Dual-State Slot Attention (DSSA) is a self‑supervised framework that separates per‑frame appearance from temporally stable identity within each slot, addressing conflicts that cause slot swapping in video object‑centric learning. It introduces a learned recurrent transition for identity updates and competition‑modulated aggregation to suppress weakly matching slots, thereby improving segmentation quality and temporal consistency. Experiments on MOVi‑C, MOVi‑D, and YouTube‑VIS show DSSA outperforms prior methods and enhances downstream object recognition and video dynamics prediction.
By Sieu Tran, Duc Nguyen, Hao Vo, Khoa Vo, Ngan Le
Semantic Slots for Video Object-Centric Learning proposes a Transformer-based decoder that frees slots from spatial anchoring, enabling them to act as semantic queries invariant to object position. This design removes the need for complex temporal predictors, allowing a single-frame slot to decompose subsequent video frames. On YouTube-VIS, the method outperforms prior approaches, improving mBO by 31 points over VideoSAUR and achieving state-of-the-art results with 86.6% ARI and 62.8% mBO.
By Khalil Sabri, Guillaume-Alexandre Bilodeau, Nicolas Saunier, Wassim Bouachir
TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation proposes a lightweight mechanism that learns per-slot, per-frame activation scores to control slot lifecycle in unsupervised video object-centric learning. By gating slot updates and decoder participation when objects are absent or occluded, TSA reduces state drift and reconstruction interference, improving object decomposition and temporal identity preservation. The method is evaluated on MOVi-C, MOVi-E, YTVIS-HQ, and OVIS benchmarks, showing significant gains, especially on long, heavily occluded videos.
By Duc Nguyen, Sieu Tran, Hao Vo, Khoa Vo, Duy Minh Ho Nguyen, Nghi D. Q. Bui, Anh Nguyen, Long Mai, Ngan Le
arXiv:2506.01004v3 Announce Type: replace-cross
Abstract: Unlike traditional video editing or inpainting, video semantic mixing fuses a reference concept with a moving target entity to produce a hybr...
By Tong Zhang, Victor Escorcia, Juan C Leon Alcazar, Bernard Ghanem
arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.
By Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, Kun Zhang
TrajTok introduces an end‑to‑end video tokenizer that replaces traditional patchification with a lightweight, trajectory‑based tokenization process. It performs implicit clustering over space‑time pixels to generate object trajectories in a single forward pass, dynamically adjusting token granularity to semantic complexity while remaining independent of video duration. The resulting TrajViT2 model outperforms existing methods on classification and retrieval benchmarks, and TrajTok can also serve as a probing head (TrajAdapter) or alignment connector (TrajVLM) for vision‑language tasks, notably improving long‑video reasoning.
By Chenhao Zheng, Jieyu Zhang, Jianing Zhang, Weikai Huang, Ashutosh Kumar, Quan Kong, Oncel Tuzel, Chun-Liang Li, Ranjay Krishna