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Scene Parsing

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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/YesianRohn/TextSSR

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TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition

https://github.com/YingjieYin/Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation

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Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation

https://github.com/YorkUCVIL/Static-Dynamic-Interpretability

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A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information

https://github.com/Yuqifan1117/CaCao

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Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World

https://github.com/ZENGXH/DMM_Net

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DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

https://github.com/ZJULearning/CariFaceParsing

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Weakly-supervised Caricature Face Parsing through Domain Adaptation

https://github.com/Zengyi-Qin/MonoGRNet

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MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection

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Weakly Supervised 3D Object Detection from Point Clouds

https://github.com/ZhengyuZhao/Adi-Red-Scene

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From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition

https://github.com/ZhilingYan/Biomedical-SAM-2

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Biomedical SAM 2: Segment Anything in Biomedical Images and Videos

https://github.com/Zhourobotics/Query-3DGS-LLM

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Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D Gaussian

https://github.com/aa200647963/sgg-dhl

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Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation

https://github.com/abourki/sota-semantically-aware-nerfs

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Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review

https://github.com/acaglayan/CNN_randRNN

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When CNNs Meet Random RNNs: Towards Multi-Level Analysis for RGB-D Object and Scene Recognition

https://github.com/acecreamu/color-constancy-googlenet

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Artificial Color Constancy via GoogLeNet with Angular Loss Function

https://github.com/activevisionlab/awesome-llm-3d

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When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models

https://github.com/adeline-cs/GTR

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Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

https://github.com/affect2mm/emotion-timeseries

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Affect2MM: Affective Analysis of Multimedia Content Using Emotion Causality

https://github.com/ahmedssabir/Visual-Semantic-Relatedness-with-Word-Embedding

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Visual Re-ranking with Natural Language Understanding for Text Spotting

https://github.com/aicps/rs2g

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RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding

https://github.com/aigeeksgroup/dc-scene

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DC-Scene: Data-Centric Learning for 3D Scene Understanding

https://github.com/aim-skku/box-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-skku/nyudv2-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-skku/sun-rgbd-is

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IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks

https://github.com/aim-uofa/omni-r1

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Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration

https://github.com/aimerykong/Recurrent-Scene-Parsing-with-Perspective-Understanding-in-the-loop

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Recurrent Scene Parsing with Perspective Understanding in the Loop

https://github.com/aimi-lab/robust-pose-estimator

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Learning How To Robustly Estimate Camera Pose in Endoscopic Videos

https://github.com/air-discover/vibus

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VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling

https://github.com/ais-bonn/nimbro_api

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Leveraging Vision-Language Models for Open-Vocabulary Instance Segmentation and Tracking

https://github.com/ait-assistive-autonomous-systems/hopomop

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Few-shot Structure-Informed Machinery Part Segmentation with Foundation Models and Graph Neural Networks

https://github.com/akash2907/semi-sup-active-learning

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Semi-supervised Active Learning for Video Action Detection

https://github.com/akash2907/stable_mean_teacher

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Stable Mean Teacher for Semi-supervised Video Action Detection

https://github.com/alanlukezic/d3s

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D3S -- A Discriminative Single Shot Segmentation Tracker

https://github.com/albertgassol1/vf-nerf

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VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction

https://github.com/alexanderjaus/PPS

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Panoramic Panoptic Segmentation: Towards Complete Surrounding Understanding via Unsupervised Contrastive Learning

https://github.com/alexberian/crossmodalitydiffusion

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CrossModalityDiffusion: Multi-Modal Novel View Synthesis with Unified Intermediate Representation

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