Perception Test: A Diagnostic Benchmark for Multimodal Video Models
Authors: name: Viorica Pătrăucean; name: Lucas Smaira; name: Ankush Gupta; name: Adrià Recasens Continente; name: Larisa Markeeva; name: Dylan Banarse; name: Skanda Koppula; name: Joseph Heyward; name: Mateusz Malinowski; name: Yi Yang; name: Carl Doersch; name: Tatiana Matejovicova; name: Yury Sulsky; name: Antoine Miech; name: Alex Frechette; name: Hanna Klimczak; name: Raphael Koster; name: Junlin Zhang; name: Stephanie Winkler; name: Yusuf Aytar; name: Simon Osindero; name: Dima Damen; name: Andrew Zisserman; name: João Carreira
Published: 2023-05-23 07:54:37

Abstract: We propose a novel multimodal video benchmark - the Perception Test - to
evaluate the perception and reasoning skills of pre-trained multimodal models
(e.g. Flamingo, BEiT-3, or GPT-4). Compared to existing benchmarks that focus
on computational tasks (e.g. classification, detection or tracking), the
Perception Test focuses on skills (Memory, Abstraction, Physics, Semantics) and
types of reasoning (descriptive, explanatory, predictive, counterfactual)
across video, audio, and text modalities, to provide a comprehensive and
efficient evaluation tool. The benchmark probes pre-trained models for their
transfer capabilities, in a zero-shot / few-shot or limited finetuning regime.
For these purposes, the Perception Test introduces 11.6k real-world videos, 23s
average length, designed to show perceptually interesting situations, filmed by
around 100 participants worldwide. The videos are densely annotated with six
types of labels (multiple-choice and grounded video question-answers, object
and point tracks, temporal action and sound segments), enabling both language
and non-language evaluations. The fine-tuning and validation splits of the
benchmark are publicly available (CC-BY license), in addition to a challenge
server with a held-out test split. Human baseline results compared to
state-of-the-art video QA models show a significant gap in performance (91.4%
vs 43.6%), suggesting that there is significant room for improvement in
multimodal video understanding.
  Dataset, baselines code, and challenge server are available at
https://github.com/deepmind/perception_test