https://github.com/ut-amrl/coda-devkit
공식 코드Towards Robust Robot 3D Perception in Urban Environments: The UT Campus Object Dataset
Papers with Code 아카이브
Semantic Segmentation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/uta-smile/ASSUDA
공식 코드Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/utkuozbulak/adaptive-segmentation-mask-attack
공식 코드Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
https://github.com/uva-dsa/auto_surgical_context2gesture
공식 코드Towards Surgical Context Inference and Translation to Gestures
https://github.com/uva-dsa/runtime_robscene_seg_2context
공식 코드Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference
https://github.com/uvavision/chair-segments
공식 코드Chair Segments: A Compact Benchmark for the Study of Object Segmentation
https://github.com/uverma/ManipalUAVid
공식 코드UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information
https://github.com/uwsbel/polar-digital
공식 코드POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
https://github.com/uwsbel/polar-sim
공식 코드POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
https://github.com/ux-decoder/find
공식 코드https://github.com/ux-decoder/semantic-sam
공식 코드Semantic-SAM: Segment and Recognize Anything at Any Granularity
https://github.com/uzh-rpg/ess
공식 코드ESS: Learning Event-based Semantic Segmentation from Still Images
https://github.com/uzh-rpg/rpg_vid2e
공식 코드https://github.com/uzh-rpg/svit
공식 코드Revisiting Token Pruning for Object Detection and Instance Segmentation
https://github.com/v7labs/xmem_icl
공식 코드Flexible visual prompts for in-context learning in computer vision
https://github.com/vacany/let-it-flow
공식 코드Let-It-Flow: Simultaneous Optimization of 3D Flow and Object Clustering
https://github.com/val-iisc/MixupDA
공식 코드Balancing Discriminability and Transferability for Source-Free Domain Adaptation
https://github.com/val-iisc/SFDA-Seg
공식 코드Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
https://github.com/val-iisc/gd-uap
공식 코드Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
https://github.com/valentinkoch/svdna
공식 코드Noise transfer for unsupervised domain adaptation of retinal OCT images
https://github.com/valeoai/3DGenZ
공식 코드Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/ConfidNet
공식 코드https://github.com/valeoai/DADA
공식 코드DADA: Depth-aware Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/FKAConv
공식 코드FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
https://github.com/valeoai/LaRa
공식 코드LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation
https://github.com/valeoai/MTAF
공식 코드Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/MVRSS
공식 코드https://github.com/valeoai/MuHDi
공식 코드Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/Occfeat
공식 코드OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
https://github.com/valeoai/WoodScape/tree/master/omnidet
공식 코드OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
https://github.com/valeoai/ZS3
공식 코드https://github.com/valeoai/also
공식 코드ALSO: Automotive Lidar Self-supervision by Occupancy estimation
https://github.com/valeoai/bevcontrast
공식 코드BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.