Qi Wu

Dr Qi Wu

Senior Lecturer

School of Computer Science

Faculty of Engineering, Computer and Mathematical Sciences

Eligible to supervise Masters and PhD - email supervisor to discuss availability.


Dr Qi Wu is currently a Senior Lecturer in the University of Adelaide and he was an ARC Senior Research Associate in the Australian Centre for Robotic Vision (ACRV) in the University of Adelaide, Australia. Before that, he works as a Postdoc Researcher in the Australian Centre for Visual Technologies (ACVT). He received an MSc in Global Computing and Media Technology, a PhD in Computer Science from the University of Bath (United Kingdom), in 2011 and 2015. His research interests include cross-depictive style object modelling, object detection and Vision-to-Language problems. He is especially interested in the problem of Image Captioning and Visual Question Answering. His image captioning model produced the best result in the Microsoft COCO Image Captioning Challenges in the last year and his VQA model is the current state-of-the-art in the area. His work has been published in prestigious journals and conferences such as TPAMI, CVPR, ICCV and ECCV.

My research interests are mainly in computer vision and machine learning. My previous research projects include modeling visual objects regardless of depictive styles and image understanding using contextual cues. I am currently leading a small team at the Adelaide to research on the topic of Vision-and-Language.

I have been in the computer vision filed for nearly 6 years and I have a strong track record in this field. Currently, I am working on the vision to language problem and I am especially an expert in the image captioning and visual question answering (VQA). In 2015, my image captioning model and VQA model achieved the leading performance in the Microsoft COCO Image Captioning Challenges and VQA Challenges. I have published several papers in the top journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Signal Processing Magazine (SPM), Computer Vision and Image Understanding (CVIU). I also have published several papers on the top conference, such as International Joint Conference on Artificial Intelligence (IJCAI), AAAI, The Conference on Computer Vision and Pattern Recognition (CVPR) and the European Conference on Computer Vision (ECCV), and so on.

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  • Appointments

    Date Position Institution name
    2018 Lecturer in Computer Science University of Adelaide, Adelaide
    2017 - 2018 ARC Senior Research Associate Australia Centre for Robotic Vision, University of Adelaide
    2015 - 2017 Senior Research Associate University of Adelaide
    2014 Research Intern Lenovo
    2011 - 2015 PhD University of Bath
  • Language Competencies

    Language Competency
    Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
    English Can read, write, speak, understand spoken and peer review
  • Education

    Date Institution name Country Title
    2011 - 2015 University of Bath United Kingdom PhD
    2010 - 2011 University of Bath United Kingdom MSc
    2006 - 2010 China Jiliang University China BSc
  • Research Interests

