Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning

Abstract: While Multimodal LLMs (MLLMs) have advanced video understanding, achieving precise temporal and cross-modal alignment in audiovisual video captioning remains a formidable challenge. Most existing approaches suffer from modality detachment and temporal incoherence, failing to accurately bind auditory events to visual entities or capture complex causal dynamics. To address these deficiencies, we propose TCA-Captioner, a framework specifically engineered to enhance Temporal and Cross-Modal Alignment for audiovisual video captioning. We first introduce the Observer-Checker-Corrector (OCC) framework, an iterative refinement strategy that generates high-fidelity, meticulously grounded training data. Leveraging a curated high-density human interaction dataset, TCA-Captioner is optimized to model sophisticated audiovisual interactions. Furthermore, we present TCA-Bench, a diagnostic benchmark utilizing a Decoupled Evaluation Protocol to isolate and quantify model proficiency in audiovisual binding and temporal relational reasoning. Extensive experiments demonstrate that TCA-Captioner sets a new standard for temporally-coherent and synchronized audiovisual narratives.

• The paper introduces the Observer-Checker-Corrector (OCC) framework to iteratively refine captions and eliminate hallucinations in audiovisual data.
• It employs a high-density human interaction dataset and the TCA-Bench diagnostic to meticulously evaluate cross-modal and temporal coherence.
• Experimental results reveal that TCA-Captioner achieves SOTA performance with notable gains in AV Binding (73.2) and AV Temporal (76.9) metrics, outperforming closed-source models.

Temporal and Cross-Modal Alignment in Audiovisual Video Captioning: An Expert Analysis

Motivation and Problem Formulation

Audiovisual video captioning remains a focal research area within the trajectory of Multimodal LLMs (MLLMs). Despite considerable progress, the domain continues to wrestle with two persistent challenges: audio-visual cross-modal binding—the precise attribution and association of auditory events to visual entities—and audio-visual temporal coherence, which demands correct modeling of chronological and causal relationships across modalities. Existing MLLM-based video captioners (e.g., Qwen3-Omni, Gemini-3-Pro, AVoCaDO) often exhibit modality detachment and temporal incoherence, resulting in disjointed or hallucinated associations between sound events and their physical or contextual sources.

Figure 1: Existing models’ output failures in cross-modal binding and temporal coherence compared to TCA-Captioner’s synchronized, grounded descriptions.

These issues are not just theoretical; they systematically degrade the representational quality and downstream utility of captions in tasks such as VideoQA, action recognition, and generative video synthesis.

The Observer-Checker-Corrector Framework

To address these deficiencies, the authors introduce the Observer-Checker-Corrector (OCC) framework, a rigorous, iterative data synthesis and annotation pipeline designed to systematically eliminate factual hallucinations and enforce fine-grained grounding.

Figure 2: Overview of the OCC pipeline architecture, detailing the Observer’s global audiovisual scan, Checker’s unimodal and cross-modal audits, and the Corrector’s iterative refinement.

Within the OCC, the Observer produces a high-density, strictly timestamped description emphasizing objective multimodal reporting. The Checker component is split into three decohered subsystems:

• Audio Forensic Checker: Detects hallucinations, transcription errors, and auditory omissions, focusing on phonetic and prosodic fidelity.
• Visual Forensic Checker: Recovers granular visual details, corrects entity-action misattributions, and validates temporal logic on silent video data.
• Audio-Visual Clip Checker: Core to cross-modal binding, precisely localizes diegetic/off-screen/narrator sounds, identifies lip-sync issues, and employs fine temporal slicing for clip-based verification.

The Corrector performs violation-driven refinement, synthesizing inputs from all Checkers to output a maximally faithful caption. This pipeline is computationally expensive (e.g., ~22k tokens per 10s video), but it delivers highly structured, erroneous detail-minimized data for supervised fine-tuning.

