Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation (2003.13045v2)

Abstract: Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a lightweight network with multiple frames by a highly-shared flow decoder. Our method consistently gets a leap of performance on several benchmarks with the best accuracy among deep unsupervised methods. Also, our method achieves competitive results to recent fully supervised methods while with much fewer parameters.

• The paper introduces 'Learning by Analogy,' a novel unsupervised framework for optical flow estimation that leverages transformations and a dual-forward pass mechanism for reliable self-supervision.
• The proposed method achieves competitive performance on MPI Sintel and KITTI benchmarks with reduced AEPE, demonstrating strong generalization across datasets.
• This approach offers a pathway for more robust, generalizable, and efficient unsupervised optical flow models applicable in real-world settings lacking labeled data.

Insights into Unsupervised Optical Flow Estimation: Learning by Analogy

The paper "Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation" presents a novel approach for estimating optical flow without requiring labeled data, utilizing a framework that leverages reliable supervision from transformations within the paradigm of unsupervised learning. By employing a learning strategy that incorporates self-supervision and a lightweight network architecture, this research addresses the deficiencies of traditional unsupervised methods in challenging visual environments.

Contribution to Optical Flow Estimation

Optical flow predicts motion between two consecutive video frames, integral to various computer vision tasks such as object tracking and video segmentation. However, generating dense optical flow data is resource-intensive, making unsupervised learning attractive. Previous unsupervised approaches predominantly used view synthesis, relying on the brightness constancy assumption, which fails in scenarios with significant brightness changes or occlusions. The authors propose an innovative framework where transformations, applied to images and their corresponding optical flow predictions, serve as a source of self-supervision. This method circumvents the need for direct photometric consistency, which can often be unreliable.

Technical Approach and Methodology

The paper proposes a dual-forward pass mechanism: the original and a transformed forward pass. The transformations include spatial adjustments and appearance changes, generating challenging data scenarios. The framework employs transformed predictions from the first forward pass as pseudo-ground truth to supervise the second pass. This strategy contrasts with the typical augmentation methods, which can cause reliability concerns.

The paper introduces a lightweight extension of PWC-Net, a well-known optical flow architecture, with a semi-dense layer connectivity structure and decoder sharing across different scales and time steps. These modifications aim to enhance efficiency without sacrificing performance. The network supports multi-frame input, promoting better temporal consistency in flow estimation.

Results and Analysis

The proposed method demonstrates notable performance improvements across several well-established benchmarks: MPI Sintel and KITTI (2012 and 2015 datasets), achieving performance competitive with even certain supervised approaches. Specifically, the results show a significant reduction in average end-point error (AEPE) for both non-occluded and occluded regions while utilizing fewer parameters—a compelling feature for deployment in computationally constrained environments.

Furthermore, the cross-dataset evaluations highlight the generalization prowess of this unsupervised approach. Models trained solely on CityScapes data outmatch previous unsupervised models trained directly on KITTI, showcasing the model's adaptability to unseen domains.

Implications and Future Directions

This research offers substantial implications for both theoretical and practical components of optical flow estimation. The method paves the way for more robust and generalizable unsupervised models that can potentially lower the barrier for optical flow applications in diverse real-world settings. By tackling the issue of unreliable supervision head-on, there's a clear pathway for deploying these models in applications lacking labeled data.

Looking forward, this approach can be foundational in exploring other self-supervised techniques involving more complex transformations or hierarchical learning strategies that further enhance performance. Moreover, extending the analogous self-supervision methodology to other visual sensor modalities and tasks such as depth estimation or scene flow suggests an exciting direction for future research in unsupervised learning frameworks.

In conclusion, the framework of "Learning by Analogy" provides a compelling case for integrating transformation-based self-supervision in unsupervised optical flow estimation, effectively bridging the performance gap with supervised methods while enhancing model efficiency and utility.