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Anticipating Visual Representations from Unlabeled Video

Carl Vondrick, Hamed Pirsiavash, Antonio Torralba

arXiv:1504.08023v2cs.CV

TL;DR

Anticipating future actions and objects is difficult because it requires world knowledge that is hard to write down. The paper learns from unlabeled video by predicting future visual representations and applying recognition algorithms to them. Experiments suggest this can help anticipate actions and objects, though results remain far from human performance.

  • Problem

    Anticipating actions and objects before they occur is difficult because machines lack the extensive world knowledge people use to infer what comes next.

    Source: §1 · p. 1

  • Method

    The framework learns from unlabeled video to predict future visual representations, then applies recognition algorithms to forecast categories.

    Source: §1 · pp. 1–2, §3.5 · p. 4

  • Results

    Experiments using 600 hours of web video suggest that forecasting representations may help machines anticipate actions and objects, though performance remains far from human.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Unlabeled video is presented as a promising resource for acquiring knowledge that can help machines anticipate future concepts.

    Source: §5 · p. 8

Abstract

from arXiv · show

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.

1. Introduction

Anticipating future actions and objects matters for applications such as robot interaction, but machines lack the extensive world knowledge people use to predict what comes next. This work learns from unlabeled video by predicting future visual representations and applying recognition algorithms to them.

1. Introduction

  • Anticipation is difficult because people draw on extensive lifetime knowledge to infer what will happen next, while machines lack that resource.The introduction frames this capability as useful for applications including robot planning and interaction, recommendations, and surveillance alerts.Source: §1 · p. 1
  • Unlabeled videos offer rich signals at massive scale, with temporal ordering available for free as a useful forecasting asset.The passages describe videos as economical to obtain at massive scales despite lacking ground-truth annotations.Source: §1 · p. 1
  • The method predicts future-frame visual representations from unlabeled video, then applies recognition algorithms to forecast concepts such as actions and objects.Representations capture semantic information and are automatic to compute, unlike concepts that are not readily accessible in unlabeled video.Source: §1 · pp. 1–2, Figure 1 · p. 1
  • Experiments using 600 hours of web video suggest that forecasting representations can help machines anticipate actions and objects, though performance remains far from human.The method forecasts representations 1 to 5 seconds ahead and is evaluated on television-show action and egocentric activity datasets.Source: §1 · p. 2
  • The paper’s primary contribution is a method that leverages unlabeled video to forecast high-level concepts, with multiple predictions addressing uncertainty about the future.The paper also reports plans to make its trained models and code publicly available.Source: §1 · p. 2

2. Related Work

Prior work uses visual data to predict pixels, motions, paths, or action categories, while this paper targets future visual representations to anticipate both actions and objects. Its approach mines unlabeled video for semantic forecasting rather than learning a representation itself.

2. Related Work

  • Unlike prior work predicting low-level pixels or motions from large video collections, this paper uses unlabeled video to predict high-level concepts such as objects and actions.The cited prior approaches include motion transfer, object-trajectory animation, and future-pixel prediction [31].Source: §2 · p. 2
  • Earlier action-prediction approaches use hierarchical representations, early-stage models, scene affordances, or inferred intentions; this work predicts future representations from large-scale unlabeled data for both actions and objects.The related-work discussion names Lan et al., Ryoo, Hoai and De la Torre, Vu et al., Pei et al., and Xie et al..Source: §2 · p. 2
  • Where prior work predicts possible human paths or action movements, this approach forecasts human actions and objects.The cited examples include surveillance paths, scene paths, and human-robot interaction movements.Source: §2 · p. 2
  • Related motion-prediction work includes ordering events, predicting object movement, future distributions, and optical flow; this paper hopes to extrapolate motion in representation space for future recognition.Examples include,,, and.Source: §2 · p. 2
  • Prior web-data work mines images for recognition, place elements, or photo-story recommendations, whereas this paper uses online videos to learn semantic anticipation.Examples cited include,,,, and.Source: §2 · pp. 2–3
  • Other unsupervised vision work uses unlabeled data to build face datasets, annotate or discover actions, or discover objects; this paper instead learns to predict visual representations.The examples cited are,,,, and.Source: §2 · p. 3
  • Unlike representation-learning work that builds visual representations, this paper aims to anticipate representations in the future and could in principle predict any representation.The discussion contrasts its goal with visual representation learning from images [4] or videos.Source: §2 · p. 3

3. Anticipating Visual Representations

The section forecasts future visual representations from unlabeled video, then applies recognition algorithms to predicted representations to anticipate categories. It extends regression to multiple possible futures and learns mixture assignments alongside network weights.

