https://github.com/LinearPi/OCR_Chinese
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 3
- 보존된 평가 기록
- 5
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/LiwenWang919/M3-UDA
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/Lkruitwagen/remote-sensing-solar-pv
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/LoopTactics/tc-cim
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/Lxrd-AJ/Advanced_ML
https://github.com/MS-Mind/MS-Code-06/tree/main/PSPNet
https://github.com/MS-Mind/MS-Code-08/tree/main/MGN
MGNet: Monocular Geometric Scene Understanding for Autonomous Driving
https://github.com/MS-P3/code7/tree/main/upernet
https://github.com/Magicboomliu/Accelerator-Simple-Template
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/MartinBuessemeyer/Artistic-Texture-Control
https://github.com/MartinHahner/FoggySynscapes
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
https://github.com/MartinHahner88/FoggySynscapes
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
https://github.com/Media-Smart/vedaseg
https://github.com/MichalKacprzak99/reconstruction_particle_mass_spectra
https://github.com/Mikoto10032/AutomaticWeightedLoss
https://github.com/Mind23-2/MindCode-169
https://github.com/Mind23-2/MindCode-5/tree/main/PSPNet
https://github.com/Mind23-2/MindCode-63
https://github.com/MindCode-4/code-1/tree/main/upernet
https://github.com/MindCode-4/code-11/tree/main/auxiliary-tasks-in-multi-task-learning
https://github.com/MindCode-4/code-4/tree/main/sam
https://github.com/MindCode-4/code-5/tree/main/upernet
https://github.com/MindCode-4/code-5/tree/main/videomae
https://github.com/MindCode-4/code-6/tree/main/auxiliary-tasks-in-multi-task-learning
https://github.com/MindSpore-paper-code-2/code2/tree/main/OSVOS
https://github.com/MindSpore-paper-code-2/code2/tree/main/PSPNet
https://github.com/MindSpore-paper-code-3/code10/tree/main/FCN8s
https://github.com/MindSpore-scientific-2/code-14/tree/main/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/MindSpore-scientific-2/code-2/tree/main/Super-Resolution-for-Root-Imaging
ROOT: VLM based System for Indoor Scene Understanding and Beyond
https://github.com/MindSpore-scientific/code-10/tree/main/Super-Resolution-for-Root-Imaging
ROOT: VLM based System for Indoor Scene Understanding and Beyond
https://github.com/MindSpore-scientific/code-14/tree/main/SGDR
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
https://github.com/MindSpore-scientific/code-5/tree/main/SGDR
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
https://github.com/MindSpore-scientific/code-7/tree/main/SAM
https://github.com/MindSpore-scientific/code-7/tree/main/UPerNet
https://github.com/MuchHair/PD-Net-Extended-Version
Polysemy Deciphering Network for Robust Human-Object Interaction Detection
https://github.com/NVlabs/neuralrgbd
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.