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Visual Representation Learning with Stochastic Frame Prediction
Huiwon Jang, Dongyoung Kim, Junsu Kim, Jinwoo Shin, Pieter Abbeel, Younggyo Seo
TL;DR
Future-frame prediction is difficult because a single current frame can correspond to multiple plausible futures, limiting deterministic representation learning. RSP addresses this with stochastic future-frame prediction plus masked image modeling through a shared decoder, and it achieves competitive or superior performance across video and robot-learning tasks, including 36.0% average RLBench manipulation success versus 13.5% for MAE.
Problem
Deterministic future-frame prediction struggles with under-determined videos because one current frame can have multiple plausible futures.
Method
RSP learns temporal information with stochastic future-frame prediction and dense within-frame information with an auxiliary masked autoencoding objective using a shared decoder.
Results
RSP achieves competitive or superior performance across video label propagation and vision-based robot-learning tasks, including 36.0% average RLBench manipulation success versus 13.5% for MAE.
Takeaways & Limitations
Stochastic future prediction can learn strong image representations from complex real-world videos while combining temporal and dense visual information.
Takeaways & Limitations
Generated frames are not high quality, and computational constraints prevented large-scale experiments with longer training budgets and larger models.
Abstract
from arXiv · showhide
Self-supervised learning of image representations by predicting future frames is a promising direction but still remains a challenge. This is because of the under-determined nature of frame prediction; multiple potential futures can arise from a single current frame. To tackle this challenge, in this paper, we revisit the idea of stochastic video generation that learns to capture uncertainty in frame prediction and explore its effectiveness for representation learning. Specifically, we design a framework that trains a stochastic frame prediction model to learn temporal information between frames. Moreover, to learn dense information within each frame, we introduce an auxiliary masked image modeling objective along with a shared decoder architecture. We find this architecture allows for combining both objectives in a synergistic and compute-efficient manner. We demonstrate the effectiveness of our framework on a variety of tasks from video label propagation and vision-based robot learning domains, such as video segmentation, pose tracking, vision-based robotic locomotion, and manipulation tasks. Code is available on the project webpage: https://sites.google.com/view/2024rsp.
1. Introduction
Video future-frame prediction can learn temporal representations, but deterministic prediction struggles because one current frame may have multiple plausible futures. RSP addresses this uncertainty with stochastic prediction and combines it with masked image modeling, achieving strong results across video and robot-learning tasks.
- Multiple plausible futures make deterministic frame prediction poorly suited to learning representations from complex real-world videos.The paper attributes this difficulty to the under-determined nature of future-frame prediction.
- RSP augments stochastic prediction with masked autoencoding and a shared decoder to learn dense information within individual frames.The shared decoder lets masked tokens attend to different inputs for the two objectives.
- RSP learns temporal information with stochastic frame prediction and captures uncertainty through a learned distribution over possible future frames.Its model uses posterior and prior distributions, with the prior conditioned only on the current frame.
- 36.0% average success rate on RLBench manipulation tasks versus 13.5% for the MAE baseline demonstrates RSP’s effectiveness in robotic learning.Across robot-learning and video label-propagation benchmarks, RSP achieves competitive or superior performance to self-supervised baselines.
2. Related Work
Prior video self-supervised methods mainly learn spatiotemporal representations for video understanding, while RSP focuses on image representations learned through future-frame prediction. It also differs from masked future-frame approaches that resolve ambiguity using unmasked future patches.
- Most video self-supervised learning methods target spatiotemporal representations for tasks such as action recognition.
- RSP instead focuses on learning useful image representations from videos through future-frame prediction.
- Gupta et al. (2023) resolves future ambiguity by conditioning on unmasked future patches, whereas RSP aims to learn representations without that conditioning.
3. Method
RSP reconfigures stochastic video generation for representation learning and combines stochastic future-frame prediction with masked autoencoding through a shared decoder. The objectives complement one another while preserving computational efficiency.
- Representation Learning from Videos with Stochastic Frame Prediction: RSP samples current and future frames, encodes them with a shared ViT, and learns temporal representations through stochastic future-frame prediction.The framework processes the frames separately while sharing encoder parameters.
- Representation Learning from Videos with Stochastic Frame Prediction: The posterior uses current and future-frame representations, while the learned prior uses only the current frame to predict future uncertainty.The decoder predicts the future frame from the current representation and a latent variable sampled from the posterior or prior.
- Auxiliary Representation Learning from Images: A shared cross-attention decoder makes the prediction and masked-autoencoding objectives synergistic, while adding negligible training time.A parallel decoder performs worse, indicating that decoder sharing is important for the combination.
- Representation Learning from Videos with Stochastic Frame Prediction: The training objective combines future-frame reconstruction with KL divergence between posterior and prior distributions, encouraging predictable future representations.The hyperparameter β balances decoding and KL losses.
- Auxiliary Representation Learning from Images: The auxiliary masked autoencoding objective addresses static, dense frame information that future prediction may underemphasize because it accesses the previous frame.Masked patches are reconstructed through the decoder from unmasked representations of the same frame.
