  Existing computer vision methods mainly focus on the recognition of rigid
objects, whereas the recognition of flexible objects remains unexplored.
Recognizing flexible objects poses significant challenges due to their
inherently diverse shapes and sizes, translucent attributes, ambiguous
boundaries, and subtle inter-class differences. In this paper, we claim that
these problems primarily arise from the lack of object saliency. To this end,
we propose the Flexible Vision Graph Neural Network (FViG) to optimize the
self-saliency and thereby improve the discrimination of the representations for
flexible objects. Specifically, on one hand, we propose to maximize the
channel-aware saliency by extracting the weight of neighboring nodes, which
adapts to the shape and size variations in flexible objects. On the other hand,
we maximize the spatial-aware saliency based on clustering to aggregate
neighborhood information for the centroid nodes, which introduces local context
information for the representation learning. To verify the performance of
flexible objects recognition thoroughly, for the first time we propose the
Flexible Dataset (FDA), which consists of various images of flexible objects
collected from real-world scenarios or online. Extensive experiments evaluated
on our Flexible Dataset demonstrate the effectiveness of our method on
enhancing the discrimination of flexible objects.
