https://github.com/medaitech/tcmn
공식 코드One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
Papers with Code 아카이브
Scene Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/mehmetaygun/4d-pls
공식 코드https://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/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/minghanli/univs
공식 코드UniVS: Unified and Universal Video Segmentation with Prompts as Queries
https://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/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/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/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 코드https://github.com/mostafaelaraby/bacs-continual-semantic-segmentation
공식 코드https://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/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/nankepan/VIPMT
공식 코드Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.