https://github.com/NeuronDroid/GVSS-S.A.Drone
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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/NiallEHunt/MonocularDepth-Using-LightFields
https://github.com/Nicholasli1995/EgoNet
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/OpenNeuroDatasets/ds001499
https://github.com/Osdel/ssnets
https://github.com/OzHsu23/chineseocr
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/PRBonn/bonnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/PaddlePaddle/Paddle3D
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/segmentation/cfbi.md
Collaborative Video Object Segmentation by Foreground-Background Integration
https://github.com/Paultool/segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/PhanTom2003/PSPnet
https://github.com/Pointcept/Pointcept
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
https://github.com/PuchatekwSzortach/voc_fcn
https://github.com/QingyongHu/SQN
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
https://github.com/RajkumarPreetham/Road-scene-understanding
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Rintarooo/PSPNet
https://github.com/RituYadav92/Image-segmentation
https://github.com/Rosie-Brigham/sesmeg
https://github.com/RuiZhou-cn/U3HS
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
https://github.com/RuijieZhu94/mmdepth
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation
https://github.com/RylanSchaeffer/prismatic-vlms
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/SaifeddineBarkia/Multi-view-radar-semantic-segmentation-for-scene-understanding-in-autonomous-driving
https://github.com/SamvitJ/Accel
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
https://github.com/Scalsol/mega.pytorch
https://github.com/Sentient07/HeadHunter-T
https://github.com/Shun14/UperNet
https://github.com/ShunyuYao/blitznet_instance_segment
https://github.com/SilvioGiancola/SoccerNetv2-DevKit
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/SkyWa7ch3r/ImageSegmentation
https://github.com/SkyWa7ch3r/SceneSegmentation
https://github.com/SonpKing/semantic-segmentation-pytorch
https://github.com/Srijan2001/Keypoint-regression
https://github.com/StanLei52/GEBD
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/SunDaDenny/DuLa-Net
DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.