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Papers: temporal action proposals & detection

  • Constraining Temporal Relationship for Action Localization (arxiv 2020)
  • (AGCN-P-3DCNNs) Graph Attention Based Proposal 3D ConvNets for Action Detection (AAAI20)
  • (PBRNet) Progressive Boundary Refinement Network for Temporal Action Detection (AAAI20)
  • (RapNet) Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network (AAAI20)
  • (S-2D-TAN) Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization (HACS Temporal Action Localization Challenge at ICCV 2019)
  • (G-TAD) G-TAD: Sub-Graph Localization for Temporal Action Detection (CVPR 2020) CODE
  • (CMSN) CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation (arxiv 2019)
  • (DBG) Fast Learning of Temporal Action Proposal via Dense Boundary Generator (AAAI 2020) CODE
  • (AFO-TAD) AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection (arxiv 2019)
  • (semi-supervised) Learning Temporal Action Proposals With Fewer Labels (ICCV 2019)
  • (DPP.AnchorFree) Deep Point-wise Prediction for Action Temporal Proposal (ICONIP 2019)
  • (P-GCN) Graph Convolutional Networks for Temporal Action Localization (ICCV 2019) CODE.pytorch
  • (C-TCN) Deep Concept-wise Temporal Convolutional Networks for Action Localization (arxiv 2019) CODE.PadddlePaddle
  • (TSANet) Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos (ICME2020)
  • (BMN) BMN: Boundary-Matching Network for Temporal Action Proposal Generation (ICCV 2019)
  • (TGM) Temporal Gaussian Mixture Layer for Videos (ICML 2019) CODE.pytorch
  • (MGG) Multi-granularity Generator for Temporal Action Proposal (CVPR 2019)
  • (GTAN) Gaussian Temporal Awareness Networks for Action Localization (CVPR 2019)

Papers: weakly temporal action detection

  • Relational Prototypical Network for Weakly Supervised Temporal Action Localization (AAAI20)
  • (BaSNet) Background Suppression Network for Weakly-supervised Temporal Action Localization (AAAI20)CODE.pytorch
  • (LPAT) LPAT: Learning to Predict Adaptive Threshold for Weakly-supervised Temporal Action Localization (arxiv 2019)
  • (3C-Net) 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization (ICCV2019)CODE.pytorch
  • (TSM) Temporal Structure Mining for Weakly Supervised Action Detection (ICCV2019)
  • (CleanNet) Weakly Supervised Temporal Action Localization through Contrast based Evaluation Networks (ICCV2019)
  • (BM) Weakly-supervised Action Localization with Background Modeling (ICCV 2019)
  • (ASSG) Adversarial Seeded Sequence Growing for Weakly-Supervised Temporal Action Localization (ACM MM19)
  • (CMCS) Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization (CVPR19)CODE.pytorch
  • Weakly-Supervised Temporal Localization via Occurrence Count Learning (ICML 2019)
  • (MAAN) Marginalized Average Attentional Network for Weakly-Supervised Learning (ICLR2019)CODE.pytorch
  • (WSGN) Weakly Supervised Gaussian Networks for Action Detection (Arxiv 2019.4)
  • (RefineLoc) RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization (Arxiv 2019.4)
  • (STAR) Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection (AAAI 2019)
  • (TSRNet) Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision (AAAI 2019)
  • (StepByStep) Step-by-step Erasion, One-by-one Collection: AWeakly Supervised Temporal Action Detector (MM 2018)
  • (W-TALC) W-TALC: Weakly-supervised Temporal Activity Localization and Classification (ECCV 2018) CODE.pytorch
  • (AutoLoc) AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos (ECCV 2018) CODE.caffe
  • (STPN) Weakly Supervised Action Localization by Sparse Temporal Pooling Network (CVPR 2018) CODE.tensorflow.unofficial
  • (H&S) Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization (ICCV 2017)
  • (UNet) UntrimmedNets for Weakly Supervised Action Recognition and Detection (CVPR 2017) CODE.caffe

Features: Download link

THUMOS14

  • C3D: link
  • I3D: Video is sampled at 25 frames per second. 16 frames as a video unit. link
  • UNet: link
  • ANet2016-cuhk(4096dims): 6 frames as a video unit. link
  • ANet2016-cuhk(3072dims): 5 frames as a video unit. link

ActivityNet v1.2

  • I3D: Video is sampled at 25 frames per second. 16 frames as a video unit. link
  • UNet: link

ActivityNet v1.3

  • C3D: link
  • ANet2016-cuhk(400dims): 16 frames as a video unit. link
  • I3D: 16 frames as a video unit. link
  • ANet2016-cuhk(3072dims): 16 frames as a video unit. link

Benchmark Results (THUMOS14 Results)

These methods are listed in chronological order.

Method Feature IoU-> 0.1 0.2 0.3 0.4 0.5 0.6 0.7
BaSNet I3D 58.2 52.3 44.6 36.0 27.0 18.6 10.4
3C-Net I3D 59.1 53.5 44.2 34.1 26.6 8.1
TSM I3D 39.5 31.9 24.5 13.8 7.1
CleanNet UNet 37.0 30.9 23.9 13.9 7.1
BM I3D 60.4 56.0 46.6 37.5 26.8 17.6 8.6
ASSG I3D 65.6 59.4 50.4 38.7 25.4 15.0 6.6
MAAN I3D 59.8 50.8 41.1 30.6 20.3 12.0 6.9
CMCS I3D 57.4 50.8 41.2 32.1 23.1 15.0 7.0
WSGN I3D 55.3 47.6 38.9 30.0 21.1 13.9 8.3
RefineLoc UNet 33.9 22.1 6.1
STAR I3D 68.8 60.0 48.7 34.7 23.0
TSRNet 2-Stream(ResNet101) 55.9 46.9 38.3 28.1 18.6 11.0 5.59
StepByStep TSN 45.8 39.0 31.1 22.5 15.9
W-TALC UNet 49.0 42.8 32.0 26.0 18.8 6.2
W-TALC I3D 55.2 49.6 40.1 31.1 22.8 7.6
AutoLoc UNet 35.8 29.0 21.2 13.4 5.8
STPN I3D 52.0 44.7 35.5 25.8 16.9 9.9 4.3
H&S C3D 36.4 27.8 19.5 12.7 6.8
UNet UNet 44.4 37.7 28.2 21.1 13.7