Catastrophic forgetting emerges when a neural network’s parameters undergo continuous updates during the sequential training of multiple tasks. The ongoing adaptation, while enhancing the model’s suitability for new tasks, inadvertently leads to a degradation in performance on previously learned tasks. This challenge significantly impedes the sequential learning capabilities essential for the advancement of artificial general intelligence. The phenomenon of catastrophic forgetting also occurs in the field of quantum machine learning, where parametric quantum circuits serve a role analogous to neural networks. In this study, our focus is to explore strategies for mitigating catastrophic forgetting within quantum learning models for quantum data. In this context, we employ a task-based hard attention mechanism, which automatically generates masks for each task to regulate the network’s learning process and resist catastrophic forgetting. To our knowledge, the proposed method is the first one in the field of quantum machine learning to adjust the model structure to prevent catastrophic forgetting. Numerical simulation results demonstrate that our approach preserves the model’s performance on previous tasks without compromising its ability to acquire new knowledge. This effective resistance to catastrophic forgetting marks a significant stride in advancing quantum continual learning.

Mobile navigation is a critical component in mobile maps. Yawing detection (does a vehicle yaw) is an important task in mobile navigation. In regions containing parallel and close elevated and surface roads, it is hard to detect yawing events using traditional methods, which mainly rely on low-accuracy positions and moving directions. Recognizing whether a vehicle is moving on an elevated road can significantly improve the performance of yawing detection.

Maximally Stable Extremal Regions (MSER) algorithms are based on the component tree and are used to detect invariant regions. OpenCV MSER, the most popular MSER implementation, uses a linked list to associate pixels with ERs. The data-structure of an ER contains the attributes of a head and a tail linked node, which makes OpenCV MSER hard to be performed in parallel using existing parallel component tree strategies. Besides, pixel extraction (i.e. extracting the pixels in MSERs) in OpenCV MSER is very slow. In this paper, we propose two novel MSER algorithms, called Fast MSER V1 and V2. They first divide an image into several spatial partitions, then construct sub-trees and doubly linked lists (for V1) or a labelled image (for V2) on the partitions in parallel. A novel sub-tree merging algorithm is used in V1 to merge the sub-trees into the final tree, and the doubly linked lists are also merged in the process. While V2 merges the sub-trees using an existing merging algorithm. Finally, MSERs are recognized, the pixels in them are extracted through two novel pixel extraction methods taking advantage of the fact that a lot of pixels in parent and child MSERs are duplicated. Both V1 and V2 outperform three open source MSER algorithms (28 and 26 times faster than OpenCV MSER), and reduce the memory of the pixels in MSERs by 78%.

The generation of text proposals (i.e. local candidate regions most likely containing textual components) is one critical and prerequisite step in scene text detection task. As one popular text proposal algorithm, the Maximally Stable Extremal Region (MSER), has been exploited by many successful text detection methods, while on the other hand has difficulties in handling complicated scene text involving touching characters and characters composed of multiple unconnected parts (e.g. Chinese characters and text in dot matrix fonts). In this paper, we propose a novel text proposal method for localizing text in natural images, which integrates the MSER algorithm with the multi-scale sliding window framework and efficiently extracts Windowed Maximally Stable Extremal Regions (WMSERs) as text proposals. We further present effective proposal filtering and grouping algorithms for exploiting WMSER-based proposals in text detection task. Experiments on public scene text datasets demonstrate the promising aspects of the proposed method in dealing with complicated scene text.

Failures are not uncommon in production data center networks (DCNs) nowadays. It takes long time for the DCN routing to recover from a failure and find new forwarding paths, significantly impacting realtime and interactive applications at the upper layer. In this paper, we present a fault-tolerant DCN solution, called F2Tree, which is readily deployed in existing DNCs. F2Tree can significantly improve the failure recovery time only through a small amount of link rewiring and switch configuration changes. Through testbed and emulation experiments, we show that F2Tree can greatly reduce the routing recovery time after failure (by 78%) and improve the performance of upper layer applications when routing failure happens (96% less deadline-missing requests).

