Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking
Jointly exploiting multiple different yet complementary domain information has been proven to be an effective way to perform robust object tracking. This paper focuses on effectively representing and utilizing complementary features from the frame domain and event domain for boosting object tracking performance in challenge scenarios. Specifically, we propose Common Features Extractor (CFE) to learn potential common representations from the RGB domain and event domain. For learning the unique features of the two domains, we utilize a Unique Extractor for Event (UEE) based on Spiking Neural Networks to extract edge cues in the event domain which may be missed in RGB in some challenging conditions, and a Unique Extractor for RGB (UER) based on Deep Convolutional Neural Networks to extract texture and semantic information in RGB domain. Extensive experiments on standard RGB benchmark and real event tracking dataset demonstrate the effectiveness of the proposed approach. We show our approach outperforms all compared state-of-the-art tracking algorithms and verify event-based data is a powerful cue for tracking in challenging scenes.
Code (0)
등록된 구현이 없습니다.
Tasks
Object TrackingVisual Object TrackingSimilar Papers 제목 키워드 기반
Collaborative Normalization for Unsupervised Domain Adaptation
Batch Normalization (BN) as an important component assists Deep Neural Networks achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches. However…
Domain AdaptationUnsupervised Domain AdaptationFORLA:Federated Object-centric Representation Learning with Slot Attention
Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative acros…
DecoderFederated LearningObject DiscoveryRepresentation LearningCFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Medical image anomaly detection remains challenging because networks pretrained on natural images often exhibit limited adaptability to medical images, where abnormal patterns appear as fine-grained local shifts, multi-s…
Anomaly ClassificationAnomaly DetectionIFTR: An Instance-Level Fusion Transformer for Visual Collaborative Perception
Multi-agent collaborative perception has emerged as a widely recognized technology in the field of autonomous driving in recent years. However, current collaborative perception predominantly relies on LiDAR point clouds,…
Autonomous DrivingVCP-DCN: Beyond Visual Concealed Property via Depth Collaborative Network for Camouflaged Object Detection
Camouflaged Object Detection (COD) aims to identify and segment camouflaged objects in complex environments, which are often concealed because their color and texture are similar to the background. Several existing COD m…
Contrastive LearningObject Detection