Learning the Best Pooling Strategy for Visual Semantic Embedding (2011.04305v5)

Abstract: 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.

Learning the Best Pooling Strategy for Visual Semantic Embedding

The paper "Learning the Best Pooling Strategy for Visual Semantic Embedding" proposes an innovative approach to enhance the performance of Visual Semantic Embedding (VSE) models, a dominant method for vision-language retrieval tasks. The central problem addressed is the selection of pooling strategies for aggregating features from different data modalities in VSE. The authors argue that while recent VSE models employ complex methods to contextualize multi-modal features, simple global pooling functions can outperform these complicated models when correctly chosen. However, identifying the optimal pooling strategy manually is cumbersome and inefficient, especially when dealing with varying feature sizes.

Core Contributions

The paper introduces the Generalized Pooling Operator (GPO), a novel method that automates the selection of the best pooling strategy across different data modalities and feature extractors. The GPO is designed to learn optimal pooling strategies without manual intervention. By using GPO, the paper extends the standard VSE framework, which then significantly improves performance on both image-text and video-text retrieval benchmarks compared to previous state-of-the-art methods.

Methodology

The approach has multiple steps:

1. Generalized Pooling Operator (GPO): The GPO is formulated to adapt over a variety of pooling functions, including average pooling, max pooling, and K-Max pooling. It achieves this by learning to generate pooling coefficients, which weight the elements of sorted feature vectors to produce the pooling output. This procedure is efficient and reduces the need for exhaustive manual tuning.
2. Coefficient Generation: The GPO utilizes a sequence model that takes positional encodings, generated through trigonometric functions, to produce pooling coefficients. This sequence model is a small bi-directional GRU that handles variable input sizes effectively.
3. Empirical Validation: On popular benchmarks like COCO and Flickr30K for image-text retrieval, and MSR-VTT and VaTeX for video-text retrieval, the extension of VSE with GPO outperformed existing methods with significantly less computational cost.

Results and Implications

The empirical results demonstrate the effectiveness of GPO, with notable improvement in recall metrics. It surpasses traditional methods that rely on complex aggregation schemes by a notable margin across multiple model architectures and datasets. The introduction of GPO not only facilitates a robust and flexible integration of simple pooling strategies but also underscores the potential to simplify model architecture without sacrificing performance.

Theoretical and Practical Implications:

• Theoretical: The GPO reveals that simple, well-chosen pooling strategies can harness the efficiency of classical approaches while maintaining competitive performance to complex models. This suggests a reevaluation of overly complex feature aggregation mechanisms, highlighting the importance of strategic model simplification.
• Practical: In practical scenarios, the GPO serves as an efficient plug-and-play module, making VSE models more robust across various feature spaces and data modalities. This is particularly beneficial in deployment environments where computational resources are limited.

Future Directions

The paper opens avenues for incorporating GPO in diverse multi-modal learning tasks beyond retrieval, such as summarization and translation, where feature aggregation remains a challenge. Future work may focus on further optimizing the sequence models within GPO to handle even larger and more diverse datasets, or integrating with newer architectures responding to emerging trends in V+L models.

In conclusion, this research underscores the potency of well-grounded simplicity in model design, offering a sleek yet powerful upgrade to existing VSE frameworks. The GPO not only optimizes current performances but also charts a path for future advancements in multi-modal AI.