Dataset Curation: High-Density Human Interaction

Recognizing the limits of extant datasets, the authors curate a High-Density Human Interaction (HDI) Dataset. These clips feature frequent multi-party speech, rapid environmental actions, and complex, intertwined causal chains, providing the necessary "hard cases" for robust model training under fine-grained alignment constraints. They supplement the dataset with curated samples from FineVideo and TikTok-10M, streamlining annotation for lower-density content.

TCA-Bench: Diagnostic Benchmark for Cross-Modal and Temporal Entanglement

Assessment of cross-modal capabilities necessitates more than global detail recall. The TCA-Bench is proposed as a diagnostic benchmark that shifts beyond unordered "bag-of-events" paradigms. Its curation pipeline leverages automated entity detection (YOLOv8) and speech activity detection (Silero VAD) for clip selection, followed by dense LLM-assisted, human-validated annotation.

Figure 3: The TCA-Bench curation and annotation pipeline, integrating parallel visual and auditory analyses for dense event selection.

Its annotation schema comprises:

1. Exhaustive ground-truth captions detailing visual and auditory observations.
2. JSON-based Binding Lists mapping each sound event to its visual source (character, off-screen, or environment).
3. Temporal Relational Lists encoding the precedence and simultaneity of cross-modal events (e.g., $A\rightarrow V$, $A\rightleftharpoons V$).

   Figure 4: TCA-Bench annotation showcase with dense caption, binding pairs, and temporal triples to enable decoupled metric evaluation.

The evaluation protocol is decoupled: base perception (unimodal) is scored via LLM-based comparison to GT, whereas cross-modal metrics—Binding Accuracy and Temporal $F_1$—are computed through targeted checklist verification, isolating integration performance from content/retrieval errors.

Experimental Results and Ablations

TCA-Captioner is realized by LoRA-finetuning Qwen3-Omni on the OCC-refined dataset, with optional DPO preference alignment. On public benchmarks (Video-SALMONN-2, UGC-VideoCap), TCA-Captioner achieves state-of-the-art (SOTA) performance among all open-source models and remains highly competitive against top-tier closed-source systems.

On TCA-Bench, experimental results reveal that TCA-Captioner dramatically reduces the gap with closed-source models in the critical AV Binding (73.2) and AV Temporal (76.9) dimensions versus Qwen3-Omni (42.5, 47.9 respectively), and even surpasses Gemini-3.0-Flash in all evaluated dimensions, validating the effectiveness of multi-stage error correction (Table 2, main paper). Specific ablations confirm the essential role of each OCC component: the audio checker boosts audio scores, the visual checker enhances visual fidelity, and the A/V-Clip-Checker is uniquely necessary for cross-modal and temporal reasoning gains.

Implications and Future Directions

This work provides an actionable methodology for engineering robust multimodal alignment in LLM-centric video captioning, combining systematic annotation, error decoupling, and bench-level evaluation. TCA-Captioner’s empirical superiority in temporal and cross-modal alignment demonstrates that fine-grained, structure-aware data curation is indispensable for transcending modality detachment.

Practically, the TCA-Bench protocol will catalyze future efforts in:

• Isolated diagnosis and targeted mitigation of cross-modal failure modes,
• Advancement of omni-modal LLM architectures with stronger, benchmarkable grounding of complex, real-world audiovisual phenomena,
• Downstream tasks—multi-modal QA, controlled video generation—that require temporally and semantically integrated representations.

Theoretically, this framework points towards hierarchically structured supervision as a necessary condition for scalable, generalizable multimodal reasoning, with the OCC paradigm representing a promising blueprint for future data-centric approaches.

Conclusion

The presented work advances audiovisual video captioning by formalizing and addressing the fundamental obstacles of cross-modal binding and temporal alignment. Through the OCC iterative annotation pipeline and the TCA-Bench benchmark, it sets new standards for both model capability and evaluation rigor. The data-centric methodology and diagnostic infrastructure herein are poised to inform further innovation in multimodal learning, enabling more coherent and contextually valid narrative generation across research and practical deployments.

---

References:

"Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning" (2607.01667)