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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/henrytsui000/YOLO
공식 코드YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary
https://github.com/henryyu23/das
공식 코드Towards Unsupervised Model Selection for Domain Adaptive Object Detection
https://github.com/hertasecurity/gpu-nms
공식 코드https://github.com/heshitian/plug
공식 코드Learning Remote Sensing Object Detection with Single Point Supervision
https://github.com/hexiao-cs/s2adet
공식 코드Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation
https://github.com/hfthair/emerald_crawler
공식 코드Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
https://github.com/hhguo/EA-SVC
공식 코드Phonetic Posteriorgrams based Many-to-Many Singing Voice Conversion via Adversarial Training
https://github.com/hht1996ok/ea-bev
공식 코드EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection
https://github.com/hhu-machine-learning/hdc2021-psfnn
공식 코드Deblurring Photographs of Characters Using Deep Neural Networks
https://github.com/hhx0320/asnn
공식 코드https://github.com/hi-ft/erd
공식 코드Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation
https://github.com/hieutrong512/Yoloz
공식 코드YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles
https://github.com/hik-lab/ssod
공식 코드https://github.com/hikvision-research/SSOD
공식 코드https://github.com/hikvision-research/davar-lab-ml
공식 코드https://github.com/hikvision-research/probabilisticteacher
공식 코드Learning Domain Adaptive Object Detection with Probabilistic Teacher
https://github.com/hila-chefer/Transformer-MM-Explainability
공식 코드Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
https://github.com/hila-chefer/targetclip
공식 코드https://github.com/hirl-team/eurnet-image
공식 코드EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
https://github.com/hitachi-rd-cv/CDB-loss
공식 코드Class-Difficulty Based Methods for Long-Tailed Visual Recognition
https://github.com/hitsz-nrsl/de6d
공식 코드Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness
https://github.com/hiyouga/easyr1
공식 코드VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning
https://github.com/hkbu-hpml/easnet
공식 코드EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching
https://github.com/hkbunlp/wsdms-emnlp2023
공식 코드https://github.com/hku-tasr/anywheredoor
공식 코드AnywhereDoor: Multi-Target Backdoor Attacks on Object Detection
https://github.com/hkust-drive-ai-lab/follownet
공식 코드FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling
https://github.com/hkust-vgd/architectural_style_transfer
공식 코드Time-of-Day Neural Style Transfer for Architectural Photographs
https://github.com/hkxiao/zs-cosod
공식 코드https://github.com/hkzhang91/edgeformer
공식 코드ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
https://github.com/hkzhang91/pacc-net
공식 코드ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
https://github.com/hkzhang91/parc-net
공식 코드ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
https://github.com/hkzhang95/DynamicRCNN
공식 코드Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
https://github.com/hlesmqh/WS3D
공식 코드Weakly Supervised 3D Object Detection from Lidar Point Cloud
https://github.com/hli2020/feature_intertwiner
공식 코드https://github.com/hli2020/lps_tip15
공식 코드Inner and Inter Label Propagation: Salient Object Detection in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.