Papers with Code 아카이브
Scene Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/mcg-nju/trace
공식 코드Target Adaptive Context Aggregation for Video Scene Graph Generation
https://github.com/mcgrche/LetsPi--Planning-Safety-Trajectories-with-Dual-Phase-Physics-Informed-LLM
공식 코드https://github.com/mdyao/polarfree
공식 코드https://github.com/medaitech/rab
공식 코드https://github.com/medaitech/tcmn
공식 코드One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
https://github.com/mehmetaygun/4d-pls
공식 코드https://github.com/melosy/cam
공식 코드Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
https://github.com/menglcool/segic
공식 코드SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
https://github.com/mguti97/soccernet-v3d
공식 코드SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding
https://github.com/mhashas/Exploiting-Temporality-For-Semi-Supervised-Video-Segmentation
공식 코드Exploiting Temporality for Semi-Supervised Video Segmentation
https://github.com/mhayoz/online_endo_track
공식 코드Online 3D reconstruction and dense tracking in endoscopic videos
https://github.com/michaelramamonjisoa/monteboxfinder
공식 코드MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud
https://github.com/microsoft/GenerativeImage2Text
공식 코드GIT: A Generative Image-to-text Transformer for Vision and Language
https://github.com/microsoft/scene_graph_benchmark
공식 코드https://github.com/microsoft/swin3d
공식 코드Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
https://github.com/microsoft/transductive-vos.pytorch
공식 코드https://github.com/microsoft/unilm/tree/master/trocr
공식 코드TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
https://github.com/milomilo0320/a-semi-automatic-annotation-software-for-scene-graph
공식 코드GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph
https://github.com/minghanli/univs
공식 코드UniVS: Unified and Universal Video Segmentation with Prompts as Queries
https://github.com/mingliangzhang2018/PGDP
공식 코드https://github.com/minhoooo1/catmae
공식 코드Concatenated Masked Autoencoders as Spatial-Temporal Learner
https://github.com/mit-spark/llm_scene_understanding
공식 코드Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
https://github.com/mit-spark/midiffusion
공식 코드https://github.com/mjmjeong/physgaia
공식 코드PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis
https://github.com/mkg1204/rhmnet-for-ssvos
공식 코드Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation
https://github.com/mkoshkina/jersey-number-pipeline
공식 코드A General Framework for Jersey Number Recognition in Sports Video
https://github.com/mlvlab/speaq
공식 코드https://github.com/mlvlab/vidchain
공식 코드VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
https://github.com/mobarakol/AP-MTL
공식 코드https://github.com/mobarakol/Surgical_SceneGraph_Generation
공식 코드Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
https://github.com/modelstreaming/ams
공식 코드Real-Time Video Inference on Edge Devices via Adaptive Model Streaming
https://github.com/mods333/energy-based-scene-graph
공식 코드https://github.com/mohamedac29/P2AT
공식 코드P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation
https://github.com/monster-ghost/monodistill
공식 코드MonoDistill: Learning Spatial Features for Monocular 3D Object Detection
https://github.com/moonseokha/streamocc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.