Multi-scale Recurrent LSTM and Transformer Network for Depth Completion

Lidar depth completion is a new and hot topic of depth estimation. In this task, it is the key and difficult point to fuse the features of color space and depth space. In this paper, we migrate the classic LSTM and Transformer modules from NLP to depth completion and redesign them appropriately. Specifically, we use Forget gate, Update gate, Output gate, and Skip gate to achieve the efficient fusion of color and depth features and perform loop optimization at multiple scales. Finally, we further fuse the deep features through the Transformer multi-head attention mechanism. Experimental results show that without repetitive network structure and post-processing steps, our method can achieve state-of-the-art performance by adding our modules to a simple encoder-decoder network structure. Our method ranks first on the current mainstream autonomous driving KITTI benchmark dataset. It can also be regarded as a backbone network for other methods, which likewise achieves state-of-the-art performance

USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model

Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects. Existing methods typically estimate the object likelihood with an additional objectness branch, but ignore the conflict in learning objectness and classification boundaries, which oppose each other on the semantic manifold and training objective. To address this issue, we propose a simple yet effective learning strategy, namely Decoupled Objectness Learning (DOL), which divides the learning of these two boundaries into suitable decoder layers. Moreover, detecting unknown objects comprehensively requires a large amount of annotations, but labeling all unknown objects is both difficult and expensive. Therefore, we propose to take advantage of the recent Large Vision Model (LVM), specifically the Segment Anything Model (SAM), to enhance the detection of unknown objects. Nevertheless, the output results of SAM contain noise, including backgrounds and fragments, so we introduce an Auxiliary Supervision Framework (ASF) that uses a pseudo-labeling and a soft-weighting strategies to alleviate the negative impact of noise. Extensive experiments on popular benchmarks, including Pascal VOC and MS COCO, demonstrate the effectiveness of our approach. Our proposed Unknown Sensitive Detector (USD) outperforms the recent state-of-the-art methods in terms of Unknown Recall, achieving significant improvements of 14.3\%, 15.5\%, and 8.9\% on the M-OWODB, and 27.1\%, 29.1\%, and 25.1\% on the S-OWODB

Capacity consideration of wireless ad hoc networks

The focus of this dissertation is on the fundamental capacity bounds of wireless ad hoc networks. We establish upper and lower bounds on the capacity to shed light on what is theoretically possible and what is currently achievable. In the first part of the dissertation, we introduce and describe a method to find an upper bound on the capacity of wireless networks with arbitrary topology, size and traffic demands. The upper bound not only provides a yardstick against which the throughput of an existing wireless ad hoc network scheme can be gauged, it also provides insight into how to design better routing and medium access control protocols for wireless networks. Using the upper bound, we examine the behavior of networks of different size, under different channel conditions, and with different traffic patterns. Numeric results indicate that, when the channel conditions are known precisely, shadow and multipath fading increase capacity; and that the capacity increases with network size when full traffic patterns are considered but decreases when directional traffic patterns are considered. In the second part, we obtain the performance of an optimistic protocol based on CSMA/CA and compare it against the upper bound. There is a significant gap between the two results, especially when considering large networks operating in the high SNR region. In the third part, we describe a new time-division scheduling scheme derived from the upper bound. In addition, we make an improvement to the schedules that increases the capacity significantly, even for small to medium size networks. Our schedules perform better than the protocol based on CSMA/CA, for medium to high SNR regions. Moreover, they also perform well against the upper bound when there is a directional traffic pattern, but not as well when there is a full traffic pattern. Finally, we examine the effects of time-varying fading and mobility on the schedules. We conclude that our schedules perform well in an environment where the channel changes slowly relative to the schedule update rat

Capacity consideration of wireless ad hoc networks

The focus of this dissertation is on the fundamental capacity bounds of wireless ad hoc networks. We establish upper and lower bounds on the capacity to shed light on what is theoretically possible and what is currently achievable. In the first part of the dissertation, we introduce and describe a method to find an upper bound on the capacity of wireless networks with arbitrary topology, size and traffic demands. The upper bound not only provides a yardstick against which the throughput of an existing wireless ad hoc network scheme can be gauged, it also provides insight into how to design better routing and medium access control protocols for wireless networks. Using the upper bound, we examine the behavior of networks of different size, under different channel conditions, and with different traffic patterns. Numeric results indicate that, when the channel conditions are known precisely, shadow and multipath fading increase capacity; and that the capacity increases with network size when full traffic patterns are considered but decreases when directional traffic patterns are considered. In the second part, we obtain the performance of an optimistic protocol based on CSMA/CA and compare it against the upper bound. There is a significant gap between the two results, especially when considering large networks operating in the high SNR region. In the third part, we describe a new time-division scheduling scheme derived from the upper bound. In addition, we make an improvement to the schedules that increases the capacity significantly, even for small to medium size networks. Our schedules perform better than the protocol based on CSMA/CA, for medium to high SNR regions. Moreover, they also perform well against the upper bound when there is a directional traffic pattern, but not as well when there is a full traffic pattern. Finally, we examine the effects of time-varying fading and mobility on the schedules. We conclude that our schedules perform well in an environment where the channel changes slowly relative to the schedule update rat

Collective Entity Linking Method in Chinese Text Based on Topic Consistency

Entity Linking refers to the task of linking entity mentions in the given text with their referent entities in a knowledge base, which is a key technology of knowledge base expansion. However, the performance of traditional Chinese entity linking methods are affected by the incomplete Chinese knowledge base. Also they rarely use the semantic relevance between entities. Therefore, we propose a Chinese collective entity linking method based on the consistency of the topic, which considers both the content similarity and topic relevance of the co-occurrence entities, and propose a method for calculating the topic consistency of entities. This method implements batch links for multiple ambiguous entities that appear in the same text, and reduces the reliance on the local knowledge base by using the combination of the local knowledge base and the external knowledge base. Experimental results show that our method performs well over the traditional methods. And it is potentially effective

Collective Entity Linking Method in Chinese Text Based on Topic Consistency

Entity Linking refers to the task of linking entity mentions in the given text with their referent entities in a knowledge base, which is a key technology of knowledge base expansion. However, the performance of traditional Chinese entity linking methods are affected by the incomplete Chinese knowledge base. Also they rarely use the semantic relevance between entities. Therefore, we propose a Chinese collective entity linking method based on the consistency of the topic, which considers both the content similarity and topic relevance of the co-occurrence entities, and propose a method for calculating the topic consistency of entities. This method implements batch links for multiple ambiguous entities that appear in the same text, and reduces the reliance on the local knowledge base by using the combination of the local knowledge base and the external knowledge base. Experimental results show that our method performs well over the traditional methods. And it is potentially effective