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Scene Parsing

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전체 기법 전체 데이터셋

하위 태스크: 9
평가표: 2
보존된 평가 기록: 3

Scene parsing is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Description

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/bionicvisionlab/2021-han-scene-simplification

공식 코드

Deep Learning--Based Scene Simplification for Bionic Vision

https://github.com/biqiwhu/agos

공식 코드

All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification

https://github.com/birlrobotics/PMN

공식 코드

Pose-based Modular Network for Human-Object Interaction Detection

https://github.com/birlrobotics/vs-gats

공식 코드

Visual-Semantic Graph Attention Networks for Human-Object Interaction Detection

https://github.com/bknyaz/sgg

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Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

https://github.com/blackprotoss/gsdm

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Text Image Inpainting via Global Structure-Guided Diffusion Models

https://github.com/blvlab/pimae

공식 코드

PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection

https://github.com/bo-miao/HTR

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Temporally Consistent Referring Video Object Segmentation with Hybrid Memory

https://github.com/bo-miao/MAMP

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Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation

https://github.com/bo-miao/sgmg

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Spectrum-guided Multi-granularity Referring Video Object Segmentation

https://github.com/bobwan1995/PMFNet

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Pose-aware Multi-level Feature Network for Human Object Interaction Detection

https://github.com/boschresearch/relationfield

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RelationField: Relate Anything in Radiance Fields

https://github.com/bowang-lab/medsam

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Segment Anything in Medical Images and Videos: Benchmark and Deployment

https://github.com/bowang-lab/medsam2

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MedSAM2: Segment Anything in 3D Medical Images and Videos

https://github.com/bowen-upenn/scene_graph_commonsense

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Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation

https://github.com/brownvc/shapefromtracing

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Shape From Tracing: Towards Reconstructing 3D Object Geometry and SVBRDF Material from Images via Differentiable Path Tracing

https://github.com/buaahugegun/dcfm

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Deep Common Feature Mining for Efficient Video Semantic Segmentation

https://github.com/buaavrcg/LEGaussians

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Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding

https://github.com/bupt-ai-cz/Meta-SelfLearning

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Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark

https://github.com/buxiangzhiren/vd-it

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Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation

https://github.com/bxiang233/panopticsegforlargescalepointcloud

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Towards accurate instance segmentation in large-scale LiDAR point clouds

https://github.com/bxiang233/panopticsegformobilemappingpointclouds

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A Review of Panoptic Segmentation for Mobile Mapping Point Clouds

https://github.com/bytedance/MTVQA

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MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering

https://github.com/bytedance/e2str

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Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer

https://github.com/caganselim/tltm

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Two-Level Temporal Relation Model for Online Video Instance Segmentation

https://github.com/caipeide/VTGNet

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VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

https://github.com/cake-lab/PointAR

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PointAR: Efficient Lighting Estimation for Mobile Augmented Reality

https://github.com/cake-lab/Xihe

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Xihe: A 3D Vision-based Lighting Estimation Framework for Mobile Augmented Reality

https://github.com/camma-public/surglatentgraph

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Optimizing Latent Graph Representations of Surgical Scenes for Zero-Shot Domain Transfer

https://github.com/canjie-luo/real-300k

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SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization

https://github.com/carrierlxk/AGNN

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Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

https://github.com/carrierlxk/COSNet

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See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

https://github.com/carrierlxk/GraphMemVOS

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Video Object Segmentation with Episodic Graph Memory Networks

https://github.com/carrierlxk/MuG

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Learning Video Object Segmentation from Unlabeled Videos

https://github.com/catalina17/VideoNavQA

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VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

https://github.com/ch3cook-fdu/vote2cap-detr

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Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning

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