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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/huangzhe885/roco

공식 코드

RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment

https://github.com/huckiyang/MOT-Kuzushiji-Fashion-Video

공식 코드

Robust Unsupervised Multi-Object Tracking in Noisy Environments

https://github.com/hughw19/NOCS_CVPR2019

공식 코드

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

https://github.com/hunterj-lin/activeteacher

공식 코드

Active Teacher for Semi-Supervised Object Detection

https://github.com/huochf/StackFLOW

공식 코드

StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset

https://github.com/huochf/WildHOI

공식 코드

Monocular Human-Object Reconstruction in the Wild

https://github.com/huodongjian0603/crt

공식 코드

Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

https://github.com/huskykingdom/dita_acml2023

공식 코드

Learning to Terminate in Object Navigation

https://github.com/hustvl/BMaskR-CNN

공식 코드

Boundary-preserving Mask R-CNN

https://github.com/hustvl/LSFA

공식 코드

Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation

https://github.com/hustvl/QueryInst

공식 코드

Instances as Queries

https://github.com/hustvl/YOLOS

공식 코드

You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

https://github.com/hustvl/featurized-queryrcnn

공식 코드

Featurized Query R-CNN

https://github.com/hustvl/mimdet

공식 코드

Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection

https://github.com/hustvl/sparseinst

공식 코드

Sparse Instance Activation for Real-Time Instance Segmentation

https://github.com/hutaihang/tome

공식 코드

Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis

https://github.com/huuxiaobin/hitnet

공식 코드

High-resolution Iterative Feedback Network for Camouflaged Object Detection

https://github.com/huyvvo/LOD

공식 코드

Large-Scale Unsupervised Object Discovery

https://github.com/huyvvo/OSD

공식 코드

Unsupervised Image Matching and Object Discovery as Optimization

https://github.com/huyvvo/bib

공식 코드

Active Learning Strategies for Weakly-supervised Object Detection

https://github.com/hvision-nku/camoformer

공식 코드

CamoFormer: Masked Separable Attention for Camouflaged Object Detection

https://github.com/hxzhong1997/fsba

공식 코드

Few-Shot Backdoor Attacks on Visual Object Tracking

https://github.com/hy0523/mtnet

공식 코드

Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation

https://github.com/hydragon516/dpa

공식 코드

Dual Prototype Attention for Unsupervised Video Object Segmentation

https://github.com/hydragon516/gsanet

공식 코드

Guided Slot Attention for Unsupervised Video Object Segmentation

https://github.com/hyenal/relate

공식 코드

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces

https://github.com/hyjhkoh/mgtanet

공식 코드

MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection

https://github.com/hyperwjf/logiccheckgpt

공식 코드

Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

https://github.com/hyz-xmaster/VarifocalNet

공식 코드

VarifocalNet: An IoU-aware Dense Object Detector

https://github.com/hyz-xmaster/swa_object_detection

공식 코드

SWA Object Detection

https://github.com/hzai-zjnu/mamba-yolo

공식 코드

Mamba YOLO: A Simple Baseline for Object Detection with State Space Model

https://github.com/hzai-zjnu/remdet

공식 코드

RemDet: Rethinking Efficient Model Design for UAV Object Detection

https://github.com/hzhupku/DCNet

공식 코드

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection

https://github.com/hzxie/RMNet

공식 코드

Efficient Regional Memory Network for Video Object Segmentation

https://github.com/hzxie/Stereo-3D-Reconstruction

공식 코드

Toward 3D Object Reconstruction from Stereo Images

https://github.com/hzzone/pseco

공식 코드

Point, Segment and Count: A Generalized Framework for Object Counting

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