https://github.com/tusimple/centerformer
공식 코드CenterFormer: Center-based Transformer for 3D Object Detection
Papers with Code 아카이브
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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/tusimple/simpledet
공식 코드https://github.com/tusimple/simpletrack
공식 코드SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
https://github.com/tusimple/sst
공식 코드Embracing Single Stride 3D Object Detector with Sparse Transformer
https://github.com/tustailab/hmpnet
공식 코드https://github.com/tvpian/project-tar
공식 코드https://github.com/twangnh/Distilling-Object-Detectors
공식 코드Distilling Object Detectors with Fine-grained Feature Imitation
https://github.com/ty-shi/liot
공식 코드Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
https://github.com/tydpan/gpc
공식 코드Pre-Training LiDAR-Based 3D Object Detectors Through Colorization
https://github.com/tyroneli/esol_wsss
공식 코드Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
https://github.com/tztztztztz/eql.detectron2
공식 코드https://github.com/tzzcl/PSOL
공식 코드Rethinking the Route Towards Weakly Supervised Object Localization
https://github.com/uber/sbnet
공식 코드https://github.com/ucas-vg/TinyBenchmark
공식 코드https://github.com/ucas-vg/p2bnet
공식 코드Point-to-Box Network for Accurate Object Detection via Single Point Supervision
https://github.com/ucas-vg/pointtinybenchmark
공식 코드https://github.com/ucas-vg/sambor
공식 코드Boosting Segment Anything Model Towards Open-Vocabulary Learning
https://github.com/ucbdrive/few-shot-object-detection
공식 코드https://github.com/ucsb-nlp-chang/diffusion_resampling
공식 코드https://github.com/ucwxb/nto3d
공식 코드NTO3D: Neural Target Object 3D Reconstruction with Segment Anything
https://github.com/ukyh/RemovingSpuriousAlignment
공식 코드https://github.com/umd-fire-coml/kitti-orientation-learning
공식 코드SoK: Vehicle Orientation Representations for Deep Rotation Estimation
https://github.com/umd-huang-lab/perceptionclip
공식 코드PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
https://github.com/unibwtas/yolopoint
공식 코드https://github.com/unique-chan/nbbox
공식 코드NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection
https://github.com/upeee/GOO-GAZE2021
공식 코드GOO: A Dataset for Gaze Object Prediction in Retail Environments
https://github.com/uscresl/dmfd
공식 코드Learning Deformable Object Manipulation from Expert Demonstrations
https://github.com/usp-lrm/dcme
공식 코드Distance to Center of Mass Encoding for Instance Segmentation
https://github.com/ut-amrl/coda-devkit
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut-amrl/coda-models
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
https://github.com/ut-vision/actionvos
공식 코드https://github.com/utokyo-fieldphenomics-lab/doda
공식 코드DODA: Diffusion for Object-detection Domain Adaptation in Agriculture
https://github.com/uulm-mrm/mmood3d
공식 코드Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
https://github.com/uvavision/scord
공식 코드SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data
https://github.com/uw-cmg/cavity_defect_detection
공식 코드Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests
https://github.com/uzh-rpg/svit
공식 코드Revisiting Token Pruning for Object Detection and Instance Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.