O pen VNA : A Framework for Analyzing the Behavior of Multimodal Language Understanding System under Noisy Scenarios (2024)

Abstract: We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at https://github.com/thuiar/OpenVNA , with a demonstration video available at https://youtu.be/0Z9cW7RGct4 .

## introduction the multimodal language understanding (mlu) task aims to empower artificial intelligence agents with the capability to comprehensively understand human communication, discerning the speaker's affective states @xcite and intentions @xcite . despite the proliferation of multimodal large language models has yielded remarkable achievements @xcite @xcite , their application in real-world scenarios is still under development. analyzing the behaviors of mlu systems under carefully constructed noise offers a potential avenue for researchers to gain deeper insights into the possible limitations and underlying mechanisms of current mlu systems @xcite . specifically, by evaluating the global behavior of mlu system under homologous manually constructed noise, researchers can ensure their model operate effectively in practical usage scenarios. moreover, analyzing the local behavior of the mlu system under customized perturbed instances provides an understanding of how the model makes decisions, pinpointing which aspects of multimodal signals are pivotal for prediction. in recent years, researchers in this field have faced obstacles in imitating real-world noise in multimodal systems and quantitatively assessing the global robustness of mlu methods @xcite . due to the lack of open-source noise injection toolkits and evaluation benchmarks, researchers frequently provide the model performance under specific simulated noise, leading to inequitable comparisons and susceptibility to overfitting on such noise patterns @xcite . to bridge the gap between simulated scenario and real world multimodal noise, benchmark current approaches, and provide intuitive local behavior analysis, we introduce openvna, an opensource framework dedicated to analyze mlu system behavior under noisy scenario. the composition of the openvna and the interconnections among its components are illustrated in figure 1 . firstly, openvna serves as a python library providing easy-to-use application programming interfaces (apis) for noise simulation, model reproduction, and global robustness evaluation. presently, it incorporates fifteen noise injection techniques, eight integrated baseline models pertaining to two video understanding tasks, and can be easily extend to user defined tasks, noise configurations and models. moreover, openvna provide researchers a gui-based interface to interactively analyze the local model behavior under user specific noisy scenario. the contributions of this work can be succinctly summarized as follows, 1. the openvna contains one of the most com- noise injection noise type random / preset feature level / raw video level audio / visual noise periods noise degree missing rates start-end times signal-to-noise ratios … … models tpfn mmin ctfn gcnet t2fn tfr-net niat emt-dflr datasets mosi (s) mosei (s) sims v2 (s) mintrec (i) evaluation -global arbitrary interval robustness evaluation -local case / error study model diagnosis augmented data noisy test data customized data performance curve figure 1: openvna is an comprehensive python library for analyzing mlu system behavior under noise, which consists of noise injection module, datasets module, models module, instance-level (local) and batch-level (global) evaluation module. for instance level model behavior evaluation, an online platform is also available. prehensive video noise injection toolkits, covering the most cases in real world applications. 2. the openvna framework serves as a robust mlu benchmark, which providing unified noisy dataset construction, benchmark model reproducing and global robustness evaluation. the modular pipeline makes it very easy to integrate new models for a reliable comparison with existing baselines. 3. the openvna framework offers a gui-based interface, facilitating users to effortlessly apply user-defined noise to a given video and compare extracted features and model predictions, thus enabling the analysis of local model behavior and even model diagnostics. 2 related works multibench. multibench (liang et al., 2021) is a comprehensive benchmark that presents three fundamental challenges in multimodal representation learning, including generalization, complexity, and robustness. concerning robustness, multibench initially outlines potential perturbations across diverse heterogeneous sources and introduces several criteria for measuring robustness. however, multi-bench primarily treats video resources as timeseries and restricts the discussion to feature-level imperfections, overlooking general raw video-level perturbations. in this study, we concentrate on perturbations in video applications and further extend the previous quantitative robustness criteria to encompass all types of video noise. robust-msa. the robust-msa (mao et al., 2022b) is developed primarily as a demonstration platform to showcase the impact of raw video-level perturbations on msa models. the instance level evaluation platform integrated in openvna is carefully enhanced, derives partially from the robust-msa system. it now provides support for a broader spectrum of perturbation types, allows for custom noise injection in json format, and includes other notable advancements to enhance its capabilities. furthermore, the openvna framework presented in this paper strives for a more comprehensive scope by establishing a standardized evaluation process, facilitating seamless integration of newly developed models, and providing noise generation apis to enable large-scale data generation with real-world imperfections, specifically tailored for human-centered video understanding applications. ## system design in this section, we present the functionality of the noise injection toolkit, the global robustness evaluation benchmark, and the gui-based interface for local behavior analysis. we commence with the noise injection toolkit, as it occupies a central position within the openvna. ## conclusion in this work, we introduce openvna, an opensource framework tailored for analyzing the behavior of multimodal language understanding systems under noisy conditions. this developed framework facilitates future researchers in two key ways. firstly, openvna serves as a highly extensible global robustness evaluation benchmark, integrating two video understanding tasks, four databases, and eight robust baselines. with a unified evaluation pipeline, convenient baseline reproduction is achievable, which enables a fair performance comparison. moreover, with flexible noise injection toolkits, the provided pipeline further empowers researchers to assess designed models under analogous noise, which can be regarded as a superior simulation for real-world application scenarios. secondly, openvna provides a gui-based interface for local model behavior analysis. through detailed comparisons of extracted feature sequences and model predictions, ad-hoc model behavior explanations can be formulated, facilitating error case 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