https://github.com/okn-yu/SegNet-A-Deep-Convolutional-Encoder-Decoder-Architecture-for-Image-Segmentation
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
- 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/omkar13/MaskTrack
https://github.com/onyame/Git2PROV
https://github.com/open-mmlab/mmaction2
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmdetection3d
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmsegmentation
https://github.com/open-mmlab/mmtracking
https://github.com/opendilab/DI-engine
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/osmr/imgclsmob
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/pa56/SegNet_on_Cityscapes
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/pa56/SegNetonCityscapes
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/palver7/CFLPytorch
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/palver7/EquiConvPytorch
Corners for Layout: End-to-End Layout Recovery from 360 Images
https://github.com/pavankumar186/Paper-Implementation-Correspondence-b-w-DeepNN-and-Human-representations
Adapting Deep Network Features to Capture Psychological Representations
https://github.com/peizeli96/Object-attribute-matters-in-visual-question-answering
https://github.com/pengsongyou/openscene
https://github.com/peterfws/structure-plp-slam
https://github.com/petko-nikolov/pysemseg
https://github.com/pmeletis/panoptic_parts
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
https://github.com/prajjwal1/autonomous-object-detection
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
https://github.com/preddy5/segnet
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/princeton-computational-imaging/SeeingThroughFog
https://github.com/pwc-1/Paper-10/tree/main/seggpt
https://github.com/pwc-1/Paper-5/tree/main/mask2former
https://github.com/pwc-1/Paper-5/tree/main/mctct
MCTS with Refinement for Proposals Selection Games in Scene Understanding
https://github.com/pwc-1/Paper-9/tree/main/2/mctct
MCTS with Refinement for Proposals Selection Games in Scene Understanding
https://github.com/pwc-1/Paper-9/tree/main/5/upernet
https://github.com/pwc-1/Paper-9/tree/main/groupvit
GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/pytorch/vision
https://github.com/qrzyang/pseudo-stereo
https://github.com/qubvel/segmentation_models
https://github.com/rajatashhpa/1
https://github.com/raunak222/Image-Captioning
https://github.com/ray-project/ray/tree/master/rllib
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.