Complementary Random Masking for RGB-Thermal Semantic Segmentation

30 Mar 2023  ·  Ukcheol Shin, Kyunghyun Lee, In So Kweon, Jean Oh ·

RGB-thermal semantic segmentation is one potential solution to achieve reliable semantic scene understanding in adverse weather and lighting conditions. However, the previous studies mostly focus on designing a multi-modal fusion module without consideration of the nature of multi-modality inputs. Therefore, the networks easily become over-reliant on a single modality, making it difficult to learn complementary and meaningful representations for each modality. This paper proposes 1) a complementary random masking strategy of RGB-T images and 2) self-distillation loss between clean and masked input modalities. The proposed masking strategy prevents over-reliance on a single modality. It also improves the accuracy and robustness of the neural network by forcing the network to segment and classify objects even when one modality is partially available. Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities. Based on the proposed method, we achieve state-of-the-art performance over three RGB-T semantic segmentation benchmarks. Our source code is available at https://github.com/UkcheolShin/CRM_RGBTSeg.

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Thermal Image Segmentation KP day-night CRM_RGBTSeg mIoU 55.2 # 1
Thermal Image Segmentation MFN Dataset CRM_RGBT_Seg mIOU 61.4 # 1
Thermal Image Segmentation PST900 CRM_RGBTSeg mIoU 88 # 1

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