The field of multimodal learning is witnessing significant advancements, driven by the integration of diverse sensory modalities such as vision, audio, and text. Researchers are exploring innovative ways to combine these modalities to improve accuracy and robustness in various applications, including video saliency prediction, sound source localization, and idling vehicle detection. A key trend is the use of transformer-based architectures and attention mechanisms to effectively align and fuse multimodal features, leading to state-of-the-art performance in several benchmarks. Notably, the development of models that can handle multiple sound sources and naturally mixed audio is paving the way for more realistic and generalizable sound separation systems. Noteworthy papers include:

• A Text-Audio-Visual-conditioned Diffusion Model for video saliency prediction, which improves upon existing methods by 1.03%, 2.35%, 2.71%, and 0.33% on SIM, CC, NSS, and AUC-J metrics.
• A novel sound source localization method using joint slot attention on image and audio, which achieved the best results in almost all settings on three public benchmarks.
• ClearSep, a framework for universal sound separation that employs a data engine to decompose complex naturally mixed audio into independent tracks, demonstrating state-of-the-art performance across multiple sound separation tasks.