Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding

• URL: http://arxiv.org/abs/2608.28192v1
• Date: Fri, 28 Aug 2026 11:05:30 GMT
• Title: Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding
• Abstract summary: multimodal large language models typically serialize trajectories autoregressively.<n>We introduce Parallel Tube Decoding (PTD), a generative formulation that decomposes grounding into a temporal block.<n>On VidSTG, PTD Tube reduces Completion by 79x and increases spatial decoding by 92x over standard autoregressive decoding.
• Score: 80.10744910189997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification [11.171207895220578]
  temporal-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries.<n>We propose a practical pipeline that shifts from frame-level to second-level tracking and performs cross-second annotations to preserve continuity while reducing sequence length.
  arXiv  Detail & Related papers  (2026-06-27T17:42:32Z)
• Towards Long-Form Spatio-Temporal Video Grounding [37.582576273199955]
  Long-term videos contain much longer temporal span and spans more irrelevant information.<n>Unlike conventional STVG methods that require the entire video sequence to make predictions at once, ART-STVG treats the video as streaming input frames and processes sequentially.<n>Since memories from different moments are not always relevant to the current frame, we introduce simple yet effective memory selection strategies.
  arXiv  Detail & Related papers  (2026-02-26T18:04:09Z)
• LSA: Localized Semantic Alignment for Enhancing Temporal Consistency in Traffic Video Generation [44.62533878314138]
  Localized Semantic Alignment (LSA) is a framework for fine-tuning pre-trained video generation models.<n>LSA enhances temporal consistency by aligning semantic features between ground-truth and generated video clips.<n> experiments on nuScenes and KITTI datasets show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2026-02-05T18:21:02Z)
• Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers [95.68243351895107]
  We propose a holistic, video-centric paradigm named textbfLocal textbfDiffusion textbfForcing for textbfVideo textbfFrame textbfInterpolation (LDF-VFI)<n>Our framework is built upon an auto-regressive diffusion transformer that models the entire video sequence to ensure long-range temporal coherence.<n>LDF-VFI achieves state-of-the-art performance on challenging long-sequence benchmarks, demonstrating superior per
  arXiv  Detail & Related papers  (2026-01-21T12:58:52Z)
• Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events [71.2439653098351]
  Continuous space-time video super-STVSR has garnered increasing interest for its capability to reconstruct high-resolution and high-frame-rate videos at arbitrary temporal scales.<n>We present EvEnhancer, a novel approach that marries unique properties of high temporal and high dynamic range encapsulated in event streams.<n>Our method achieves state-of-the-art performance on both synthetic and real-world datasets, while maintaining generalizability at OOD scales.
  arXiv  Detail & Related papers  (2025-10-04T15:23:07Z)
• Exploiting Temporal State Space Sharing for Video Semantic Segmentation [53.8810901249897]
  Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes.<n>Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements.<n>We introduce a Temporal Video State Space Sharing architecture to leverage Mamba state space models for temporal feature sharing.<n>Our model features a selective gating mechanism that efficiently propagates relevant information across video frames, eliminating the need for a memory-heavy feature pool.
  arXiv  Detail & Related papers  (2025-03-26T01:47:42Z)
• Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression [59.632286735304156]
  We propose a spatial decomposition and temporal fusion based inter prediction for learned video compression. With the SDD-based motion model and long short-term temporal fusion, our proposed learned video can obtain more accurate inter prediction contexts.
  arXiv  Detail & Related papers  (2024-01-29T03:30:21Z)
• Temporally Consistent Transformers for Video Generation [80.45230642225913]
  To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. No established benchmarks on complex data exist for rigorously evaluating video generation with long temporal dependencies. We introduce the Temporally Consistent Transformer (TECO), a generative model that substantially improves long-term consistency while also reducing sampling time.
  arXiv  Detail & Related papers  (2022-10-05T17:15:10Z)
• TubeDETR: Spatio-Temporal Video Grounding with Transformers [89.71617065426146]
  We consider the problem of encoder localizing a-temporal tube in a video corresponding to a given text query. To address this task, we propose TubeDETR, a transformer- architecture inspired by the recent success of such models for text-conditioned object detection.
  arXiv  Detail & Related papers  (2022-03-30T16:31:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.