BARISTA: A Multi-Task Egocentric Benchmark for Compositional Visual Understanding

TL;DR

BARISTA is a comprehensive egocentric benchmark dataset with detailed annotations for procedural video understanding, enabling evaluation of multiple interconnected visual reasoning tasks.

Contribution

It introduces a new densely annotated dataset and benchmark for egocentric procedural videos, facilitating multi-task evaluation of physical scene understanding.

Findings

Models show high variability across tasks.

No single model family dominates performance.

BARISTA is a challenging diagnostic tool for procedural understanding.

Abstract

Scene understanding is central to general physical intelligence, and video is a primary modality for capturing both state and temporal dynamics of a scene. Yet understanding physical processes remains difficult, as models must combine object localization, hand-object interactions, relational parsing, temporal reasoning, and step-level procedural inference. Existing benchmarks usually evaluate these capabilities separately, limiting diagnosis of why models fail on procedural tasks. We introduce BARISTA, a densely annotated egocentric dataset and benchmark of 185 real-world coffee-preparation videos covering fully automatic, portafilter-based, and capsule-based workflows. BARISTA provides verified per-frame scene graphs linking persistent object identities to masks, tracks, boxes, attributes, typed relations, hand-object interactions, activities, and process steps. From these graphs, we derive zero-shot language-based tasks spanning phrase grounding, hand-object interaction recognition, referring, activity recognition, relation extraction, and temporal visual question answering. Experiments reveal strong variation across task families and no consistently dominant model family, positioning BARISTA as a challenging diagnostic benchmark for procedural video understanding. Code and dataset available at https://huggingface.co/datasets/ramblr/BARISTA.