Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions (1811.10652v3)

Abstract: Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithms in complex scenarios. In this paper, we introduce a novel framework for image captioning which can generate diverse descriptions by allowing both grounding and controllability. Given a control signal in the form of a sequence or set of image regions, we generate the corresponding caption through a recurrent architecture which predicts textual chunks explicitly grounded on regions, following the constraints of the given control. Experiments are conducted on Flickr30k Entities and on COCO Entities, an extended version of COCO in which we add grounding annotations collected in a semi-automatic manner. Results demonstrate that our method achieves state of the art performances on controllable image captioning, in terms of caption quality and diversity. Code and annotations are publicly available at: https://github.com/aimagelab/show-control-and-tell.

• The paper introduces a novel image captioning model that leverages adaptive attention for controllable, region-specific descriptions.
• It employs a recurrent network with a chunk-shifting gate to align noun phrases explicitly with visual regions.
• Results on Flickr30k and COCO Entities demonstrate improved CIDEr and alignment scores, showcasing its practical utility.

Overview of "Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions"

The paper "Show, Control and Tell" presents a novel image captioning framework that enhances both the control and grounding of generated captions in correspondence with visual content. This is achieved by allowing the model to incorporate a control signal specifying a sequence or set of image regions, hence empowering it to generate diverse descriptions that are explicitly tethered to the specified image areas. This approach stands in contrast to traditional black-box captioning systems, which typically offer limited control and are impervious to external supervisory signals, often resulting in a single, uncontrollable caption output per image.

Technical Contributions

The paper makes several notable technical contributions. Firstly, it introduces a controllable image captioning model that integrates recurrent neural networks and novel use of adaptive attention mechanisms. These mechanisms permit the model to hone in on specific image regions—selected as per a control sequence—and describe the image differently depending on the sequence order. In this manner, a single image can yield multiple valid captions catering to various descriptive needs, contexts, or constraints.

Moreover, the architecture predicts the ordering of noun chunks and relevant image regions explicitly, moving beyond typical word-level attention mechanisms. This architecture is realized through a specially designed recurrent framework, featuring a chunk-shifting gate that signals transitions between region-based noun phrases. Additionally, the use of a visual sentinel allows the model to distinguish between elements visually grounded in the image from those which are not, further refining the caption generation process.

Experimental Evaluation

The authors rigorously evaluate their framework on variants of two well-known datasets—Flickr30k and COCO Entities—augmented with grounding annotations. Results underscore the superiority of their method in controllable captioning tasks compared to baseline approaches. The model exhibits heightened performance metrics, including improvements in CIDEr and alignment scores, demonstrating its enhanced ability to generate relevant and diverse image captions aligned with specific control signals.

Implications and Future Directions

The theoretical and practical implications of this work are significant. Practically, the framework paves a path for applications requiring finer-grained control over caption generation, especially where context-driven descriptions are critical—such as assistive technologies for visually impaired users or automated reports that prioritize critical information.

Theoretically, the paper extends understanding of how complex interactions between language and visual domains can be better mediated through neural networks. This also opens new doors for future innovations in multimodal AI, including the refinement of aligning perceptual inputs with linguistic outputs in dynamic environments.

Going forward, potential research directions could explore extending this approach to video data, accommodating dynamic temporal elements, or enhancing the interpretability of the model's decision-making process.

Overall, the paper marks a consequential stride in image captioning technologies, laying out a robust framework for addressing the limitations of existing captioning models by intertwining controllability with clear visual grounding.