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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/aryanshar/swarm-detection

공식 코드

Lightweight Multi-Drone Detection and 3D-Localization via YOLO

https://github.com/asbroad/geom_rcnn

공식 코드

Geometry-Based Region Proposals for Real-Time Robot Detection of Tabletop Objects

https://github.com/aselsan-research-imaging-team/bicubic-plusplus

공식 코드

Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network

https://github.com/asharakeh/bayes-od-rc

공식 코드

BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

https://github.com/asharakeh/pod_compare

공식 코드

A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

https://github.com/asharakeh/probdet

공식 코드

Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors

https://github.com/ashep29/confluence

공식 코드

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

https://github.com/ashishkumar822/cred-detr

공식 코드

Cross Resolution Encoding-Decoding For Detection Transformers

https://github.com/ashu1069/perceptual-piercing

공식 코드

Perceptual Piercing: Human Visual Cue-based Object Detection in Low Visibility Conditions

https://github.com/ashual/scene_generation

공식 코드

Specifying Object Attributes and Relations in Interactive Scene Generation

https://github.com/asindel/VSRF

공식 코드

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests

https://github.com/asra-aslam/unseennet

공식 코드

UnseenNet: Fast Training Detector for Any Unseen Concept

https://github.com/asrafulashiq/transfer_broad

공식 코드

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

https://github.com/ast-fortiss-tum/perturbation-drive

공식 코드

Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems

https://github.com/asterisci/language-assisted-3d

공식 코드

Language-Assisted 3D Feature Learning for Semantic Scene Understanding

https://github.com/asterisci/point-gcc

공식 코드

Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

https://github.com/astra-vision/poda

공식 코드

PØDA: Prompt-driven Zero-shot Domain Adaptation

https://github.com/atonderski/future-object-detection

공식 코드

Future Object Detection with Spatiotemporal Transformers

https://github.com/atten4vis/cae

공식 코드

Context Autoencoder for Self-Supervised Representation Learning

https://github.com/atten4vis/conditionaldetr

공식 코드

Conditional DETR for Fast Training Convergence

https://github.com/atten4vis/demystifylocalvit

공식 코드

On the Connection between Local Attention and Dynamic Depth-wise Convolution

https://github.com/atten4vis/groupdetr

공식 코드

Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment

https://github.com/atten4vis/lw-detr

공식 코드

LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

https://github.com/atten4vis/ms-detr

공식 코드

MS-DETR: Efficient DETR Training with Mixed Supervision

https://github.com/augcog/dttd2

공식 코드

Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset

https://github.com/augcog/robust-digital-twin-tracking

공식 코드

Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset

https://github.com/aulrichsen/sronn

공식 코드

Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-Resolution

https://github.com/aupendu/sr-uncertainty

공식 코드

Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network

https://github.com/aurangau/ICIP2024

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A Dictionary Based Approach for Removing Out-of-Focus Blur

https://github.com/auto-dog/moac_deep

공식 코드

Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network

https://github.com/automl/GoLU

공식 코드

Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics

https://github.com/automl/NASLib

공식 코드

NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy

https://github.com/autonomousvision/good

공식 코드

GOOD: Exploring Geometric Cues for Detecting Objects in an Open World

https://github.com/autonomousvision/graf

공식 코드

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis

https://github.com/autonomousvision/transfuser

공식 코드

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/avc2-uestc/craft-sr

공식 코드

Exploring Frequency-Inspired Optimization in Transformer for Efficient Single Image Super-Resolution

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.