ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2406.02652
  4. Cited By
RepCNN: Micro-sized, Mighty Models for Wakeword Detection

RepCNN: Micro-sized, Mighty Models for Wakeword Detection

4 June 2024
Arnav Kundu
Prateeth Nayak
Priyanka Padmanabhan
Devang Naik
ArXivPDFHTML

Papers citing "RepCNN: Micro-sized, Mighty Models for Wakeword Detection"

4 / 4 papers shown
Title
HEiMDaL: Highly Efficient Method for Detection and Localization of
  wake-words
HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words
Arnav Kundu
Mohammad Samragh Razlighi
Minsik Cho
Priyanka Padmanabhan
Devang Naik
21
2
0
26 Oct 2022
RepVGG: Making VGG-style ConvNets Great Again
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding
X. Zhang
Ningning Ma
Jungong Han
Guiguang Ding
Jian-jun Sun
117
1,531
0
11 Jan 2021
Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution
Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution
Ximin Li
Xiaodong Wei
Xiaowei Qin
31
36
0
20 Oct 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,471
0
17 Apr 2017
1