Papers with Code 아카이브
Scene Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 2
- 보존된 평가 기록
- 3
Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kklemon/bgan-pytorch
https://github.com/krishnapalS/Human-Pose-Estimation-using-ShelfNet
https://github.com/krutikabapat/Crowd_Counting
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/kuangzijian/Flow-Based-Video-Matting
https://github.com/kukby/Mish-semantic-segmentation-pytorch
https://github.com/kulkarnikeerti/SegNet-Semantic-Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/kurapan/CRNN
https://github.com/kyungsu-lee-ksl/ash
https://github.com/l1997i/lim3d
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
https://github.com/l1997i/rapid_seg
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
https://github.com/lalithjets/domain-generalization-for-surgical-scene-graph
Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection
https://github.com/larkinandy/PlacePulse_PSP_RemoteSensing
https://github.com/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/laura-wang/video_repres_mas
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leeyeehoo/CSRNet-pytorch
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
https://github.com/lidongliang666/cv_deep_learning
https://github.com/liuch37/sar-pytorch
Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition
https://github.com/llmbev/talk2bev
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
https://github.com/logictensornetworks/logictensornetworks
Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation
https://github.com/lojzezust/lars_evaluator
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
https://github.com/looooongchen/sortedap
https://github.com/lostsword/character_recognition
https://github.com/louisfinner/him2sam
https://github.com/lpiccinelli-eth/unidepth
iDisc: Internal Discretization for Monocular Depth Estimation
https://github.com/lufan31/comprecap
https://github.com/lunaproject22/aar
Spatial-Temporal Transformer for Dynamic Scene Graph Generation
https://github.com/luozhilin99/cnnctc
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/lxx1991/VS-ReID
https://github.com/lynx97/convolutional-attention-network-for-scene-text-recognition
A Holistic Representation Guided Attention Network for Scene Text Recognition
https://github.com/lzmisscc/emoran
A Multi-Object Rectified Attention Network for Scene Text Recognition
https://github.com/m-serra/action-inference-for-video-prediction-benchmarking
https://github.com/mangonihao/BiLinear_CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.