| Active Generative Adversarial Network for Image Classification Q Kong, B Tong, M Klinkigt, Y Watanabe, N Akira, T Murakami Proceedings of the AAAI Conference on Artificial Intelligence 33, 4090-4097, 2019 | 144* | 2019 |
| MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding Q Kong, Z Wu, Z Deng, M Klinkigt, B Tong, T Murakami Proceedings of the IEEE International Conference on Computer Vision, 8658-8667, 2019 | 64 | 2019 |
| Cycle-Contrast for Self-Supervised Video Representation Learning Q Kong, W Wei, Z Deng, T Yoshinaga, T Murakami NeurIPS 2020, 2020 | 46 | 2020 |
| Adversarial Zero-shot Learning With Semantic Augmentation. B Tong, M Klinkigt, J Chen, X Cui, Q Kong, T Murakami, Y Kobayashi AAAI, 2018 | 26 | 2018 |
| Selecting home appliances with smart glass based on contextual information Q Kong, T Maekawa, T Miyanishi, T Suyama Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 18 | 2016 |
| Detecting and correcting WiFi positioning errors Y Tsuda, Q Kong, T Maekawa Proceedings of the 2013 ACM international joint conference on Pervasive and …, 2013 | 17 | 2013 |
| Egocentric Video Search via Physical Interactions. T Miyanishi, J Hirayama, Q Kong, T Maekawa, H Moriya, T Suyama AAAI, 330-337, 2016 | 10 | 2016 |
| Image retrieving apparatus, image retrieving method, and setting screen used therefor Y Watanabe, K Morita, T Murakami, A Hiroike, K Quan US Patent 10,977,515, 2021 | 8 | 2021 |
| Sharing training data among different activity classes Q Kong, T Maekawa Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing …, 2013 | 6 | 2013 |
| Multi-stream Adaptive Graph Convolutional Network Using Inter-and Intra-body Graphs for Two-person Interaction Recognition Y Ito, K Morita, Q Kong, T Yoshinaga IEEE Access, 2021 | 5 | 2021 |
| Identifying outlets at which electrical appliances are used by electrical wire sensing to gain positional information about appliance use Q Kong, T Maekawa Proceedings of the 2014 ACM International Joint Conference on Pervasive and …, 2014 | 5 | 2014 |
| Reusing training data with generative/discriminative hybrid model for practical acceleration-based activity recognition Q Kong, T Maekawa Computing 96 (9), 875-895, 2014 | 5 | 2014 |
| A MULTI-MODAL FUSION APPROACH FOR AUDIO-VISUAL SCENE CLASSIFICATION ENHANCED BY CLIP VARIANTS S Okazaki, Q Kong, T Yoshinaga | 4* | |
| LDSLVISION SUBMISSIONS TO DCASE’21: A MULTI-MODAL FUSION APPROACH FOR AUDIO-VISUAL SCENE CLASSIFICATION ENHANCED BY CLIP VARIANTS S Okazaki, Q Kong, T Yoshinaga | 4* | |
| Hitachi at TRECVID DSDI 2020 S Okazaki, Q Kong, M Klinkigt, T Yoshinaga | 4* | |
| Towards Efficient Instance Segmentation with Hierarchical Distillation Z Deng, Q Kong, T Murakami Proceedings of the IEEE International Conference on Computer Vision …, 2019 | 3 | 2019 |
| Mask Atari for deep reinforcement learning as POMDP benchmarks Y Shao, Q Kong, T Matsumura, T Fuji, K Ito, H Mizuno arXiv preprint arXiv:2203.16777, 2022 | 2 | 2022 |
| Anticipating the Start of User Interaction for Service Robot in the Wild K Ito, Q Kong, S Horiguchi, T Sumiyoshi, K Nagamatsu 2020 IEEE International Conference on Robotics and Automation (ICRA), 9687-9693, 2020 | 2 | 2020 |
| Multimodal Deep Neural Networks Based Ensemble Learning for X-Ray Object Recognition Q Kong, N Akira, B Tong, Y Watanabe, D Matsubara, T Murakami Asian Conference on Computer Vision, 523-538, 2018 | 2 | 2018 |
| NII Hitachi UIT at TRECVID 2019 M Klinkigt, DD Le, A Hiroike, HQ Vo, Q Kong, VT Nguyen, T Murakami, ... | 2* | |
Takuya MaekawaOsaka UniversityGeverifieerd e-mailadres voor acm.org
