[Paper Review] Area Attention

Yang Li, Łukasz Kaiser+2 more|arXiv (Cornell University)|Oct 23, 2018

Multimodal Machine Learning Applications9 references4 citations

TL;DR

This paper introduces area attention, a novel attention mechanism that allows models to attend to contiguous regions (areas) of memory—spatially or temporally adjacent items—rather than individual items. By dynamically learning area sizes based on item coherence and leveraging summed area tables for efficient computation, area attention improves performance on neural machine translation and image captioning without additional parameters.

ABSTRACT

Existing attention mechanisms, are mostly item-based in that a model is designed to attend to a single item in a collection of items (the memory). Intuitively, an area in the memory that may contain multiple items can be worth attending to as a whole. We propose area attention: a way to attend to an area of the memory, where each area contains a group of items that are either spatially adjacent when the memory has a 2-dimensional structure, such as images, or temporally adjacent for 1-dimensional memory, such as natural language sentences. Importantly, the size of an area, i.e., the number of items in an area, can vary depending on the learned coherence of the adjacent items. By giving the model the option to attend to an area of items, instead of only a single item, we hope attention mechanisms can better capture the nature of the task. Area attention can work along multi-head attention for attending to multiple areas in the memory. We evaluate area attention on two tasks: neural machine translation and image captioning, and improve upon strong (state-of-the-art) baselines in both cases. These improvements are obtainable with a basic form of area attention that is parameter free. In addition to proposing the novel concept of area attention, we contribute an efficient way for computing it by leveraging the technique of summed area tables.

Motivation & Objective

• To address the limitation of existing item-based attention mechanisms that focus only on single items, which may not capture structural or contextual coherence in data.
• To propose a new attention mechanism that allows models to attend to groups of adjacent items (areas) in memory, improving modeling of spatial or temporal coherence.
• To enable dynamic area size learning based on the coherence of adjacent items, allowing the model to adaptively focus on meaningful regions.
• To provide an efficient computation method for area attention using summed area tables, ensuring scalability.
• To evaluate the effectiveness of area attention on real-world tasks such as neural machine translation and image captioning.

Proposed method

• Area attention operates by defining an area as a contiguous region of items in memory, either spatially adjacent (e.g., in 2D feature maps) or temporally adjacent (e.g., in sequences).
• The size of each area is dynamically determined by the model’s learned coherence of adjacent items, allowing variable-sized receptive fields.
• The mechanism integrates naturally with multi-head attention, enabling multiple areas to be attended in parallel across different heads.
• Efficient computation is achieved via summed area tables (also known as integral images), which allow constant-time computation of area sums over rectangular or contiguous regions.
• The method is parameter-free, meaning no additional learnable parameters are introduced beyond standard attention mechanisms.
• The attention weights are computed over entire areas rather than individual tokens, with attention scores reflecting the relevance of each area to the query.

Experimental results

Research questions

• RQ1 Can attending to contiguous regions of memory (areas) instead of individual items improve performance on sequence modeling and vision tasks?
• RQ2 How does dynamic area size selection based on item coherence affect model performance and generalization?
• RQ3 Can area attention be efficiently computed without introducing additional parameters or computational overhead?
• RQ4 Does area attention outperform standard item-based attention in tasks like neural machine translation and image captioning?
• RQ5 Can the use of summed area tables enable practical deployment of area attention in real-world models?

Key findings

• Area attention improves performance on neural machine translation, achieving better results than strong state-of-the-art baselines.
• In image captioning, area attention leads to significant improvements over strong baseline models, demonstrating its effectiveness in vision-language tasks.
• The improvements are achieved using a basic, parameter-free form of area attention, indicating that the mechanism itself is the key innovation.
• The use of summed area tables enables efficient computation of area attention, making it scalable and practical for real-world applications.
• The dynamic area size selection based on coherence allows the model to focus on semantically meaningful groups of items, enhancing representation learning.
• Area attention generalizes across different memory structures—2D (images) and 1D (sequences)—demonstrating broad applicability.