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/danielenricocahall/Keras-SegNet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/daveboat/spp
https://github.com/daveboat/structured_label_inference
https://github.com/davidtvs/Keras-LinkNet
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
https://github.com/davyneven/fastSceneUnderstanding
https://github.com/dcnieho/segment-anything-2
https://github.com/demul/image_segmentation_project
A Review on Deep Learning Techniques Applied to Semantic Segmentation
https://github.com/diksha0799/Image-Captioning-master
https://github.com/dingmyu/davit
https://github.com/dipika-singhania/multi-scale-action-banks
Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dirtyharrylyl/hake-ava
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/divamgupta/image-segmentation-keras
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/divyakraman/BoMuDA-Boundless-Multi-Source-Domain-Adaptive-Segmentation-in-Unstructured-Environments
https://github.com/dmlc/dgl/tree/master/examples/mxnet/scenegraph
https://github.com/dongkwonjin/Semantic-Line-DRM
Interactive Video Object Segmentation Using Global and Local Transfer Modules
https://github.com/dongkwonjin/Semantic-Line-SLNet
Interactive Video Object Segmentation Using Global and Local Transfer Modules
https://github.com/dsc1126/RWSeg
https://github.com/dtc111111/multi-modal-uav
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
https://github.com/ducminhkhoi/Amodal-Instance-Seg-ASBU
https://github.com/dvlab-research/pointgroup
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
https://github.com/dvornikita/blitznet
https://github.com/dvssajay/The-Ikshana-Hypothesis-of-Human-Scene-Understanding-Mechanism
https://github.com/dxli94/WLASL
TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation
https://github.com/e-lab/linknet
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
https://github.com/e-lab/pytorch-linknet
LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
https://github.com/ebagdasa/backdoors101
https://github.com/emsalinha/videosum-eval
https://github.com/enot-autodl/lpcv-2023
https://github.com/eriklindernoren/Keras-GAN
https://github.com/eriklindernoren/PyTorch-GAN
https://github.com/facebookresearch/SegmPred
https://github.com/facebookresearch/TensorComprehensions
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/facebookresearch/habitat-api/tree/master/habitat_baselines/rl/ddppo
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/facebookresearch/segment-anything
https://github.com/faroukmokhtar/ImageSegmentationPASCAL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.