Abstract
GuessWhich is an engaging visual dialogue game that involves interaction between a Questioner Bot (QBot) and an Answer Bot (ABot) in the context of image-guessing. In this game, QBot’s objective is to locate a concealed image solely through a series of visually related questions posed to ABot. However, effectively modeling visually related reasoning in QBot’s decision-making process poses a significant challenge. Current approaches either lack visual information or rely on a single real image sampled at each round as decoding context, both of which are inadequate for visual reasoning. To address this limitation, we propose a novel approach that focuses on visually related reasoning through the use of a mental model of the undisclosed image. Within this framework, QBot learns to represent mental imagery, enabling robust visual reasoning by tracking the dialogue state. The dialogue state comprises a collection of representations of mental imagery, as well as representations of the entities involved in the conversation. At each round, QBot engages in visually related reasoning using the dialogue state to construct an internal representation, generate relevant questions, and update both the dialogue state and internal representation upon receiving an answer. Our experimental results on the VisDial datasets (v0.5, 0.9, and 1.0) demonstrate the effectiveness of our proposed model, as it achieves new state-of-the-art performance across all metrics and datasets, surpassing previous state-of-the-art models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, A., et al.: VQA: visual question answering. In: ICCV, pp. 2425–2433 (2015)
Das, A., et al.: Visual dialog. In: CVPR, pp. 326–335 (2017)
Das, A., Kottur, S., Moura, J.M., Lee, S., Batra, D.: Evaluating visual conversational agents via cooperative human-AI games. In: HCOMP (2017)
Das, A., Kottur, S., Moura, J.M., Lee, S., Batra, D.: Learning cooperative visual dialog agents with deep reinforcement learning. In: ICCV, pp. 2951–2960 (2017)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017)
Lee, S.W., Gao, T., Yang, S., Yoo, J., Ha, J.W.: Large-scale answerer in questioner’s mind for visual dialog question generation. In: ICLR (2019)
Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: NeurIPS (2019)
Murahari, V., Chattopadhyay, P., Batra, D., Parikh, D., Das, A.: Improving generative visual dialog by answering diverse questions. In: EMNLP, pp. 1449–1454 (2019)
Paivio, A.: Imagery and verbal processes (1971)
Pang, W.: Multi-round dialogue state tracking by object-entity alignment in visual dialog. In: CICAI (Oral) (2023)
Pang, W., Wang, X.: Guessing state tracking for visual dialogue. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 683–698. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_40
Pang, W., Wang, X.: Visual dialogue state tracking for question generation. In: Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 11831–11838. AAAI (Oral) (2020)
Shuster, K., Humeau, S., Bordes, A., Weston, J.: Image-chat: engaging grounded conversations. In: ACL, pp. 2414–2429 (2020)
Testoni, A., Shekhar, R., Fernández, R., aella Bernardi, R.: The devil is in the details: a magnifying glass for the guesswhich visual dialogue game. In: Proceedings of the 23rd SemDial Workshop on the Semantics and Pragmatics of Dialogue, pp. 15–24 (2019)
Vaswani, A., et al: Attention is all you need. In: NeurIPS (2017)
de Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., Courville, A.: GuessWhat?! visual object discovery through multi-modal dialogue. In: CVPR, pp. 4466–4475 (2017)
Zhang, Y., et al.: Generating informative and diverse conversational responses via adversarial information maximization. In: NeurIPS (2018)
Zhang, Y., Galley, M., Gao, J., Schwing, A., Forsyth, D.: Fast, diverse and accurate image captioning guided by part-of-speech. In: CVPR, pp. 10695–10704 (2019)
Zhao, L., Lyu, X., Song, J., Gao, L.: GuessWhich? visual dialog with attentive memory network. Pattern Recogn. 114, 107823 (2021)
Zheng, D., Xu, Z., Meng, F., Wang, X., Wang, J., Zhou, J.: Enhancing visual dialog questioner with entity-based strategy learning and augmented guesser. In: Findings of EMNLP, pp. 1839–1851 (2021)
Zhou, M., Arnold, J., Yu, Z.: Building task-oriented visual dialog systems through alternative optimization between dialog policy and language generation. In: EMNLP, pp. 143–153 (2019)
Acknowledgements
We thank the reviewers for their comments and suggestions. This paper was partially supported by the National Natural Science Foundation of China (NSFC 62076032), Huawei Noah’s Ark Lab, MoECMCC “Artificial Intelligence” Project (No. MCM20190701), Beijing Natural Science Foundation (Grant No. 4204100), and BUPT Excellent Ph.D. Students Foundation (No. CX2020309).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pang, W., Duan, R., Yang, J., Li, N. (2024). Multi-modal Dialogue State Tracking for Playing GuessWhich Game. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14473. Springer, Singapore. https://doi.org/10.1007/978-981-99-8850-1_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-8850-1_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8849-5
Online ISBN: 978-981-99-8850-1
eBook Packages: Computer ScienceComputer Science (R0)