4. Experiments
RSP is evaluated across robot learning, video label propagation, and ablation settings, showing benefits from stochastic prediction, auxiliary MAE, and carefully matched augmentation.
- Vision-Based Robot Learning: RSP consistently outperforms all baselines on aggregate vision-based robot learning tasks, including 35.6% versus 6.0% success rates for SiamMAE in RLBench.The aggregate metric is an interquartile mean over benchmark tasks, while RLBench uses a difficult single-camera, no-depth setup.
- Ablation Study and Analysis: Stochastic frame prediction improves representation learning over deterministic prediction, raising performance from 54.4% to 60.1%.The comparison uses the same auxiliary MAE objective and shared decoder for both methods.
- Ablation Study and Analysis: Categorical latent variables outperform Gaussian latents, supporting discrete stochastic modeling for complex real-world video prediction.The authors hypothesize that discrete labels are easier to predict than an accurate continuous Gaussian distribution.
- Ablation Study and Analysis: Adding the auxiliary MAE objective improves performance by 2.4%p, while a shared decoder reaches 60.1% versus 58.1% for a parallel decoder.The shared decoder both makes the objectives synergistic and is more parameter-efficient than using separate decoders.
- Ablation Study and Analysis: Performance worsens with excessively strong or weak KL weighting, mismatched frame augmentations, or aggressive future-frame masking.Mild Gaussian noise on future frames improves performance, whereas strong masking makes the prior model augmentation-induced stochasticity.
5. Conclusion
RSP learns video representations by stochastic future-frame prediction and achieves competitive or superior performance across evaluated tasks. The authors identify low-quality generated frames, limited scaling, and single-frame prediction as future directions.
- RSP learns temporal representations from videos by training a stochastic future-frame prediction model.
- Experiments show RSP consistently achieves competitive or superior performance to various baselines.
- Generated frames are not high quality because the framework prioritizes representation learning over high-fidelity generation.
- Future work includes diffusion-based generation, larger models and training budgets, and extending the framework to multiple frames.
Impact Statement
Because RSP generates video outputs, it could potentially be misused to create fake videos. Its current outputs remain clearly distinguishable from real frames, reducing that risk.
- RSP may be misused for malicious purposes such as generating fake videos.
- Current outputs are clearly distinguishable from real frames, which significantly reduces misuse risk.
A. Implementation Details
The implementation uses ViT-S/16 with a transformer decoder and follows established stochastic-latent design choices. Hyperparameters for pre-training and evaluation are summarized in Table 5.
- The stochastic latent design uses 32 categorical variables with 32 classes for both posterior and prior distributions.
- Evaluation hyperparameters are identified separately in the implementation details.
- Pre-training and video-label-propagation hyperparameters are summarized in Table 5.
- Architectural details: The encoder is standard ViT-S/16, while each decoder block uses cross-attention, self-attention, and feed-forward MLP layers.
B. Additional Ablation Study and Analysis
The additional ablation study evaluates RSP variants on DAVIS after identical ViT-S/16 pre-training on Kinetics-400. Default settings are highlighted for comparison.
- The ablation study compares multiple RSP variants on the DAVIS benchmark.
- All ablation variants use ViT-S/16 pre-trained on Kinetics-400 for 400 epochs.
- Default settings are highlighted in gray to identify the reference configuration.
C. Experimental Results with 95% Confidence Interval
The section reports vision-based robot-learning results with 95% confidence intervals across three benchmarks and specifies the number of runs used for each benchmark.
- Table 7 reports imitation-learning performance on CortexBench, RLBench, and Franka Kitchen with 95% confidence intervals.
- The reported robot-learning evaluations cover CortexBench, RLBench, and Franka Kitchen.These benchmarks are associated with Majumdar et al. (2023), James et al. (2020), and Gupta et al. (2019), respectively.
- The experiments use 5 runs for CortexBench, 4 for RLBench, and 4 for Franka Kitchen.
D. Comparison with ImageNet Pre-trained SSLs
This section compares Kinetics-400-pre-trained approaches with ImageNet-pre-trained approaches across video label-propagation tasks.
- Table 8 compares Kinetics-400-pre-trained approaches with ImageNet-pre-trained approaches as a reference.
- The comparison covers video segmentation, video part segmentation, and pose tracking.
- The tasks use DAVIS, VIP, and JHMDB benchmarks, respectively.The cited benchmarks are associated with Pont-Tuset et al. (2017), Zhou et al. (2018), and Jhuang et al. (2013).
E. Additional Qualitative Results
The section adds qualitative results for RSP's predicted propagation on DAVIS video object-segmentation benchmarks at multiple propagation ratios.
- Figure 6 presents additional qualitative results of RSP-predicted propagation on DAVIS video object segmentation benchmarks.
- The visualizations use ground-truth annotations as the reference, marked “ref.”
- The results show propagated video ratios of 25%, 50%, and 100%.