https://github.com/henghuiding/MeViS
공식 코드MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
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/hengliusky/few_shot_rvos
공식 코드Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples
https://github.com/hermannsblum/scim
공식 코드SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
https://github.com/heshuting555/dshmp
공식 코드Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
https://github.com/hhyhrhy/owmm-agent
공식 코드OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
https://github.com/hiltonml/camera_trap_tools
공식 코드An Image Processing Pipeline for Camera Trap Time-Lapse Recordings
https://github.com/hitcslj/ssd
공식 코드Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
https://github.com/hithqd/unim-ov3d
공식 코드https://github.com/hitottiez/sdof-tracker
공식 코드SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow
https://github.com/hkchengrex/Cutie
공식 코드https://github.com/hkchengrex/MiVOS
공식 코드https://github.com/hkchengrex/STCN
공식 코드Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation
https://github.com/hkchengrex/Tracking-Anything-with-DEVA
공식 코드https://github.com/hkchengrex/XMem
공식 코드XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/hkust-aerial-robotics/emsgc
공식 코드Event-based Motion Segmentation with Spatio-Temporal Graph Cuts
https://github.com/hkust-vgd/riconv
공식 코드Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
https://github.com/hluo29/rcnet
공식 코드RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement
https://github.com/hmxiong/3ur-llm
공식 코드3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene Understanding
https://github.com/hongje/hmmn
공식 코드Hierarchical Memory Matching Network for Video Object Segmentation
https://github.com/hszhao/PSPNet
공식 코드https://github.com/huaaaliu/rgbx_semantic_segmentation
공식 코드CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
https://github.com/huangkun101230/360mtlgeometricestimation
공식 코드Multi-task Geometric Estimation of Depth and Surface Normal from Monocular 360° Images
https://github.com/huixiancheng/cenet
공식 코드CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
https://github.com/hustvl/senna
공식 코드Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
https://github.com/hwansoo-choi/cognitive-transfuser
공식 코드https://github.com/hy0523/mtnet
공식 코드Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
https://github.com/hydragon516/dpa
공식 코드Dual Prototype Attention for Unsupervised Video Object Segmentation
https://github.com/hydragon516/gsanet
공식 코드Guided Slot Attention for Unsupervised Video Object Segmentation
https://github.com/hysonlab/tesgraph
공식 코드https://github.com/hzxie/RMNet
공식 코드Efficient Regional Memory Network for Video Object Segmentation
https://github.com/icitylab/hspr
공식 코드Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
https://github.com/imatge-upc/rvos-mots
공식 코드https://github.com/imoneoi/carla_env
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/imoneoi/onerl
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/imran2205/logicrag
공식 코드https://github.com/imsy-dkfz/htc
공식 코드Robust deep learning-based semantic organ segmentation in hyperspectral images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.