  Visual representation learning is ubiquitous in various real-world
applications, including visual comprehension, video understanding, multi-modal
analysis, human-computer interaction, and urban computing. Due to the emergence
of huge amounts of multi-modal heterogeneous spatial/temporal/spatial-temporal
data in big data era, the lack of interpretability, robustness, and
out-of-distribution generalization are becoming the challenges of the existing
visual models. The majority of the existing methods tend to fit the original
data/variable distributions and ignore the essential causal relations behind
the multi-modal knowledge, which lacks unified guidance and analysis about why
modern visual representation learning methods easily collapse into data bias
and have limited generalization and cognitive abilities. Inspired by the strong
inference ability of human-level agents, recent years have therefore witnessed
great effort in developing causal reasoning paradigms to realize robust
representation and model learning with good cognitive ability. In this paper,
we conduct a comprehensive review of existing causal reasoning methods for
visual representation learning, covering fundamental theories, models, and
datasets. The limitations of current methods and datasets are also discussed.
Moreover, we propose some prospective challenges, opportunities, and future
research directions for benchmarking causal reasoning algorithms in visual
representation learning. This paper aims to provide a comprehensive overview of
this emerging field, attract attention, encourage discussions, bring to the
forefront the urgency of developing novel causal reasoning methods, publicly
available benchmarks, and consensus-building standards for reliable visual
representation learning and related real-world applications more efficiently.
