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/yangyucheng000/University/tree/main/model-2/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/yangyucheng000/University/tree/main/model-3/sam
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
https://github.com/yangyucheng000/University/tree/main/model-3/seggpt
https://github.com/yashdani/Object-Detection-Using-YOLOv3-Algorithm
https://github.com/ycszen/ContextPrior
https://github.com/yelantingfeng/pyLucid
https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
https://github.com/yformer/EfficientSAM
https://github.com/yinanzhu12/SegNet-keras
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/yinanzhu12/SegNet-keras-implementation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/yingkaisha/keras-unet-collection
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/youngsean/unseenobjectswithmeanshift
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
https://github.com/yubaoliu/caffe-segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/yubaoliu/rds-slam
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/yuhsijen/Object-Detector
https://github.com/yuhuan-wu/EDN
https://github.com/yuhuan-wu/RDPNet
https://github.com/yuhuan-wu/mobilesal
https://github.com/yui-mhcp/base_dl_project
https://github.com/yunzeman/lexicon3d
Lexicon3D: Probing Visual Foundation Models for Complex 3D Scene Understanding
https://github.com/yuranusduke/TransCNN
https://github.com/ywcmaike/TianchiVideoCharacterSegmentationPreliminary
https://github.com/zawlin/cvpr17_vtranse
Visual Translation Embedding Network for Visual Relation Detection
https://github.com/zh320/realtime-semantic-segmentation-pytorch
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/zhang-tao-whu/DVIS
DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
https://github.com/zhihaozhang97/ru-ai
https://github.com/zhouxian/FluidLab
Diffusion-based Generation, Optimization, and Planning in 3D Scenes
https://github.com/zhucheng725/MobileNetv1_Segnet_keras_tensorrt
https://github.com/zhuotongchen/self-healing-robust-neural-networks-via-closed-loop-control
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/zixuan-chen/M3VOS_Experiment
M$^3$-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation
https://github.com/zj603252818/FCN8s
https://github.com/zkyntu/UnLanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/zkyseu/PPlanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/zlw9161/carrada-rac
https://github.com/zlw9161/pkc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.