Learning the Best Pooling Strategy for Visual Semantic Embedding

By Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, Changhu Wang. 2020

337 citations recorded by the source corpus.

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∞.Without bells and whistles, VSE∞ outperforms previous VSE methods significantly on image-text retrieval benchmarks across popular feature extractors. With a simple adaptation, variants of VSE∞ 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 http://jcchen.me/vse_infty/

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Multimodal Learning, Representation & Self-Supervised Learning

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Citations in the corpus

Florence: A New Foundation Model for Computer Vision (2021, methodology): “Thus, the object detector has been a de facto tool for image feature extraction, followed by a fusion network for prediction in many works (Anderson et al., 2018; Li et al., 2020; Zhang et al., 2021b; Wang et al., 2020; Fang et al., 2021; Chen et al., 2020d).”
Combined Scaling for Zero-shot Transfer Learning (2021, neutral): “…Hodosh et al., 2013; Frome et al., 2013; Norouzi et al., 2013; Kiros et al., 2014; Socher et al., 2014; Akata et al., 2015b,a; Nam et al., 2017; Faghri et al., 2017; Li et al., 2019; Liu et al., 2019; Lu et al., 2019; Messina et al., 2020; Chen et al., 2020d; Huang et al., 2020; Chen et al., 2021).”
LiT: Zero-Shot Transfer with Locked-image text Tuning (2021, background): “Another approach, which we adopt in this work, is to learn an alignment between image and text embedding spaces [6, 15, 21, 22, 31, 70].”
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (2021, methodology): “This is one of the most effective loss functions for both self-supervised (Chen et al., 2020b) and supervised (Zhai & Wu, 2019; Musgrave et al., 2020) representation learning.”
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (2022, positive): “Many propose to learn visual-semantic embedding for vision-text retrieval (Liu et al., 2019; Wu et al., 2019; Lu et al., 2019; Huang et al., 2020; Chen et al., 2021) by attention or objection detection models; and by vision-text contrastive learning (Zhang et al., 2020; Jia et al., 2021; Yuan et…”
Combined Scaling for Open-Vocabulary Image Classiﬁcation (2022, methodology): “Similar to CLIP and ALIGN, our work also learns the mapping between images and texts, which is related to many previous works, such as (Hironobu et al., 1999; Weston et al., 2010; Socher and Fei-Fei, 2010; Socher et al., 2013; Hodosh et al., 2013; Frome et al., 2013; Norouzi et al., 2013; Kiros et a”