https://github.com/lolrudy/lapose
공식 코드LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation
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/lolrudy/rbp_pose
공식 코드RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
https://github.com/longyin880815/STCNN
공식 코드https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings
공식 코드https://github.com/looooongchen/instseg
공식 코드Instance Segmentation of Dense and Overlapping Objects via Layering
https://github.com/looooongchen/sortedap
공식 코드SortedAP: Rethinking evaluation metrics for instance segmentation
https://github.com/lorebianchi98/fg-clip
공식 코드Is CLIP the main roadblock for fine-grained open-world perception?
https://github.com/lorebianchi98/fg-ovd
공식 코드https://github.com/lorenzovaquero/siammotion
공식 코드Real-Time Siamese Multiple Object Tracker with Enhanced Proposals
https://github.com/lovelyqian/CDFSOD-benchmark
공식 코드Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
https://github.com/lovelyqian/ntire2025_cdfsod
공식 코드NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
https://github.com/lsh9832/edgeyolo
공식 코드https://github.com/ltguo19/VSUA-Captioning
공식 코드Aligning Linguistic Words and Visual Semantic Units for Image Captioning
https://github.com/lucabutera/graphose_ocrrig
공식 코드Object-Centric Relational Representations for Image Generation
https://github.com/luckybird1994/hqsod
공식 코드https://github.com/luckybird1994/samcod
공식 코드Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
https://github.com/luisfelipewb/potato
공식 코드PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects
https://github.com/luiszeni/Boosted-OICR
공식 코드Distilling Knowledge from Refinement in Multiple Instance Detection Networks
https://github.com/lukasmut/vice
공식 코드https://github.com/luluho1208/diffusion-ss3d
공식 코드Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
https://github.com/luo-z13/pointobb
공식 코드PointOBB: Learning Oriented Object Detection via Single Point Supervision
https://github.com/luoyadan/crb-active-3ddet
공식 코드Open-CRB: Towards Open World Active Learning for 3D Object Detection
https://github.com/luping-liu/detector-guidance
공식 코드https://github.com/lutingwang/head
공식 코드HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
https://github.com/lutingwang/oadp
공식 코드Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection
https://github.com/luyongxi/az-net
공식 코드Adaptive Object Detection Using Adjacency and Zoom Prediction
https://github.com/luyukang/DCTNet
공식 코드Depth-Cooperated Trimodal Network for Video Salient Object Detection
https://github.com/lvsn/rgbde_tracking
공식 코드RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
https://github.com/lwzeng/GC-Net
공식 코드https://github.com/lxj-drifter/uiou_files
공식 코드https://github.com/lxtGH/DecoupleSegNets
공식 코드Improving Semantic Segmentation via Decoupled Body and Edge Supervision
https://github.com/lxtgh/denseworld-1m
공식 코드DenseWorld-1M: Towards Detailed Dense Grounded Caption in the Real World
https://github.com/lxycopper/plu
공식 코드Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
https://github.com/lyessaadsaoud/adod
공식 코드ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments
https://github.com/lym29/EasyHOI
공식 코드https://github.com/lyongo/nwpu-moc
공식 코드NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.