HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning

TL;DR

This paper introduces HAAV, a hierarchical approach that effectively leverages multiple heterogeneous image encodings as augmented views, improving image captioning performance through view-specific aggregation and contrastive learning.

Contribution

It proposes a novel hierarchical decoder and contrastive loss to efficiently utilize diverse image encodings as augmented views for enhanced captioning.

Findings

+5.6% CIDEr on MS-COCO

+12.9% CIDEr on Flickr30k

Demonstrates effectiveness of view aggregation and contrastive learning

Abstract

A great deal of progress has been made in image captioning, driven by research into how to encode the image using pre-trained models. This includes visual encodings (e.g. image grid features or detected objects) and more recently textual encodings (e.g. image tags or text descriptions of image regions). As more advanced encodings are available and incorporated, it is natural to ask: how to efficiently and effectively leverage the heterogeneous set of encodings? In this paper, we propose to regard the encodings as augmented views of the input image. The image captioning model encodes each view independently with a shared encoder efficiently, and a contrastive loss is incorporated across the encoded views in a novel way to improve their representation quality and the model's data efficiency. Our proposed hierarchical decoder then adaptively weighs the encoded views according to their effectiveness for caption generation by first aggregating within each view at the token level, and then across views at the view level. We demonstrate significant performance improvements of +5.6% CIDEr on MS-COCO and +12.9% CIDEr on Flickr30k compared to state of the arts, and conduct rigorous analyses to demonstrate the importance of each part of our design.