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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/andr345/frtm-vos

공식 코드

Learning Fast and Robust Target Models for Video Object Segmentation

https://github.com/andrewjlee0/evaluating_compositionality_vlms

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Evaluating Compositional Scene Understanding in Multimodal Generative Models

https://github.com/andy-cheng/tempura

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TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action

https://github.com/andyj1/doubly-contrastive-semseg

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Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather

https://github.com/aneesh3108/AeroRIT

공식 코드

AeroRIT: A New Scene for Hyperspectral Image Analysis

https://github.com/angeloucn/min_max_similarity

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Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation

https://github.com/angelvillar96/MCDS-VSS

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MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and Motion

https://github.com/anger-man/unsupervised-depth-estimation

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Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction

https://github.com/apple/ARKitScenes

공식 코드

ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data

https://github.com/apple/ml-hypersim

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Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

https://github.com/appletea233/al-ref-sam2

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Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Audio and Language Referenced Video Object Segmentation

https://github.com/arplaboratory/PENet

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PENet: A Joint Panoptic Edge Detection Network

https://github.com/arplaboratory/coped

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CoPeD-Advancing Multi-Robot Collaborative Perception: A Comprehensive Dataset in Real-World Environments

https://github.com/artonson/autoinst

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AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans

https://github.com/ashawkey/dimr

공식 코드

Point Scene Understanding via Disentangled Instance Mesh Reconstruction

https://github.com/ashesham/tv3s

공식 코드

Exploiting Temporal State Space Sharing for Video Semantic Segmentation

https://github.com/aspirinone/catr.github.io

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CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation

https://github.com/asterisci/language-assisted-3d

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Language-Assisted 3D Feature Learning for Semantic Scene Understanding

https://github.com/asterisci/point-gcc

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Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast

https://github.com/atapour/temporal-depth-segmentation

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Veritatem Dies Aperit- Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach

https://github.com/atr-dbi/scanqa

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ScanQA: 3D Question Answering for Spatial Scene Understanding

https://github.com/atrovast/thgs

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Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs

https://github.com/audiollms/audiobench

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AudioBench: A Universal Benchmark for Audio Large Language Models

https://github.com/aurora-hao/hct

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

https://github.com/autonomousvision/kitti360labeltool

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/autonomousvision/kitti360scripts

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KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D

https://github.com/aymanbegh/hybrid-gcnn-cnn

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A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference

https://github.com/azhuantou/aiscene

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Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation

https://github.com/baaivision/painter

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SegGPT: Segmenting Everything In Context

https://github.com/baegwangbin/surface_normal_uncertainty

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Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

https://github.com/baldassarreFe/ws-vrd

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Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks

https://github.com/bcv-uniandes/grasp

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Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/matis

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Pixel-Wise Recognition for Holistic Surgical Scene Understanding

https://github.com/bcv-uniandes/tapir

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Towards Holistic Surgical Scene Understanding

https://github.com/beileicui/ms-tfal

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Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation

https://github.com/beltransen/velo2cam_calibration

공식 코드

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.