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  • Journals

    Year Citation
    2021 Parvaneh, A., Abbasnejad, E., Wu, Q., & Shi, J. (2021). Show, price and negotiate: a hierarchical attention recurrent visual negotiator. IEEE Transactions on Multimedia, abs/1905.03721, 10 pages.
    DOI
    2021 Zhang, W., Ma, C., Wu, Q., & Yang, X. (2021). Language-guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning. IEEE Transactions on Circuits and Systems for Video Technology, 31(9), 3469-3481.
    DOI
    2021 Wang, Y., Qi, Y., Yao, H., Gong, D., & Wu, Q. (2021). Image editing with varying intensities of processing. Computer Vision and Image Understanding, 211, 1-13.
    DOI
    2021 Yu, J., Jiang, X., Qin, Z., Zhang, W., Hu, Y., & Wu, Q. (2021). Learning Dual Encoding Model for Adaptive Visual Understanding in Visual Dialogue. IEEE TRANSACTIONS ON IMAGE PROCESSING, 30, 220-233.
    DOI
    2020 Chen, Q., Wu, Q., Chen, J., Wu, Q., Van Den Hengel, A., & Tan, M. (2020). Scripted Video Generation with a Bottom-Up Generative Adversarial Network. IEEE Transactions on Image Processing, 29, 7454-7467.
    DOI Scopus2 WoS1
    2020 Qiao, Y., Deng, C., & Wu, Q. (2020). Referring Expression Comprehension: A Survey of Methods and Datasets. IEEE Transactions on Multimedia, 1.
    DOI
    2020 Yu, J., Zhang, W., Lu, Y., Qin, Z., Hu, Y., Tan, J., & Wu, Q. (2020). Reasoning on the Relation: Enhancing Visual Representation for Visual Question Answering and Cross-Modal Retrieval. IEEE Transactions on Multimedia, 22(12), 3196-3209.
    DOI Scopus3 WoS1
    2020 Huang, Y., Wu, Q., Wang, W., & Wang, L. (2020). Image and Sentence Matching via Semantic Concepts and Order Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3), 636-650.
    DOI Scopus3 WoS2
    2019 Liu, W., Li, Y., & Wu, Q. (2019). An Attribute-Based High-Level Image Representation for Scene Classification. IEEE Access, 7, 4629-4640.
    DOI Scopus3 WoS1
    2019 Lyu, F., Wu, Q., Hu, F., Wu, Q., & Tan, M. (2019). Attend and Imagine: Multi-Label Image Classification with Visual Attention and Recurrent Neural Networks. IEEE Transactions on Multimedia, 21(8), 1971-1981.
    DOI Scopus19 WoS16
    2019 Zhang, J., Wu, Q., Zhang, J., Shen, C., Lu, J., & Wu, Q. (2019). Heritage image annotation via collective knowledge. Pattern Recognition, 93, 204-214.
    DOI Scopus2 WoS2
    2019 Zhang, J., Xie, Y., Wu, Q., & Xia, Y. (2019). Medical image classification using synergic deep learning. Medical Image Analysis, 54, 10-19.
    DOI Scopus84 WoS62 Europe PMC8
    2018 Wu, Q., Shen, C., Wang, P., Dick, A., & van den Hengel, A. (2018). Image captioning and visual question answering based on attributes and external knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1367-1381.
    DOI Scopus137 WoS96 Europe PMC2
    2018 Zhang, J., Wu, Q., Shen, C., Zhang, J., & Lu, J. (2018). Multilabel image classification with regional latent semantic dependencies. IEEE Transactions on Multimedia, 20(10), 2801-2813.
    DOI Scopus62 WoS44
    2017 Teney, D., Wu, Q., & Van Den Hengel, A. (2017). Visual Question Answering: a tutorial. IEEE Signal Processing Magazine, 34(6), 63-75.
    DOI Scopus15 WoS11
    2017 Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). FVQA: fact-based Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 2413-2427.
    DOI Scopus75 WoS51 Europe PMC3
    2017 Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017). Visual question answering: a survey of methods and datasets. Computer Vision and Image Understanding, 163, 21-40.
    DOI Scopus109 WoS78
    2015 Hall, P., Cai, H., Wu, Q., & Corradi, T. (2015). Cross-depiction problem: recognition and synthesis of photographs and artwork. Computational Visual Media, 1(2), 91-103.
    DOI Scopus14
    Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. V. D. (n.d.). Care about you: towards large-scale human-centric visual relationship
    detection.
    Moghaddam, M. K., Abbasnejad, E., Wu, Q., Shi, J., & Hengel, A. V. D. (n.d.). Learning for Visual Navigation by Imagining the Success.
  • Conference Papers

