title: No Time to Waste: Squeeze Time into Channel for Mobile Video
    Understanding
summary: Current architectures for video understanding mainly build upon 3D
  convolutional blocks or 2D convolutions with additional operations for temporal
  modeling. However, these methods all regard the temporal axis as a separate
  dimension of the video sequence, which requires large computation and memory
  budgets and thus limits their usage on mobile devices. In this paper, we
  propose to squeeze the time axis of a video sequence into the channel dimension
  and present a lightweight video recognition network, term as
  SqueezeTime, for mobile video understanding. To enhance the temporal
  modeling capability of the proposed network, we design a Channel-Time Learning
  (CTL) Block to capture temporal dynamics of the sequence. This module has two
  complementary branches, in which one branch is for temporal importance learning
  and another branch with temporal position restoring capability is to enhance
  inter-temporal object modeling ability. The proposed SqueezeTime is much
  lightweight and fast with high accuracies for mobile video understanding.
  Extensive experiments on various video recognition and action detection
  benchmarks, i.e., Kinetics400, Kinetics600, HMDB51, AVA2.1 and THUMOS14,
  demonstrate the superiority of our model. For example, our SqueezeTime achieves
  +1.2% accuracy and +80% GPU throughput gain on Kinetics400 than prior
  methods. Codes are publicly available at
  https://github.com/xinghaochen/SqueezeTime and
  https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime.
paper_authors._id: 6644fc9e8264e57bf9445843
paper_authors.name: Yingjie Zhai
paper_authors._id: 6644fc9e8264e57bf9445844
paper_authors.name: Wenshuo Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-15T20:11:12.734Z
paper_authors._id: 65f9312ac1d406d7161d6141
paper_authors.avatarUrl: /avatars/b249df8e7e9fb1133365db2ce5bc3f03.svg
paper_authors.fullname: LiWenshuo
paper_authors.type: user
paper_authors.user: meteorh
paper_authors._id: 6644fc9e8264e57bf9445845
paper_authors.name: Yehui Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-15T20:11:20.999Z
paper_authors._id: 64d5deb154bb9eb704f83122
paper_authors.avatarUrl: /avatars/86ce09bcca903319051e2307581a43f4.svg
paper_authors.fullname: Yehui Tang
paper_authors.type: user
paper_authors.user: tangyehui
paper_authors._id: 6644fc9e8264e57bf9445846
paper_authors.name: Xinghao Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-16T10:43:02.545Z
paper_authors._id: 65878c774f9d2b955e025019
paper_authors.avatarUrl: /avatars/c039c29e131a8cba96b38048da94971b.svg
paper_authors.fullname: Xinghao Chen
paper_authors.type: user
paper_authors.user: xinghaochen
paper_authors._id: 6644fc9e8264e57bf9445847
paper_authors.name: Yunhe Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-15T20:12:06.407Z
paper_authors._id: 658bdf7b925aadd43304f05c
paper_authors.avatarUrl: /avatars/64d9e9dea27c376c3bc7b2a54efc2a46.svg
paper_authors.fullname: Yunhe Wang
paper_authors.type: user
paper_authors.user: MightyCrane
paper_title: No Time to Waste: Squeeze Time into Channel for Mobile Video
    Understanding
paper_summary: Current architectures for video understanding mainly build upon 3D
  convolutional blocks or 2D convolutions with additional operations for temporal
  modeling. However, these methods all regard the temporal axis as a separate
  dimension of the video sequence, which requires large computation and memory
  budgets and thus limits their usage on mobile devices. In this paper, we
  propose to squeeze the time axis of a video sequence into the channel dimension
  and present a lightweight video recognition network, term as
  SqueezeTime, for mobile video understanding. To enhance the temporal
  modeling capability of the proposed network, we design a Channel-Time Learning
  (CTL) Block to capture temporal dynamics of the sequence. This module has two
  complementary branches, in which one branch is for temporal importance learning
  and another branch with temporal position restoring capability is to enhance
  inter-temporal object modeling ability. The proposed SqueezeTime is much
  lightweight and fast with high accuracies for mobile video understanding.
  Extensive experiments on various video recognition and action detection
  benchmarks, i.e., Kinetics400, Kinetics600, HMDB51, AVA2.1 and THUMOS14,
  demonstrate the superiority of our model. For example, our SqueezeTime achieves
  +1.2% accuracy and +80% GPU throughput gain on Kinetics400 than prior
  methods. Codes are publicly available at
  https://github.com/xinghaochen/SqueezeTime and
  https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/SqueezeTime.
paper_ai_summary: The proposed SqueezeTime network enhances video understanding on mobile devices by integrating the temporal axis into the channel dimension, improving accuracy and GPU throughput.
paper_ai_keywords: 3D convolutions 2D convolutions temporal modeling SqueezeTime Channel-Time Learning (CTL) Block temporal importance learning inter-temporal object modeling Kinetics400 Kinetics600 HMDB51 AVA2.1 THUMOS14