DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion

• URL: http://arxiv.org/abs/2402.04558v1
• Date: Wed, 7 Feb 2024 03:36:41 GMT
• Title: DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion
• Authors: Guoqiang Liang, Jiahao Hu, Qingyue Wang, Shizhou Zhang
• Abstract summary: Human de-occlusion aims to infer the appearance of invisible human parts from an occluded image. This paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions. Experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods.
• Score: 5.901982216097867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods.

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