Papers Box-supervised Instance Segmentation
“Box-supervised Instance Segmentation” 태그가 달린 논문 19편 · 필터 해제
BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation
Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-stu…
Box-supervised Instance SegmentationInstance SegmentationSemantic SegmentationAdvancing SEM Based Nano-Scale Defect Analysis in Semiconductor Manufacturing for Advanced IC Nodes
In this research, we introduce a unified end-to-end Automated Defect Classification-Detection-Segmentation (ADCDS) framework for classifying, detecting, and segmenting multiple instances of semiconductor defects for adva…
Box-supervised Instance SegmentationDefect DetectionInstance SegmentationSegmentation+1Boosting Box-supervised Instance Segmentation with Pseudo Depth
The realm of Weakly Supervised Instance Segmentation (WSIS) under box supervision has garnered substantial attention, showcasing remarkable advancements in recent years. However, the limitations of box supervision become…
Box-supervised Instance SegmentationDepth EstimationDepth PredictionInstance Segmentation+3P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Point-level Supervised Instance Segmentation (PSIS) aims to enhance the applicability and scalability of instance segmentation by utilizing low-cost yet instance-informative annotations. Existing PSIS methods usually rel…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+2Label-efficient Segmentation via Affinity Propagation
Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniq…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1BoxSnake: Polygonal Instance Segmentation with Box Supervision
Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-supervised instance segmentation models mainl…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationSIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without expli…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization loss term such as pairwise color affinity…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution
In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention. This paper…
Box-supervised Instance SegmentationDecoderInstance SegmentationSegmentationH2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fi…
AllAutonomous DrivingBox-supervised Instance SegmentationInstance Segmentation+6BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with p…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+2Box-supervised Instance Segmentation with Level Set Evolution
In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In …
Box-supervised Instance SegmentationInstance SegmentationSegmentationDeep Level Set for Box-supervised Instance Segmentation in Aerial Images
Box-supervised instance segmentation has recently attracted lots of research efforts while little attention is received in aerial image domain. In contrast to the general object collections, aerial objects have large int…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationAffinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic s…
Box-supervised Instance SegmentationGraph Neural NetworkInstance SegmentationModel Optimization+4DiscoBox: 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+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+4BBAM: 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+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+2Weakly 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+3