# Knowledge Consultation for Semi-Supervised Semantic Segmentation

Source: [Knowledge Consultation for Semi-Supervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa77631e209131cea72efd>)

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

Data updated: 2025-10-26T04:17:49.742Z

Authors: Thuan Than, Nhat-Anh Nguyen-Dang, Dung Nguyen, Salwa K. Al Khatib, Ahmed Elhagry, Hai Phan, Yihui He, Zhiqiang Shen, Marios Savvides, Dang Huynh

Published: 2025-03-12T04:32:37.000Z

## Abstract

Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the success of deep co-training methods, their underlying mechanisms remain underexplored. This work revisits Cross Pseudo Supervision with dual heterogeneous backbones and introduces Knowledge Consultation (SegKC) to further enhance segmentation performance. The proposed SegKC achieves significant improvements on Pascal and Cityscapes benchmarks, with mIoU scores of 87.1%, 89.2%, and 89.8% on Pascal VOC with the 1/4, 1/2, and full split partition, respectively, while maintaining a compact model architecture.

## 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/2503.10693>)

[PDF](<https://arxiv.org/pdf/2503.10693.pdf>)