Semantic Video Segmentation by Gated Recurrent Flow Propagation
This addresses the challenge of processing large video data efficiently for applications like autonomous driving, though it is incremental as it builds on existing static segmentation methods.
The paper tackles semantic video segmentation by proposing an end-to-end trainable model that uses gated recurrent flow propagation to leverage unlabeled temporal frames, improving accuracy and consistency without extra annotation or significant computation, achieving gains on CityScapes and Camvid datasets.
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.