Attend to You: Personalized Image Captioning with Context Sequence Memory Networks (1704.06485v2)

Abstract: We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.

Attend to You: Personalized Image Captioning with Context Sequence Memory Networks

The paper "Attend to You: Personalized Image Captioning with Context Sequence Memory Networks" by Cesc Chunseong Park, Byeongchang Kim, and Gunhee Kim proposes a novel approach to image captioning that incorporates user-specific preferences to enhance the personalization of generated captions. The paper addresses the limitations of conventional image captioning models that primarily focus on generating generic descriptions, thereby lacking in adaptability to individual user contexts.

Methodology

The core methodology centers around the integration of Context Sequence Memory (CSM) networks within the captioning framework. These networks are designed to capture and store user-specific contextual information which is subsequently leveraged during the caption generation process. The authors employ an attention mechanism that allows the model to 'attend' to personal memory features, facilitating the creation of captions that are customized to fit the user's unique style, interests, and historical preferences.

Experimental Setup

The authors implement this personalized image captioning system using a dataset collected from various users, taking into account their past interactions and feedback related to generated captions. The experimental setup involves a series of controlled tests comparing this personalized captioning model against baseline models that do not incorporate personalization features.

Results

Key quantitative results reveal that the model significantly outperforms traditional image captioning systems on metrics such as BLEU, METEOR, and CIDEr, with notable improvements observed in user satisfaction rates. For instance, user survey feedback indicates a marked preference for the outputs generated by the proposed method, showcasing the model's efficacy in producing captions that align more closely with user expectations.

Implications and Future Work

This research advances the landscape of image captioning by introducing personalization as a crucial aspect of automated caption generation. The implications extend beyond mere user satisfaction, suggesting potential applications in personalized content delivery systems, assistive technologies, and user-centric digital experiences. Furthermore, the methodology established in this paper could inform future explorations in other areas of AI where personalization is paramount.

Looking forward, the paper suggests several paths for further investigation, including the refinement of the memory networks to accommodate a richer array of user cues, the adaptation of the model for real-time applications, and the exploration of scalability aspects in terms of dataset size and model complexity. As AI systems continually evolve, integrating the user-specific dimension promises to remain a significant focus for research in personalizing user interactions.