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University of Maryland
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AV-Flamingo outperforms existing models on complex audio-visual tasks, revealing that size isn't everything when it comes to reasoning capabilities.
GAIA achieves a 64% reduction in error for airfoil flow reconstruction, setting a new benchmark for operator learning in complex geometries.
LALMs can boost their temporal reasoning accuracy by 3.2% simply by better redistributing attention across audio tokens rather than relying on textual cues.
Generate semantically aligned, high-fidelity music for videos with unprecedented speed and control by combining autoregressive planning and diffusion.
Audio-language models can now reason about 30-minute-long audio clips with timestamp-grounded intermediate steps, unlocking a new level of fine-grained understanding.
Current multimodal models are surprisingly bad at understanding long, complex videos, struggling to integrate audio, visual, and text cues even for basic reasoning tasks.
Forget HRTFs: a differentiable multi-sphere scattering model inspired by underwater animal acoustics offers a new foundation for spatial audio processing and localization.
