Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on the source knowledge. Without measuring the transferability of the source knowledge, these methods insufficiently exploit the source knowledge, and fail to identify the reliability of the inferred target semantics. However, existing transferability measurements require either source data or target labels, which are infeasible in SFUDA. To this end, firstly , we propose a novel Uncertainty-induced Transferability Representation (UTR), which leverages uncertainty as the tool to analyse the channel-wise transferability of the source encoder in the absence of the source data and target labels. The domain-level UTR unravels how transferable the encoder channels are to the target domain and the instance-level UTR characterizes the reliability of the inferred target semantics. Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i) the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii) the target semantics calibration module that calibrates the unreliable semantics. With the help of the calibrated source knowledge and the target semantics, the model adapts to the target domain safely and ultimately better. We verified the effectiveness of our method using experimental results and demonstrated that the proposed method achieves state-of-the-art performances on the three SFUDA benchmarks. Code is available at https://github.com/SPIresearch/UTR.

Indoor location-based service is a hot research topic whose application market size is expected to grow from $41 billion by 2022 and $58 billion by 2023. As a portable communication device, smartphones have inherent advantages (such as an essential gadget in daily life, embedding various sensors, having powerful computing and storage ability), which provides great opportunity for human activity recognition and pedestrian navigation. Smartphone-based pedestrian inertial navigation systems (PINS) utilizes measurements from inertial sensors embedded in smartphone to reckon the pedestrian’s location. Compared with other indoor position technologies, PINS have the advantage of autonomy and continuity, and can achieve high-precision positioning in a short period. Though various PINS methods based on smartphone have been proposed, up-to-date review papers that summarize relevant technologies, methods and solutions of PINS are relatively less so far in the literature. This paper aims to provide an elaborated, timely, and valuable survey for different infrastructure-free pedestrian positioning and navigation techniques, including pedestrian inertial navigation methods, performance evaluation and applications. Finally, current challenges and future research trends are discussed. The work propels a better understanding of existing pedestrian inertial positioning methods. It is helpful for researchers to further design and develop more accurate and robust pedestrian positioning and navigation systems based on inertial sensors embedded in smartphone.

There is a gap between recent development of 4K display technologies and the short storage of 4K contents. Super-Resolution (SR) serves as a bridge to harmonize the need and demand. Recently, Convolutional Neural Network (CNN) based networks have demonstrated great property in image SR. However, most existing methods require large model capacity and consume expensive computation for high performance. Besides, most methods keep the upscaling part relatively simple compared with the feature extraction part. For feature fusion, some methods directly concatenate the features of multi-levels, which is suboptimal due to ignoring the importance of different features. In this work, we propose a recursive multi-stage upscaling network (RMUN) with multiple sub-upscaling modules (SUMs) and a discriminative self-ensemble module (SEM). Specifically, we extract local hierarchical features by using a novel feature extraction module (FEM) which is recursive to reduce the number of parameters. Then, we construct multiple sub-upscaling modules to produce various high-resolution features in forward propagation. This strategy enhances the upscaling part and provides multiple error feedback routes. Furthermore, we employ an SEM for global hierarchical feature recalibration, which can selectively emphasize informative features and surpass less useful ones. Extensive quantitative and qualitative evaluations on benchmark datasets show that our proposed method performs comparable with the state-of-the-art methods in terms of the balance of model size and model performance.

Location-based service (LBS) is the core of Internet of Things (IoT), which serves tracking, navigation, and monitoring. The ubiquitous magnetic signals are temporally stable and spatially distinguishable and can achieve high-precision and ubiquitous positioning results without additional infrastructure, which is favored by researchers and has become a major research hotspot. Although there has been extensive research in the field of indoor magnetic positioning, there is still room for optimization in terms of positioning accuracy and robustness. Aiming at the problem that the magnetometer is offset and susceptible to environmental interference, we propose an online magnetometer calibration algorithm without user perception. Aiming at the inconsistency of magnetic data spatial scale problem caused by differences in device sampling frequency and user walking speed, we leverage different scales to segment the magnetic data, extract the magnetic sequence features of the corresponding scales through Transformer, utilize the attention mechanism to score the weights of the different scale features, and finally fuse the multiple scale features for positioning. We conduct extensive and well-designed experiments on public data sets and self-collected data sets. The experimental results indicate that the proposed method effectively solves the magnetic spatial scale problem and improves indoor magnetic positioning accuracy.