  Compositional zero-shot learning aims to recognize unseen state-object
compositions by leveraging known primitives (state and object) during training.
However, effectively modeling interactions between primitives and generalizing
knowledge to novel compositions remains a perennial challenge. There are two
key factors: object-conditioned and state-conditioned variance, i.e., the
appearance of states (or objects) can vary significantly when combined with
different objects (or states). For instance, the state "old" can signify a
vintage design for a "car" or an advanced age for a "cat". In this paper, we
argue that these variances can be mitigated by predicting composition
categories based on pre-observed primitive. To this end, we propose Progressive
Language-based Observations (PLO), which can dynamically determine a better
observation order of primitives. These observations comprise a series of
concepts or languages that allow the model to understand image content in a
step-by-step manner. Specifically, PLO adopts pre-trained vision-language
models (VLMs) to empower the model with observation capabilities. We further
devise two variants: 1) PLO-VLM: a two-step method, where a pre-observing
classifier dynamically determines the observation order of two primitives. 2)
PLO-LLM: a multi-step scheme, which utilizes large language models (LLMs) to
craft composition-specific prompts for step-by-step observing. Extensive
ablations on three challenging datasets demonstrate the superiority of PLO
compared with state-of-the-art methods, affirming its abilities in
compositional recognition.
