Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Rudinac, Stevan"'
We propose Set2Seq Transformer, a novel sequential multiple instance architecture, that learns to rank permutation aware set representations of sequences. First, we illustrate that learning temporal position-aware representations of discrete timestep
Externí odkaz:
http://arxiv.org/abs/2408.03404
Autor:
Huang, Jia-Hong, Zhu, Hongyi, Shen, Yixian, Rudinac, Stevan, Pacces, Alessio M., Kanoulas, Evangelos
Evaluating the quality of automatically generated image descriptions is challenging, requiring metrics that capture various aspects such as grammaticality, coverage, correctness, and truthfulness. While human evaluation offers valuable insights, its
Externí odkaz:
http://arxiv.org/abs/2408.01723
Autor:
Wang, Shuai, Zhang, David W., Huang, Jia-Hong, Rudinac, Stevan, Kackovic, Monika, Wijnberg, Nachoem, Worring, Marcel
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intric
Externí odkaz:
http://arxiv.org/abs/2405.13372
Image search stands as a pivotal task in multimedia and computer vision, finding applications across diverse domains, ranging from internet search to medical diagnostics. Conventional image search systems operate by accepting textual or visual querie
Externí odkaz:
http://arxiv.org/abs/2404.18746
Autor:
Wang, Shuai, Shen, Jiayi, Efthymiou, Athanasios, Rudinac, Stevan, Kackovic, Monika, Wijnberg, Nachoem, Worring, Marcel
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable of utilizing the full richness of the HINs. Existing methods for mode
Externí odkaz:
http://arxiv.org/abs/2309.13092
Multimodal demand forecasting aims at predicting product demand utilizing visual, textual, and contextual information. This paper proposes a method for multimodal product demand forecasting using convolutional, graph-based, and transformer-based arch
Externí odkaz:
http://arxiv.org/abs/2307.02578
Large collections of geo-referenced panoramic images are freely available for cities across the globe, as well as detailed maps with location and meta-data on a great variety of urban objects. They provide a potentially rich source of information on
Externí odkaz:
http://arxiv.org/abs/2208.14295
Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawb
Externí odkaz:
http://arxiv.org/abs/2109.10683
Publikováno v:
In International Journal of Information Management Data Insights November 2024 4(2)
We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations. First, we illustrate the significant ad
Externí odkaz:
http://arxiv.org/abs/2105.08190