{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/texttt-avrobustbench-benchmarking-the","title":"$\\texttt{AVROBUSTBENCH}$: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time","arxiv_id":"2506.00358","date":"2025-05-31","proceeding":null,"authors":["Sarthak Kumar Maharana","Saksham Singh Kushwaha","Baoming Zhang","Adrian Rodriguez","Songtao Wei","Yapeng Tian","Yunhui Guo"],"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 $\\href{https://github.com/sarthaxxxxx/AV-C-Robustness-Benchmark}{here}$.","url_abs":"https://arxiv.org/abs/2506.00358v1","url_pdf":"https://arxiv.org/pdf/2506.00358v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"texttt-avrobustbench-benchmarking-the","repo_url":"https://github.com/sarthaxxxxx/av-c-robustness-benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"test-time-adaptation","task_name":"Test-time Adaptation"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.00358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.00358"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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