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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jacobi93/alpha-iou
공식 코드Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
https://github.com/jacobyuan7/ocn-hoi-benchmark
공식 코드Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics
https://github.com/jacobyuan7/rlipv2
공식 코드RLIPv2: Fast Scaling of Relational Language-Image Pre-training
https://github.com/jaehakim97/sr4ir
공식 코드Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
https://github.com/jaeill/CVPR23-VNE
공식 코드VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
https://github.com/jaewon-lee-b/lte
공식 코드Local Texture Estimator for Implicit Representation Function
https://github.com/jalalilabucla/phycv
공식 코드VEViD: Vision Enhancement via Virtual diffraction and coherent Detection
https://github.com/james128333/HarDNet-MSEG
공식 코드https://github.com/jammer345/SL-CycleGAN-Blind-Motion-Deblurring-in-Cycles-using-Sparse-Learning
공식 코드SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning
https://github.com/janghyuncho/ecm-loss
공식 코드https://github.com/janmarcelkezmann/metadetect3d
공식 코드LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
https://github.com/jannctu/FINED
공식 코드https://github.com/jaraxxus-me/airdet
공식 코드AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
https://github.com/jarchv/solverip
공식 코드Generative Flows as a General Purpose Solution for Inverse Problems
https://github.com/jarintotiondin/ziragroundingdino
공식 코드Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
https://github.com/jaryp/adaptivesemantictokencommunication
공식 코드Adaptive Semantic Token Communication for Transformer-based Edge Inference
https://github.com/jason-lim26/dipex
공식 코드DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection
https://github.com/jasonli0707/hklut
공식 코드Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
https://github.com/jaychempan/ETS
공식 코드https://github.com/jaychempan/LAE-DINO
공식 코드Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community
https://github.com/jayleicn/moment_detr
공식 코드QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
https://github.com/jbadiat/supertoroidfitting
공식 코드Supertoroid fitting of objects with holes for robotic grasping and scene generation
https://github.com/jbji/repvf
공식 코드RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception
https://github.com/jbwang1997/OBBDetection
공식 코드https://github.com/jbwang1997/aopg
공식 코드Anchor-free Oriented Proposal Generator for Object Detection
https://github.com/jbwang1997/crosskd
공식 코드CrossKD: Cross-Head Knowledge Distillation for Object Detection
https://github.com/jcjohnson/densecap
공식 코드DenseCap: Fully Convolutional Localization Networks for Dense Captioning
https://github.com/jdfxzzy/dpmn
공식 코드Improving Scene Text Image Super-resolution via Dual Prior Modulation Network
https://github.com/jdfxzzy/pean
공식 코드PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
https://github.com/jdsobek/medyolo
공식 코드https://github.com/jean50621/Badminton_Challenge
공식 코드https://github.com/jeetv/gmvd
공식 코드Bringing Generalization to Deep Multi-View Pedestrian Detection
https://github.com/jefferyzhan/griffon
공식 코드https://github.com/jeongjin0/invisible-backdoor-object-detection
공식 코드https://github.com/jerpelhan/GeCo
공식 코드A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.