kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies

• URL: http://arxiv.org/abs/2404.09447v3
• Date: Tue, 13 Aug 2024 13:24:33 GMT
• Title: kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
• Authors: Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, Philip Torr,
• Abstract summary: kNN-CLIP is a training-free strategy for continual segmentation. It can adapt to continually growing vocabularies without the need for retraining or large memory costs. It achieves state-of-the-art performance across large-vocabulary semantic and panoptic segmentation datasets.
• Score: 22.51592283786031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually expanding vocabularies. We discover that traditional continual training results in severe catastrophic forgetting, failing to outperform a zero-shot segmentation baseline. We introduce a novel training-free strategy, kNN-CLIP, which augments the model with a database of instance embeddings for semantic and panoptic segmentation that achieves zero forgetting. We demonstrate that kNN-CLIP can adapt to continually growing vocabularies without the need for retraining or large memory costs. kNN-CLIP enables open-vocabulary segmentation methods to expand their vocabularies on any domain with a single pass through the data, while only storing compact embeddings. This approach minimizes both compute and memory costs. kNN-CLIP achieves state-of-the-art performance across large-vocabulary semantic and panoptic segmentation datasets. We hope kNN-CLIP represents a significant step forward in enabling more efficient and adaptable continual segmentation, paving the way for advances in real-world large-vocabulary continual segmentation methods.

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