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cs.CV2026

Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +9

The paper describes the POLY-SIM 2026 challenge, which focuses on developing multimodal speaker identification systems that remain robust when audio or visual data are missing and…

cs.CV2026

SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

Markus Essl, Marta Moscati, Mubashir Noman +4

Multimodal sensor fusion has demonstrated remarkable performance improvements over unimodal approaches in 3D object detection for autonomous vehicles. Typically, existing methods t…

cs.CV2026

POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan

Marta Moscati, Muhammad Saad Saeed, Marina Zanoni +8

Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-w…

cs.CV2026

Face-Voice Association with Inductive Bias for Maximum Class Separation

Marta Moscati, Oleksandr Kats, Mubashir Noman +4

Face-voice association is widely studied in multimodal learning and is approached representing faces and voices with embeddings that are close for a same person and well separated…

cs.CV2025

Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge

Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed +7

Over half of the world's population is bilingual and people often communicate under multilingual scenarios. The Face-Voice Association in Multilingual Environments (FAME) 2026 Chal…

cs.CV2025

Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan

Marta Moscati, Ahmed Abdullah, Muhammad Saad Saeed +7

The advancements of technology have led to the use of multimodal systems in various real-world applications. Among them, audio-visual systems are among the most widely used multimo…