Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation

• URL: http://arxiv.org/abs/2012.03603v1
• Date: Mon, 7 Dec 2020 11:43:10 GMT
• Title: Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
• Authors: Gengwei Zhang, Yiming Gao, Hang Xu, Hao Zhang, Zhenguo Li, Xiaodan Liang
• Abstract summary: We propose an automated multi-loss adaptation (named Ada-Segment) to flexibly adjust multiple training losses over the course of training. With an end-to-end architecture, Ada-Segment generalizes to different datasets without the need of re-tuning hyper parameters. Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset.
• Score: 95.31590177308482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention. While most existing methods focus on designing novel architectures, we steer toward a different perspective: performing automated multi-loss adaptation (named Ada-Segment) on the fly to flexibly adjust multiple training losses over the course of training using a controller trained to capture the learning dynamics. This offers a few advantages: it bypasses manual tuning of the sensitive loss combination, a decisive factor for panoptic segmentation; it allows to explicitly model the learning dynamics, and reconcile the learning of multiple objectives (up to ten in our experiments); with an end-to-end architecture, it generalizes to different datasets without the need of re-tuning hyperparameters or re-adjusting the training process laboriously. Our Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset. The extensive ablation studies reveal the ever-changing dynamics throughout the training process, necessitating the incorporation of an automated and adaptive learning strategy as presented in this paper.

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