Approximate Policy Iteration for Budgeted Semantic Video Segmentation

TL;DR

This paper introduces a novel budgeted inference framework for semantic video segmentation that intelligently selects descriptors within a time limit, maintaining accuracy while reducing computation time.

Contribution

It presents a new budgeted inference method for CRFs and a learning algorithm based on Approximate Policy Iteration to optimize descriptor selection under time constraints.

Findings

Significantly reduces computation time for video segmentation.

Maintains competitive accuracy across different time budgets.

Demonstrates effectiveness on multiple video datasets.

Abstract

This paper formulates and presents a solution to the new problem of budgeted semantic video segmentation. Given a video, the goal is to accurately assign a semantic class label to every pixel in the video within a specified time budget. Typical approaches to such labeling problems, such as Conditional Random Fields (CRFs), focus on maximizing accuracy but do not provide a principled method for satisfying a time budget. For video data, the time required by CRF and related methods is often dominated by the time to compute low-level descriptors of supervoxels across the video. Our key contribution is the new budgeted inference framework for CRF models that intelligently selects the most useful subsets of descriptors to run on subsets of supervoxels within the time budget. The objective is to maintain an accuracy as close as possible to the CRF model with no time bound, while remaining within the time budget. Our second contribution is the algorithm for learning a policy for the sparse selection of supervoxels and their descriptors for budgeted CRF inference. This learning algorithm is derived by casting our problem in the framework of Markov Decision Processes, and then instantiating a state-of-the-art policy learning algorithm known as Classification-Based Approximate Policy Iteration. Our experiments on multiple video datasets show that our learning approach and framework is able to significantly reduce computation time, and maintain competitive accuracy under varying budgets.