EPOC: Efficient Perception via Optimal Communication
Authors: Masoumeh Heidari Kapourchali, Bonny Banerjee4107-4114
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Human activity recognition from multimodal, multisource and heterogeneous sensor data is used as a testbed to evaluate the proposed model where each sensor is assumed to be monitored by an agent. The recognition accuracy on benchmark datasets is comparable to the state-of-the-art even though our model uses significantly fewer parameters and infers the state in a localized manner. |
| Researcher Affiliation | Academia | Masoumeh Heidari Kapourchali, Bonny Banerjee Institute for Intelligent Systems, and Department of Electrical and Computer Engineering, University of Memphis Memphis, TN 38152, USA {mhdrkprc, bbnerjee}@memphis.edu |
| Pseudocode | No | The paper describes methods through equations and text, and includes a block diagram, but does not contain a formal 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any statements about the release of source code or links to a code repository. |
| Open Datasets | Yes | To test the model for larger number of agents, we use Kinect skeleton data from UTDMHAD (Chen, Jafari, and Kehtarnavaz 2015) and KARD (Gaglio, Re, and Morana 2015) datasets where each joint in the skeleton is monitored by an agent. |
| Dataset Splits | Yes | In order to compare with baselines, the new person setup, as in Gaglio, Re, and Morana (2015), is used where data of one subject is reserved for testing while the model is trained on data of other subjects. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions conceptual frameworks and models but does not provide specific software dependencies with version numbers used for its implementation or experiments. |
| Experiment Setup | Yes | Communication stops if the change in VFE is less than ϵ (= 10^-3). First, a dictionary of 50 features is learned from the training set. |