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

TL;DR

This paper introduces INO generative learning, a unified framework combining image-level and pixel-level optimization for unsupervised video object segmentation using Vision Transformers, achieving state-of-the-art results.

Contribution

It proposes a novel INO generative learning approach that unifies high-level and pixel-level optimization in a single framework for VOS.

Findings

Outperforms previous state-of-the-art methods on DAVIS-2017 and YouTube-VOS datasets.

Effectively captures high-level semantics and fine-grained details.

Enhances temporal consistency in video segmentation.

Abstract

In this paper, we focus on unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., the similarity between pixel-level features) from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in image level or pixel level. Image-level optimization (e.g., the spatially pooled feature of ResNet) learns robust high-level semantics but is sub-optimal since the pixel-level features are optimized implicitly. By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation. To complementarily perform these two levels of optimization in a unified framework, we propose the In-aNd-Out (INO) generative learning from a purely generative perspective with the help of naturally designed class tokens and patch tokens in Vision Transformer (ViT). Specifically, for image-level optimization, we force the out-view imagination from local to global views on class tokens, which helps capture high-level semantics, and we name it as out-generative learning. As to pixel-level optimization, we perform in-view masked image modeling on patch tokens, which recovers the corrupted parts of an image via inferring its fine-grained structure, and we term it as in-generative learning. To discover the temporal information better, we additionally force the inter-frame consistency from both feature and affinity matrix levels. 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. Code is available: https://github.com/pansanity666/INO_VOS