Papers Weakly-supervised instance segmentation
“Weakly-supervised instance segmentation” 태그가 달린 논문 39편 · 필터 해제
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. Specifically, we propose a self-ensembling framework where instance segmentat…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic correspondence+1Pointly-Supervised Instance Segmentation
We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each…
Instance SegmentationObjectSegmentationSemantic Segmentation+1Weakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images
Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based …
Box-supervised Instance SegmentationInstance SegmentationMulti-Task LearningObject+4Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) parti…
Instance SegmentationRelation NetworkSegmentationSemantic Segmentation+1BBAM: 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. Existing methods typically depend on a class-agnostic mask generator, which …
Box-supervised Instance SegmentationInstance SegmentationObjectSegmentation+4Weakly Supervised Multi-Object Tracking and Segmentation
We introduce the problem of weakly supervised Multi-Object Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation. …
Instance SegmentationMulti-Object TrackingMulti-Object Tracking and SegmentationMulti-Task Learning+6Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation
Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention. To date, bottom-up WSIS methods refine discriminative cues from classifiers with sophisticated multi-stag…
Instance Segmentationobject-detectionObject DetectionSegmentation+4BoxInst: 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. While this setting has been studied in the literature, here we show significantly st…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+2Leveraging Instance-, Image- and Dataset-Level Information for Weakly Supervised Instance Segmentation
Weakly supervised semantic instance segmentation with only image-level supervision, instead of relying on expensive pixel wise masks or bounding box annotations, is an important problem to alleviate the data-hungry natur…
Image-level Supervised Instance SegmentationInstance SegmentationMultiple Instance LearningSegmentation+3Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances
Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model that provides instances which are consistent with a given annotation; and (ii) an instance segm…
Image-level Supervised Instance SegmentationInstance SegmentationPseudo LabelSegmentation+2Weakly Supervised Instance Segmentation by Deep Community Learning
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentatio…
Image-level Supervised Instance SegmentationInstance Segmentationobject-detectionObject Detection+4Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each boundi…
Box-supervised Instance SegmentationInstance SegmentationMultiple Instance LearningSegmentation+3Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation
Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only. Unlike previous methods which are composed of multiple offline stages, we propose Sequential La…
General ClassificationImage-level Supervised Instance SegmentationInstance SegmentationMulti-Label Classification+6Learning Instance Activation Maps for Weakly Supervised Instance Segmentation
Discriminative region responses residing inside an object instance can be extracted from networks trained with image-level label supervision. However, learning the full extent of pixel-level instance response in a weakly…
Image-level Supervised Instance SegmentationInstance SegmentationObjectobject-detection+7Weakly Supervised Instance Segmentation Using Hybrid Network
Weakly-supervised instance segmentation, which could greatly save labor and time cost of pixel mask annotation, has attracted increasing attention in recent years. The commonly used pipeline firstly utilizes conventional…
Image SegmentationInstance SegmentationSegmentationSemantic Segmentation+2Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation
Effectively bridging between image level keyword annotations and corresponding image pixels is one of the main challenges in weakly supervised semantic segmentation. In this paper, we use an instance-level salient object…
Clusteringgraph partitioningImage-level Supervised Instance SegmentationInstance Segmentation+6Weakly- 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. In …
Instance SegmentationPanoptic SegmentationSegmentationSemantic Segmentation+3Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products
Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly.…
Instance SegmentationObjectSemantic SegmentationWeakly-supervised instance segmentationWeakly Supervised Instance Segmentation using Class Peak Response
Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable …
General ClassificationImage-level Supervised Instance SegmentationInstance SegmentationSegmentation+2