https://github.com/neurai-lab/cude-monodepthcl
공식 코드Continual Learning of Unsupervised Monocular Depth from Videos
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 6
- 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/neurai-lab/mimdepth
공식 코드Image Masking for Robust Self-Supervised Monocular Depth Estimation
https://github.com/neurai-lab/mono-pose-attack
공식 코드https://github.com/neurai-lab/mt-sfmlearner
공식 코드https://github.com/nianticlabs/monodepth2
공식 코드https://github.com/nicholasli1995/s-3d-rcnn
공식 코드Joint stereo 3D object detection and implicit surface reconstruction
https://github.com/niessner/Matterport
공식 코드Matterport3D: Learning from RGB-D Data in Indoor Environments
https://github.com/nik-dim/pamal
공식 코드Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
https://github.com/nik-v9/airobject
공식 코드AirObject: A Temporally Evolving Graph Embedding for Object Identification
https://github.com/nomiaro/OPA
공식 코드Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
https://github.com/nust-machine-intelligence-laboratory/hcpn
공식 코드Co-attention Propagation Network for Zero-Shot Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/hgpu
공식 코드https://github.com/nust-machine-intelligence-laboratory/hpan
공식 코드https://github.com/nust-machine-intelligence-laboratory/hvc
공식 코드Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/videomac
공식 코드https://github.com/oceanhao/CoNav
공식 코드CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation
https://github.com/olivesgatech/implicit-background-estimation
공식 코드https://github.com/olivesgatech/segmentation_corruption
공식 코드On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
https://github.com/open-air-sun/cerberus
공식 코드Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
https://github.com/open-air-sun/pq-transformer
공식 코드PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
https://github.com/opendatalab/urbench
공식 코드https://github.com/opendilab/InterFuser
공식 코드Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
https://github.com/opendrivelab/elm
공식 코드https://github.com/opendrivelab/toponet
공식 코드https://github.com/opengvlab/mutr
공식 코드Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation
https://github.com/opengvlab/vinci
공식 코드An Egocentric Vision-Language Model based Portable Real-time Smart Assistant
https://github.com/opennlplab/avsbench
공식 코드https://github.com/openrobotlab/embodiedscan
공식 코드EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI
https://github.com/openrobotlab/vlm-grounder
공식 코드https://github.com/openseg-group/mlseg
공식 코드RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
https://github.com/openseg-group/rankseg
공식 코드RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
https://github.com/opradelle/2dguidedlight3d
공식 코드Lightweight integration of 3D features to improve 2D image segmentation
https://github.com/oravus/lostX
공식 코드LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
https://github.com/oskarnatan/deepipcv2
공식 코드https://github.com/palaashagrawal/stupd
공식 코드STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning
https://github.com/palmdong/smmcl
공식 코드Understanding Dark Scenes by Contrasting Multi-Modal Observations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.