Recursive Visual Attention in Visual Dialog (1812.02664v2)

Abstract: Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1) How to answer visually-grounded questions, which is the core challenge in visual question answering (VQA); (2) How to infer the co-reference between questions and the dialog history. An example of visual co-reference is: pronouns (\eg, they'') in the question (\eg,Are they on or off?'') are linked with nouns (\eg, lamps'') appearing in the dialog history (\eg,How many lamps are there?'') and the object grounded in the image. In this work, to resolve the visual co-reference for visual dialog, we propose a novel attention mechanism called Recursive Visual Attention (RvA). Specifically, our dialog agent browses the dialog history until the agent has sufficient confidence in the visual co-reference resolution, and refines the visual attention recursively. The quantitative and qualitative experimental results on the large-scale VisDial v0.9 and v1.0 datasets demonstrate that the proposed RvA not only outperforms the state-of-the-art methods, but also achieves reasonable recursion and interpretable attention maps without additional annotations. The code is available at \url{https://github.com/yuleiniu/rva}.

Recursive Visual Attention in Visual Dialog

The paper "Recursive Visual Attention in Visual Dialog" presents a novel approach to enhance performance in the task of visual dialog, where an agent engages in multi-round question answering about a given image. This work tackles two primary issues: visually-grounded question answering, a challenge shared with visual question answering (VQA), and co-reference resolution within dialog history, which involves linking pronouns in questions to nouns from previous dialog turns. The authors introduce a novel attention mechanism called Recursive Visual Attention (RvA), designed to resolve visual co-reference and improve the visual dialog agent's ability to refine attention recursively.

Key Contributions

1. Recursive Visual Attention Mechanism: The core contribution is the RvA mechanism, which allows the dialog agent to selectively review dialog history by comparing questions with previous dialog turns. This recursive browsing continues until the agent gains sufficient confidence in resolving visual co-references, leading to a more refined visual attention process.
2. Quantitative and Qualitative Advances: The RvA approach was demonstrated to outperform several state-of-the-art methods on the VisDial datasets (versions 0.9 and 1.0). The model delivered improved mean reciprocal rank (MRR) and recall rates, showcasing its effectiveness over existing models such as LF, HRE, and CorefNMN. Furthermore, RvA provides interpretable attention maps, which is an essential step towards explainable AI.
3. Novel Use of Gumbel-Softmax: The method employs the Gumbel-softmax trick to facilitate end-to-end training while making discrete decisions during the recursive attention mechanism. This innovative use improves the model’s capability to handle complex dialog structures and co-references effectively.
4. Differentiated Language Features: The authors emphasize the use of reference-aware and answering-aware language features for different stages of the attention mechanism, enhancing the model’s understanding of both dialog structure and question semantics.

Implications and Future Directions

The RvA model's architecture, with its recursive element, mimics human-like dialog comprehension by allowing for dynamic review and refinement based on historical context. This not only strengthens the understanding of visual co-reference within dialog scenarios but also advances the interpretability of attention mechanisms in complex AI systems.

Practically, such a model can significantly impact applications that require robust interaction with AI systems over successive dialog turns, such as AI-guided customer service interfaces, human-robot interaction, and multi-turn machine translation.

Future directions might include integrating more sophisticated natural language understanding components to further refine historical context awareness. Additionally, exploring the integration of RvA with other visual understanding tasks could broaden the applicability of recursive attention mechanisms. Lastly, expanding the training datasets to include more diverse dialog interactions could enhance the model's generalization capabilities across various dialog domains.

Overall, this paper contributes a robust methodology for improving visual dialog agents and offers a platform for further research on integrating sophisticated attention mechanisms within vision-language tasks.