https://github.com/unmannedlab/point_labeler
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
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/usuyama/pytorch-unet
https://github.com/ux-decoder/find
https://github.com/v1viswan/RANet_modifications
RANet: Ranking Attention Network for Fast Video Object Segmentation
https://github.com/valeoai/VideoActionModel
The Cityscapes Dataset for Semantic Urban Scene Understanding
https://github.com/vevenom/pytorchgeonodes
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
https://github.com/vigansub/StructureAwareShapeTemplates
https://github.com/vik-goel/MOREL
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
https://github.com/vinceecws/SegNet_PyTorch
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/visaVita/mmaction2
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/vlcekl/n2n-tomo
https://github.com/voidrank/SaccadeNet
https://github.com/vpariza/open-hummingbird-eval
https://github.com/vqdang/hover_net
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/vqdang/xy_net
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/vsitzmann/light-field-networks
Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
https://github.com/warmspringwinds/pytorch-segmentation-detection
https://github.com/waspinator/deep-learning-explorer
https://github.com/wind754203900/SCNN-TF2
Spatial As Deep: Spatial CNN for Traffic Scene Understanding
https://github.com/witcher425/CHINESEOCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/wkentaro/pytorch-fcn
https://github.com/wudao001/LSMVOS
LSMVOS: Long-Short-Term Similarity Matching for Video Object
https://github.com/xahidbuffon/SUIM
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
https://github.com/xiaofeng-han-res/mf-rv
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
https://github.com/xiaofengShi/CHINESE-OCR
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
https://github.com/xiaohangzhan/deocclusion
https://github.com/xiaolonw/TimeCycle
https://github.com/xilin1991/ClusterNet
Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering
https://github.com/xmed-lab/cardiacnet
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmed-lab/m3-uda
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xujxyang/opentrans
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
https://github.com/xy-guo/Learning-Monocular-Depth-by-Stereo
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
https://github.com/y-ouali/pytorch_segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/yangchris11/samurai
https://github.com/yangxuntu/vrd
Visual Translation Embedding Network for Visual Relation Detection
https://github.com/yangysc/resinet
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.