https://github.com/hustdml/cttrack
공식 코드Compact Transformer Tracker with Correlative Masked Modeling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Compact Transformer Tracker with Correlative Masked Modeling
Robust Estimation of Similarity Transformation for Visual Object Tracking
Multiple Object Tracking with Kernelized Correlation Filters in Urban Mixed Traffic
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Towards Robust Video Object Segmentation with Adaptive Object Calibration
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
Context-aware Deep Feature Compression for High-speed Visual Tracking
A Distractor-Aware Memory for Visual Object Tracking with SAM2
ProContEXT: Exploring Progressive Context Transformer for Tracking
Exploring Enhanced Contextual Information for Video-Level Object Tracking
Ground-truth or DAER: Selective Re-query of Secondary Information
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking
Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking
TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator
SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe
Deformable Siamese Attention Networks for Visual Object Tracking
CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking
RTracker: Recoverable Tracking via PN Tree Structured Memory
Unifying Visual and Vision-Language Tracking via Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.