https://github.com/Bob-cheng/MDE_Attack
공식 코드Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
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/Boese0601/2021-National-Underwater-Robotics-Vision-Optics
공식 코드CDNet is all you need: Cascade DCN based underwater object detection RCNN
https://github.com/Boshrakh/Yolov8-Small-Object-Detection-Arial-Images
공식 코드SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenes
https://github.com/BrunoSauvalle/AST
공식 코드Unsupervised Multi-object Segmentation Using Attention and Soft-argmax
https://github.com/ByeongHyunPak/tqdm
공식 코드Textual Query-Driven Mask Transformer for Domain Generalized Segmentation
https://github.com/ByungKwanLee/CoLLaVO
공식 코드https://github.com/CAPTAIN-WHU/iSAID_Devkit
공식 코드iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
https://github.com/CASIA-IVA-Lab/ACSL
공식 코드Adaptive Class Suppression Loss for Long-Tail Object Detection
https://github.com/CHENGEZ/Attacking-by-Aligning
공식 코드Attacking by Aligning: Clean-Label Backdoor Attacks on Object Detection
https://github.com/CMFiltenborg/lingually_constrained_tracking
공식 코드https://github.com/CMURobotAndVisualLearningLab/Pushing-Dataset
공식 코드Object-centric Forward Modeling for Model Predictive Control
https://github.com/CQUlearningsystemgroup/YicongPeng
공식 코드MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
https://github.com/CSAILVision/gandissect
공식 코드GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
https://github.com/CSIPlab/context-aware-attacks
공식 코드https://github.com/CVC-Lab/3D_ADV_Mesh_pytorch3d
공식 코드Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors
https://github.com/CVMI-Lab/ST3D
공식 코드ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
https://github.com/Canjie-Luo/MORAN_v2
공식 코드A Multi-Object Rectified Attention Network for Scene Text Recognition
https://github.com/Caoang327/vis_det
공식 코드https://github.com/Caojunxu/AC-FPN
공식 코드Attention-guided Context Feature Pyramid Network for Object Detection
https://github.com/Championchess/Tri-Layer_Plugin_Occluded_Detection
공식 코드https://github.com/Chasel-Tsui/mmdet-aitod
공식 코드Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
https://github.com/ChenJoya/objnessdet
공식 코드Is Heuristic Sampling Necessary in Training Deep Object Detectors?
https://github.com/ChenJoya/sampling-free
공식 코드Is Heuristic Sampling Necessary in Training Deep Object Detectors?
https://github.com/Chenglin-Yang/MOS_Meticulous-Object-Segmentation
공식 코드https://github.com/Chengxiao-Luo/Untargeted-Backdoor-Attack-against-Object-Detection
공식 코드https://github.com/ChristianMarzahl/EIPH_WSI
공식 코드Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides
https://github.com/Cli98/DMNet
공식 코드https://github.com/Cloud-CV/object-proposals
공식 코드https://github.com/CodeMonsterPHD/GaTector-A-Unified-Framework-for-Gaze-Object-Prediction
공식 코드https://github.com/CognitiveModeling/Loci
공식 코드Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
https://github.com/Cold-Winter/BachGAN
공식 코드BachGAN: High-Resolution Image Synthesis from Salient Object Layout
https://github.com/CongliangLi/LabelImg3D
공식 코드https://github.com/CongliangLi/SMOPE-Net
공식 코드https://github.com/CrossmodalGroup/GSMN
공식 코드https://github.com/CuiRuikai/SAM-SOD
공식 코드Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection
https://github.com/CupidJay/Tobias
공식 코드A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.