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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jiaxi-wu/MPSR
공식 코드Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
https://github.com/jiaxiongq/neus-hsr
공식 코드Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections
https://github.com/jiayuyang/consistnet
공식 코드ConsistNet: Enforcing 3D Consistency for Multi-view Images Diffusion
https://github.com/jiehonglin/sam-6d
공식 코드SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
https://github.com/jiehonglin/self-dpdn
공식 코드https://github.com/jiehonglin/vi-net
공식 코드https://github.com/jiepku/understand-ssl-part-aware
공식 코드Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
https://github.com/jieyuz2/taskmeanything
공식 코드https://github.com/jimmyyhwu/pose-interpreter-networks
공식 코드Real-Time Object Pose Estimation with Pose Interpreter Networks
https://github.com/jindongjiang/latent-slot-diffusion
공식 코드https://github.com/jinhseo/od-wscl
공식 코드Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
https://github.com/jinlong17/da-detect
공식 코드Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
https://github.com/jinyang06/SamGOP
공식 코드Boosting Gaze Object Prediction via Pixel-level Supervision from Vision Foundation Model
https://github.com/jinyangyuan/acquired-deep-impressions
공식 코드Knowledge-Guided Object Discovery with Acquired Deep Impressions
https://github.com/jinyangyuan/learnable-deep-priors
공식 코드Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping
https://github.com/jiuntian/interactdiffusion
공식 코드InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models
https://github.com/jiutongbro/multiview_inpaint
공식 코드Generative Object Insertion in Gaussian Splatting with a Multi-View Diffusion Model
https://github.com/jixingwu/ss-lcd
공식 코드Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
https://github.com/jizhizili/rim
공식 코드https://github.com/jnnan/chairs
공식 코드https://github.com/joaromi/Spiking-RetinaNet
공식 코드RetinaNet Object Detector based on Analog-to-Spiking Neural Network Conversion
https://github.com/joe-siyuan-qiao/DetectoRS
공식 코드DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
https://github.com/joellliu/segmentandcomplete
공식 코드https://github.com/johndreamer/twfa
공식 코드https://github.com/johnmaijer/x-few-_cd-fsod
공식 코드NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
https://github.com/johnwuzh/uc-owod
공식 코드https://github.com/jorisguerin/SemanticViewSelection_dataset
공식 코드https://github.com/joshuafeinglass/vl-detector-eval
공식 코드https://github.com/josselinsomervilleroberts/reachbot-predictor
공식 코드A Skeleton-based Approach For Rock Crack Detection Towards A Climbing Robot Application
https://github.com/jp-lan/procontext
공식 코드ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/jshilong/SEPC
공식 코드https://github.com/jshilong/ddq
공식 코드https://github.com/juliantrue/movex
공식 코드https://github.com/julimueller/disparity-sliding-window
공식 코드Disparity Sliding Window: Object Proposals From Disparity Images
https://github.com/juliozhao97/efftrans_fsdet
공식 코드Exploring Effective Knowledge Transfer for Few-shot Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.