Papers › TensorMask: A Foundation for Dense Object Segmentation

TensorMask: A Foundation for Dense Object Segmentation

28 Mar 2019ICCV 2019 10arXiv:1903.12174archive 2025-07-28

Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár

Sliding-window object detectors that generate bounding-box object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense sliding-window instance segmentation, which is surprisingly under-explored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.

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Code

facebookresearch/detectron2 officialmentioned on GitHubpytorch report

suvasis/birdnet2cs231n mentioned on GitHubpytorchApache-2.0 report

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Tasks

Instance Segmentation Object Object Detection Prediction Segmentation Semantic Segmentation object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Instance Segmentation	COCO test-dev	TensorMask (ResNet-101-FPN)	mask AP	37.3%	#92 of 112	Archive leaderboard	report

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Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Kaiming Initialization Mask R-CNN Max Pooling RPN ReLU Residual Block Residual Connection RoIAlign Softmax

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