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   https://doi.org/10.1109/IROS.2018.8593720  

   Ye, Xin ; Lin, Zhe ; Li, Haoxiang ; Zheng, Shibin ; Yang, Yezhou ( October 2018 , 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
2. TagAttention: Mobile Object Tracing without Object Appearance Information by Vision-RFID Fusion

   https://doi.org/10.1109/ICNP.2019.8888149  

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3. Multi-Object Search using Object-Oriented POMDPs

   https://doi.org/10.1109/ICRA.2019.8793888  

   Wandzel, Arthur ; Oh, Yoonseon ; Fishman, Michael ; Kumar, Nishanth ; Wong, Lawson L.S. ; Tellex, Stefanie ( May 2019 , IEEE International Conference on Robotics and Automation (ICRA))
4. You reap what you sow: Using Videos to Generate High Precision Object Proposals for Weakly-supervised Object Detection

   Singh, Krishna ; Lee, Yong Jae ( January 2019 , IEEE Conference on Computer Vision and Pattern Recognition (CVPR))

   We propose a novel way of using videos to obtain high precision object proposals for weakly-supervised object detection. Existing weakly-supervised detection approaches use off-the-shelf proposal methods like edge boxes or selective search to obtain candidate boxes. These methods provide high recall but at the expense of thousands of noisy proposals. Thus, the entire burden of finding the few relevant object regions is left to the ensuing object mining step. To mitigate this issue, we focus instead on improving the precision of the initial candidate object proposals. Since we cannot rely on localization annotations, we turn to video and leverage motionmore »cues to automatically estimate the extent of objects to train a Weakly-supervised Region Proposal Network (W-RPN). We use the W-RPN to generate high precision object proposals, which are in turn used to re-rank high recall proposals like edge boxes or selective search according to their spatial overlap. Our W-RPN proposals lead to significant improvement in performance for state-of-the-art weakly-supervised object detection approaches on PASCAL VOC 2007 and 2012.« less
5. Multi-Object Search using Object-Oriented POMDPs

   Arthur Wandzel, Yoonseon Oh ( January 2019 , IEEE International Conference on Robotics and Automation)

   Abstract— A core capability of robots is to reason about mul- tiple objects under uncertainty. Partially Observable Markov Decision Processes (POMDPs) provide a means of reasoning under uncertainty for sequential decision making, but are computationally intractable in large domains. In this paper, we propose Object-Oriented POMDPs (OO-POMDPs), which represent the state and observation spaces in terms of classes and objects. The structure afforded by OO-POMDPs support a factorization of the agent’s belief into independent object distributions, which enables the size of the belief to scale linearly versus exponentially in the number of objects. We formulate a novel Multi-Object Search (MOS)more »task as an OO-POMDP for mobile robotics domains in which the agent must find the locations of multiple objects. Our solution exploits the structure of OO-POMDPs by featuring human language to selectively update the belief at task onset. Using this structure, we develop a new algorithm for efficiently solving OO-POMDPs: Object- Oriented Partially Observable Monte-Carlo Planning (OO- POMCP). We show that OO-POMCP with grounded language commands is sufficient for solving challenging MOS tasks both in simulation and on a physical mobile robot.« less