Auto-Encoding Scene Graphs for Image Captioning (1812.02378v3)

Abstract: We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation person on bike', it is natural to replaceon' with ride' and inferperson riding bike on a road' even the `road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ($\mathcal{G}$) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ($\mathcal{I}$) and sentence ($\mathcal{S}$). In the textual domain, we use SGAE to learn a dictionary ($\mathcal{D}$) that helps to reconstruct sentences in the $\mathcal{S}\rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline, where $\mathcal{D}$ encodes the desired language prior; in the vision-language domain, we use the shared $\mathcal{D}$ to guide the encoder-decoder in the $\mathcal{I}\rightarrow \mathcal{G}\rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art $127.8$ CIDEr-D on the Karpathy split, and a competitive $125.5$ CIDEr-D (c40) on the official server even compared to other ensemble models.

Auto-Encoding Scene Graphs for Image Captioning: An Expert Overview

The paper "Auto-Encoding Scene Graphs for Image Captioning" introduces an innovative method to enhance image captioning by incorporating a language inductive bias into the encoder-decoder framework. This method is designed to generate captions that are more human-like and contextually rich by leveraging the structure provided by scene graphs.

Scene Graph Auto-Encoder (SGAE)

The core of the proposed approach is the Scene Graph Auto-Encoder (SGAE), which aims to capture the language inductive bias. This bias represents the human ability to infer contextual information and create coherent discourse from visual scenes. SGAE employs scene graphs as a unifying representation mapping between visual data and natural language. It auto-encodes the scene graph into a trainable shared dictionary, which is subsequently utilized to guide the image captioning process.

Methodology

The paper details the process through which scene graphs are used to represent objects, attributes, and relationships in both images and text. The SGAE framework reconstructs sentences in a self-supervised manner via a $$\mathcal{S} \rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$$ pipeline, where $\mathcal{D}$ serves as the dictionary that holds the encoded language prior. The image captioning then utilizes this shared dictionary in a $$\mathcal{I} \rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$$ pipeline, thus effectively transferring the inductive bias from the language domain to the vision-language domain.

A Multi-modal Graph Convolutional Network (MGCN) is applied to modulate scene graph features into more informative representations suitable for the encoder-decoder setup. The method emphasizes the semantic richness offered by integrating objects, attributes, and relationships, which significantly boosts the descriptiveness of the generated captions.

Results

The proposed model has been empirically validated on the MS-COCO benchmark, where it achieves a CIDEr-D score of 127.8 on the Karpathy split and 125.5 on the official test set. These results indicate a substantial improvement over existing models, including ensemble approaches. This performance is notable considering the complexity and variability of the scenes within the dataset.

Implications and Future Directions

The introduction of SGAE suggests that embedding language inductive bias into machine learning models can substantially enhance the quality of generated language in image captioning tasks. This approach potentially addresses the limitations posed by dataset biases that have historically plagued encoder-decoder models.

The research opens several avenues for further development. Future work could explore more comprehensive scene graph extraction from images and sentences, and refining the encoding processes to minimize domain gaps in feature representations. Additionally, SGAE's framework could be applied to other vision-language tasks, potentially broadening its impact on the field.

In conclusion, the paper presents a compelling case for the integration of symbolic reasoning and neural models, offering a promising pathway toward more advanced artificial intelligence systems that can engage in complex, high-level reasoning akin to human cognition.