169916 20260316125348.0 doi 10.1109/TPAMI.2026.3652831 sideral 148448 ART-2026-148448 eng Mur-Labadia, Lorenzo Universidad de Zaragoza Integrating Affordances and Attention models for Short-Term Object Interaction Anticipation 2026 Access copy available to the general public Unrestricted Short-Term object-interaction Anticipation (STA) consists in detecting the location of the next-active objects, the noun and verb categories of the interaction, as well as the time to contact from the observation of egocentric video. This ability is fundamental for wearable assistants to understand user's goals and provide timely assistance, or to enable human-robot interaction. In this work, we present a method to improve the performance of STA predictions. Our contributions are two-fold: 1) We propose STAformer and STAformer++, two novel attention-based architectures integrating frame-guided temporal pooling, dual image-video attention, and multiscale feature fusion to support STA predictions from an image-input video pair; 2) We introduce two novel modules to ground STA predictions on human behavior by modeling affordances. First, we integrate an environment affordance model which acts as a persistent memory of interactions that can take place in a given physical scene. We explore how to integrate environment affordances via simple late fusion and with an approach which adaptively learns how to best fuse affordances with end-to-end predictions. Second, we predict interaction hotspots from the observation of hands and object trajectories, increasing confidence in STA predictions localized around the hotspot. Our results show significant improvements on Overall Top-5 mAP, with gain up to +23% on Ego4D and +31% on a novel set of curated EPIC-Kitchens STA labels. We released the code, annotations, and pre-extracted affordances on Ego4D and EPIC-Kitchens to encourage future research in this area. info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Martinez-Cantin, Ruben Universidad de Zaragoza (orcid)0000-0002-6741-844X Guerrero, Jose J. Universidad de Zaragoza (orcid)0000-0001-5209-2267 Farinella, Giovanni Maria Furnari, Antonino 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. (2026), [17 pp.] IEEE trans. pattern anal. mach. intell. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 0162-8828 2370573 http://zaguan.unizar.es/record/169916/files/texto_completo.pdf Postprint 3279813 http://zaguan.unizar.es/record/169916/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:169916 articulos driver 2026-03-16-08:29:38 ARTICLE