# SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment

Source: [SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment](<https://www.sota2.com/research/paper/68fa70e61e209131cea4dca0>)

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

Data updated: 2025-10-27T06:27:18.233Z

Authors: Tatiana Zemskova, Margarita Kichik, Dmitry Yudin, Aleksei Staroverov, Aleksandr Panov

Published: 2023-10-18T15:15:13.000Z

## Abstract

This paper presents an adaptive transformer model named SegmATRon for embodied image semantic segmentation. Its distinctive feature is the adaptation of model weights during inference on several images using a hybrid multicomponent loss function. We studied this model on datasets collected in the photorealistic Habitat and the synthetic AI2-THOR Simulators. We showed that obtaining additional images using the agent's actions in an indoor environment can improve the quality of semantic segmentation. The code of the proposed approach and datasets are publicly available at https://github.com/wingrune/SegmATRon.

## 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/2310.12031>)

[PDF](<https://arxiv.org/pdf/2310.12031.pdf>)