Learning to Describe Differences Between Pairs of Similar Images (1808.10584v1)

Abstract: In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a firstpass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.

• The paper introduces a novel task and dataset of 13,192 image pairs for generating textual descriptions of subtle visual differences.
• It presents the DDLA model which segments pixel differences into clusters and uses latent alignments, outperforming attention-only baselines in BLEU and ROUGE scores.
• The study offers practical implications for enhanced surveillance and media monitoring, advancing automated visual content analysis.

Analysis of the "Learning to Describe Differences Between Pairs of Similar Images" Study

This paper introduces a new research task focused on automatically generating text that describes the differences between pairs of similar images. The paper presents a novel dataset, derived from video-surveillance footage via crowd-sourcing, which is aimed at capturing human-generated descriptions of visual differences. This dataset, referred to as the "Spot-the-diff," contains descriptions for 13,192 image pairs, providing a substantial benchmark for exploring models that align language with visual salience and improve coherent multi-sentence generation.

The problem this paper addresses is situated at the intersection of image captioning and data summarization. It poses significant challenges, including identifying salient differences that warrant description and managing different levels of abstraction in human descriptions. For instance, human annotators might describe coordinated changes in multiple objects as a single comprehensive description. The paper extends previous research in visual data interpretation and highlights the potential application in contexts such as assisted surveillance and media asset monitoring.

The authors propose a model that goes beyond conventional attention mechanisms by incorporating a visual analysis stage. This stage identifies clusters of differing pixels, which act as proxies for object-level differences. The model utilizes latent variables to directly align these difference clusters with generated sentences, enhancing the capture of visual salience. Compared to baseline models using only attention-based mechanisms, the proposed model demonstrates superior performance in generating both single and multi-sentence descriptions.

Key numerical results underline the model's efficacy—improvements in BLEU and ROUGE scores across tasks further substantiate the approach. For instance, in single-sentence generation, the proposed Difference Description with Latent Alignment (DDLA) models achieved significant improvements over baseline methods like Capt (traditional captioning with attention) and Capt-masked (captioning with masking).

The methodological innovation introduced includes segmenting pixel differences into meaningful clusters and leveraging these in a neural generation model through latent alignments. This methodological rigor makes the approach a noteworthy contribution to tasks requiring semantic, spatial, and pragmatic reasoning, especially in the generation of multi-sentence descriptions.

The implications of this research span both theoretical and practical domains. Theoretically, the work provides insights into modeling visual salience and semantic alignment between image regions and textual descriptions. Practically, applications could range from developing more sophisticated surveillance systems to improving interaction technologies for visually impaired individuals through automated description systems.

Future developments might focus on refining object-tracking capabilities within image pairs, incorporating explicit modeling of object movements and a more nuanced handling of visual grammar. Furthermore, enhancing the model's ability to predict the number of sentences necessary for comprehensive difference description and exploring unsupervised methods for object detection in differences could extend its applicability.

In conclusion, this paper makes an important contribution by introducing a challenging task and dataset, providing a solid benchmark for future research into the integration of vision and language in AI. It sets a foundational precedent for the exploration of visual difference description, offering substantial promise for advancements in automated visual content analysis.