https://github.com/GCVCG/VolETA-MetaFood
XMem++: Production-level Video Segmentation From Few Annotated Frames
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GLUS-video/GLUS
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation
https://github.com/GT-RIPL/robo-vln
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/Gaoyiminggithub/Graphonomy-Panoptic
Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation
https://github.com/GatorSense/MICI
Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty
https://github.com/GriffinLiang/vrd-dsr
https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/HAN-ARK/GVSS-S.A.Drone
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/HCPLab-SYSU/SR
https://github.com/Harsharma2308/PoseRefinement
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/HenonBamboo/PSPNet-MindSpore
https://github.com/Huawei-MRC-OSI/optml-zoo
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/HuynhThanhQuan/skin-detective
https://github.com/IBM/MAX-Object-Detector
https://github.com/IDEA-Research/Grounded-Segment-Anything
https://github.com/IRVLab/SUIM
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/IRVLab/SUIM-Net
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/IRVLab/srdrm
Underwater Image Super-Resolution using Deep Residual Multipliers
https://github.com/IcarusWizard/monodepth2-paddle
https://github.com/IuliiaZaitova/code_slingers
https://github.com/Ivan-LZY/SG-Cyclingscapes
The Cityscapes Dataset for Semantic Urban Scene Understanding
https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models
https://github.com/Jeff-HOU/ML_Note
https://github.com/JerryX1110/awesome-rvos
https://github.com/JhihYangWu/UnofficialGeNVS
CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation
https://github.com/Jiang-Muyun/Open3D-Semantic-KITTI-vis
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/JonDoe-297/cross-view
https://github.com/JonasFrey96/ml-hypersim-pytorch
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
https://github.com/JonathonLuiten/PReMVOS
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/JosephPB/XNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/JuliaChae/M3D-RPN-Waymo
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/JunLi-Galios/CDFL
Weakly Supervised Energy-Based Learning for Action Segmentation
https://github.com/Jungjaewon/Visual-Relationship-Detection
Visual Relationship Detection with Language prior and Softmax
https://github.com/KPeng9510/MASS
https://github.com/KangchengLiu/RM3D
https://github.com/Lawhy/Searchive
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.