Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics (1904.03597v1)

Abstract: We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.

Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

The paper "Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics" addresses the challenge of video representation learning without relying on manually annotated labels. The motivation behind this paper is to design a self-supervised learning approach that effectively extracts spatio-temporal features from video data, which are essential for numerous video analysis tasks such as action recognition, video captioning, and temporal action localization.

Key Contributions

1. Novel Learning Task: The authors propose a unique self-supervised learning task that predicts motion and appearance statistics derived directly from the video content. This task utilizes unlabeled video data to train spatio-temporal convolutional networks (such as C3D) to learn expressive video representations.
2. Statistical Concepts for Video Analysis: The task is grounded in predicting several numerical labels that capture:
   • The location with the largest motion and its dominant direction.
   • The spatio-temporal color diversity, including regions with dominant and stable colors.
3. Use of Optical Flow and Motion Boundary: The approach leverages optical flow and motion boundary information to derive motion statistics that are robust against camera motion. This is crucial for focusing on relevant motion within the scene rather than background changes.
4. Effective Learning Framework: By modifying the C3D network for self-supervised learning, the authors enable the model to predict motion and appearance statistics through regression, bypassing the need for labeled data while capturing complex spatio-temporal dynamics.

Experimental Results

The paper reports extensive experiments demonstrating the effectiveness of the proposed self-supervised learning approach. The authors observe substantial performance improvements in action recognition tasks, particularly when pre-training CNNs on unlabeled datasets like UCF101 and Kinetics-400 before fine-tuning on smaller labeled datasets such as HMDB51.

• Performance Gains: The approach significantly boosts C3D's performance on the UCF101 benchmark from 45.4% (random initialization) to 61.2% when pre-trained using the proposed self-supervised task, showing its efficacy in extracting and refining video representations.
• Comparison with State-of-the-art: The paper showcases the superiority of their approach against existing self-supervised methods like sequence verification and space-time puzzle solving across multiple datasets, bolstering its potential utility in broader applications.

Implications and Future Directions

The implications of this research are profound for the video analytics field. By removing the dependence on labeled data, the approach reduces annotation costs and enables the exploitation of vast amounts of unlabeled video data available online. The learned spatio-temporal features are not only applicable to video recognition tasks but also transferable to other video-related tasks such as scene understanding and video similarity labeling.

The theoretical implications include advancing self-supervised learning methodologies by reinforcing the value of biologically-inspired learning tasks in AI. The authors suggest potential extensions of the approach could involve experimenting with more complex partitioning patterns and feature extraction techniques.

In conclusion, this paper provides a robust framework for self-supervised spatio-temporal representation learning, paving the way for further advancements in unsupervised video analysis and understanding within the AI community.