Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/serenos/lwsis
공식 코드LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
https://github.com/shangweihung/LDFNet
공식 코드https://github.com/shanshuo/social-fabric
공식 코드Social Fabric: Tubelet Compositions for Video Relation Detection
https://github.com/shaohuadong2021/dplnet
공식 코드Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
https://github.com/shaoyuanlo/stpl
공식 코드https://github.com/sharpershape/eclair-dataset
공식 코드ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
https://github.com/shelhamer/revolver
공식 코드https://github.com/shenzheng2000/instance-warp
공식 코드Instance-Warp: Saliency Guided Image Warping for Unsupervised Domain Adaptation
https://github.com/shenzheng2000/tpsence
공식 코드TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain
https://github.com/shibshib/Moe-VRD
공식 코드https://github.com/shikha-gist/image-captioning
공식 코드Label-Attention Transformer with Geometrically Coherent Objects for Image Captioning
https://github.com/shilinyan99/panovos
공식 코드PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
https://github.com/shinke-li/Campus3D
공식 코드Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
https://github.com/showlab/sam-i2v
공식 코드SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost
https://github.com/showlab/videolisa
공식 코드One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
https://github.com/shuangjiexu/MHP-VOS
공식 코드MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation
https://github.com/shuhongll/sgs-slam
공식 코드https://github.com/shvdiwnkozbw/ssl-uvos
공식 코드Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
https://github.com/silky1708/locate
공식 코드LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training
https://github.com/simarkareer/unifiedvideoda
공식 코드We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
https://github.com/simondemoreau/led
공식 코드https://github.com/simonzeng7108/video-swinunet
공식 코드Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
https://github.com/simplexsigil/omnifall-experiments
공식 코드OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection
https://github.com/sirkosophia/dip
공식 코드DIP: Unsupervised Dense In-Context Post-training of Visual Representations
https://github.com/siyandong/NeuralRouting
공식 코드https://github.com/sjtu-deepvisionlab/laga
공식 코드Tackling View-Dependent Semantics in 3D Language Gaussian Splatting
https://github.com/sjtu-visys/textslam
공식 코드https://github.com/skmhrk1209/VSRD
공식 코드VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
https://github.com/skyshoumeng/pc-bev
공식 코드PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation
https://github.com/skyworkai/daq-vs
공식 코드DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
https://github.com/slytheringe/rsteller
공식 코드https://github.com/sophiabano/endovis-fetreg2021
공식 코드https://github.com/speedinghzl/CCNet
공식 코드https://github.com/spengliang/weakm3d
공식 코드WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
https://github.com/spla-tam/splatam
공식 코드SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.