Papers › Video Representation Learning by Dense Predictive Coding
Video Representation Learning by Dense Predictive Coding
Tengda Han, Weidi Xie, Andrew Zisserman
The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition. We make three contributions: First, we introduce the Dense Predictive Coding (DPC) framework for self-supervised representation learning on videos. This learns a dense encoding of spatio-temporal blocks by recurrently predicting future representations; Second, we propose a curriculum training scheme to predict further into the future with progressively less temporal context. This encourages the model to only encode slowly varying spatial-temporal signals, therefore leading to semantic representations; Third, we evaluate the approach by first training the DPC model on the Kinetics-400 dataset with self-supervised learning, and then finetuning the representation on a downstream task, i.e. action recognition. With single stream (RGB only), DPC pretrained representations achieve state-of-the-art self-supervised performance on both UCF101(75.7% top1 acc) and HMDB51(35.7% top1 acc), outperforming all previous learning methods by a significant margin, and approaching the performance of a baseline pre-trained on ImageNet.
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Code
Syntology Ran 1 of 11 code samples harvested from 1 repository linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
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Code Syntology ran Syntology
11 samples harvested; 1 ran; 0 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D Resnet-34) | Frozen | false | #39 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D Resnet-34) | Pre-Training Dataset | Kinetics400 | #39 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D Resnet-34) | Top-1 Accuracy | 35.7 | #39 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D ResNet-18) | Frozen | false | #40 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D ResNet-18) | Pre-Training Dataset | Kinetics400 | #40 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | HMDB51 | DPC (Modified 3D ResNet-18) | Top-1 Accuracy | 34.5 | #40 of 48 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (Modified 3D Resnet-34) | 3-fold Accuracy | 75.7 | #33 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (Modified 3D Resnet-34) | Frozen | false | #33 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (Modified 3D Resnet-34) | Pre-Training Dataset | Kinetics400 | #33 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18) | 3-fold Accuracy | 68.2 | #39 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18) | Frozen | false | #39 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18) | Pre-Training Dataset | Kinetics400 | #39 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18, Split 1) | 3-fold Accuracy | 60.6 | #46 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18, Split 1) | Frozen | false | #46 of 53 | Archive leaderboard | report |
| Self-Supervised Action Recognition | UCF101 | DPC (3D ResNet-18, Split 1) | Pre-Training Dataset | UCF101 | #46 of 53 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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