Papers Zero-Shot Instance Segmentation
“Zero-Shot Instance Segmentation” 태그가 달린 논문 13편 · 필터 해제
SA3DIP: Segment Any 3D Instance with Potential 3D Priors
The proliferation of 2D foundation models has sparked research into adapting them for open-world 3D instance segmentation. Recent methods introduce a paradigm that leverages superpoints as geometric primitives and incorp…
3D Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation
This paper presents GoodSAM++, a novel framework utilizing the powerful zero-shot instance segmentation capability of SAM (i.e., teacher) to learn a compact panoramic semantic segmentation model, i.e., student, without r…
Domain AdaptationInstance SegmentationSemantic SegmentationZero-Shot Instance SegmentationGoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact pano…
Domain AdaptationInstance SegmentationSemantic SegmentationTransfer Learning+1EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Loss
We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM's lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, w…
DecoderGPUKnowledge DistillationZero-Shot Instance SegmentationEfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transforme…
Decoderimage-classificationImage ClassificationInstance Segmentation+5Fast Segment Anything
The recently proposed segment anything model (SAM) has made a significant influence in many computer vision tasks. It is becoming a foundation step for many high-level tasks, like image segmentation, image caption, and i…
Edge DetectionImage SegmentationInstance SegmentationObject Proposal Generation+4Segment Anything in High Quality
The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's ma…
DecoderZero-Shot Instance SegmentationZero Shot SegmentationZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes
Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose estimation methods are object-specific, whic…
6D Pose EstimationInstance SegmentationObjectPose Estimation+2Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation
Zero-shot instance segmentation aims to detect and precisely segment objects of unseen categories without any training samples. Since the model is trained on seen categories, there is a strong bias that the model tends t…
Instance SegmentationSegmentationSemantic SegmentationZero-Shot Instance SegmentationSegment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with…
Event-based Object SegmentationImage SegmentationRobot Manipulation GeneralizationSegmentation+3SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments
Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object.…
Instance SegmentationObjectSemantic SegmentationZero-Shot Instance SegmentationEfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
High-resolution dense prediction enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost makes deploying state-of-the-art high-reso…
Autonomous DrivingCPUGPUImage Classification+8Zero-Shot Instance Segmentation
Deep learning has significantly improved the precision of instance segmentation with abundant labeled data. However, in many areas like medical and manufacturing, collecting sufficient data is extremely hard and labeling…
Instance Segmentationobject-detectionObject DetectionSegmentation+3