Modality adaptation via feature difference learning for depth human parsing

In the field of human parsing, depth data offers unique advantages over RGB data due to its illumination invariance and geometric detail, which motivates us to explore human parsing with only depth input. However, depth data is challenging to collect at scale due to the specialized equipment required. In contrast, RGB data is readily available in large quantities, presenting an opportunity to enhance depth-only parsing models with semantic knowledge learned from RGB data. However, fully finetuning the RGB-pretrained encoder leads to high training costs and inflexible domain generalization, while keeping the encoder frozen suffers from a large RGB-depth modality gap and restricts the parsing performance. To alleviate the limitations of these naive approaches, we introduce a Modality Adaptation pipeline via Feature Difference Learning (MAFDL) which leverages the RGB knowledge to facilitate depth human parsing. A Difference-Guided Depth Adapter (DGDA) is proposed within MAFDL to learn the feature differences between RGB and depth modalities, adapting depth features into RGB feature space to bridge the modality gap. Furthermore, we also design a Feature Alignment Constraint (FAC) to impose explicit alignment supervision at pixel and batch levels, making the modality adaptation more comprehensive. Extensive experiments on the NTURGBD-Parsing-4K dataset show that our method surpasses previous state-of-the-art approaches.