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.
By Alberto Presta, Michal Byra, Grzegorz Stefa\'nski, Karol Szurkowski, Eryk Ko{\l}odziejczyk, Krzysztof Arendt
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.
By Kai Chen, Ming Dai, Wenxuan Cheng, Wankou Yang
arXiv:2607. 24570v1 Announce Type: cross Abstract: Large-scale video platforms process millions of uploads hourly, requiring moderation systems that can localize when and where policy violations occur within each video.
By Jiameng Zhang, Srikanth Madikeri
VTrajEmbed is an embedding model that aligns text with video object trajectories by decomposing videos into individual trajectories and embedding each trajectory separately. It introduces a Slow‑Fast Trajectory Encoding dual‑branch architecture that captures high‑frequency motion and scene context, and a Mask‑Guided Contrastive Learning objective that prevents false negatives during training. The model is evaluated on a new trajectory‑level benchmark, Text2Traj‑Bench, and outperforms existing large‑language‑model embeddings by significant margins on both text‑to‑trajectory and trajectory‑to‑text retrieval tasks.
By Ruizhi Chen, Zining Fan, Yudi Guo, Jiang Liu, Zhiyu Lin, Zhongjiang He, Shouqian Shi, Hao Sun
arXiv:2605.08158v2 Announce Type: replace-cross
Abstract: Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB fr...
By Haopeng Jin, Wenlong Zhao, Tao Yu, Tiankun Yang, Zhenyu Guan, ShiQuan Dong, Yujia Yang, Shani Ye, Chubin Chen, Hongzhu Yi, Jinwen Luo
arXiv:2606.06158v2 Announce Type: replace
Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous...
By Kevin Dave, Sai Aditya Patkuri, Chhaya Kumar Das, Gouranga Bala, Rajeshkumar SA, R. Venkatesh Babu