Context-aware Captions from Context-agnostic Supervision (1701.02870v3)

Abstract: We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siamese cat" and "tiger cat", we generate language that describes the "siamese cat" in a way that distinguishes it from "tiger cat". Our key novelty is that we show how to do joint inference over a LLM that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.

Context-aware Captions from Context-agnostic Supervision

The paper presents an innovative approach to generate context-aware image captions from context-agnostic training data, addressing the limitations of existing models in distinguishing between closely related visual concepts. The authors propose a novel inference method that allows a LLM, traditionally trained on context-agnostic data, to produce discriminative context-aware captions. This framework is particularly relevant for applications where explicit contextual training data is unavailable or data collection is prohibitively expensive.

Key Contributions

1. Novel Inference Technique: The primary contribution of this work is the development of an introspective speaker model. This model performs joint inference using a context-agnostic LLM and a discriminative listener. The technique leverages a log-likelihood ratio to score sentences based on their discriminative potential. This allows the model to produce captions that not only describe an image but also emphasize its differences from another related image or concept.
2. Tasks for Pragmatic Language Processing: The authors validate their method on two tasks requiring context-sensitive language generation: justification and discriminative image captioning. In the justification task, the model justifies why an image belongs to a particular category rather than a distractor category. Discriminative image captioning involves generating captions that distinctly refer to one of two semantically similar images. Both tasks are novel applications in the vision domain that emphasize the need for pragmatic reasoning.
3. CUB-Justify Dataset: The authors introduce the CUB-Justify dataset, comprising fine-grained bird images annotated with captions that highlight discriminative features between closely related bird species. This dataset is utilized to evaluate the effectiveness of their justification framework and serves as an important resource for benchmarking fine-grained visual understanding.
4. Empirical Evaluation: The performance of the proposed introspective speaker model is empirically validated on the CUB-Justify dataset and through human evaluations on the COCO dataset. The model significantly outperforms baseline generative approaches and speaker-listener setups in creating context-aware, discriminative captions.

Implications and Future Directions

The proposed technique has broad implications for the development of image captioning systems where nuanced understanding and differentiative capabilities are crucial. This is particularly useful in domains like ornithology or botany, where fine-grained distinctions are important. Additionally, the application of this approach extends to human-robot interaction, machine teaching, and potentially other domains requiring natural language explanations with contextual sensitivity.

The method also suggests a direction for future research in adapting existing LLMs to more context-aware tasks. It opens avenues for exploring pragmatic language use without the need for extensive and costly context-dependent training data. Further investigations could include the integration of more advanced LLMs, exploration of different contextual embedding techniques, and adaptation to various application-specific requirements.

In conclusion, this paper thoughtfully expands the boundaries of image captioning by integrating pragmatic reasoning through an efficient and adaptable framework, presenting a significant stride in the field of vision-LLMs.