One-stage Modality Distillation for Incomplete Multimodal Learning

Learning based on multimodal data has attracted increasing interest recently. While a variety of sensory modalities can be collected for training, not all of them are always available in development scenarios, which raises the challenge to infer with incomplete modality. To address this issue, this paper presents a one-stage modality distillation framework that unifies the privileged knowledge transfer and modality information fusion into a single optimization procedure via multi-task learning. Compared with the conventional modality distillation that performs them independently, this helps to capture the valuable representation that can assist the final model inference directly. Specifically, we propose the joint adaptation network for the modality transfer task to preserve the privileged information. This addresses the representation heterogeneity caused by input discrepancy via the joint distribution adaptation. Then, we introduce the cross translation network for the modality fusion task to aggregate the restored and available modality features. It leverages the parameters-sharing strategy to capture the cross-modal cues explicitly. Extensive experiments on RGB-D classification and segmentation tasks demonstrate the proposed multimodal inheritance framework can overcome the problem of incomplete modality input in various scenes and achieve state-of-the-art performance.

MMANet: Margin-aware Distillation and Modality-aware Regularization for Incomplete Multimodal Learning

Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations. Specifically, we propose a novel margin-aware distillation (MAD) to assist the information transfer by weighing the sample contribution with the classification uncertainty. This encourages the deployment network to focus on the samples near decision boundaries and acquire the refined inter-class margin. Besides, we design a modality-aware regularization (MAR) algorithm to mine the weak modality combinations and guide the regularization network to calculate prediction loss for them. This forces the deployment network to improve its representation ability for the weak modality combinations adaptively. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art significantly. Code is available at: https://github.com/shicaiwei123/MMANet