Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 3
- 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/zonmgin-zhang/asi-seg
공식 코드ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
https://github.com/zouchuhang/LayoutNetv2
공식 코드LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
https://github.com/zrrskywalker/personalize-sam
공식 코드https://github.com/zty0304/sgdraw
공식 코드SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
https://github.com/zubair-irshad/NeRF-MAE
공식 코드https://github.com/zyp123494/dynavol
공식 코드Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering
https://github.com/zzhhfut/ccnet-aaai2025
공식 코드https://github.com/zzy-zhou/dsec-mos
공식 코드Event-Free Moving Object Segmentation from Moving Ego Vehicle
https://github.com/zzzkkkyyy/padenet
공식 코드Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes
https://gitlab.com/mihaicristianpirvu/cycleconcepts
공식 코드Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
https://gitlab.com/mihaicristianpirvu/neural-graph-consensus
공식 코드Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
https://gitlab.com/nct_tso_public/dsad-segmentation
공식 코드One model to use them all: Training a segmentation model with complementary datasets
https://gitlab.com/neural-graph-consensus/semisup-multitask-scene-understanding
공식 코드Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
https://github.com/0bserver07/Keras-SegNet-Basic
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/163GitHub/AI
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/fcn-4
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FCN8s
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PSPNet
https://github.com/2023-MindSpore-1/ms-code-42
https://github.com/2023-MindSpore-1/ms-code-46
https://github.com/2023-MindSpore-4/Code10/tree/main/FCN8s
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/9p15p/frtm-vos
Learning Fast and Robust Target Models for Video Object Segmentation
https://github.com/AIwithSwift/TFWorld2019-SwiftIn3Hours
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/Akhilesh64/ResUnet-a
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/AmrElsersy/PointPainting
https://github.com/AndPuQing/ContextPrior_Paddle
https://github.com/AndresPMD/Pytorch-yolo-phoc
https://github.com/AnirudhAchal/Human-Parsing
https://github.com/ArantxaCasanova/fc-drn
On the iterative refinement of densely connected representation levels for semantic segmentation
https://github.com/ArkaJU/SegNet---Chromosome
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Aryavir07/Detecting-Brain-Tumor-Using-Deep-Learning
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/Asthestarsfalll/PSPNet-MegEngine
https://github.com/AyaLotfy/PReMVOS-trial
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/AyaLotfy/premvos-tmp
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.