To detect salient image regions containing textual patterns in the video is valuable to many content-based video applications such as video retrieval, abstraction, classification and analysis. In this paper, we present an effective textual saliency detection method for natural scene videos. We first compute text-alike confidence values of local image regions, which capture the basic visual cues of textual components in the video frames, using an efficient cascaded prediction model. Next, we construct patch features depicting the statistical and spatial distribution of confidence values and combine them with general visual features like colors. We then employ a saliency detection model based on random walk with restart on the graph of local video regions, which effectively integrates both the spatial and the temporal saliency maps. The experiment result demonstrates the effectiveness of the proposed method.

IEEE 802.11-based wireless LAN, commonly referred to as Wi-Fi, has become a universal solution for the last-hop network access. In large and public assembly places, people may use their mobile devices to view the video of the same popular events via the same wireless access points (APs). However, current 802.11 APs transmit the same video stream multiple times via separate unicast sessions due to the well-known poor reliability and low data rate of the legacy Wi-Fi multicast. Besides, in traditional single-layer-coded video streams, all clients have to settle with the lowest video bitrate limited by the client with the worst channel quality. To address these problems, we propose M3, a practical and reliable multi-layer video multicast solution over multi-rate Wi-Fi networks. The aims of our system are, in the premise of no change to APs, not only to ensure that all clients can smoothly watch the video at least with the lowest quality, but also to maximize the overall video quality received by all clients. To meet these design goals, the video server selects certain clients as unicast receivers to transmit different SVC video layers, and other clients listen for the packets in the promiscuous mode. It is challenging to select specific unicast receivers and allocate different SVC layers to fully utilize the available bandwidth because of dynamic network conditions. To overcome this challenge, we use a periodical feedback mechanism to collect necessary statistics from clients, and use them to derive an optimal SVC-layer allocation strategy to maximize the video quality. We implemented a prototype in a real Wi-Fi testbed consisting of one AP and one M3server and 8 clients. Compared with the single-layer video multicast, our M3system can improve the total received video rate by up to 200%

Text detection in natural scene images is challenging due to the significant variations of the appearance of the text itself and its interaction with the context. The popular stroke width transform (SWT) algorithm is highly efficient but sensitive to the defects of the edges extracted from the input image when searching for the matching edge pixels for computing potential stroke width. In this paper, we propose a novel seed-based variant of SWT that enhances significantly the robustness of the original algorithm to complicated image contextual interference and varied text appearance. We first search for the seed segment of strokes, which is defined as a consecutive sequence of neighbouring rays (pairs of edge pixels) with regular length and satisfying certain constraints, and grow from them to localize more stroke segments. We then exploit the principal width and direction information of stroke captured by the stroke segments detected to rectify inaccurate stroke width and recover missed stroke parts, which are resulted from erroneous and noisy edges in complex natural images. The stroke segments detected are also exploited to improve the accuracy of candidate character localization. The experimental results on public datasets demonstrated the effectiveness of the proposed method.

Detecting the text in natural scene images is often challenging due to the complexity and variety of text's appearance and its interaction with the scene context. In this paper, we present a novel hierarchical text detection method exploiting textual characteristics at both character and text line scales for improved accuracy. First, seed candidate characters are detected with discriminative deep convolutional features learned within the maximally stable extremal regions extracted from the image, and are further grown to localize other degraded candidate characters. Next, as a finer filtering of text in the richer text line context, the random forest classifier is exploited on statistical features of text line characterizing the geometrical and conformability properties of constituent character components, to predict the text and non-text label. The effectiveness of the proposed method is demonstrated by the state-of-the-art results achieved on the public datasets.

Text detection in natural scene images is an open and challenging problem due to the significant variations of the appearance of the text itself and its interaction with the context. In this paper, we present a novel text detection method based on robust localization and adaptive growing of seed text components. The method consists of two main ingredients. First, convolutional neural network is exploited to localize seed candidate characters from the maximally stable extremal regions of the image with learned discriminative deep convolutional features. Next, an iterative and adaptive growing algorithm is employed to grow from seed characters to search for other degraded text components in same text line based on their conformity to the seed, and an associative quality is learned to measure the conformity combining both the geometric and appearance constraints between two neighbouring text components. The effectiveness of the proposed method is demonstrated by the state-of-the-art results achieved on the public datasets.