Papers for

event organizers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Temporal knowledge graph predicts music festival lineups years ahead

A Temporal Knowledge Graph for Music Festival Lineup Forecasting

Abstract: Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future lineups a natural fit for temporal knowledge graph (TKG) forecasting. In this work, we present a TKG covering 380 festivals over 55 years, comprising more than 90K festival performance quadruples along with information on festivals, artist tours, and artist metadata, and release it as a resource for TKG forecasting evaluation. We formalize festival lineup forecasting as temporal link prediction between artists and festivals at future timestamps. We evaluate six TKG forecasting models on this task, analyze their capabilities and limitations, and compare them against Large Language Models applied zero-shot. Our resource complements existing TKG benchmarks by grounding evaluation in a concrete, real-world application domain.

Mon 21 SeptMachine Learning
The gist
Predicting which artists will perform at future music festivals is tricky because it depends on many factors like genres, tours, and past shows. The authors created a large dataset covering decades of festival lineups and related artist information to help make better predictions. They treated the problem as forecasting connections between artists and festivals over time and tested several computer models to see how well each could predict future lineups. They also compared these models to large language models that have no prior training for this task. This dataset can help improve future prediction tools and study how artists and festivals are related over time.
Open → 2609.24467v1

Event level emotion recognition works reliably on wild photos

Event-Level Emotion Recognition in the Wild Using Deep Facial Expression Analysis

Abstract: Facial emotion recognition (FER) in real-world environments remains challenging due to unconstrained imaging conditions, including multiple faces, occlusions, pose variations, and complex lighting. Most existing studies focus on individual facial emotion classification and do not address the analysis of collective emotional states at the event level. This paper proposes an end-to-end pipeline for event-level emotion recognition from photographs. The approach detects faces in each image, classifies facial expressions using a deep convolutional neural network, and aggregates face-level emotion probabilities to estimate the overall emotional distribution of a public event. A comparative evaluation of several CNN architectures on the FER- 2013 and RAF-DB datasets demonstrates that transfer learning with EfficientNet-B2 trained on RAF-DB is more suitable for real-world RGB data. The proposed method is evaluated on a real-world event dataset containing 1658 images. Experimental results show stable emotion distributions across event subsets, confirming the effectiveness of event-level aggregation for emotion analysis in the wild.

Sat 12 SeptComputer Vision and Pattern Recognition
The gist
Recognizing emotions from faces in crowded, real-life photos is hard because of different lighting, poses, and overlapping faces. The authors developed a system that first finds faces, then uses a smart neural network trained on expression data to identify emotions on each face. Finally, it combines all these to figure out the overall mood at public events from photos. They tested various neural nets and found one called EfficientNet-B2 works best for photos taken in the wild. Their method gave consistent results on many real event photos, showing it can tell the general emotional atmosphere of an event.
Open → 2609.13854v1