# $\\texttt{AVROBUSTBENCH}$: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time Source: [$\\texttt{AVROBUSTBENCH}$: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time]() Collection index: [llms.txt]() Data updated: 2025-10-29T05:03:10.559Z Authors: Sarthak Kumar Maharana, Saksham Singh Kushwaha, Baoming Zhang, Adrian Rodriguez, Songtao Wei, Yapeng Tian, Yunhui Guo Published: 2025-05-31T02:56:07.000Z ## Abstract While recent audio-visual models have demonstrated impressive performance, their robustness to distributional shifts at test-time remains not fully understood. Existing robustness benchmarks mainly focus on single modalities, making them insufficient for thoroughly assessing the robustness of audio-visual models. Motivated by real-world scenarios where shifts can occur $\\textit{simultaneously}$ in both audio and visual modalities, we introduce $\\texttt{AVROBUSTBENCH}$, a comprehensive benchmark designed to evaluate the test-time robustness of audio-visual recognition models. $\\texttt{AVROBUSTBENCH}$ comprises four audio-visual benchmark datasets, $\\texttt{AUDIOSET-2C}$, $\\texttt{VGGSOUND-2C}$, $\\texttt{KINETICS-2C}$, and $\\texttt{EPICKITCHENS-2C}$, each incorporating 75 bimodal audio-visual corruptions that are $\\textit{co-occurring}$ and $\\textit{correlated}$. Through extensive evaluations, we observe that state-of-the-art supervised and self-supervised audio-visual models exhibit declining robustness as corruption severity increases. Furthermore, online test-time adaptation (TTA) methods, on $\\texttt{VGGSOUND-2C}$ and $\\texttt{KINETICS-2C}$, offer minimal improvements in performance under bimodal corruptions. We further propose $\\texttt{AV2C}$, a simple TTA approach enabling on-the-fly cross-modal fusion by penalizing high-entropy samples, which achieves improvements on $\\texttt{VGGSOUND-2C}$. We hope that $\\texttt{AVROBUSTBENCH}$ will steer the development of more effective and robust audio-visual TTA approaches. Our code is available $\\href{https://github.com/sarthaxxxxx/AV-C-Robustness-Benchmark}{here}$. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()