Papers with Code 아카이브
Object Proposal Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 4
Object proposal generation is a preprocessing technique that has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images.
<span style="color:grey; opacity: 0.6">( Image credit: Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Towards Addressing the Misalignment of Object Proposal Evaluation for Vision-Language Tasks via Semantic Grounding
Small, but important: Traffic light proposals for detecting small traffic lights and beyond
Fast Segment Anything
SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask
Localizing Small Apples in Complex Apple Orchard Environments
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues
Class-agnostic Object Detection with Multi-modal Transformer
DeepFH Segmentations for Superpixel-based Object Proposal Refinement
Superpixel-based Refinement for Object Proposal Generation
3D Object Detection with Pointformer
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
What leads to generalization of object proposals?
Real-time 3D object proposal generation and classification under limited processing resources
Relation Graph Network for 3D Object Detection in Point Clouds
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects
Deep Learning for Generic Object Detection: A Survey
Semantic Edge Detection with Diverse Deep Supervision
A New Target-specific Object Proposal Generation Method for Visual Tracking
Object cosegmentation using deep Siamese network
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
Recurrent Pixel Embedding for Instance Grouping
SalProp: Salient object proposals via aggregated edge cues
CASENet: Deep Category-Aware Semantic Edge Detection
Object Discovery via Cohesion Measurement
ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond
Object proposal generation applying the distance dependent Chinese restaurant process
Semantic Instance Segmentation via Deep Metric Learning
Boundary-aware Instance Segmentation
Learning to Segment Object Candidates via Recursive Neural Networks
Multi-View 3D Object Detection Network for Autonomous Driving
Multi-stage Object Detection with Group Recursive Learning
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.