arXiv Computer Vision
iSEE: Object Permanence Through Self-Supervision
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jul 9
Whareformer: Learning to Track What is Where in Long Egocentric Videos
The recently established 'Out of Sight, Not out of Mind' (OSNOM) task for egocentric videos focuses on tracking objects that are moved by the camera wearer, online, maintaining knowledge of instance locations throughout the video even when they leave the field of view or become heavily occluded. In this paper, we propose the first learning-based solution to the OSNOM task: Whareformer, a transformer-based model with two components: an updatable memory of established tracks and a track assignment module that associates observations with existing tracks in a feed-forward manner.
arXiv Computer Vision
Aug 31
WALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes
arXiv:2608.28216v1 Announce Type: new Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is ab...
arXiv Machine Learning
Jul 3
Object-centric LeJEPA
arXiv:2607. 02404v1 Announce Type: cross Abstract: Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets.
arXiv Computer Vision
Sep 4
Temporal Self-Distillation: Learning Visual State Tracking in Videos Without Supervision
The paper introduces S$^3$T, a fully self‑contained framework for continuous video state tracking that uses temporal self‑distillation. It treats denser temporal sampling as privileged information, letting a dense‑view teacher guide a sparse‑view student to match its next‑token distribution without external labels or reward signals. Experiments on LLaVA-OneVision-2-8B show significant accuracy gains on VSTAT and MVBench benchmarks, and the learned capability transfers from synthetic to real videos.
arXiv AI
Jun 17
GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
arXiv AI
Jul 21
VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.