Context-Aware Group Captioning via Self-Attention and Contrastive Features

Abstract

While image captioning has progressed rapidly, existing works focus mainly on describing single images. In this paper, we introduce a new task, context-aware group captioning, which aims to describe a group of target images in the context of another group of related reference images. Context-aware group captioning requires not only summarizing information from both the target and reference image group but also contrasting between them. To solve this problem, we propose a framework combining selfattention mechanism with contrastive feature construction to effectively summarize common information from each image group while capturing discriminative information between them. To build the dataset for this task, we propose to group the images and generate the group captions based on single image captions using scene graphs matching. Our datasets are constructed on top of the public Conceptual Captions dataset and our new Stock Captions dataset. Experiments on the two datasets show the effectiveness of our method on this new task. 1 * This work has been done during the first author's internship at Adobe.1 Related Datasets and code are released at https://lizw14. github.io/project/groupcap.

Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions

“…In an effort to incorporate the relational reasoning ability into the model, a scene graph representation -a structured description that captures semantic summaries of entities and their relationshipshas been presented recently [7]. Since then, a number of works have proposed deep network-based approaches for generating the scene graphs, confirming its importance to the field [8,9,10,11,12,13,14]. While scene graph representation holds tremendous promise, extracting scene graphs from images is known to be challenging.…”