https://github.com/anuragkulkarni/lidar-based_object_detection_with_real-time_voice_specifications
공식 코드LiDAR-based Object Detection with Real-time Voice Specifications
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aoki-media-lab/event_based_segmentation_vcmax
공식 코드Iterative Event-based Motion Segmentation by Variational Contrast Maximization
https://github.com/aosokin/cnn_head_detection
공식 코드https://github.com/aosokin/os2d
공식 코드OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
https://github.com/aounon/center-smoothing
공식 코드Center Smoothing: Certified Robustness for Networks with Structured Outputs
https://github.com/apple/ARKitScenes
공식 코드ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
https://github.com/apple/ml-cvnets
공식 코드MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
https://github.com/apple/ml-dr
공식 코드Reinforce Data, Multiply Impact: Improved Model Accuracy and Robustness with Dataset Reinforcement
https://github.com/apple/ml-gaudi
공식 코드https://github.com/apple/ml-gsn
공식 코드Unconstrained Scene Generation with Locally Conditioned Radiance Fields
https://github.com/apple/ml-mobileone
공식 코드https://github.com/apple/ml-ncg
공식 코드Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training
https://github.com/apple/ml-tune-mode-convbn
공식 코드https://github.com/apple1986/residual_learning_for_images
공식 코드https://github.com/applied-ai-lab/genesis
공식 코드GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
https://github.com/applied-ai-lab/object_matching
공식 코드Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement
https://github.com/arashfree/TLW-in-Super-Resolution
공식 코드https://github.com/arashsajjadi/TGraphX
공식 코드TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning
https://github.com/arashwan/matrixnet
공식 코드MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
https://github.com/arcananana/cdcn
공식 코드Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
https://github.com/arcananana/dssr
공식 코드Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
https://github.com/arcaninez/odm3d
공식 코드ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
https://github.com/archiplab-linfengzhang/contrastive-deep-supervision
공식 코드https://github.com/arctichare105/s3diff
공식 코드Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
https://github.com/ardagen/stem-automated-nanoparticle-analysis-yolov8-sam
공식 코드https://github.com/arekavandi/transformer-sod
공식 코드Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art
https://github.com/areuther/ai-accelerators
공식 코드https://github.com/arezou-pakzad/circle
공식 코드CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions
https://github.com/argoverse/av2-api
공식 코드Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
https://github.com/aritrabhowmik/uoi
공식 코드Union-over-Intersections: Object Detection beyond Winner-Takes-All
https://github.com/arm-software/ml-restructurable-activation-networks
공식 코드https://github.com/arminmasoumian/gcndepth
공식 코드https://github.com/arojsubedi/cct20_processed_dataset
공식 코드Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection
https://github.com/arojsubedi/improved-yolov8s
공식 코드Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection
https://github.com/arpitbansal297/universal-guided-diffusion
공식 코드https://github.com/arturjordao/WearableSensorData
공식 코드Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.