Unsupervised Representation Learning by Sorting Sequences (1708.01246v1)

Abstract: We present an unsupervised representation learning approach using videos without semantic labels. We leverage the temporal coherence as a supervisory signal by formulating representation learning as a sequence sorting task. We take temporally shuffled frames (i.e., in non-chronological order) as inputs and train a convolutional neural network to sort the shuffled sequences. Similar to comparison-based sorting algorithms, we propose to extract features from all frame pairs and aggregate them to predict the correct order. As sorting shuffled image sequence requires an understanding of the statistical temporal structure of images, training with such a proxy task allows us to learn rich and generalizable visual representation. We validate the effectiveness of the learned representation using our method as pre-training on high-level recognition problems. The experimental results show that our method compares favorably against state-of-the-art methods on action recognition, image classification and object detection tasks.

• The paper introduces an unsupervised method that uses sequence sorting as a supervisory signal to learn robust visual representations from video data.
• It proposes an Order Prediction Network that leverages pairwise feature extraction to accurately predict the correct chronological order of frames.
• Experimental results demonstrate enhanced performance in action recognition, image classification, and object detection compared to state-of-the-art techniques.

Unsupervised Representation Learning by Sorting Sequences

This paper presents a novel approach to unsupervised representation learning using videos without semantic labels. The primary contribution is leveraging temporal coherence as a supervisory signal by redefining representation learning as a sequence sorting task. The proposed methodology employs a convolutional neural network (CNN) to sort frames shuffled in non-chronological order, drawing parallels to comparison-based sorting algorithms, and extracting features from frame pairs to predict sequence order.

The proxy task of sorting shuffled image sequences necessitates an understanding of the statistical temporal structures inherent in videos, facilitating the learning of rich and generalizable visual representations. The effectiveness of the learned representation is validated through pre-training on high-level recognition tasks, demonstrating favorable comparisons against state-of-the-art methods in areas such as action recognition, image classification, and object detection.

Methodology Overview

The approach centers around utilizing CNNs within a self-supervised learning paradigm. A key methodology involves using tuple-based frames from videos, which are randomly shuffled, as input. The CNN model is tasked with placing these sequences into the correct chronological order. The sequence sorting problem is framed as a multi-class classification task, further detailed by grouping forward and backward sequences to handle actions that exhibit bidirectional coherence.

An Order Prediction Network (OPN) architecture is introduced, which employs pairwise feature extraction rather than simultaneous frame feature extraction, capitalizing on the concept of ordering akin to comparison-based sorting methods. The network's architecture includes crucial components such as frame feature extraction, pairwise feature extraction, and order prediction, and is trained end-to-end for efficiency.

Experimental Validation

Extensive experiments validate the proposed method's efficacy. When tested as a pre-training strategy on standard benchmark datasets like UCF-101 and HMDB-51, the method outperforms existing state-of-the-art approaches, showcasing particularly strong results in action recognition tasks. The method also demonstrates noteworthy performance on the PASCAL VOC 2007 dataset for image classification and object detection, rivaling top-performing models.

Contributions and Implications

This work offers several contributions to the field:

1. Introduction of Sequence Sorting: Sequence sorting is posited as an effective self-supervised learning approach, fundamentally differing from binary order verification tasks by handling complex permutations.
2. Order Prediction Network: The development of an Order Prediction Network utilizing pairwise feature extraction marks a significant advancement, allowing for meaningful performance improvements.
3. Generalizable Representation: The research highlights the potential for the learned representations to serve as robust pre-trained models for various computer vision tasks.

Future Directions

The implications of this research extend into several potential future directions. One prominent avenue includes integrating recurrent neural networks or other architectures adept at modeling long-term temporal dynamics in videos, which may bridge existing performance gaps between unsupervised and supervised pre-training methodologies. Additionally, expanding training data with large-scale and diverse video datasets could further enhance the method’s generalizability and practical applications in computer vision.

This approach contributes significantly to the ongoing exploration of effective unsupervised learning strategies, capitalizing on unlabeled data's vast potential while addressing scalability challenges inherent in manual data annotation processes.