https://github.com/lemmersj/ground-truth-or-daer
공식 코드Ground-truth or DAER: Selective Re-query of Secondary Information
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/leonlha/kinematics-afr
공식 코드https://github.com/leonnnop/locater
공식 코드Local-Global Context Aware Transformer for Language-Guided Video Segmentation
https://github.com/lhc1224/OSAD_Net
공식 코드https://github.com/lhoangan/341n143
공식 코드Three for one and one for three: Flow, Segmentation, and Surface Normals
https://github.com/lhoangan/multas
공식 코드Box for Mask and Mask for Box: weak losses for multi-task partially supervised learning
https://github.com/liat-s/radar_occupancy_grid
공식 코드https://github.com/liavassif/BU-TD
공식 코드Image interpretation by iterative bottom-up top-down processing
https://github.com/lijunzhang01/cefa
공식 코드A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap
https://github.com/likojack/odam
공식 코드ODAM: Object Detection, Association, and Mapping using Posed RGB Video
https://github.com/lilydaytoy/openpvsg
공식 코드https://github.com/lilydaytoy/pvsgannotation
공식 코드https://github.com/limingxing00/rde-vos-cvpr2022
공식 코드https://github.com/linfengyuan1997/losh
공식 코드LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
https://github.com/linjieyangsc/video_seg
공식 코드https://github.com/lironui/A2-FPN
공식 코드A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
https://github.com/lironui/FPN-MHA
공식 코드A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
https://github.com/liuyi1989/pwrf
공식 코드Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding
https://github.com/liuzhengzhe/One-Thing-One-Click
공식 코드You Only Need One Thing One Click: Self-Training for Weakly Supervised 3D Scene Understanding
https://github.com/liuziyang123/faminet
공식 코드https://github.com/lizhaoliu-Lec/CPCM
공식 코드CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
https://github.com/lkeab/gaussian-grouping
공식 코드https://github.com/lluisgomez/single-shot-str
공식 코드https://github.com/lmd0311/hermes
공식 코드HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
https://github.com/lmm077/SWEM
공식 코드SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
https://github.com/lofrienger/onetomany_toolsynseg
공식 코드Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
https://github.com/lojzezust/mmsegmentation-macvi
공식 코드LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmark
https://github.com/lojzezust/wasr-t
공식 코드https://github.com/longbai1006/cat-vil
공식 코드https://github.com/longbai1006/cs-vqla
공식 코드https://github.com/longmalongma/desa2va
공식 코드Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
https://github.com/longyin880815/STCNN
공식 코드https://github.com/lrp-ivc/stsbench
공식 코드https://github.com/lu-zhan/pano-nerf
공식 코드https://github.com/lucaspk512/vrdone
공식 코드https://github.com/lukasliebel/MultiDepth
공식 코드MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.