Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation

• URL: http://arxiv.org/abs/2309.08020v1
• Date: Thu, 14 Sep 2023 20:31:06 GMT
• Title: Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation
• Authors: Zhaochong An, Guolei Sun, Zongwei Wu, Hao Tang, Luc Van Gool
• Abstract summary: Video semantic segmentation dataset has limited categories per video. Less than 10% of queries could be matched to receive meaningful gradient updates during VSS training. Our method achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.
• Score: 62.275143240798236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern approaches have proved the huge potential of addressing semantic segmentation as a mask classification task which is widely used in instance-level segmentation. This paradigm trains models by assigning part of object queries to ground truths via conventional one-to-one matching. However, we observe that the popular video semantic segmentation (VSS) dataset has limited categories per video, meaning less than 10% of queries could be matched to receive meaningful gradient updates during VSS training. This inefficiency limits the full expressive potential of all queries.Thus, we present a novel solution THE-Mask for VSS, which introduces temporal-aware hierarchical object queries for the first time. Specifically, we propose to use a simple two-round matching mechanism to involve more queries matched with minimal cost during training while without any extra cost during inference. To support our more-to-one assignment, in terms of the matching results, we further design a hierarchical loss to train queries with their corresponding hierarchy of primary or secondary. Moreover, to effectively capture temporal information across frames, we propose a temporal aggregation decoder that fits seamlessly into the mask-classification paradigm for VSS. Utilizing temporal-sensitive multi-level queries, our method achieves state-of-the-art performance on the latest challenging VSS benchmark VSPW without bells and whistles.

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