Recognizing ingredients in cooking images is a challenging task due to
the significant visual changes that ingredients undergo throughout the
cooking process. As ingredients are prepared, cooked, and served,
their appearances vary greatly between the beginning, intermediate,
and finishing stages. Traditional object recognition methods, which
assume constant object appearances, struggle with this variability and
are often not good at accurately identifying ingredients at different
cooking stages. To address this challenge, we propose a stage-aware
recognition method specifically designed for dynamically changing
ingredients in cooking images. Our approach introduces two
techniques: 1. Stage-Wise Model Learning: This technique involves
training separate models for each stage of the cooking process. By
adapting models to specific stages, we can better capture the distinct
visual characteristics of ingredients as their appearances change.
2. Stage-Aware Curriculum Learning: This technique begins training
with data from the beginning cooking stages and progressively
incorporates data from later stages. This gradual approach helps the
model adapt to the evolving appearances of ingredients. Our
experimental results, using our published dataset, demonstrate that
our stage-aware methods significantly outperform models trained
without stage considerations, achieving higher accuracy in ingredient
recognition.
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