让模型忽略纹理差异，更准确分割没见过的新物体。
v-CLR: View-Consistent Learning for Open-World Instance Segmentation
- 通过多视角图像增强纹理变化，强制模型学习不变特征。
- 在跨类别和跨数据集测试中均达顶尖性能，显著提升新物体识别率。
- 适合做开放世界分割的科研与工业应用，尤其关注泛化能力。
本文针对开放世界实例分割的挑战提出v-CLR框架。现有视觉模型常依赖外观特征（如纹理）进行识别，导致在开放世界中无法检测具有未见纹理的新物体。为解决此问题，我们引入多视角图像，使纹理大幅改变但保持结构一致，通过强制不同视图间物体特征的一致性，引导模型学习外观无关表示。利用现成无监督模型生成类无关物体提议，实现跨视图特征匹配，显著降低对纹理的依赖并增强对象感知能力。我们在公开基准上进行了全面评估，在跨类别与跨数据集设置下均达到当前最优表现。
原文摘要 · Abstract (English)
In this paper, we address the challenging problem of open-world instance segmentation. Existing works have shown that vanilla visual networks are biased toward learning appearance information, \eg texture, to recognize objects. This implicit bias causes the model to fail in detecting novel objects with unseen textures in the open-world setting. To address this challenge, we propose a learning framework, called view-Consistent LeaRning (v-CLR), which aims to enforce the model to learn appearance-invariant representations for robust instance segmentation. In v-CLR, we first introduce additional views for each image, where the texture undergoes significant alterations while preserving the image's underlying structure. We then encourage the model to learn the appearance-invariant representation by enforcing the consistency between object features across different views, for which we obtain class-agnostic object proposals using off-the-shelf unsupervised models that possess strong object-awareness. These proposals enable cross-view object feature matching, greatly reducing the appearance dependency while enhancing the object-awareness. We thoroughly evaluate our method on public benchmarks under both cross-class and cross-dataset settings, achieving state-of-the-art performance. Project page: https://visual-ai.github.io/vclr
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