    Year Citation
    2021 Moghaddam, M. K., Wu, Q., Abbasnejad, E., & Shi, J. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. In 2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 (pp. 3732-3741). ELECTR NETWORK: IEEE.
    DOI
    2021 Zheng, Y., Wen, Z., Tan, M., Zeng, R., Chen, Q., Wang, Y., & Wu, Q. (2021). Modular graph attention network for complex visual relational reasoning. In Proceedings of the 15th Asian Conference on Computer Vision (ACCV 2020), as published in Lecture Notes in Computer Science Vol. 12627 (pp. 137-153). Cham, Switzerland: Springer.
    DOI
    2021 Zhu, Q., Gao, C., Wang, P., & Wu, Q. (2021). Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps. In THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE Vol. 35 (pp. 3608-3615). ELECTR NETWORK: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    2021 Wang, Z., Bao, R., Wu, Q., & Liu, S. (2021). Confidence-aware Non-repetitive Multimodal Transformers for TextCaps. In THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE Vol. 35 (pp. 2835-2843). ELECTR NETWORK: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    2021 Liu, L., He, M., Xu, G., Tan, M., & Wu, Q. (2021). How to Train Your Agent to Read and Write. In THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE Vol. 35 (pp. 13397-13405). ELECTR NETWORK: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    2020 Jiang, X., Yu, J., Qin, Z., Zhuang, Y., Zhang, X., Hu, Y., & Wu, Q. (2020). DualVD: An adaptive dual encoding model for deep visual understanding in visual dialogue. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence Vol. 34 (pp. 11125-11132). online: AAAI.
    Scopus8 WoS1
    2020 Jing, C., Wu, Y., Zhang, X., Jia, Y., & Wu, Q. (2020). Overcoming language priors in VQA via decomposed linguistic representations. In AAAI 2020 - 34th AAAI Conference on Artificial Intelligence Vol. 34 (pp. 11181-11188). online: AAAI.
    Scopus6 WoS1
    2020 Zhang, C., Yao, Y., Shu, X., Li, Z., Tang, Z., & Wu, Q. (2020). Data-driven Meta-set Based Fine-Grained Visual Recognition. In MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia (pp. 2372-2381). online: ACM.
    DOI Scopus1
    2020 Wang, P., Liu, D., Li, H., & Wu, Q. (2020). Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge. In MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia (pp. 28-36). online: ACM.
    DOI Scopus1
    2020 Jing, C., Wu, Y., Pei, M., Hu, Y., Jia, Y., & Wu, Q. (2020). Visual-Semantic Graph Matching for Visual Grounding. In MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia (pp. 4041-4050). online: ACM.
    DOI
    2020 Liu, F., Xu, G., Wu, Q., Du, Q., Jia, W., & Tan, M. (2020). Cascade Reasoning Network for Text-based Visual Question Answering. In MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia (pp. 4060-4069). online: ACM.
    DOI
    2020 Hong, Y., Rodriguez-Opazo, C., Qi, Y., Wu, Q., & Gould, S. (2020). Language and visual entity relationship graph for agent navigation. In Advances in Neural Information Processing Systems Vol. 2020-December (pp. 1-12). online: NIPS.
    Scopus2
    2020 Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). Medical data inquiry using a question answering model. In Proceedings: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020) Vol. 2020-April (pp. 1490-1493). online: IEEE.
    DOI Scopus2
    2020 Wang, H., Wu, Q., & Shen, C. (2020). Soft Expert Reward Learning for Vision-and-Language Navigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12354 LNCS (pp. 126-141). Switzerland: Springer Nature.
    DOI
    2020 Tang, R., Ma, C., Zhang, W. E., Wu, Q., & Yang, X. (2020). Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12364 LNCS (pp. 437-453). Switzerland: Springer International Publishing.
    DOI
    2020 Deng, C., Ding, N., Tan, M., & Wu, Q. (2020). Length-Controllable Image Captioning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12358 LNCS (pp. 712-729). Switzerland: Springer International Publishing.
    DOI
    2020 Qi, Y., Pan, Z., Zhang, S., van den Hengel, A., & Wu, Q. (2020). Object-and-Action Aware Model for Visual Language Navigation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12355 LNCS (pp. 303-317). Switzerland: Springer International Publishing.
    DOI Scopus1
    2020 Jiang, X., Yu, J., Sun, Y., Qin, Z., Zhu, Z., Hu, Y., & Wu, Q. (2020). DAM: Deliberation, abandon and memory networks for generating detailed and non-repetitive responses in visual dialogue. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2021-January (pp. 687-693). online: AAAI Press.
    Scopus1
