Titlebook: Digital Multimedia Communications; 20th International F Guangtao Zhai,Jun Zhou,Xiaokang Yang Conference proceedings 2024 The Editor(s) (if

Tremor

https://doi.org/10.1007/978-3-642-11460-1ur-dimensional spatial data. However, the restriction of sensor resolution results in a trade-off between angular and spatial resolution, which hinders our ability to simultaneously acquire light field with high spatial and angular resolution. In this paper, we focus on the sparse light field recons

连词

学术讨论会

Bertrand Beckert,Benoît Masquidateness in each separate training task. To date, the average accuracy and forgetting rate are the two most popular metrics for continual learning evaluation. However, these two metrics only care about the overall increment of mistaken samples when a model updated by the new task is applied to the old

Alveoli

Vivian Bellofatto,Jennifer B. Palencharsual confounding factors and language confounding factors in images and annotations of datasets, by computing co-occurrence probabilities between objects and between words. These methods can effectively deconfound the visual and language confounders simultaneously. However, the impact of language pr

几何学家

Introduction: The Sage of Love,tion. To accurately model the complexity of rumor propagation dynamics in real social media and effectively ameliorate the effects of rumors on society, a new rumor propagation model named V-SEIR is proposed in this paper. The proposed V-SEIR model considers the node heterogeneity, individual behavi

轻快走过

Enrique A. Thomann,Edward C. Waymireindispensable component across various fields. Traditional recommendation systems typically rely on either user historical preferences or item similarity for generating suggestions. However, cross-domain recommendation systems transcend these traditional boundaries by harnessing not only user histor

百灵鸟

关节炎

Intuitive

ble information about user concerns and preferences in specific domains. However, there has been limited research exploring the user-related information contained in such conversational data for constructing individual user knowledge graphs. We propose a method for learning to construct a personal k

健谈