Papers › Mean Shift Mask Transformer for Unseen Object Instance Segmentation

Mean Shift Mask Transformer for Unseen Object Instance Segmentation

21 Nov 2022arXiv:2211.11679archive 2025-07-28

Yangxiao Lu, Yuqiao Chen, Nicholas Ruozzi, Yu Xiang

Segmenting unseen objects from images is a critical perception skill that a robot needs to acquire. In robot manipulation, it can facilitate a robot to grasp and manipulate unseen objects. Mean shift clustering is a widely used method for image segmentation tasks. However, the traditional mean shift clustering algorithm is not differentiable, making it difficult to integrate it into an end-to-end neural network training framework. In this work, we propose the Mean Shift Mask Transformer (MSMFormer), a new transformer architecture that simulates the von Mises-Fisher (vMF) mean shift clustering algorithm, allowing for the joint training and inference of both the feature extractor and the clustering. Its central component is a hypersphere attention mechanism, which updates object queries on a hypersphere. To illustrate the effectiveness of our method, we apply MSMFormer to unseen object instance segmentation. Our experiments show that MSMFormer achieves competitive performance compared to state-of-the-art methods for unseen object instance segmentation. The project page, appendix, video, and code are available at https://irvlutd.github.io/MSMFormer

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Code

youngsean/unseenobjectswithmeanshift officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Clustering Image Segmentation Instance Segmentation Object Robot Manipulation Segmentation Semantic Segmentation Unseen Object Instance Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Unseen Object Instance Segmentation	OCID	MSMFormer	Boundary F-measure	87.3	#1 of 1	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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