VidAudio-Bench benchmarks V2A and VT2A models across four audio categories, revealing poor speech/singing performance and a tension between visual alignment and text instruction following.
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TIQA introduces datasets and a model that predict human perceptual quality of rendered text in AI images, achieving PLCC 0.942 on crops and improving selected image text quality by 0.36 MOS.
MASQ claims up to 16.06x speedup and 4.18x energy gain over A100 for masked diffusion via stage-wise multi-precision quantization and specialized hardware units while preserving quality.
citing papers explorer
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VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories
VidAudio-Bench benchmarks V2A and VT2A models across four audio categories, revealing poor speech/singing performance and a tension between visual alignment and text instruction following.
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TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images
TIQA introduces datasets and a model that predict human perceptual quality of rendered text in AI images, achieving PLCC 0.942 on crops and improving selected image text quality by 0.36 MOS.
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MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization
MASQ claims up to 16.06x speedup and 4.18x energy gain over A100 for masked diffusion via stage-wise multi-precision quantization and specialized hardware units while preserving quality.