Papers Human Instance Segmentation
“Human Instance Segmentation” 태그가 달린 논문 18편 · 필터 해제
SAM-pose2seg: Pose-Guided Human Instance Segmentation in Crowds
Segment Anything (SAM) provides an unprecedented foundation for human segmentation, but may struggle under occlusion, where keypoints may be partially or fully invisible. We adapt SAM 2.1 for pose-guided segmentation wit…
Human Instance SegmentationLeveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing
Multi-human parsing is the task of segmenting human body parts while associating each part to the person it belongs to, combining instance-level and part-level information for fine-grained human understanding. In this wo…
Human Instance SegmentationMulti-Human ParsingDetection, Pose Estimation and Segmentation for Multiple Bodies: Closing the Virtuous Circle
Human pose estimation methods work well on separated people but struggle with multi-body scenarios. Recent work has addressed this problem by conditioning pose estimation with detected bounding boxes or bottom-up-estimat…
Human Instance SegmentationPose-Based Human Instance SegmentationPose EstimationSegmentationCrowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes
In computer vision, object detection is an important task that finds its application in many scenarios. However, obtaining extensive labels can be challenging, especially in crowded scenes. Recently, the Segment Anything…
Human Instance SegmentationInstance Segmentationobject-detectionObject Detection+1You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception
Human-centric perception (e.g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision. This paper introduces a unified and versatile framework (HQNet) for single-…
AttributeHuman Instance SegmentationMulti-Task LearningPose EstimationObject-Centric Multi-Task Learning for Human Instances
Human is one of the most essential classes in visual recognition tasks such as detection, segmentation, and pose estimation. Although much effort has been put into individual tasks, multi-task learning for these three ta…
Human DetectionHuman Instance SegmentationMulti-Task LearningObject+2Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
We consider the problem of improving the human instance segmentation mask quality for a given test image using keypoints estimation. We compare two alternative approaches. The first approach is a test-time adaptation (TT…
Human Instance SegmentationInstance SegmentationSemantic SegmentationTest-time AdaptationHumans need not label more humans: Occlusion Copy & Paste for Occluded Human Instance Segmentation
Modern object detection and instance segmentation networks stumble when picking out humans in crowded or highly occluded scenes. Yet, these are often scenarios where we require our detectors to work well. Many works have…
Human Instance SegmentationInstance Segmentationobject-detectionObject Detection+2Occlusion-Aware Instance Segmentation via BiLayer Network Architectures
Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previous instance segmentation methods, we mod…
Human Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition, camera images do not preserve privacy. T…
Action RecognitionAction UnderstandingHuman Instance SegmentationInstance Segmentation+3Human Instance Segmentation and Tracking via Data Association and Single-stage Detector
Human video instance segmentation plays an important role in computer understanding of human activities and is widely used in video processing, video surveillance, and human modeling in virtual reality. Most current VIS …
Human Instance SegmentationInstance SegmentationPositionSegmentation+4Contextual Instance Decoupling for Robust Multi-Person Pose Estimation
Crowded scenes make it challenging to differentiate persons and locate their pose keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline for multi-person pose estimatio…
Human Instance SegmentationMulti-Person Pose EstimationPose EstimationReal-time Human-Centric Segmentation for Complex Video Scenes
Most existing video tasks related to "human" focus on the segmentation of salient humans, ignoring the unspecified others in the video. Few studies have focused on segmenting and tracking all humans in a complex video, i…
Human Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1Fashion-Guided Adversarial Attack on Person Segmentation
This paper presents the first adversarial example based method for attacking human instance segmentation networks, namely person segmentation networks in short, which are harder to fool than classification networks. We p…
Adversarial AttackHuman Instance SegmentationInstance SegmentationSegmentation+1SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation
Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passiv…
Human DetectionHuman Instance SegmentationInstance Segmentationreinforcement-learning+2Count- and Similarity-aware R-CNN for Pedestrian Detection
Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal simi…
Human Instance SegmentationInstance SegmentationPedestrian DetectionSemantic SegmentationPoSeg: Pose-Aware Refinement Network for Human Instance Segmentation
Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and sha…
Human Instance SegmentationInstance SegmentationPose EstimationScene Understanding+2Pose2Seg: Detection Free Human Instance Segmentation
The standard approach to image instance segmentation is to perform the object detection first, and then segment the object from the detection bounding-box. More recently, deep learning methods like Mask R-CNN perform the…
2D Human Pose EstimationHuman Instance SegmentationInstance SegmentationKeypoint Detection+6