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Scene Understanding

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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/resuldagdanov/robot-learning-human-trust

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Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales

https://github.com/rheinzler/PointCloudDeNoising

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CNN-based Lidar Point Cloud De-Noising in Adverse Weather

https://github.com/rkzheng99/villa

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ViLLa: Video Reasoning Segmentation with Large Language Model

https://github.com/rll-research/sim2seg

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Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data

https://github.com/robert80203/GSVNet

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GSVNet: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video

https://github.com/robertluo1/iccv2023_rvos_challenge

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1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation

https://github.com/robertwyq/panoocc

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PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation

https://github.com/robotvisionmu/densemonoslam

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A Hybrid Sparse-Dense Monocular SLAM System for Autonomous Driving

https://github.com/rolsheng/mm-vufm4ds

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Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

https://github.com/rongfu-dsb/MPG-SAM2

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MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation

https://github.com/root-ai/depth-quality

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Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures

https://github.com/rrrajjjj/flowgrad

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FlowGrad: Using Motion for Visual Sound Source Localization

https://github.com/rtous/lester

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Lester: rotoscope animation through video object segmentation and tracking

https://github.com/ruipingl/opensu

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Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments

https://github.com/ruipingl/skr_pea

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TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

https://github.com/ruipingl/transkd

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TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

https://github.com/runnanchen/clip2scene

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CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

https://github.com/runnanchen/label-free-scene-understanding

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Towards Label-free Scene Understanding by Vision Foundation Models

https://github.com/sabarim/3DC-Seg

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Making a Case for 3D Convolutions for Object Segmentation in Videos

https://github.com/safouaneelg/FusionVision

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FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything

https://github.com/saitpublic/doctr

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DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding

https://github.com/sallymmx/TransVOS

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TransVOS: Video Object Segmentation with Transformers

https://github.com/salmanmaq/segmentationNetworks

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m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks

https://github.com/sam1224/fssam

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Unlocking the Power of SAM 2 for Few-Shot Segmentation

https://github.com/samsunglabs/swissdino

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Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search

https://github.com/sangminwoo/Temporal-Span-Proposal-Network-VidVRD

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What and When to Look?: Temporal Span Proposal Network for Video Relation Detection

https://github.com/sarashoouri/efficientmtl

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Efficient Computation Sharing for Multi-Task Visual Scene Understanding

https://github.com/sbrunoberenguel/fredsnet

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FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions

https://github.com/scenediffuser/Scene-Diffuser

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Diffusion-based Generation, Optimization, and Planning in 3D Scenes

https://github.com/schmiddo/d2conv3d

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D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

https://github.com/scott-yjyang/vivim

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Vivim: a Video Vision Mamba for Medical Video Segmentation

https://github.com/seanzhuh/awesome-open-vocabulary-detection-and-segmentation

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A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future

https://github.com/segmentationblwx/sssegmentation

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MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation

https://github.com/selgroup/sit-hss

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Hierarchical Superpixel Segmentation via Structural Information Theory

https://github.com/sephirex-x/latentdriver

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Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving

https://github.com/sercant/mobile-segmentation

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An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions

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