    2020 Zhu, Z., Yu, J., Wang, Y., Sun, Y., Hu, Y., & Wu, Q. (2020). Mucko: Multi-layer cross-modal knowledge reasoning for fact-based visual question answering. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2021-January (pp. 1097-1103). online: AAAI Press.
    Scopus4
    2020 Chen, Z., Wang, P., Ma, L., Wong, K. Y. K., & Wu, Q. (2020). Cops-Ref: A New Dataset and Task on Compositional Referring Expression Comprehension. In Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 10083-10092). online: IEEE.
    DOI
    2020 Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & Van Den Hengel, A. (2020). Reverie: Remote embodied visual referring expression in real indoor environments. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 9979-9988). online: IEEE.
    DOI Scopus12
    2020 Liao, Z., Wu, Q., Shen, C., Van Den Hengel, A., & Verjans, J. (2020). AIML at VQA-Med 2020: Knowledge inference via a skeleton-based sentence mapping approach for medical domain visual question answering. In L. Cappellato, C. Eickhoff, N. Ferro, & A. Névéol (Eds.), Proceedings of the 11th International Conference of the CLEF Initiative (CLEF 2020), as published in CEUR Workshop Proceedings Vol. 2696 (pp. 1-14). online: CEUR-WS.
    2020 Abbasnejad, M., Abbasnejad, I., Wu, Q., Shi, Q., & Van Den Hengel, A. (2020). Gold seeker: Information gain from policy distributions for goal-oriented vision-and-langauge reasoning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 13447-13456). online: IEEE.
    DOI
    2020 Chen, S., Jin, Q., Wang, P., & Wu, Q. (2020). Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 9959-9968). online: IEEE.
    DOI Scopus13
    2020 Chen, S., Zhao, Y., Jin, Q., & Wu, Q. (2020). Fine-grained video-text retrieval with hierarchical graph reasoning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 10635-10644). online: IEEE.
    DOI Scopus12
    2020 Chen, Q., Wu, Q., Tang, R., Wang, Y., Wang, S., & Tan, M. (2020). Intelligent home 3D: Automatic 3D-house design from linguistic descriptions only. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 12622-12631). online: IEEE.
    DOI Scopus2
    2019 Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., Van Den Hengel, A., & Zhu, W. (2019). Watch, reason and code: Learning to represent videos using program. In Proceedings of the 27th ACM International Conference on Multimedia (ACM Multimedia 2019), MM '19 (pp. 1543-1551). online: Association for Computing Machinery.
    DOI Scopus2 WoS1
    2019 Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2019). What's to know? uncertainty as a guide to asking goal-oriented questions. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 4150-4159). online: IEEE.
    DOI Scopus8 WoS1
    2019 Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind your neighbours: Image annotation with metadata neighbourhood graph co-attention networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 2951-2959). online: IEEE.
    DOI Scopus8 WoS2
    2019 Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Hengel, A. (2019). Neighbourhood watch: Referring expression comprehension via language-guided graph attention networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2019-June (pp. 1960-1968). online: IEEE.
    DOI Scopus46 WoS9
    2018 Cao, I., Guo, Y., Wu, Q., Shen, C., Huang, J., & Tan, M. (2018). Adversarial learning with local coordinate coding. In 35th International Conference on Machine Learning, ICML 2018 Vol. 2 (pp. 1104-1117). online: PMLR.
    Scopus12 WoS11
    2018 Zhuang, Z., Tan, M., Zhuang, B., Liu, J., Guo, Y., Wu, Q., . . . Zhu, J. (2018). Discrimination-aware Channel Pruning for Deep Neural Networks. In Advances in Neural Information Processing Systems Vol. 2018-December (pp. 875-886). online: NIPS.
    Scopus138 WoS1
    2018 Zhang, J., Zhang, J., Wu, Q., Wu, Q., Xu, J., Lu, J., . . . Tang, Z. (2018). Historical image annotation by exploring the tag relevance. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 (pp. 646-651). Nanjing, PEOPLES R CHINA: IEEE.
    DOI Scopus1 WoS1
    2018 Zhuang, B., Wu, Q., Shen, C., Reid, I., & Van Den Hengel, A. (2018). HCVRD: A benchmark for large-scale human-centered visual relationship detection. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7631-7638). New Orleans: Association for the Advancement of Artificial Intelligence.
    Scopus12 WoS4
    2018 Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2018). Kill two birds with one stone: Weakly-supervised neural network for image annotation and tag refinement. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 7550-7557). New Orleans: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    Scopus4 WoS3
