ReFormer: The Relational Transformer for Image Captioning (2107.14178v2)

Abstract: Image captioning is shown to be able to achieve a better performance by using scene graphs to represent the relations of objects in the image. The current captioning encoders generally use a Graph Convolutional Net (GCN) to represent the relation information and merge it with the object region features via concatenation or convolution to get the final input for sentence decoding. However, the GCN-based encoders in the existing methods are less effective for captioning due to two reasons. First, using the image captioning as the objective (i.e., Maximum Likelihood Estimation) rather than a relation-centric loss cannot fully explore the potential of the encoder. Second, using a pre-trained model instead of the encoder itself to extract the relationships is not flexible and cannot contribute to the explainability of the model. To improve the quality of image captioning, we propose a novel architecture ReFormer -- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image. ReFormer incorporates the objective of scene graph generation with that of image captioning using one modified Transformer model. This design allows ReFormer to generate not only better image captions with the bene-fit of extracting strong relational image features, but also scene graphs to explicitly describe the pair-wise relation-ships. Experiments on publicly available datasets show that our model significantly outperforms state-of-the-art methods on image captioning and scene graph generation

ReFormer: The Relational Transformer for Image Captioning

Image captioning represents a critical task in computer vision, involving the synthesis of textual descriptions from the visual content of images. The exploration of encoder-decoder models for this task has driven considerable advancements, notably through the integration of scene graphs into image representation. This essay provides an expert analysis of the research paper titled "ReFormer: The Relational Transformer for Image Captioning," which advocates a novel methodology for enhancing image captioning via a relational perspective.

The authors highlight existing challenges in integrating relational information into image captions. Traditional approaches typically employ Graph Convolutional Networks (GCNs) to encode object relationships derived from pre-trained scene graph generators, often suffering from limited expressiveness and inflexibility. Such models are constrained by the maximum likelihood estimation objective and lack explicit relational encoding, limiting their potential for generating relationally informative captions.

Addressing these limitations, the proposed Relational Transformer, ReFormer, introduces a refined architecture combining scene graph generation with image captioning in a single model. The ReFormer advances prior work by embedding relational features directly into the image captioning process using a modified Transformer model. The paper emphasizes two pivotal contributions: the concurrent learning of scene graphs alongside captions, and the use of scene graph-based encoding to enrich image features, fostering more expressive and explainable captions.

Experimentally, ReFormer achieves significant enhancements over state-of-the-art methodologies in both image captioning and scene graph generation tasks. The model demonstrates superior performance across standardized evaluation metrics (BLEU, ROUGE, METEOR, CIDEr, and SPICE) on the COCO dataset. Specifically, the inclusion of weighted encoder layers and the application of multi-head self-attention enable ReFormer to exceed the capabilities of conventional CNN-LSTM and GCN-based models in capturing and utilizing relational image content.

The implications of ReFormer's design are substantial. By integrating scene graph objectives into the training of image captioning models, ReFormer not only elevates the quality of the generated captions but also enhances the transparency and interpretability of the model's outputs through visually grounded scene graphs. These contributions pave the way for more nuanced and semantically aware image captioning systems, offering promising applications in areas requiring detailed visual-linguistic understanding, such as automated content generation and assistive technologies.

Future research directions stemming from this work could explore the refinement of scene graph annotations for broader datasets and the adaptation of ReFormer to more complex image understanding tasks. Additionally, the integration of more sophisticated attention mechanisms and adaptation to multimodal settings hold potential to further enhance relational understanding in visual contexts.