Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth

• URL: http://arxiv.org/abs/2206.10562v1
• Date: Tue, 21 Jun 2022 17:40:55 GMT
• Title: Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth
• Authors: Nitin Bansal, Pan Ji, Junsong Yuan, Yi Xu
• Abstract summary: We tackle the MTL problem of two dense tasks, ie, semantic segmentation and depth estimation, and present a novel attention module called Cross-Channel Attention Module (CCAM) In a true symbiotic spirit, we then formulate a novel data augmentation for the semantic segmentation task using predicted depth called AffineMix, and a simple depth augmentation using predicted semantics called ColorAug. Finally, we validate the performance gain of the proposed method on the Cityscapes dataset, which helps us achieve state-of-the-art results for a semi-supervised joint model based on depth and semantic
• Score: 83.94528876742096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-task learning (MTL) paradigm focuses on jointly learning two or more tasks, aiming for significant improvement w.r.t model's generalizability, performance, and training/inference memory footprint. The aforementioned benefits become ever so indispensable in the case of joint training for vision-related {\bf dense} prediction tasks. In this work, we tackle the MTL problem of two dense tasks, \ie, semantic segmentation and depth estimation, and present a novel attention module called Cross-Channel Attention Module ({CCAM}), which facilitates effective feature sharing along each channel between the two tasks, leading to mutual performance gain with a negligible increase in trainable parameters. In a true symbiotic spirit, we then formulate a novel data augmentation for the semantic segmentation task using predicted depth called {AffineMix}, and a simple depth augmentation using predicted semantics called {ColorAug}. Finally, we validate the performance gain of the proposed method on the Cityscapes dataset, which helps us achieve state-of-the-art results for a semi-supervised joint model based on depth and semantic segmentation.

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