Papers with Code 아카이브
Visual Object Tracking
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Visual Object Tracking is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wenbowen123/catgrasp
공식 코드You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
https://github.com/wencheng256/SiamBAN-ACM
공식 코드Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
https://github.com/wenruicai/hiptrack
공식 코드https://github.com/wenruicai/spmtrack
공식 코드https://github.com/xl-sr/THOR
공식 코드https://github.com/xuefeng-zhu5/RGBD1K
공식 코드RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
https://github.com/xvjiarui/VFS
공식 코드Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
https://github.com/yangchris11/samurai
공식 코드SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
https://github.com/yoxu515/mits
공식 코드Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
https://github.com/z-x-yang/AOT
공식 코드Associating Objects with Transformers for Video Object Segmentation
https://github.com/z-z188/umdatrack
공식 코드UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
https://github.com/zhang-pengyu/Multimodal-Tracking-Survey
공식 코드Multi-modal Visual Tracking: Review and Experimental Comparison
https://github.com/zhang-pengyu/Multimodal_tracking_survey
공식 코드Multi-modal Visual Tracking: Review and Experimental Comparison
https://github.com/zhangyong-tang/dfat
공식 코드Exploring Fusion Strategies for Accurate RGBT Visual Object Tracking
https://github.com/zhangyong-tang/gmmt
공식 코드https://github.com/zhangyong-tang/taat
공식 코드https://github.com/zhiqic/procontext
공식 코드ProContEXT: Exploring Progressive Context Transformer for Tracking
https://github.com/111qqz/caffe-ssd
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/ecolite
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/OSVOS
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/ocean_model
https://github.com/2023-MindSpore-1/ms-code-32
https://github.com/2023-MindSpore-4/Code10/tree/main/SSD
https://github.com/2023-MindSpore-4/Code11/tree/main/SiamFC
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
https://github.com/2023-MindSpore-4/Code11/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/SiamFC
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/siamRPN
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
https://github.com/2023-MindSpore-4/Code3/tree/main/ecolite
https://github.com/AVAuco/ssd_people
https://github.com/AbhinavJhanwar/object-detection-classification
https://github.com/AmirDavoodi/Hand-Gestures-Human-Robot-Interaction
https://github.com/Ander02/FaceRecognition
https://github.com/Andrew-Qibin/ssdlite-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.