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/salesforce/lavis
공식 코드Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/salesforce/pb-ovd
공식 코드Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
https://github.com/salmank255/ROAD_Waymo_Baseline
공식 코드ROAD-Waymo: Action Awareness at Scale for Autonomous Driving
https://github.com/saltoricristiano/SF-UDA-3DV
공식 코드SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
https://github.com/samarth-robo/py-faster-rcnn
공식 코드https://github.com/samiksome/faster-rcnn.pytorch
공식 코드https://github.com/samuelyu2002/ImVisible
공식 코드Street Crossing Aid Using Light-weight CNNs for the Visually Impaired
https://github.com/samvadidar/rgbt
공식 코드https://github.com/samwilso/safe_official
공식 코드SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
https://github.com/sanchit2843/Indian_LPR
공식 코드https://github.com/sandipan211/ZSD-SC-Resolver
공식 코드Resolving Semantic Confusions for Improved Zero-Shot Detection
https://github.com/sanghoon/pva-faster-rcnn
공식 코드PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
https://github.com/sanjay-810/aydiv2
공식 코드AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer
https://github.com/sanjay-810/qicvt
공식 코드https://github.com/sanjay-810/savid
공식 코드https://github.com/sankin97/logonet
공식 코드LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
https://github.com/sanmin0312/P2D
공식 코드Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images
https://github.com/sanmin0312/labeldistill
공식 코드LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection
https://github.com/sarwarmurshed/supermarket_hazard_detection
공식 코드Hazard Detection in Supermarkets using Deep Learning on the Edge
https://github.com/sashamatsun/lskdiffdet
공식 코드Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection
https://github.com/sashankmvv/Emotion-recognition
공식 코드Facial Emotion Recognition: A multi-task approach using deep learning
https://github.com/satellogic/iquaflow-airport-use-case
공식 코드Object Detection performance variation on compressed satellite image datasets with iquaflow
https://github.com/satellogic/iquaflow-dota-obb-use-case
공식 코드Object Detection performance variation on compressed satellite image datasets with iquaflow
https://github.com/sauradip/muppet
공식 코드https://github.com/sawhney-medha/memtrack
공식 코드https://github.com/say2l/dcdet
공식 코드https://github.com/say2l/ffam
공식 코드FFAM: Feature Factorization Activation Map for Explanation of 3D Detectors
https://github.com/sbasu276/GBCNet
공식 코드Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning
https://github.com/sbasu276/Weakly_DETR
공식 코드Gall Bladder Cancer Detection from US Images with Only Image Level Labels
https://github.com/sberbank-ai/fusion_brain_aij2021
공식 코드https://github.com/scailab/paser
공식 코드https://github.com/scale-lab/AdaCon
공식 코드AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices
https://github.com/scheckmedia/centernet-uda
공식 코드Unsupervised Domain Adaptation from Synthetic to Real Images for Anchorless Object Detection
https://github.com/schreiber-lab/mlgixd
공식 코드https://github.com/schubertm/MetaDetect
공식 코드MetaDetect: Uncertainty Quantification and Prediction Quality Estimates for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.