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Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understanding.
Reinforcement learning: An introduction
Richard S Sutton, Andrew G Barto, Francis Bach, et al · 1998
Earlier work this paper cites.
Actor-critic algorithms
Vijay R Konda and John N Tsitsiklis · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
The optimal reward baseline for gradient-based reinforcement learning
Lex Weaver and Nigel Tao · 2001
Earlier work this paper cites.
An empirical study of context in object detection
Santosh K Divvala, Derek Hoiem, James H Hays, Alexei A Efros, and Martial Hebert · 2009
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
Philipp Krähenbühl and Vladlen Koltun · 2011
Earlier work this paper cites.
Active object localization with deep reinforcement learning
Juan C Caicedo and Svetlana Lazebnik · 2015
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
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Image retrieval using scene graphs
Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Multiagent cooperation and competition with deep reinforcement learning
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, and Raul Vicente · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
Earlier work this paper cites.
Conditional random fields as recurrent neural networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr · 2015
Earlier work this paper cites.
Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, and Shimon Whiteson · 2016
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Tree-structured reinforcement learning for sequential object localization
Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, and Shuicheng Yan · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
Earlier work this paper cites.
Visual relationship detection with language priors
Cewu Lu, Ranjay Krishna, Michael Bernstein, and Li Fei-Fei · 2016
Earlier work this paper cites.
Reinforcement learning for visual object detection
Stefan Mathe, Aleksis Pirinen, and Cristian Sminchisescu · 2016
Earlier work this paper cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
Earlier work this paper cites.
An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio · 2017
Earlier work this paper cites.
Query-guided regression network with context policy for phrase grounding
Kan Chen, Rama Kovvuri, and Ram Nevatia · 2017
Earlier work this paper cites.
Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning
Long Chen, Hanwang Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, and Tat-Seng Chua · 2017
Earlier work this paper cites.
Detecting visual relationships with deep relational networks
Bo Dai, Yuqi Zhang, and Dahua Lin · 2017
Earlier work this paper cites.
Learning cooperative visual dialog agents with deep reinforcement learning
Abhishek Das, Satwik Kottur, José MF Moura, Stefan Lee, and Dhruv Batra · 2017
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Stabilising experience replay for deep multi-agent reinforcement learning
Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip HS Torr, Pushmeet Kohli, and Shimon Whiteson · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Learning to reason: End-to-end module networks for visual question answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko · 2017
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Inferring and executing programs for visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C Lawrence Zitnick, and Ross B Girshick · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Tensorize, factorize and regularize: Robust visual relationship learning
Seong Jae Hwang, Sathya N Ravi, Zirui Tao, Hyunwoo J Kim, Maxwell D Collins, and Vikas Singh · 2018
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Factorizable net: An efficient subgraph-based framework for scene graph generation
Yikang Li, Wanli Ouyang, Bolei Zhou, Yawen Cui, Jianping Shi, and Xiaogang Wang · 2018
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Context-aware visual policy network for sequence-level image captioning
Daqing Liu, Zheng-Jun Zha, Hanwang Zhang, Yongdong Zhang, and Feng Wu · 2018
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Learning conditioned graph structures for interpretable visual question answering
Will Norcliffe-Brown, Stathis Vafeias, and Sarah Parisot · 2018
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Learning globally optimized object detector via policy gradient
Yongming Rao, Dahua Lin, Jiwen Lu, and Jie Zhou · 2018
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Dual-stream recurrent neural network for video captioning
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Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Vip-cnn: Visual phrase guided convolutional neural network
Yikang Li, Wanli Ouyang, Xiaogang Wang, and Xiao’ou Tang · 2017
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Scene graph generation from objects, phrases and region captions
Yikang Li, Wanli Ouyang, Bolei Zhou, Kun Wang, and Xiaogang Wang · 2017
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Deep variation-structured reinforcement learning for visual relationship and attribute detection
Xiaodan Liang, Lisa Lee, and Eric P Xing · 2017
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Improved image captioning via policy gradient optimization of spider
Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, and Kevin Murphy · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Pixels to graphs by associative embedding
Alejandro Newell and Jia Deng · 2017
Cited alongside, same era.
Ning Xu, An-An Liu, Yongkang Wong, Yongdong Zhang, Weizhi Nie, Yuting Su, and Mohan Kankanhalli · 2018
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Graph r-cnn for scene graph generation
Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh · 2018
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Shuffle-then-assemble: learning object-agnostic visual relationship features
Xu Yang, Hanwang Zhang, and Jianfei Cai · 2018
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Exploring visual relationship for image captioning
Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei · 2018
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Zoom-net: Mining deep feature interactions for visual relationship recognition
Guojun Yin, Lu Sheng, Bin Liu, Nenghai Yu, Xiaogang Wang, Jing Shao, and Chen Change Loy · 2018
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Neural motifs: Scene graph parsing with global context
Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi · 2018
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Learning to count objects in natural images for visual question answering
Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett · 2018
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Deep structured learning for visual relationship detection
Yaohui Zhu and Shuqiang Jiang · 2018
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Knowledge-embedded routing network for scene graph generation
Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin · 2019
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Scene graph generation with external knowledge and image reconstruction
Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling · 2019
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It’s not about the journey; it’s about the destination: Following soft paths under question-guidance for visual reasoning
Monica Haurilet, Alina Roitberg, and Rainer Stiefelhagen · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Dense relational captioning: Triple-stream networks for relationship-based captioning
Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, and In So Kweon · 2019
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Recursive visual attention in visual dialog
Yulei Niu, Hanwang Zhang, Manli Zhang, Jianhong Zhang, Zhiwu Lu, and Ji-Rong Wen · 2019
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Attentive relational networks for mapping images to scene graphs
Mengshi Qi, Weijian Li, Zhengyuan Yang, Yunhong Wang, and Jiebo Luo · 2019
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Video relation detection with spatio-temporal graph
Xufeng Qian, Yueting Zhuang, Yimeng Li, Shaoning Xiao, Shiliang Pu, and Jun Xiao · 2019
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Explainable and explicit visual reasoning over scene graphs
Jiaxin Shi, Hanwang Zhang, and Juanzi Li · 2019
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Learning to compose dynamic tree structures for visual contexts
Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu · 2019
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Exploring context and visual pattern of relationship for scene graph generation
Wenbin Wang, Ruiping Wang, Shiguang Shan, and Xilin Chen · 2019
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Auto-encoding scene graphs for image captioning
Xu Yang, Kaihua Tang, Hanwang Zhang, and Jianfei Cai · 2019
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Learning to collocate neural modules for image captioning
Xu Yang, Hanwang Zhang, and Jianfei Cai · 2019
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Graphical contrastive losses for scene graph parsing
Ji Zhang, Kevin J. Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro · 2019
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