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/TWJianNuo/panoptic-scene-understanding
https://github.com/TanyaChutani/Monodepth-Tf2.x
https://github.com/Tashnim-Chowdhury/FloodNet-Challenge-EARTHVISION2021
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/TejasBajania/Mtech_pro
https://github.com/TejasBajania/Mtech_thesis_project
https://github.com/Tez01/SegNet-Keras-Implementation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/TheUser0815/segnet-pytorch
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/TimoSaemann/caffe-segnet-cudnn5
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/TingdiRen/URSCT-SESR
Underwater Image Super-Resolution using Deep Residual Multipliers
https://github.com/TqDavid/td
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/TranThienDat-Nguyen/VisionEvaluation
https://github.com/TripleJoy/SAM2MOT
https://github.com/Turoad/lanedet
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/UBC-CIC/COVID19-L3-Net
https://github.com/VCL3D/DeepPanoramaLighting
Deep Lighting Environment Map Estimation from Spherical Panoramas
https://github.com/VICO-UoE/KD4MTL
https://github.com/Violet981/Breast_mass_Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/VisualComputingInstitute/mots_trackingonly_tools
PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation
https://github.com/Whiffe/mmaction2_YF
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/XXXVincent/MonoDepth2
https://github.com/XiangbingJi/Stanford-cs230-final-project
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
https://github.com/YanjieZe/Virtual-Multi-View-Fusion
https://github.com/YeLyuUT/UAVidToolKit
https://github.com/YeLyuUT/semantic_segmentation
Bidirectional Multi-scale Attention Networks for Semantic Segmentation of Oblique UAV Imagery
https://github.com/YeongHyeon/Skip-GANomaly
Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
https://github.com/Yijunmaverick/GenerativeFaceCompletion
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/YininKorea/Contour-aware-equipotential-learning
https://github.com/YoungXIAO13/FewShotDetection
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
https://github.com/YoungXIAO13/PoseContrast
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
https://github.com/ZFTurbo/segmentation_models_3D
https://github.com/Zhanghongbin-github/SegNet-Tutorial
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/ZhecanJamesWang/GLAT_SGG
Learning Visual Commonsense for Robust Scene Graph Generation
https://github.com/ZhecanJamesWang/GLAT_Visual_Commonsense
Learning Visual Commonsense for Robust Scene Graph Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.