aiqbench analysis
SlotMatch proposes a simple distillation method that aligns teacher and student object-centric "slots" using cosine similarity, so the student matches the teacher's direction-encoded structure while ignoring norms. The reported result is a student with 3.6x fewer parameters and up to 2.7x faster inference that matches or slightly beats its teacher on three benchmarks. These are results on academic datasets, so practitioners should check whether the gains hold on their own video workloads before relying on them.
SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation
arXiv:2508.03411v4 Announce Type: replace-cross Abstract: 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. Unlike other distillation losses, our cosine-based distillation of slots enforces alignment of slot directions (which typically encode relational structure and semantic similarity), while ignoring their norms (which may encode confidence overestimation and dataset-specific scale artifacts). The simplicity of SlotMatch is supported by theoretical and empirical evidence, both indicating that integrating additional losses may be suboptimal. We conduct experiments on three datasets to compare state-of-the-art models, including our SlotContrast teacher, with our distilled student. The results show that our student based on SlotMatch matches and even outperforms its teacher, while using 3.6x fewer parameters and running up to 2.7x faster. Moreover, our student surpasses all other state-of-the-art unsupervised video segmentation models. We release our code at https://github.com/dianagrigore/SlotMatch.
Abstract: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. Unlike other distillation losses, our cosine-based distillation of slots enforces alignment of slot directions (which typically encode relational structure and semantic similarity), while ignoring their norms (which may encode confidence overestimation and dataset-specific scale artifacts). The simplicity of SlotMatch is supported by theoretical and empirical evidence, both indicating that integrating additional losses may be suboptimal. We conduct experiments on three datasets to compare state-of-the-art models, including our SlotContrast teacher, with our distilled student. The results show that our student based on SlotMatch matches and even outperforms its teacher, while using 3.6x fewer parameters and running up to 2.7x faster. Moreover, our student surpasses all other state-of-the-art unsupervised video segmentation models. We release our code at this https URL.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2508.03411 [cs.CV]
(or arXiv:2508.03411v4 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2508.03411 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 303 words