Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning

2026-07-03

AI Summary

This work addresses the limitations of traditional autoregressive approaches in dense video captioning, which suffer from low inference efficiency and poor scalability in long videos with high event density. The authors propose a parallelized autoregressive framework that restructures the causal dependency graph to explicitly model weak local dependencies among events. By introducing a latent global planning mechanism, the model automatically learns event structure and aggregates audio-visual semantics, enabling cross-event parallel generation through factorized event decoding. This approach maintains local semantic coherence while significantly improving inference efficiency and temporal grounding accuracy. The method achieves state-of-the-art results across multiple benchmarks, demonstrating particularly strong performance in multimodal event localization and description tasks.

Abstract

Dense video captioning aims to generate temporally grounded descriptions of video events, benefiting both event-level video understanding and generation. In this domain, autoregressive video large language models have emerged as a prevalent paradigm due to their strong generative and cross-modal modeling capacity. However, generating dense captions under the token-by-token paradigm severely limits inference efficiency and hinders scalability as video length and event density increase. In this work, we propose a parallelized autoregressive framework that not only improves generation efficiency but also enhances temporally grounded captioning performance. Our key insight is to exploit the weak local dependencies across temporally distinct events to restructure the causal dependency graph, thereby enabling lossless parallel generation. Specifically, tokens with weak cross-event dependencies can be decoded in parallel, while tightly coupled tokens within each event retain sequential decoding to preserve local semantic coherence. To realize this insight, we introduce two key components for lossless parallel decoding: (1) a latent global planning mechanism that automatically learns the event-level structure and produces compact tokens encoding global inter-event causality while adaptively aggregating event-level audio-visual semantics, guiding subsequent dependency restructuring and parallel decoding; and (2) an event-factorized parallel decoding mechanism that effectively balances local focus with global inter-event awareness. Experiments on various benchmarks demonstrate the clear advantage of our approach in both efficiency and performance in omni-modal event grounding and captioning. Project website: https://github.com/showlab/PadCaptioner.

Problem

• dense video captioning
• autoregressive decoding
• inference efficiency
• event density
• temporal grounding

Innovation

• parallelized autoregressive decoding
• dense video captioning
• event-level planning
• causal dependency restructuring
• omni-modal grounding