{"id":781180,"date":"2021-10-03T17:09:09","date_gmt":"2021-10-04T00:09:09","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=781180"},"modified":"2021-10-03T17:09:09","modified_gmt":"2021-10-04T00:09:09","slug":"learning-spatio-temporal-transformer-for-visual-tracking","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-spatio-temporal-transformer-for-visual-tracking\/","title":{"rendered":"Learning Spatio-Temporal Transformer for Visual Tracking"},"content":{"rendered":"

In this paper, we present a new tracking architecture with an encoder-decoder transformer as the key component. The encoder models the global spatio-temporal feature dependencies between target objects and search regions, while the decoder learns a query embedding to predict the spatial positions of the target objects. Our method casts object tracking as a direct bounding box prediction problem, without using any proposals or predefined anchors. With the encoder-decoder transformer, the prediction of objects just uses a simple fully-convolutional network, which estimates the corners of objects directly. The whole method is end-to-end, does not need any postprocessing steps such as cosine window and bounding box smoothing, thus largely simplifying existing tracking pipelines. The proposed tracker achieves state-of-the-art performance on five challenging short-term and long-term benchmarks, while running at real-time speed, being 6x faster than Siam R-CNN. Code and models are open-sourced and available on GitHub (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this paper, we present a new tracking architecture with an encoder-decoder transformer as the key component. The encoder models the global spatio-temporal feature dependencies between target objects and search regions, while the decoder learns a query embedding to predict the spatial positions of the target objects. Our method casts object tracking as a direct 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Yan","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Houwen Peng","user_id":38431,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Houwen Peng"},{"type":"user_nicename","value":"Jianlong Fu","user_id":32260,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jianlong Fu"},{"type":"text","value":"Dong Wang","user_id":0,"rest_url":false},{"type":"text","value":"Huchuan 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