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Video Object Tracking
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Video Object Detection aims to detect targets in videos using both spatial and temporal information. It's usually deeply integrated with tasks such as Object Detection and Object Tracking.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning
Efficient Visual Tracking with Exemplar Transformers
Evaluating and Improving Interactions with Hazy Oracles
BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
Do Different Tracking Tasks Require Different Appearance Models?
ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking
LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
Learning Spatio-Temporal Transformer for Visual Tracking
Learning Target Candidate Association to Keep Track of What Not to Track
Transformer Tracking
Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
TDIOT: Target-driven Inference for Deep Video Object Tracking
End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box
Attention over learned object embeddings enables complex visual reasoning
Contrastive Transformation for Self-supervised Correspondence Learning
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Tracking-by-Trackers with a Distilled and Reinforced Model
Ocean: Object-aware Anchor-free Tracking
Fast Template Matching and Update for Video Object Tracking and Segmentation
Know Your Surroundings: Exploiting Scene Information for Object Tracking
Learning Object Permanence from Video
Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking
Improving Human Annotation in Single Object Tracking
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
A Comparison of CNN and Classic Features for Image Retrieval
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking
Fast Visual Object Tracking with Rotated Bounding Boxes
Learning Discriminative Model Prediction for Tracking
Unsupervised learning-based long-term superpixel tracking
Deeper and Wider Siamese Networks for Real-Time Visual Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.