VALSE 论文速览 第199期：SCTNet: 融合Transformer语义信息的单分支CNN实时语义分割网 ...

论文题目：

SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation

作者列表：

徐正泽 (华中科技大学)、吴东岳 (华中科技大学)、余昌黔 (美团）、初祥祥 (美团)、桑农 (华中科技大学)、高常鑫 (华中科技大学)

B站观看网址：

https://www.bilibili.com/video/BV1voxNe8E8h/?spm_id_from=333.999.0.0&vd_source=2044ac986b5caa4c8b5f00b525441df3

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论文摘要：

Recent real-time semantic segmentation methods usually adopt an additional semantic branch to pursue rich long-range context. However, the additional branch incurs undesirable computational overhead and slows inference speed. To eliminate this dilemma, we propose SCTNet, a single branch CNN with transformer semantic information for real-time segmentation. SCTNet enjoys the rich semantic representations of an inference-free semantic branch while retaining the high efficiency of lightweight single branch CNN. SCTNet utilizes a transformer as the training-only semantic branch considering its superb ability to extract long-range context. With the help of the proposed transformer-like CNN block CFBlock and the semantic information alignment module, SCTNet could capture the rich semantic information from the transformer branch in training. During the inference, only the single branch CNN needs to be deployed. We conduct extensive experiments on Cityscapes, ADE20K, and COCO-Stuff-10K, and the results show that our method achieves the new state-of-the-art performance.

参考文献：

[1] Xu, Zhengze, Dongyue Wu, Changqian Yu, Xiangxiang Chu, Nong Sang, and Changxin Gao. "SCTNet: Single-Branch CNN with Transformer Semantic Information for Real-Time Segmentation." AAAI Conference on Artificial Intelligence (AAAI), 2024.

论文链接：

[https://arxiv.org/pdf/2312.17071.pdf]

代码链接：

[https://github.com/xzz777/SCTNet]

视频讲者简介：

徐正泽，华中科技大学硕士研究生，研究方向为语义分割、模型轻量化，师从高常鑫教授。

个人主页：

https://github.com/xzz777

特别鸣谢本次论文速览主要组织者：

月度轮值AC：傅雪阳 (中国科学技术大学)