3. Anticipating Visual Representations

  • The method forecasts visual representations rather than pixels or labeled categories, then applies recognition algorithms such as object or action classifiers to anticipate concepts.Representations are automatic to compute and encode semantic information, avoiding the need to predict pixels or train the forecasting model with category labels.Source: §3 · p. 3

3.1. Self-supervised Learning

  • A current video frame is used to predict the visual representation of a future frame with a parameterized function; the experiments focus on AlexNet’s fc7 representation.The authors chose fc7 based on its reported performance on image and video recognition tasks.Source: §3.1 · p. 3

3.2. Deep Regression Network

  • Deep regression networks are proposed to scale model complexity and training to large unlabeled datasets; the network uses five convolutional and five fully connected layers with Euclidean loss.The architecture is motivated by AlexNet but uses a regression loss and three additional fully connected layers.Source: §3.2 · p. 3

3.3. Multi-Modal Outputs

  • Multiple plausible futures make a least-squares regression mean potentially unlikely or off-manifold, so the model produces multiple output representations.The model trains a mixture of networks, each intended to predict one future mode.Source: §3.3 · pp. 3–4, Figure 3 · p. 4

3.4. Learning

  • The learning procedure treats each frame’s mixture assignment as latent, alternating network-weight fitting with reassignment under the updated weights.The authors report that this alternating process typically takes two days.Source: §3.4 · p. 4
  • The network can also be trained online from streaming video, potentially adapting in real time without storing frames.The experiments train it offline; online training is presented as an available alternative.Source: §3.4 · p. 4

3.5. Predicting Categories

  • Recognition algorithms applied to predicted future representations forecast semantic categories, using a small labeled target-task set to attach category labels.The authors also train classifiers on predicted representations to adapt to structured regression errors, and marginalize category distributions across multiple predictions.Source: §3.5 · p. 4

3.6. Implementation

  • Implementation uses more than 600 hours of YouTube video, shared early mixture layers, and stochastic gradient descent with a fixed .001 learning rate and 0.9 momentum.The architecture has five convolutional layers followed by five fully connected layers.Source: §3.6 · pp. 4–5, Figure 4 · p. 5

4. Experiments

The experiments evaluate action anticipation one second before actions begin and object anticipation five seconds before objects appear, using representations predicted from unlabeled video. Across both tasks, the method improves on baselines, while additional experiments examine training sources, prediction intervals, and multiple outputs.

4.1. Unlabeled Repository

  • The repository contains over 600 hours of YouTube television; 400 hours of THUMOS tutorials and sports videos serve as a diagnostic source with a different distribution.Nearest-neighbor checks found no overlap between the television repository and the testing sets.Source: §4.1 · p. 5

4.3. Forecasting Actions

  • At one second ahead from a single frame, representation forecasting achieves a 19% relative gain over action-prediction baselines.The evaluation uses classification accuracy and 25-fold cross-validation on the TV Human Interactions dataset.Source: Table 2 · p. 5, §4.3 · p. 6

4.2. Baselines

  • Baselines include annotated pre-action SVM and MMED classifiers, nearest-neighbor future-frame transfer, and linear regression on unlabeled-repository features.The comparisons aim to use a similar level of supervision to the proposed method.Source: §4.2 · p. 5

4.3. Forecasting Actions

  • Figure 5 shows correct forecasts of kissing, hugging, and high-fives, alongside confusion between handshakes and high-fives and a mistaken hug after an unexpected person enters.The model does not observe action frames during inference.Source: Figure 5 · p. 7, §4.3 · p. 8
  • Multiple outputs allow different networks to represent uncertain action futures; performance drops at K = 1, suggesting multiple outputs may matter in learning and inference.In an ambiguous embrace scene, networks forecast kissing or hugging.Source: Figure 7 · p. 7, §4.3 · pp. 7–8
  • Relative gains often improve as action prediction time increases, although the model is trained at one second while baselines are retrained for each interval.The figure reports that the model performs better when the time range is longer.Source: §4.3 · p. 7, Figure 6 · p. 7

4.4. Forecasting Objects

  • At five seconds, the method achieves a 30% mean relative gain over baselines in object average precision, outperforming them for many categories; multiple outputs improve over a single output.Evaluation uses egocentric videos, with three fourths of the dataset reserved for self-supervised learning and average precision measured across leave-one-out splits.Source: Table 3 · p. 8, §4.4 · p. 8

5. Conclusion

Anticipating future concepts before they begin is a key computer-vision problem with real-world applications. The paper presents unlabeled video as a resource for learning to anticipate the future.

5. Conclusion

  • Anticipating future concepts before they begin is a key computer-vision problem with many real-world applications.The conclusion identifies this capability as a key problem that will enable applications.Source: §5 · p. 8
  • Abundantly available unlabeled videos are proposed as an effective resource for acquiring knowledge about the world.Source: §5 · p. 8
  • The acquired knowledge can be used to learn to anticipate the future.Source: §5 · p. 8
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