In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

• URL: http://arxiv.org/abs/2203.15312v1
• Date: Tue, 29 Mar 2022 07:56:21 GMT
• Title: In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
• Authors: Xiao Pan, Peike Li, Zongxin Yang, Huiling Zhou, Chang Zhou, Hongxia Yang, Jingren Zhou, Yi Yang
• Abstract summary: In this paper, we focus on the unsupervised Video Object (VOS) task which learns visual correspondence from unlabeled videos. We propose the In-aNd-Out (INO) generative learning from a purely generative perspective, which captures both high-level and fine-grained semantics. Our INO outperforms previous state-of-the-art methods by significant margins.
• Score: 89.21483504654282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we focus on the unsupervised Video Object Segmentation (VOS) task which learns visual correspondence from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in pixel level or image level and show unsatisfactory scalability. Image-level optimization learns pixel-wise information implicitly therefore is sub-optimal for such dense prediction task, while pixel-level optimization ignores the high-level semantic scope for capturing object deformation. To complementarily learn these two levels of information in an unified framework, we propose the In-aNd-Out (INO) generative learning from a purely generative perspective, which captures both high-level and fine-grained semantics by leveraging the structural superiority of Vision Transformer (ViT) and achieves better scalability. Specifically, the in-generative learning recovers the corrupted parts of an image via inferring its fine-grained semantic structure, while the out-generative learning captures high-level semantics by imagining the global information of an image given only random fragments. To better discover the temporal information, we additionally force the inter-frame consistency from both feature level and affinity matrix level. Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our INO outperforms previous state-of-the-art methods by significant margins.

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