# Video Relation Detection via Tracklet based Visual Transformer Source: [Video Relation Detection via Tracklet based Visual Transformer]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:09:35.928Z Authors: Kaifeng Gao, Long Chen, Yifeng Huang, Jun Xiao Published: 2021-08-19T13:13:23.000Z ## Abstract Video Visual Relation Detection (VidVRD), has received significant attention of our community over recent years. In this paper, we apply the state-of-the-art video object tracklet detection pipeline MEGA and deepSORT to generate tracklet proposals. Then we perform VidVRD in a tracklet-based manner without any pre-cutting operations. Specifically, we design a tracklet-based visual Transformer. It contains a temporal-aware decoder which performs feature interactions between the tracklets and learnable predicate query embeddings, and finally predicts the relations. Experimental results strongly demonstrate the superiority of our method, which outperforms other methods by a large margin on the Video Relation Understanding (VRU) Grand Challenge in ACM Multimedia 2021. Codes are released at https://github.com/Dawn-LX/VidVRD-tracklets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Visual Relation Tagging on VidOR (val)](): Visual Relation Tagging; VidOR (val); P@5: 52.73. - [Visual Relation Detection on VidOR (val)](): Visual Relation Detection; VidOR (val); R@50: 0.0835. ## Original sources and implementation [Original paper]() [PDF]()