https://github.com/ifzhang/FairMOT
공식 코드FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ignc-research/habitatdyn
공식 코드HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation
https://github.com/igor-morawski/nod
공식 코드https://github.com/igor-morawski/raw-nod
공식 코드https://github.com/ihubdata-mobility/public-fgvd
공식 코드A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
https://github.com/iim-ttij/mva2023smallobjectdetection4spottingbirds
공식 코드MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
https://github.com/iit-pavis/look_around_and_learn
공식 코드Look Around and Learn: Self-Training Object Detection by Exploration
https://github.com/ika-rwth-aachen/multicorrupt
공식 코드https://github.com/ikatsamenis/cone-detection
공식 코드TraCon: A novel dataset for real-time traffic cones detection using deep learning
https://github.com/iliiliiliili/tanet-near-far
공식 코드Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
https://github.com/imalne/pbasr
공식 코드A New Dataset and Framework for Real-World Blurred Images Super-Resolution
https://github.com/imankgoyal/NonDeepNetworks
공식 코드https://github.com/imatge-upc/detection-2016-nipsws
공식 코드Hierarchical Object Detection with Deep Reinforcement Learning
https://github.com/imatge-upc/retrieval-2016-deepvision
공식 코드https://github.com/imatif17/Prototype-Mean-Teacher
공식 코드Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
https://github.com/imatif17/acia
공식 코드Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
https://github.com/imfinethanks/esbnn
공식 코드https://github.com/imlixinyang/director3d
공식 코드Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
https://github.com/imnotprepared/airshot
공식 코드AirShot: Efficient Few-Shot Detection for Autonomous Exploration
https://github.com/imoonlab/hyper-yolo
공식 코드Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
https://github.com/imoonlab/yolov13
공식 코드YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
https://github.com/implabunipr/unbiased-teacher
공식 코드https://github.com/implus/GFocal
공식 코드Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
https://github.com/implus/GFocalV2
공식 코드https://github.com/implus/PytorchInsight
공식 코드Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks
https://github.com/implus/RecursiveMix-pytorch
공식 코드https://github.com/implus/um-mae
공식 코드Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
https://github.com/imrl/umad
공식 코드UMAD: University of Macau Anomaly Detection Benchmark Dataset
https://github.com/imruljubair/BdSLImset
공식 코드Real Time Bangladeshi Sign Language Detection using Faster R-CNN
https://github.com/imt-project-lts-sr/mrunet-for-modis-super-resolution
공식 코드Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
https://github.com/inakierregueab/yolo11-jde
공식 코드YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
https://github.com/inconstance/t-shirt-natural-backdoor-dataset
공식 코드https://github.com/industryessentials/ymir
공식 코드ALBench: A Framework for Evaluating Active Learning in Object Detection
https://github.com/inexascale/pychop
공식 코드Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
https://github.com/infinigence/lveval
공식 코드LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K
https://github.com/infinity7428/hANMCL
공식 코드Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.