Semantic Video Segmentation by Gated Recurrent Flow Propagation (1612.08871v2)

Abstract: Semantic video segmentation is challenging due to the sheer amount of data that needs to be processed and labeled in order to construct accurate models. In this paper we present a deep, end-to-end trainable methodology to video segmentation that is capable of leveraging information present in unlabeled data in order to improve semantic estimates. Our model combines a convolutional architecture and a spatio-temporal transformer recurrent layer that are able to temporally propagate labeling information by means of optical flow, adaptively gated based on its locally estimated uncertainty. The flow, the recognition and the gated temporal propagation modules can be trained jointly, end-to-end. The temporal, gated recurrent flow propagation component of our model can be plugged into any static semantic segmentation architecture and turn it into a weakly supervised video processing one. Our extensive experiments in the challenging CityScapes and Camvid datasets, and based on multiple deep architectures, indicate that the resulting model can leverage unlabeled temporal frames, next to a labeled one, in order to improve both the video segmentation accuracy and the consistency of its temporal labeling, at no additional annotation cost and with little extra computation.

Semantic Video Segmentation by Gated Recurrent Flow Propagation: An Overview

The paper "Semantic Video Segmentation by Gated Recurrent Flow Propagation," authored by David Nilsson and Cristian Sminchisescu, introduces an innovative approach to semantic video segmentation that enhances temporal coherence and segmentation accuracy by leveraging unlabeled data. The proposed methodology integrates convolutional neural networks (CNNs) with a spatio-temporal transformer recurrent layer, offering a framework for temporally propagating labeling information through optical flow. This optical flow is adaptively gated according to the locally estimated uncertainty, allowing for a more robust integration of temporal information.

Methodology

At the core of the proposed framework is the Gated Recurrent Flow Propagation (GRFP) model, which employs a Spatio-Temporal Transformer Gated Recurrent Unit (STGRU). This component can transform any static segmentation model into a weakly supervised video processing architecture. Notably, the methodology is fully differentiable and end-to-end trainable, allowing it to optimize the flow, recognition, and temporal propagation modules simultaneously.

The GRFP methodology is designed to address the challenge of labeled data scarcity in video sequences by taking advantage of temporal dependencies across frames. By integrating spatio-temporal warping informed by optical flow within the CNN framework, the model mitigates annotation costs while maintaining high segmentation accuracy. The STGRUs enable the adaptive fusion of estimates from spatially warped input frames and dynamic temporal frames based on uncertainty.

Experimental Evaluation

The authors assess the performance of their methodology on benchmark datasets, including CityScapes and CamVid. Through comprehensive experimentation, it is demonstrated that GRFP significantly improves upon standard static segmentation models. For instance, leveraging multiple video frames resulted in a notable increase in mean Intersection over Union (mIoU) values, evidencing enhanced segmentation accuracy and temporal consistency. When tested on the CityScapes dataset, the GRFP model achieved a mean IoU of 69.4% compared to a baseline of 68.7% obtained with the Dilation10 network. The temporal consistency assessment further highlighted GRFP's effectiveness with a clear reduction in flickering and noise within the video sequence outputs.

The integration of forward and backward temporal models allows the GRFP framework to enhance predictions by exploiting additional frames, thereby improving inference quality. Additionally, the paper explored the potential of joint end-to-end training of optical flow networks and segmentation networks, although performance gains were modest given the limitations of current deep optical flow models.

Implications and Future Work

The implications of this work extend into various practical applications, such as robotics, autonomous navigation, and content indexing, where temporal coherence in video segmentation is critical. The model's flexibility enables its adaptation to any single-frame semantic segmentation method, suggesting broad applicability and potential for performance improvements across existing methods.

Future research could emphasize the refinement of end-to-end optical flow networks, ensuring that temporal prediction quality matches that achieved by state-of-the-art optical flow techniques. As deep optical flow models evolve, there is potential for the GRFP framework to leverage these advancements, thereby enhancing its segmentation precision and reliability.

Overall, the GRFP model represents a significant step towards efficient and effective semantic video segmentation, addressing both annotation cost and computational complexity challenges. Its ability to seamlessly integrate temporal information into existing segmentation pipelines marks a key advancement in leveraging video data for improved semantic understanding.