On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
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Integrating vision and language has gained notable attention following the success of pretrained language models. Despite that, a fraction of emerging multimodal models is suitable for text generation conditioned on images. This minority is typically developed and evaluated for image captioning, a text generation task conditioned solely on images with the goal to describe what is explicitly visible in an image. In this paper, we take a step back and ask: How do these models work for more complex generative tasks, conditioned on both text and images? Are models based on joint multimodal pretraining, visually adapted pretrained language models, or models that combine these two approaches, more promising for such tasks? We address these questions in the context of self-rationalization (jointly generating task labels/answers and free-text explanations) of three tasks: (i) visual question answering in VQA-X, (ii) visual commonsense reasoning in VCR, and (iii) visual-textual entailment in E-SNLI-VE. We show that recent advances in each modality, CLIP image representations and scaling of language models, do not consistently improve multimodal self-rationalization of tasks with multimodal inputs. We also observe that no model type works universally the best across tasks/datasets and finetuning data sizes. Our findings call for a backbone modelling approach that can be built on to advance text generation from images and text beyond image captioning.
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