    2018 Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. (2018). Are you talking to me? Reasoned visual dialog generation through adversarial learning. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 6106-6115). Salt Lake City, UT: IEEE.
    DOI Scopus50 WoS17
    2018 Deng, C., Wu, Q., Wu, Q., Hu, F., Lyu, F., & Tan, M. (2018). Visual Grounding via Accumulated Attention. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 7746-7755). online: IEEE.
    DOI Scopus56 WoS19
    2018 Anderson, P., Das, A., & Wu, Q. (2018). Connecting language and vision to actions. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference Tutorial Abstracts (pp. 10-14). Melbourne: Association for Computational Linguistics.
    DOI
    2018 Huang, Y., Wu, Q., Song, C., & Wang, L. (2018). Learning Semantic Concepts and Order for Image and Sentence Matching. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6163-6171). online: IEEE.
    DOI Scopus96 WoS46
    2018 Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Van Den Hengel, A., & Reid, I. (2018). Visual Question Answering with memory-augmented network. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 6975-6984). Salt Lake City, Utah: IEEE.
    DOI Scopus37 WoS21
    2018 Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., . . . Hengel, A. (2018). Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) Vol. abs/1711.07280 (pp. 3674-3683). Salt Lake City, UT: IEEE.
    DOI Scopus168 WoS99
    2018 Zhang, J., Xie, Y., Wu, Q., & Xia, Y. (2018). Skin lesion classification in dermoscopy images using synergic deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11071 LNCS (pp. 12-20). Switzerland: Springer.
    DOI Scopus19 WoS11
    2018 Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & van den Hengel, A. (2018). Goal-oriented visual question generation via intermediate rewards. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018: Proceedings, Part V Vol. Lecture Notes in Computer Science; vol. 11209 (pp. 189-204). Munich: Springer.
    DOI Scopus5 WoS3
    2018 Zhuang, B., Wu, Q., Shen, C., Reid, I., & van den Hengel, A. (2018). Parallel attention: a unified framework for visual object discovery through dialogs and queries. In Proceedings: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018) (pp. 4252-4261). Salt Lake City, UT: IEEE.
    DOI Scopus30 WoS7
    2017 Wang, P., Wu, Q., Shen, C., & van den Hengel, A. (2017). The VQA-machine: learning how to use existing vision algorithms to answer new questions. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017-January (pp. 3909-3918). Honolulu: IEEE.
    DOI Scopus50 WoS18
    2017 Wang, P., Wu, Q., Shen, C., Dick, A., & Van Den Hengel, A. (2017). Explicit knowledge-based reasoning for visual question answering. In C. Sierra (Ed.), Proceedings of the twenty-sixth International Joint Conference on Artificial Intelligence Vol. 0 (pp. 1290-1296). online: IJCAI.
    DOI Scopus43
    2016 Wu, Q., Wang, P., Shen, C., Dick, A., & Van Den Hengel, A. (2016). Ask me anything: free-form visual question answering based on knowledge from external sources. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 4622-4630). Las Vegas, NV: IEEE.
    DOI Scopus177 WoS83
    2016 Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016). What value do explicit high level concepts have in vision to language problems?. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2016-December (pp. 203-212). Las Vegas, NV: IEEE.
    DOI Scopus285 WoS168
    2015 Cai, H., Wu, Q., & Hall, P. (2015). Beyond Photo-Domain Object Recognition: Benchmarks for the Cross-Depiction Problem. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2015-February (pp. 74-79). Santigo: IEEE.
    DOI Scopus11 WoS2
    2014 Wu, Q., Cai, H., & Hall, P. (2014). Learning graphs to model visual objects across different depictive styles. In D. Fleet, T. Pajdia, B. Schiele, & T. Tuytelaars (Eds.), Proceedings of the 13th European Conference on Computer Vision Vol. VII (pp. 313-328). Zurich, Switzerland: Springer.
    DOI Scopus13 WoS5
    2013 Wu, Q., & Hall, P. (2013). Modelling visual objects Invariant to depictive style. In T. Burghardt, D. Damen, W. Mayol-Cuevas, & M. Mirmehdi (Eds.), Proceedings of the British Machine Vision Conference (pp. 23.1-23.12). Bristol, UK: BMVA Press.
    DOI Scopus4
    2012 Wu, Q., & Hall, P. (2012). Prime shapes in natural images. In R. Bowden, J. Collomosse, & K. Mikolajcczk (Eds.), Proceedings of the British Machine Vision Conference (pp. 45-1-45-12). Surrey, UK: BMVA Press.
    DOI Scopus4 WoS2
  • MyIP-7370, CERA grants, Anton van den Hengel, Anthony Dick, Qi Wu, Answer Me Why:Explainability is Critical if We are to Trust Automated Decision Making, 98,000 AUD

