Papers with Code 아카이브
Scene Parsing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/motern88/det-sam2
공식 코드https://github.com/mseg-dataset/mseg-semantic
공식 코드MSeg: A Composite Dataset for Multi-domain Semantic Segmentation
https://github.com/msiam/mmc-multiscalememory
공식 코드Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation
https://github.com/msiam/tti_fsvos
공식 코드Temporal Transductive Inference for Few-Shot Video Object Segmentation
https://github.com/msracver/Deep-Feature-Flow
공식 코드https://github.com/mtec-tuhh/polypnextlstm
공식 코드PolypNextLSTM: A lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM
https://github.com/mttr2021/MTTR
공식 코드End-to-End Referring Video Object Segmentation with Multimodal Transformers
https://github.com/muirbench/MuirBench
공식 코드MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
https://github.com/muktilin/nice
공식 코드The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation
https://github.com/muliphein/infonorm
공식 코드InfoNorm: Mutual Information Shaping of Normals for Sparse-View Reconstruction
https://github.com/multimediafor/truvil
공식 코드Trusted Video Inpainting Localization via Deep Attentive Noise Learning
https://github.com/multimediafor/vilocal
공식 코드https://github.com/myukzzz/eicr
공식 코드Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation
https://github.com/nankepan/VIPMT
공식 코드Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
https://github.com/naver-ai/egtr
공식 코드EGTR: Extracting Graph from Transformer for Scene Graph Generation
https://github.com/neo85824/epsnet
공식 코드EPSNet: Efficient Panoptic Segmentation Network with Cross-layer Attention Fusion
https://github.com/neurai-lab/cude-monodepthcl
공식 코드Continual Learning of Unsupervised Monocular Depth from Videos
https://github.com/neurai-lab/mimdepth
공식 코드Image Masking for Robust Self-Supervised Monocular Depth Estimation
https://github.com/neurai-lab/mono-pose-attack
공식 코드https://github.com/neurai-lab/mt-sfmlearner
공식 코드https://github.com/nianticlabs/monodepth2
공식 코드https://github.com/nicholasli1995/s-3d-rcnn
공식 코드Joint stereo 3D object detection and implicit surface reconstruction
https://github.com/nickhaidos/scenir-icml2025
공식 코드SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
https://github.com/niessner/Matterport
공식 코드Matterport3D: Learning from RGB-D Data in Indoor Environments
https://github.com/nik-dim/pamal
공식 코드Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
https://github.com/nik-v9/airobject
공식 코드AirObject: A Temporally Evolving Graph Embedding for Object Identification
https://github.com/nomewang/mcf
공식 코드https://github.com/nomiaro/OPA
공식 코드Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection
https://github.com/nust-machine-intelligence-laboratory/hcpn
공식 코드Co-attention Propagation Network for Zero-Shot Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/hgpu
공식 코드https://github.com/nust-machine-intelligence-laboratory/hpan
공식 코드https://github.com/nust-machine-intelligence-laboratory/hvc
공식 코드Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation
https://github.com/nust-machine-intelligence-laboratory/videomac
공식 코드https://github.com/nv-tlabs/atiss
공식 코드ATISS: Autoregressive Transformers for Indoor Scene Synthesis
https://github.com/oceanhao/CoNav
공식 코드CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.