# SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation

Source: [SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6de51e209131cea3cbfd>)

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

Data updated: 2025-10-26T00:46:11.149Z

Authors: Junliang Chen, Xiaodong Zhao, Minmin Liu, Linlin Shen

Published: 2022-10-22T11:17:30.000Z

## Abstract

Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity. In this paper, we propose a novel semantic learning based framework, named SLAMs (Semantic Learning based Activation Map), for WSSS.

## 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/2210.12417>)

[PDF](<https://arxiv.org/pdf/2210.12417.pdf>)