# Semantic Video Segmentation : Exploring Inference Efficiency

Source: [Semantic Video Segmentation : Exploring Inference Efficiency](<https://www.sota2.com/research/paper/68fa62121e209131ce9f7434>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:11:29.310Z

Authors: Subarna Tripathi, Serge Belongie, Youngbae Hwang, Truong Nguyen

Published: 2015-09-04T22:03:40.000Z

## Abstract

We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames. The key idea is to combine best of two worlds: semantic co-labeling and more expressive models. Our formulation enables us to perform inference over ten thousand images within seconds and makes the system amenable to perform video semantic segmentation most effectively. On CamVid dataset, with TextonBoost unaries, our proposed method achieves up to 8% improvement in accuracy over individual semantic image segmentation without additional time overhead. The source code is available at https://github.com/subtri/video\_inference

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1509.02441>)

[PDF](<https://arxiv.org/pdf/1509.02441.pdf>)