Image-level Supervised Instance Segmentation
8 papers with code • 3 benchmarks • 1 datasets
Weakly-Supervised Instance Segmentation using Image-level Labels
Benchmarks
These leaderboards are used to track progress in Image-level Supervised Instance Segmentation
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MS COCO
Most implemented papers
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries.
Object Counting and Instance Segmentation with Image-level Supervision
GuoleiSun/CountSeg
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CVPR 2019
Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
Weakly Supervised Instance Segmentation using Class Peak Response
Motivated by this, we first design a process to stimulate peaks to emerge from a class response map.
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation
shenyunhang/WS-JDS
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CVPR 2019
In this paper, we join weakly supervised object detection and segmentation tasks with a multi-task learning scheme for the first time, which uses their respective failure patterns to complement each other's learning.
Where are the Masks: Instance Segmentation with Image-level Supervision
A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective.
Towards Partial Supervision for Generic Object Counting in Natural Scenes
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
yun-liu/LIID
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For each proposal, this MIL framework can simultaneously compute probability distributions and category-aware semantic features, with which we can formulate a large undirected graph.
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement
This semantic drift occurs confusion between background and instance in training and consequently degrades the segmentation performance.
