Report title:ICASSP2023-Decoupled Visual Causality for Robust Detection
reportAnnouncement time：2023year6month16日 19:00 - 20:00
Reporting location: Electronic Building, Railway College, Central South University414
Tencent Conference:914-8957-5796
Report Summary: This work is accepted as an oral paper in the ICASSP2023 (CCF B). The abstract of the accepted paper seabet online sports betting is as follows. The existing empirical risk minimization algorithms learn the association between inputs and labels, and face substantial difficulties when apply to different distributions because of various confounders. Causal intervention becomes a solid solution to this issue by analyzing the visual causality, instead, those approaches fail at disentangling the confounders and mediators within the causality, and bring negative effects to the prediction. In this paper, we propose seabet casino review a disentangled visual causal model to eliminate the effects of confounders while reserving the corresponding mediators. Specifically, confounders are considered as different objects on the image, while mediators are formulated as some critical components of the targets that contribute to a distinctive identification. Extensive experiments on COCO datasets have demonstrated the superiority of our model over other state-of-the-art baselines.
Lecturer’s profile: Ping seabet app download Jiang received the master's degree in computer engineering from the University of Western Ontario, Canada, in 2018. He is currently pursuing a Ph.D. degree with the Electrical and Communication Engineering Department, Central South University, China. His research interests include machine learning, computer vision, and perception in vehicular networks.
