  Visual Semantic Embedding (VSE) is a dominant approach for vision-language
retrieval, which aims at learning a deep embedding space such that visual data
are embedded close to their semantic text labels or descriptions. Recent VSE
models use complex methods to better contextualize and aggregate multi-modal
features into holistic embeddings. However, we discover that surprisingly
simple (but carefully selected) global pooling functions (e.g., max pooling)
outperform those complex models, across different feature extractors. Despite
its simplicity and effectiveness, seeking the best pooling function for
different data modality and feature extractor is costly and tedious, especially
when the size of features varies (e.g., text, video). Therefore, we propose a
Generalized Pooling Operator (GPO), which learns to automatically adapt itself
to the best pooling strategy for different features, requiring no manual tuning
while staying effective and efficient. We extend the VSE model using this
proposed GPO and denote it as VSE$\infty$.
  Without bells and whistles, VSE$\infty$ outperforms previous VSE methods
significantly on image-text retrieval benchmarks across popular feature
extractors. With a simple adaptation, variants of VSE$\infty$ further
demonstrate its strength by achieving the new state of the art on two
video-text retrieval datasets. Comprehensive experiments and visualizations
confirm that GPO always discovers the best pooling strategy and can be a
plug-and-play feature aggregation module for standard VSE models. Code and
pre-trained models are available at https://vse-infty.github.io.
