https://github.com/Nacriema/Loss-Functions-For-Semantic-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Papers with Code 아카이브
Object Tracking
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Nguyendat-bit/U-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/NicoleEic/Brainhack_Oxbridge
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/NikolasEnt/soccernet-calibration-sportlight
https://github.com/Nioolek/PPYOLOE_pytorch
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Ohyeon5/MismatchPenaltySegmentation
https://github.com/Ohyeon5/MultiscaleSegmentation
https://github.com/Oichii/DeepPulse-pytorch
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks
https://github.com/OlgaFomin/Speech-enhancement
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Osdel/ssnets
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Owais-Ansari/Unet3plus
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/Paddle3D
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot
https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/mot
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
https://github.com/PaddlePaddle/PaddleScience
TGCN: Time Domain Graph Convolutional Network for Multiple Objects Tracking
https://github.com/PaddlePaddle/PaddleSeg
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleYOLO
https://github.com/PatriciaRodrigues1994/Satellite-Image-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/PaulZhangIsing/CaffeSSD-opencv4
https://github.com/Paulpauladq/HighSpeedFaceTracker
https://github.com/Pecneb/computer_vision_research
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/PenK1nG/DroneMOT
https://github.com/PettyDevil/caffe_ssd
https://github.com/PhanTom2003/SSD
https://github.com/PiseyYou/MobileNet_Caffe_Configuration
https://github.com/PlumedSerpent/tmp_perspective_map
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/PuchatekwSzortach/voc_ssd
https://github.com/PurabPatel555/Pytorch-UNet-Brain-MRI
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/QiHuangChen/CACFNet
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/QiaoLiuHit/HSSNet
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
https://github.com/QiaoLiuHit/LSOTB-TIR
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
https://github.com/QiaoLiuHit/MLSSNet
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.