In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

This paper proposes G-VIDO, a vehicle dynamics, and intermittent Global Navigation Satellite System (GNSS)-aided visual-inertial state estimator, to address the state estimation problem of autonomous vehicle localization (i.e., position and orientation estimation in the global coordinate system) under various GNSS states. A dynamics pre-integration theory is proposed on the basis of a two-degree-of-freedom (DOF) vehicle dynamics model, and dynamics constraints are built in the optimization back-end, considering the unobservable problem of the monocular visual-inertial system under degenerate motions. The proposed highly nonlinear system can be robustly initialized by loosely aligning the monocular structure from motion (SfM) results, pre-integrated IMU measurements, and vehicle motion information. GNSS is used for reference frame transformation and constraint construction in the sliding window. The cumulative error can be corrected with the aid of GNSS, and the vehicle’s position in the global coordinate system can be determined. A GNSS anomaly detection algorithm is proposed to improve the system robustness under intermittent GNSS. Experiments have shown that G-VIDO can provide real-time, robust, and seamless localization in multiple GNSS states, with an RMSE of less than 30 cm (with GNSS). Moreover, we proved that the initialization and local odometry modules in G-VIDO outperform several state-of-the-art VIO systems and our preliminary work VINS-Vehicle.

Link state measurement is the fundamental part of routing control and has received wide attention in research community. However, due to the massive number of network nodes, it is considerably expensive to measure the network timely. In this work, we propose LTSM, a lightweight and time sliced measurement approach for link state to reduce the measurement cost. In LTSM, the measurement tasks are assigned to all nodes evenly and launched at proper time. Theoretically, the measurement cost can be reduced by 50%. We design a prototype of the approach and implement it on a real-world network consisting of 37 public cloud nodes and 1, 332 links. Experimental results show that our approach can acquire the link state of the network efficiently.

Reducing network latency has always been a challenging problem due to the diversity of its potential causes. Though there have been many successful solutions, they are limited to certain applications or suffering from high resource consumption. In this paper, we propose a hybrid architecture which combines existing methods to meet the low latency requirements of future Internet services. We first make a comprehensive survey to the existing solutions and employ the classical queueing model to analyze the strengths and weaknesses of them. Then we present our hybrid solution, which makes a combination of P2P CDN, data redundancy, deterministic link and TCP optimization algorithms according to their characteristics. The related techniques we adopted work cooperatively in different scenarios to optimize network latency. Simulation results on two representative scenarios show that our architecture is powerful in offering low latency service, thus competent for future Internet applications.

Consistent object extraction plays an essential role for stereo image editing with the population of stereoscopic 3D media. Most previous methods perform segmentation on entire images for both views using dense stereo correspondence constraints. We find that for such kind of methods the computation is highly redundant since the two views are near-duplicate. Besides, the consistency may be violated due to the imperfectness of current stereo matching algorithms. In this paper, we propose a contour based method which searches for consistent object contours instead of regions. It integrates both stereo correspondence and object boundary constraints into an energy minimization framework. The proposed method has several advantages compared to previous works. First, the searching space is restricted in object boundaries thus the efficiency significantly improved. Second, the discriminative power of object contours results in a more consistent segmentation. Furthermore, the proposed method can effortlessly extend existing single-image segmentation methods to work in stereo scenarios. The experiment on the Adobe benchmark shows superior extraction accuracy and significant improvement of efficiency of our method to state-of-the-art. We also demonstrate in a few applications how our method can be used as a basic tool for stereo image editing.

Salient object detection attracts much attention for its effectiveness in numerous applications. However, how to effectively produce a high quality binary mask from a saliency map, named saliency cuts, is still an open problem. In this paper, we propose a novel saliency cuts approach using unsupervised seeds generation and GrabCut algorithm. With the input of a saliency map, we produce seeds for segmentation using adaptive triple thresholding, and feed the seeds to GrabCut algorithm. Finally, a high quality object mask is generated by iteratively optimization. The experimental results show that the proposed approach is competent to the task of saliency cuts and outperforms the state-of-the-art methods.

Most previous works on saliency detection are dedicated to 2D images. Recently it has been shown that 3D visual information supplies a powerful cue for saliency analysis. In this paper, we propose a novel saliency method that works on depth images based on anisotropic center-surround difference. Instead of depending on absolute depth, we measure the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration. Besides, two common priors based on depth and location are used for refinement. The proposed method works within a complexity of O(N) and the evaluation on a dataset of over 1000 stereo images shows that our method outperforms state-of-the-art.

Recent years, the stereo image has become an emerging media in the field of 3D technology, which leads to an urgent demand of stereo image retrieval. In this paper, we attempt to introduce a framework for object-based stereo image retrieval (OBSIR), which retrieves images containing the similar objects to the one captured in the query image by the user. The proposed approach consists of both online and offline procedures. In the offline procedure, we propose a salient object segmentation method making use of both color and depth to extract objects from each image. The extracted objects are then represented by multiple visual feature descriptors. In order to improve the image search efficiently, we construct an approximate nearest neighbor (ANN) index using cluster-based locality sensitive hashing (LSH). In the online stage, the user may supply the query object by selecting a region of interest (ROI) in the query image, or clicking one of the objects recommended by the salient object detector. For the image retrieval evaluation we build a new dataset containing over 10K stereo images. The experiments on this dataset show that the proposed method can effectively recommend the correct object and the final retrieval result is also better than other baseline methods.

In this paper, we propose an effective local stereo matching method based on extended triangular interpolation. The whole image is covered with a triangular mesh by performing triangulation on a set of initial support points. Since the disparity interpolation in some areas is ineffective, especially in the triangle which appears across the boundaries of objects or is formed by initial matching outliers, we formulate a new matching model based on the Bayesian rule to address this challenge. In the model, we introduce the concept of the triangle's reliability, and utilize the disparity planes determined by the neighboring triangles as the Bayesian prior. With the model, the disparity interpolation can be effectively performed. Experiments on the standard stereo data sets show that the method is effective, especially in dealing with the boundaries of objects.