# Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding

Source: [Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding](<https://www.sota2.com/research/paper/68fa616a1e209131ce9f440c>)

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

Data updated: 2025-10-25T19:59:37.091Z

Authors: Roozbeh Mottaghi, Sanja Fidler, Alan Yuille, Raquel Urtasun, Devi Parikh

Published: 2014-06-16T05:37:17.000Z

## Abstract

Recent trends in image understanding have pushed for holistic scene understanding models that jointly reason about various tasks such as object detection, scene recognition, shape analysis, contextual reasoning, and local appearance based classifiers. In this work, we are interested in understanding the roles of these different tasks in improved scene understanding, in particular semantic segmentation, object detection and scene recognition. Towards this goal, we "plug-in" human subjects for each of the various components in a state-of-the-art conditional random field model. Comparisons among various hybrid human-machine CRFs give us indications of how much "head room" there is to improve scene understanding by focusing research efforts on various individual tasks.

## 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/1406.3906>)

[PDF](<https://arxiv.org/pdf/1406.3906.pdf>)