  • MyIP-7370, CERA grants, Anton van den Hengel, Anthony Dick, Qi Wu, Robust long-term Autonomous Navigation, 98,000 AUD

  • Facebook’s Research and Academic Relations Program, Peter Anderson, Qi Wu, Damien Teney, Niko Sunderhauf, Stephen Gould, Anton van den Hengel, Treasure Hunt: Natural Language N

  • Computer Vision

  • Machine Learning

  • Algorithms and Data Structure Analysis

  • Research Methods

  • Advanced Topics in Computer Science

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  • Current Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2021 Principal Supervisor Generative Adversarial Networks (GANs) to Synthesize Images or Videos from Text. Doctor of Philosophy Doctorate Full Time Mr Qi Chen
    2020 Principal Supervisor General Vision and Language Methods in Real Applications Doctor of Philosophy Doctorate Full Time Miss Yanyuan Qiao
    2020 Principal Supervisor Towards Conversational Vision-Based Artificial Intelligence Doctor of Philosophy Doctorate Full Time Mr Chaorui Deng
    2019 Co-Supervisor Visual Navigation in Embodied Autonomous Agents Doctor of Philosophy Doctorate Full Time Mr Mahdi Kazemi Moghaddam
    2018 Co-Supervisor Semantic Understanding Based on 3D Data Doctor of Philosophy Doctorate Full Time Mr Wei Yin
    2018 Co-Supervisor Multi-modality Data Analysis Using Deep Learning Doctor of Philosophy Doctorate Full Time Mr Hu Wang
    2018 Co-Supervisor Instance Level Object Segmentation in Video using Deep Learning Doctor of Philosophy Doctorate Full Time Ms Yutong Dai
  • Past Higher Degree by Research Supervision (University of Adelaide)

    Date Role Research Topic Program Degree Type Student Load Student Name
    2018 - 2021 Co-Supervisor Fully Convolutional Instance-level Visual Recognition Doctor of Philosophy Doctorate Full Time Mr Zhi Tian
    2017 - 2018 Co-Supervisor Text Detection and Recognition in Natural Scene Images Doctor of Philosophy Doctorate Full Time Mrs Hui Li
  • Position: Senior Lecturer
  • Phone: 83132134
  • Email: qi.wu01@adelaide.edu.au
  • Campus: North Terrace
  • Building: Australian Institute for Machine Learning, floor 1
  • Room: 1.22.A
  • Org Unit: Australian Institute for Machine Learning

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