Papers with Code 아카이브
Video Semantic Segmentation
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The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/petko-nikolov/pysemseg
https://github.com/pwc-1/Paper-10/tree/main/seggpt
https://github.com/pwc-1/Paper-5/tree/main/mask2former
https://github.com/pytorch/vision
https://github.com/qubvel/segmentation_models
https://github.com/raymondwang987/fmnet
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
https://github.com/rickyHong/FCN-segmentation-repl
https://github.com/sagieppel/Focusing-attention-of-Fully-convolutional-neural-networks-on-Region-of-interest-ROI-input-map-
https://github.com/sagieppel/Fully-convolutional-neural-network-FCN-for-semantic-segmentation-Tensorflow-implementation
https://github.com/sajjad-sh33/yolo_sam2
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
https://github.com/saswat0/CRVOS
https://github.com/seoungwugoh/STM
https://github.com/seoungwugoh/ivs-demo
Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks
https://github.com/shashankvkt/video_object_segmentation
https://github.com/shekkizh/FCN.tensorflow
https://github.com/shelhamer/clockwork-fcn
https://github.com/shelhamer/fcn.berkeleyvision.org
https://github.com/simonzeng7108/video-transunet
Video-SwinUNet: Spatio-temporal Deep Learning Framework for VFSS Instance Segmentation
https://github.com/soccernet/sn-grounding
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/soccernet/sn-spotting
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/switchablenorms/SwitchNorm_Segmentation
https://github.com/tejaswigowda/semseg-pytorch
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/feelvos
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
https://github.com/theostoican/Guided-Research/blob/main/finetuning_odin_resnet.ipynb
https://github.com/tianyuan168326/videosemanticcompression-pytorch
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
https://github.com/tileb1/adasim
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
https://github.com/timmeinhardt/trackformer
https://github.com/udacity/MLND-CN-Capstone-TGSImage
https://github.com/usuyama/pytorch-unet
https://github.com/ux-decoder/find
https://github.com/v1viswan/RANet_modifications
RANet: Ranking Attention Network for Fast Video Object Segmentation
https://github.com/vik-goel/MOREL
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
https://github.com/visaVita/mmaction2
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/warmspringwinds/pytorch-segmentation-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.