Semanticsegmentationactivelearning
Source code for a novel active learning algorithm for efficiently annotating semantic segmentation data.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Transformer Explainability | 1,596 | 4 months ago | 9 | mit | Jupyter Notebook | |||||
| [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks. | ||||||||||
| Rethinking Text Segmentation | 222 | 6 months ago | 4 | Python | ||||||
| [CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach | ||||||||||
| Loss Family | 83 | 5 years ago | ||||||||
| Some classical and cutting-edge loss in semantic segmentation task | ||||||||||
| Iccv2019_mirrornet | 16 | 4 years ago | 3 | other | Python | |||||
| Code for the ICCV 2019 paper "Where Is My Mirror?" | ||||||||||
| Tensormask Review | 10 | 5 years ago | gpl-3.0 | |||||||
| Sliding-window object detectors that generate boundingbox 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 slidingwindow instance segmentation, which is surprisingly underexplored. 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. | ||||||||||
| Chair Segments | 7 | 3 years ago | 1 | Python | ||||||
| Chair Segments: A Compact Benchmark for the Study of Object Segmentation | ||||||||||
| Semanticsegmentationactivelearning | 5 | 3 years ago | 1 | mit | Python | |||||
| Source code for a novel active learning algorithm for efficiently annotating semantic segmentation data. | ||||||||||
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