AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning

TL;DR

AGQA is a large, balanced benchmark for evaluating models' ability to reason about complex spatio-temporal visual events, revealing significant gaps in current systems' generalization and reasoning capabilities.

Contribution

The paper introduces AGQA, a comprehensive and balanced benchmark with 192 million question-answer pairs for assessing compositional spatio-temporal reasoning in videos, and demonstrates current models' limitations.

Findings

Models perform poorly on AGQA, barely surpassing non-visual baselines.

Existing models do not generalize well to novel compositions.

AGQA reveals significant gaps in current visual reasoning systems.

Abstract

Visual events are a composition of temporal actions involving actors spatially interacting with objects. When developing computer vision models that can reason about compositional spatio-temporal events, we need benchmarks that can analyze progress and uncover shortcomings. Existing video question answering benchmarks are useful, but they often conflate multiple sources of error into one accuracy metric and have strong biases that models can exploit, making it difficult to pinpoint model weaknesses. We present Action Genome Question Answering (AGQA), a new benchmark for compositional spatio-temporal reasoning. AGQA contains $192M$ unbalanced question answer pairs for $9.6K$ videos. We also provide a balanced subset of $3.9M$ question answer pairs, $3$ orders of magnitude larger than existing benchmarks, that minimizes bias by balancing the answer distributions and types of question structures. Although human evaluators marked $86.02\%$ of our question-answer pairs as correct, the best model achieves only $47.74\%$ accuracy. In addition, AGQA introduces multiple training/test splits to test for various reasoning abilities, including generalization to novel compositions, to indirect references, and to more compositional steps. Using AGQA, we evaluate modern visual reasoning systems, demonstrating that the best models barely perform better than non-visual baselines exploiting linguistic biases and that none of the existing models generalize to novel compositions unseen during training.