SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning (2008.00975v2)

Abstract: A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learning models. In this paper, we compose a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives. We materialize the supervisory signals through determining whether a pair of samples is from one frame or from one video, and whether a triplet of samples is in the correct temporal order. We uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo). SeCo shows superior results under the linear protocol on action recognition (Kinetics), untrimmed activity recognition (ActivityNet) and object tracking (OTB-100). More remarkably, SeCo demonstrates considerable improvements over recent unsupervised pre-training techniques, and leads the accuracy by 2.96% and 6.47% against fully-supervised ImageNet pre-training in action recognition task on UCF101 and HMDB51, respectively. Source code is available at \url{https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning}.

• SeCo introduces a sequence contrastive learning framework that leverages spatial, spatiotemporal, and sequential supervision from videos for unsupervised representation learning.
• Empirical validation shows SeCo outperforms existing unsupervised and supervised methods on action recognition and other tasks, demonstrating improved accuracy.
• SeCo highlights the potential of leveraging sequential structure supervision in unsupervised learning, suggesting promising future research in video understanding and representation learning.

Exploring Sequence Supervision for Unsupervised Representation Learning: An Overview

In the field of unsupervised representation learning, the paper "SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning" introduces an innovative methodology to harness the inherent supervisory signals within video sequences to learn robust visual representations. The authors propose Sequence Contrastive Learning (SeCo), which explores the supervision originating from spatial, spatiotemporal, and sequential perspectives in videos, without relying on human-annotated labels.

Proposed Methodology

SeCo is built upon the foundation of contrastive learning, which has gained prominence in self-supervised learning paradigms due to its ability to maximize the similarity of positive pairs while minimizing it for negative pairs. In advancing this paradigm for video data, the paper delineates three primary proxy tasks to leverage the supervisory signals within video sequences:

1. Intra-frame Instance Discrimination Task: This task focuses on distinguishing frame patches from the same video frame, exploiting spatial variations within static frames to enhance representation learning.
2. Inter-frame Instance Discrimination Task: This task determines whether two frame patches originate from the same video, capitalizing on spatiotemporal correlations across different frames.
3. Temporal Order Validation Task: Designed to verify the chronological order of frame patches, this task utilizes sequential coherence as supervisory signals to refine the temporal reasoning capabilities of the learnt representations.

By synthesizing these tasks within a contrastive learning framework, SeCo optimizes the feature extractor through the formulation of a combined objective, aggregating the supervisory signals from these proxy tasks. The authors effectively remould traditional contrastive learning techniques to address the complexities introduced by video data, thereby enhancing the model's ability to capture spatiotemporal dynamics and sequential structures.

Empirical Validation

The efficacy of SeCo is validated on several downstream tasks including action recognition, untrimmed activity recognition, and object tracking. Under the "Pre-trained Representation + Linear Model" protocol, SeCo consistently outperforms existing unsupervised and supervised pre-training mechanisms across diverse benchmarks such as Kinetics400, ActivityNet, and OTB-100. Notably, SeCo demonstrates superior performance over fully-supervised ImageNet pre-training, achieving increased top-1 accuracy by 2.96% and 6.47% on UCF101 and HMDB51 datasets, respectively.

Moreover, evaluations on the "Pre-training + Fine-tuning" protocol reaffirm the transferability of SeCo's pre-trained representations, showcasing its potential to serve as effective network initialization for subsequent supervised tasks.

Implications and Future Directions

The insights presented in the paper underscore the potential of incorporating sequential structure supervision into unsupervised learning frameworks, particularly for video data, where spatiotemporal coherence and variation serve as rich sources of supervisory signals. The approach taken by SeCo suggests promising avenues for future research in leveraging sequence structures to further enhance the robustness and generalization capability of learned representations.

An intriguing future direction could involve extending SeCo to multi-modal sequences, integrating audio and text modalities with video data to learn even richer representations. Additionally, exploring the scalability of SeCo in large-scale, real-world datasets could offer practical insights into optimizing video understanding systems in various applications.

Overall, SeCo presents a compelling advancement in unsupervised representation learning, advocating for a shift towards exploiting inherent data structures rather than relying solely on supervised signals. The approach holds significant promise for developing more efficient and versatile models in video understanding and beyond.