilities, their high computational complexity and model sizes present a formidable roadblock to deployment in AIoT-based sensing applications. In this paper, we propose a novel paradigm by which peer nodes in a network can collaborate to improve their accuracy on person detection, an exemplar machine vision task. The proposed methodology requires no re-training of the DNNs and incurs minimal processing latency as it extracts scene summaries from the collaborators and injects back into DNNs of the reference cameras, on-the-fly. Early results show promise with improvements in recall as high as 10% with a single collaborator, on benchmark datasets.
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimization-based models suffer several limitations: strict assumption of shared sparse support, time-consuming optimization and "shallow" models with difficulties in encoding the rich patterns hiding in massive MRI data. In this paper, we propose the first deep learning model for multi-contrast MRI reconstruction. We achieve information sharing through feature sharing units, which significantly reduces the number of paramet…
Sharing virtual content among multiple smart glasses wearers is an essential feature of a seamless Collaborative Augmented Reality experience. To enable the sharing, local coordinate systems of the underlying 6D ego-pose trackers, running independently on each set of glasses, have to be spatially and temporally aligned with respect to each other. In this paper, we propose a novel lightweight solution for this problem, which is referred as ego-motion alignment. We show that detecting each other's face or glasses together with tracker ego-poses sufficiently conditions the problem to spatially re…
Spatio-temporal feature learning is of central importance for action recognition in videos. Existing deep neural network models either learn spatial and temporal features independently (C2D) or jointly with unconstrained parameters (C3D). In this paper, we propose a novel neural operation which encodes spatio-temporal features collaboratively by imposing a weight-sharing constraint on the learnable parameters. In particular, we perform 2D convolution along three orthogonal views of volumetric video data,which learns spatial appearance and temporal motion cues respectively. By sharing the convo…
Beijing Institute of Technology（北京理工大学）；Wuhan University（武汉大学）；Tsinghua University（清华大学）
Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) is a feasible solution for compression, its requirement on the original dataset raises privacy concerns. In addition, it is common to integrate multiple pretrained models to achieve satisfactory performance. How to compress multiple models into a tiny model is challenging, especially when the original data are unavailable. To tackle this challenge, we propose a framework termed collab…
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects…
Massachusetts Institute of Technology（麻省理工学院）；University of Leeds（利兹大学）；University of Liverpool（利物浦大学）
Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep ne…
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
As one kind of architecture from the deep learning family, deep semantic segmentation network (DSSN) achieves a certain degree of success on the semantic segmentation task and obviously outperforms the traditional methods based on hand-crafted features. As a classic data-driven technique, DSSN can be trained by an end-to-end mechanism and competent for employing the low-level and mid-level cues (i.e., the discriminative image structure) to understand images, but lacks the high-level inference ability. By contrast, human beings have an excellent inference capacity and can be able to reliably in…
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；Space Research Institute, Russian Academy of Sciences（俄罗斯科学院空间研究所）；College of Engineering, Purdue University（普渡大学工程学院）；Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University（天津大学地球系统科学学院地表-地球系统科学研究所）
Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；School of Geographical Sciences, University of Bristol（布里斯托大学地理科学学院）；Beijing Institute of Remote Sensing Equipment（北京遥感设备研究所）；Hong Kong Baptist University（香港浸会大学）
FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning
1 Center for Computational \& Data Sciences, Independent University, Bangladesh；2 Curtin University；Center for Computational \& Data Sciences Independent University, Bangladesh；School of Electrical Engineering, Computing \& Mathematical Sciences (EECMS) Curtin University
