https://github.com/srihari-humbarwadi/LinkNet-Exploiting-Encoder-Representations-for-Efficient-Semantic-Segmentation
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Papers with Code 아카이브
Scene Understanding
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- 평가표
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- 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/starkgines/PDI
https://github.com/stefan-ainetter/HOC-Search
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
https://github.com/stefan-ainetter/SCANnotateDataset
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
https://github.com/stelzner/monet
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
https://github.com/stuartchen1949/segnet_paddle
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/sunset1995/pytorch-layoutnet
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
https://github.com/sunshenggu/xc_eval_pcdet
https://github.com/suryanshkumar/online-joint-depthfusion-and-semantic
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
https://github.com/susomena/DeepSlowMotion
https://github.com/switchablenorms/SwitchNorm_Segmentation
https://github.com/tashchowdhury/FloodNet-Supervised_v1.0
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/tejaswigowda/semseg-pytorch
https://github.com/tencentyouturesearch/personretrieval-ivt
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/feelvos
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
https://github.com/ternaus/angiodysplasia-segmentation
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
https://github.com/theia-4869/semantic-poss-api
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/theostoican/Guided-Research/blob/main/finetuning_odin_resnet.ipynb
https://github.com/thimabru1010/ResUnet-a_multitasking
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data
https://github.com/thusiyuan/holistic_scene_parsing
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
https://github.com/tianyuan168326/videosemanticcompression-pytorch
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/tileb1/adasim
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
https://github.com/timmeinhardt/trackformer
https://github.com/tkuanlun350/Tensorflow-SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/tobran/DF-GAN
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis
https://github.com/torchjd/torchjd
https://github.com/towhee-io/towhee/tree/main/towhee/models/coformer
Collaborative Transformers for Grounded Situation Recognition
https://github.com/trailab/generalobjectmapping
Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior
https://github.com/trisha-c-a/Visual-Question-Answering-with-Keras
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/trypag/pytorch-unet-segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/tudelft/filled-disparity-monodepth
https://github.com/tui-nicr/emsaformer
https://github.com/tui-nicr/nicr-scene-analysis-datasets
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
https://github.com/udacity/MLND-CN-Capstone-TGSImage
https://github.com/umich-curly/bki_ros
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.