用简单方法把大模型的物体感知能力搬进小模型，速度更快、参数更少
SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation
- 通过余弦相似度对齐师生模型的注意力槽，无需额外损失函数
- 学生模型参数少3.6倍，速度提升2.7倍，性能超过教师模型
- 适合资源受限场景下的无监督视频分割任务
无监督视频分割因缺乏标注信号且场景复杂而极具挑战。现有基于槽注意力的先进模型通常依赖庞大且计算成本高的神经网络。为此，我们提出一种简洁的知识蒸馏框架SlotMatch，将教师模型的对象中心表示高效迁移到轻量级学生模型。SlotMatch通过余弦相似度对齐教师与学生模型的对应槽，无需额外蒸馏目标或辅助监督。理论与实证均表明，引入额外损失是冗余的。我们在三个数据集上将先进教师模型SlotContrast与所提学生模型进行对比，结果表明，该学生模型在参数量减少3.6倍、推理速度提升最高达2.7倍的前提下，性能不仅匹配甚至超越其教师模型，且优于所有其他先进无监督视频分割模型。
原文摘要 · Abstract (English)
Unsupervised video segmentation is a challenging computer vision task, especially due to the lack of supervisory signals coupled with the complexity of visual scenes. To overcome this challenge, state-of-the-art models based on slot attention often have to rely on large and computationally expensive neural architectures. To this end, we propose a simple knowledge distillation framework that effectively transfers object-centric representations to a lightweight student. The proposed framework, called SlotMatch, aligns corresponding teacher and student slots via the cosine similarity, requiring no additional distillation objectives or auxiliary supervision. The simplicity of SlotMatch is confirmed via theoretical and empirical evidence, both indicating that integrating additional losses is redundant. We conduct experiments on three datasets to compare the state-of-the-art teacher model, SlotContrast, with our distilled student. The results show that our student based on SlotMatch matches and even outperforms its teacher, while using 3.6x less parameters and running up to 2.7x faster. Moreover, our student surpasses all other state-of-the-art unsupervised video segmentation models.
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