Activity Recognition In Videos

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (18 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Very Deep Convolutional Networks for Large-Scale Image Recognition
  4 Sep 2014 305 repositories listed Syntology ran 12 of 122 samples · 110 unverified · 4 pointer-only (licence)
  In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
• Representation Flow for Action Recognition
  2 Oct 2018 5 repositories listed
  Our representation flow layer is a fully-differentiable layer designed to capture the `flow' of any representation channel within a convolutional neural network for action recognition.
• Large-scale weakly-supervised pre-training for video action recognition
  2 May 2019 3 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  Second, frame-based models perform quite well on action recognition; is pre-training for good image features sufficient or is pre-training for spatio-temporal features valuable for optimal transfer learning?
• ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
  9 Apr 2024 1 repository listed
  Our framework leverages both labeled and unlabelled data to robustly learn action representations in videos, combining pseudo-labeling with contrastive learning for effective learning from both types of samples.
• Dual-path Adaptation from Image to Video Transformers
  17 Mar 2023 1 repository listed
  In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters.
• TorMentor: Deterministic dynamic-path, data augmentations with fractals
  7 Apr 2022 1 repository listed
  We propose the use of fractals as a means of efficient data augmentation.
• Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
  27 Mar 2020 1 repository listed
  Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature.
• Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
  26 May 2016 1 repository listed
  In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos.
• Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
  19 May 2015 1 repository listed
  Visual features are of vital importance for human action understanding in videos.
• Pooled Motion Features for First-Person Videos
  19 Dec 2014 1 repository listed
  In this paper, we present a new feature representation for first-person videos.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.