Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors

• URL: http://arxiv.org/abs/2011.08317v3
• Date: Fri, 4 Dec 2020 17:58:14 GMT
• Title: Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors
• Authors: Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Yaser P.Fallah, Rui Guo, Hongsheng Lu
• Abstract summary: We present an in-depth analysis of the notion of Deep Feature Sharing (DFS) We explore different cooperative object detection designs and evaluate their performance in terms of average precision. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise.
• Score: 11.737037965090535
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded targets and expanding the sensing range. However, designing a reliable cooperative cognition or perception system requires addressing the challenges caused by limited network resources and discrepancies between the data shared by different sources. In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS). We explore different cooperative object detection designs and evaluate their performance in terms of average precision. We use the Volony dataset for our experimental study. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise. Furthermore, the results attest that detection gain of DFS methods caused by adding more cooperative participants in the scenes is comparable to raw information sharing technique while DFS enables flexibility in design toward satisfying communication requirements.

Related papers

• Enhanced Anomaly Detection in Industrial Control Systems aided by Machine Learning [2.2457306746668766]
  This study investigates whether combining both network and process data can improve attack detection in ICSs environments. Our findings suggest that integrating network traffic with operational process data can enhance detection capabilities. Although the results are promising, they are preliminary and highlight the need for further studies.
  arXiv  Detail & Related papers  (2024-10-25T17:41:33Z)
• Edge-device Collaborative Computing for Multi-view Classification [9.047284788663776]
  We explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden. We introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics.
  arXiv  Detail & Related papers  (2024-09-24T11:07:33Z)
• Collaborative Knowledge Infusion for Low-resource Stance Detection [83.88515573352795]
  Target-related knowledge is often needed to assist stance detection models. We propose a collaborative knowledge infusion approach for low-resource stance detection tasks.
  arXiv  Detail & Related papers  (2024-03-28T08:32:14Z)
• Attention Based Feature Fusion For Multi-Agent Collaborative Perception [4.120288148198388]
  We propose an intermediate collaborative perception solution in the form of a graph attention network (GAT) The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision.
  arXiv  Detail & Related papers  (2023-05-03T12:06:11Z)
• Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs [59.96266198512243]
  We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated. Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
  arXiv  Detail & Related papers  (2022-12-21T18:58:24Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Towards Interaction Detection Using Topological Analysis on Neural Networks [55.74562391439507]
  In neural networks, any interacting features must follow a strongly weighted connection to common hidden units. We propose a new measure for quantifying interaction strength, based upon the well-received theory of persistent homology. A Persistence Interaction detection(PID) algorithm is developed to efficiently detect interactions.
  arXiv  Detail & Related papers  (2020-10-25T02:15:24Z)
• Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection [2.064612766965483]
  Situational awareness as a necessity in the connected and autonomous vehicles (CAV) domain. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. We propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method.
  arXiv  Detail & Related papers  (2020-10-22T00:12:58Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks [11.737037965090535]
  We introduce the concept of feature sharing for cooperative object detection (FS-COD) In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.
  arXiv  Detail & Related papers  (2020-02-19T20:47:09Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.