https://github.com/594422814/TransformerTrack
공식 코드Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
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.
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
EgoTracks: A Long-term Egocentric Visual Object Tracking Dataset
LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion
Fast and Accurate Online Video Object Segmentation via Tracking Parts
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing
AiATrack: Attention in Attention for Transformer Visual Tracking
Rotation Adaptive Visual Object Tracking with Motion Consistency
MixFormer: End-to-End Tracking with Iterative Mixed Attention
UncTrack: Reliable Visual Object Tracking with Uncertainty-Aware Prototype Memory Network
Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation
Universal Instance Perception as Object Discovery and Retrieval
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
PatchNet -- Short-range Template Matching for Efficient Video Processing
LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
Siamese Keypoint Prediction Network for Visual Object Tracking
Do Different Tracking Tasks Require Different Appearance Models?
Discriminative Correlation Filter with Channel and Spatial Reliability
Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.