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AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
captionaudio-visual
Abstract:

Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present AVoCaDO, a powerful audiovisual video captioner driven by the temporal orchestration between audio and visual modalities. We propose a two-stage post-training pipeline: (1) AVoCaDO SFT, which fine-tunes the model on a newly curated dataset of 107K high-quality, temporally-aligned audiovisual captions; and (2) AVoCaDO GRPO, which leverages tailored reward functions to further enhance temporal coherence and dialogue accuracy while regularizing caption length and reducing collapse. Experimental results demonstrate that AVoCaDO significantly outperforms existing open-source models across four audiovisual video captioning benchmarks, and also achieves competitive performance on the VDC benchmark under visual-only settings. The model will be made publicly available to facilitate future research in audiovisual video understanding and generation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces AVoCaDO, an audiovisual video captioner emphasizing temporal orchestration between audio and visual modalities through a two-stage post-training pipeline. According to the taxonomy, this work resides in the 'Temporally Orchestrated Audiovisual Captioning' leaf under 'Audiovisual Caption Generation Architectures'. Notably, this leaf contains only the original paper itself with no sibling papers, suggesting this specific formulation of temporal orchestration represents a relatively sparse research direction within the broader field of 50 papers across 35 leaf nodes.

The taxonomy reveals that AVoCaDO's neighboring research directions include 'Hierarchical and Cross-Modal Attention for Captioning' (2 papers), 'Dense Video Captioning with Multimodal Fusion' (2 papers), and 'Visual-Centric Dense and Temporal Captioning' (5 papers). These adjacent leaves focus on attention mechanisms, event localization, and temporal modeling but without the explicit 'orchestration' framing. The broader 'Audiovisual Caption Generation Architectures' branch contains 8 distinct approaches, indicating moderate diversity in architectural strategies. AVoCaDO's emphasis on coordinated temporal alignment distinguishes it from methods prioritizing hierarchical fusion or memory-augmented frameworks in neighboring categories.

Among 20 candidates examined across three contributions, the core AVoCaDO system shows one refutable candidate from 10 examined, while the two-stage SFT-GRPO pipeline shows zero refutable candidates from 10 examined. The tailored reward functions contribution was not evaluated against candidates. This limited search scope suggests that while the overall audiovisual captioning concept has prior work, the specific combination of temporal orchestration with GRPO-based reinforcement learning appears less explored. The single refutable candidate for the core system indicates some overlap with existing audiovisual captioning approaches, though the extent of novelty depends on implementation details not captured in this top-20 semantic search.

Based on the limited literature search of 20 candidates, AVoCaDO appears to occupy a relatively underexplored niche within audiovisual captioning, particularly regarding its orchestration-focused architecture and reinforcement learning pipeline. The taxonomy structure shows this as an isolated leaf, though this may reflect the specific categorization criteria rather than absolute novelty. A more exhaustive search beyond top-20 semantic matches would be needed to definitively assess whether the temporal orchestration and GRPO integration represent substantial advances over the broader landscape of 50 papers in this taxonomy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: audiovisual video captioning with temporal alignment. The field addresses how to generate natural language descriptions of video content by jointly modeling visual frames and audio signals while respecting their temporal structure. The taxonomy reveals several complementary research directions. Audiovisual Representation Learning and Alignment focuses on learning shared or coordinated embeddings that capture cross-modal correspondences, often through contrastive or alignment objectives. Audiovisual Caption Generation Architectures explores encoder-decoder frameworks and attention mechanisms tailored to fuse multimodal streams and produce coherent captions. Large Language Model-Based Audiovisual Understanding investigates how pretrained language models can be adapted or prompted to incorporate video and audio inputs. Audiovisual Reasoning and Question Answering extends beyond captioning to interactive tasks requiring deeper semantic inference. Cross-Modal Generation and Synchronization examines bidirectional synthesis problems, such as generating audio from video or vice versa, which inform alignment strategies. Retrieval and Matching with Temporal Alignment studies how to index and retrieve video segments based on multimodal queries, while Datasets and Evaluation Frameworks provides the benchmarks and metrics that ground empirical progress across all branches. Several active lines of work highlight key trade-offs and open questions. One cluster emphasizes explicit temporal modeling: methods like STELLA[2] and Temporal Perceiving[12] design architectures that track event boundaries and align audio-visual cues at fine-grained time scales, whereas others adopt coarser segment-level fusion. Another line leverages large-scale pretraining and prompting strategies, as seen in Daily-Omni[1] and Video Enriched RAG[50], which integrate audiovisual encoders with language models to handle diverse reasoning tasks. The original paper, AVoCaDO[0], sits within the Temporally Orchestrated Audiovisual Captioning branch, emphasizing coordinated temporal orchestration of audio and visual streams during caption generation. Compared to earlier works like Watch Listen Describe[26] or Align and Tell[19], AVoCaDO[0] appears to prioritize tighter synchronization mechanisms and richer temporal context, aligning closely with recent efforts such as NarrativeBridge[15] and MIRA-CAP[17] that also stress fine-grained temporal alignment. The central challenge remains balancing computational efficiency with the need to capture long-range dependencies and subtle audio-visual interactions across extended video sequences.

Claimed Contributions

AVoCaDO: Audiovisual video captioner with temporal orchestration

The authors introduce AVoCaDO, a model specifically designed for audiovisual video captioning that emphasizes temporal alignment between visual and auditory events. This model addresses the limitation of existing vision-centric approaches by jointly processing both modalities to generate temporally coherent captions.

10 retrieved papers
Can Refute
Two-stage post-training pipeline with SFT and GRPO

The authors propose a two-stage training approach: first, supervised fine-tuning on 107K curated audiovisual video-caption pairs emphasizing temporal alignment; second, Group Relative Policy Optimization using tailored reward functions to improve temporal coherence, dialogue accuracy, and caption quality while reducing repetition collapse.

10 retrieved papers
Tailored reward functions for audiovisual captioning optimization

The authors design three complementary reward functions for GRPO training: a checklist-based reward for comprehensive audiovisual keypoint coverage, a dialogue-based reward for ASR fidelity and speaker identification, and a length-regularized reward to mitigate repetition collapse and control caption length.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

AVoCaDO: Audiovisual video captioner with temporal orchestration

The authors introduce AVoCaDO, a model specifically designed for audiovisual video captioning that emphasizes temporal alignment between visual and auditory events. This model addresses the limitation of existing vision-centric approaches by jointly processing both modalities to generate temporally coherent captions.

Contribution

Two-stage post-training pipeline with SFT and GRPO

The authors propose a two-stage training approach: first, supervised fine-tuning on 107K curated audiovisual video-caption pairs emphasizing temporal alignment; second, Group Relative Policy Optimization using tailored reward functions to improve temporal coherence, dialogue accuracy, and caption quality while reducing repetition collapse.

Contribution

Tailored reward functions for audiovisual captioning optimization

The authors design three complementary reward functions for GRPO training: a checklist-based reward for comprehensive audiovisual keypoint coverage, a dialogue-based reward for ASR fidelity and speaker identification, and a length-regularized reward to mitigate repetition collapse and control caption length.

Table of Contents

AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration | Novelty Validation