Papers with Code 아카이브
2D Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vdigpku/formula
공식 코드https://github.com/vdigpku/henet
공식 코드HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
https://github.com/vdigpku/nas-bnn
공식 코드NAS-BNN: Neural Architecture Search for Binary Neural Networks
https://github.com/vdigpku/rcbevdet
공식 코드RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection
https://github.com/vdigpku/t-sea
공식 코드T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
https://github.com/vdigpku/teocc
공식 코드TEOcc: Radar-camera Multi-modal Occupancy Prediction via Temporal Enhancement
https://github.com/vdorbala/LGX
공식 코드https://github.com/vectorspacelab/omnigen
공식 코드https://github.com/velatkilic/LISA
공식 코드https://github.com/velodc/oshot_detection
공식 코드Exploring Data Aggregation and Transformations to Generalize across Visual Domains
https://github.com/vibashan/irg-sfda
공식 코드Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
https://github.com/vibashan/memxformer-online-da
공식 코드https://github.com/vibashan/online-od
공식 코드https://github.com/victor-nasc/Vehicle-Licence-Plate-Recognition
공식 코드Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis
https://github.com/vidit09/domaingen
공식 코드CLIP the Gap: A Single Domain Generalization Approach for Object Detection
https://github.com/vidit09/geoshift
공식 코드Learning Transformations To Reduce the Geometric Shift in Object Detection
https://github.com/viewformerocc/viewformer-occ
공식 코드https://github.com/viktorkare/bevf
공식 코드Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data
https://github.com/vila-lab/od3
공식 코드OD3: Optimization-free Dataset Distillation for Object Detection
https://github.com/viml-cvdl/object-detection-in-foggy-scenes
공식 코드Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
https://github.com/vimslab/sodawidenetplusplus
공식 코드SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection
https://github.com/vinairesearch/counting-detr
공식 코드https://github.com/vinairesearch/lp-ovod
공식 코드https://github.com/vinits5/learning3d
공식 코드PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/vinits5/pcrnet
공식 코드PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/vinits5/pcrnet_pytorch
공식 코드PCRNet: Point Cloud Registration Network using PointNet Encoding
https://github.com/vinits5/pointnet-registration-framework
공식 코드One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
https://github.com/vishwakarmarhl/rdd2020
공식 코드https://github.com/vision-agh/lift
공식 코드LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data
https://github.com/vision-agh/mmar_2023
공식 코드Detection-segmentation convolutional neural network for autonomous vehicle perception
https://github.com/vision-agh/mt_kria
공식 코드https://github.com/vision-agh/pointpillars-backbone
공식 코드PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection
https://github.com/vision-sjtu/quadmamba
공식 코드QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
https://github.com/visionlearninggroup/mind_back
공식 코드Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
https://github.com/visionxlab/point2rbox-v2
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.