Weakly-supervised instance segmentation
19 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Weakly-supervised instance segmentation
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MS COCO
Most implemented papers
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision.
BoxInst: High-Performance Instance Segmentation with Box Annotations
We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training.
Pointly-Supervised Instance Segmentation
facebookresearch/detectron2
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CVPR 2022
Our experiments show that the new module is more suitable for the point-based supervision.
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.
Weakly- and Semi-Supervised Panoptic Segmentation
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
chengchunhsu/WSIS_BBTP
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NeurIPS 2019
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations.
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.
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation
Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object.
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.
Bounding Box Tightness Prior for Weakly Supervised Image Segmentation
wangjuan313/wsis-boundingbox
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Two variants of smooth maximum approximation, i. e., $\alpha$-softmax function and $\alpha$-quasimax function, are exploited to conquer the numeral instability introduced by maximum function of bag prediction.
