arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.
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The paper introduces Parallel Tube Decoding (PTD), a generative approach for spatio‑temporal video grounding that splits the task into a temporal block and simultaneous time‑conditioned spatial blocks, eliminating token‑level and trajectory‑level dependencies. PTD uses Decoupled Block Attention to allow parallel spatial generation while maintaining shared video‑query context, and incorporates localization‑aware policy optimization for temporal boundaries and spatial geometry. Experiments on VidSTG show PTD cuts tube completion latency by 79× and boosts spatial decoding throughput by 92× compared to autoregressive decoding, while improving grounding accuracy and performing well on related tasks such as temporal grounding, VideoQA, and referring video object tracking.
S3VD is a new video deraining framework that leverages semantic guidance and spatio‑temporal scanning to improve performance over existing State Space Models such as Mamba. It introduces a Multi‑Scale Semantic Fusion module that uses DINOv2 priors to preserve 2D spatial semantics, and a Spatio‑Temporal Scanning Fusion module that incorporates a Decoupled‑Gating Mamba layer to better model intra‑ and inter‑frame correlations. Experiments on video deraining benchmarks show that S3VD achieves state‑of‑the‑art results, improving PSNR by an average of 0.84 dB over Mamba‑based baselines.
TAME is a CLIP‑based framework for Text‑Video Retrieval that incorporates temporal modeling through three key innovations: sparse Mixture‑of‑Experts layers with frame‑consistent routing, Frame‑Temporal tokens that aggregate cross‑frame information, and a Cross‑Temporal Interaction and Aggregation module for refining frame‑wise similarities. These components enable the model to capture both local visual patterns and long‑range temporal dependencies, leading to consistent performance gains over CLIP‑based baselines on multiple TVR benchmarks, including a 4.0 R@1 improvement on MSR‑VTT. The code is publicly available on GitHub.
TAME introduces a Temporal-Aware Mixture-of-Experts framework for Text-Video Retrieval that enhances CLIP-based models by incorporating frame-level structure and temporal relations. It adds sparse Mixture-of-Experts layers with frame-consistent routing, Frame-Temporal tokens for global cross-frame aggregation, and a Cross-Temporal Interaction and Aggregation module to refine sentence-video similarities. Experiments on multiple TVR benchmarks show consistent performance gains, such as a 4.0 R@1 improvement on MSR‑VTT over CLIP4Clip.
arXiv:2609.16722v1 Announce Type: new Abstract: Scaling Multimodal Large Language Models (MLLMs) to long-form video understanding is bottlenecked by the explosion of visual tokens, which saturates co...
Pocket-STVG (P-STVG) is a lightweight cascade architecture for Spatio-Temporal Video Grounding that combines efficient pre‑trained components: a temporal‑aware video encoder based on MobileViCLIP, a spatial encoder‑decoder from MDETR, and a shared aligned text encoder. Temporal localization is achieved with a lightweight 1D U‑Net or a simple thresholding strategy, allowing the model to work in both weakly supervised and zero‑shot settings. With fewer than 90 M parameters, P-STVG matches or surpasses prior weakly supervised and zero‑shot methods while offering a more memory‑ and compute‑efficient pipeline for large‑scale video collections.