25 citations · 80 across the 27 of their papers we have counts for
8 papers · 1 filter
Results of the 1st Asynchronous CASTLE Challenge at the Joint Egocentric Vision Workshop in Conjunction with CVPR 2026
Luca Rossetto, Werner Bailer, Cathal Gurrin +5
This report summarizes the contributions and results of the 1st Asynchronous CASTLE Challenge at the Joint Egocentric Vision Workshop in conjunction with CVPR 2026.
MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
Shuai Wang, Wangyuan Ding, Yixian Shen +5
Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surface content but struggle with for…
Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding
Gowreesh Mago, Pascal Mettes, Stevan Rudinac
The automatic understanding of video content is advancing rapidly. Empowered by deeper neural networks and large datasets, machines are increasingly capable of understanding what i…
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models
Jia-Hong Huang, Hongyi Zhu, Yixian Shen +2
Evaluating the quality of automatically generated image descriptions is a complex task that requires metrics capturing various dimensions, such as grammaticality, coverage, accurac…
A Novel Evaluation Framework for Image2Text Generation
Jia-Hong Huang, Hongyi Zhu, Yixian Shen +3
Evaluating the quality of automatically generated image descriptions is challenging, requiring metrics that capture various aspects such as grammaticality, coverage, correctness, a…
Set2Seq Transformer: Temporal and Position-Aware Set Representations for Sequential Multiple-Instance Learning
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic +2
In many real-world applications, modeling both the internal structure of sets and their temporal relationships is essential for capturing complex underlying patterns. Sequential mu…
PanorAMS: Automatic Annotation for Detecting Objects in Urban Context
Inske Groenen, Stevan Rudinac, Marcel Worring
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 ur…
Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic +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-b…