Visual Abductive Reasoning

TL;DR

This paper introduces Visual Abductive Reasoning (VAR), a new task and dataset for AI to infer the best explanations for incomplete visual observations, advancing reasoning capabilities in computer vision.

Contribution

It proposes the VAR dataset and a novel Reasoner model that captures causal structures and refines hypotheses, pushing forward abductive reasoning in visual understanding.

Findings

Reasoner outperforms many video-language models on VAR

Models still lag behind human performance in abductive reasoning

The dataset and model foster future research in reasoning beyond observation

Abstract

Abductive reasoning seeks the likeliest possible explanation for partial observations. Although abduction is frequently employed in human daily reasoning, it is rarely explored in computer vision literature. In this paper, we propose a new task and dataset, Visual Abductive Reasoning (VAR), for examining abductive reasoning ability of machine intelligence in everyday visual situations. Given an incomplete set of visual events, AI systems are required to not only describe what is observed, but also infer the hypothesis that can best explain the visual premise. Based on our large-scale VAR dataset, we devise a strong baseline model, Reasoner (causal-and-cascaded reasoning Transformer). First, to capture the causal structure of the observations, a contextualized directional position embedding strategy is adopted in the encoder, that yields discriminative representations for the premise and hypothesis. Then, multiple decoders are cascaded to generate and progressively refine the premise and hypothesis sentences. The prediction scores of the sentences are used to guide cross-sentence information flow in the cascaded reasoning procedure. Our VAR benchmarking results show that Reasoner surpasses many famous video-language models, while still being far behind human performance. This work is expected to foster future efforts in the reasoning-beyond-observation paradigm.