# 视频中的任意目标定位：重新思考高效的生成式时空视频定位

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-28 08:00
- AIHOT 分数：39
- AIHOT 链接：https://aihot.virxact.com/items/cmtgx3e700cilroch1jnqhgps
- 原文链接：https://arxiv.org/abs/2608.28192

## AI 摘要

针对现有多模态大模型在时空视频定位中因自回归解码导致的延迟与误差累积问题，研究者提出并行管解码（PTD）方法，将定位分解为时间块与并行的条件空间块，将顺序解码深度固定为1+1轮。在VidSTG基准上，PTD将管完成延迟降低79倍，空间解码吞吐量提升92倍，并提升定位精度；基于40亿参数骨干的模型在VidSTG和HC-STVG上表现良好，并可零样本泛化至时间定位、视频问答等任务。

## 正文

Spatio-temporal video grounding (STVG) requires models to identify when a referred event occurs and localize the target entity throughout that interval. Existing multimodal large language models typically serialize dense localization trajectories autoregressively, causing decoding latency to grow with tube length and allowing localization errors to propagate across time. We introduce Parallel Tube Decoding (PTD), a generative formulation that decomposes grounding into a temporal block followed by time-conditioned spatial blocks decoded simultaneously. This removes both token-level and trajectory-level dependencies, reducing the sequential decoding depth to a fixed 1 + 1 rounds, independent of tube length. To enable parallel spatial generation, we introduce Decoupled Block Attention, which preserves access to shared video-query context while eliminating cross-box dependencies, together with localization-aware policy optimization for temporal boundaries and spatial geometry. On VidSTG, PTD reduces Tube Completion Latency by 79x and increases spatial decoding throughput by 92x over standard autoregressive decoding, while also improving grounding accuracy. With a compact 4B backbone, our model performs favorably well on VidSTG and HC-STVG, and generalizes zero-shot to temporal grounding, grounded VideoQA, and referring video object tracking. Our results show parallel tube generation is an efficient and effective alternative to autoregressive localization in videos.