Blar i forfatter Fakultet for naturvitenskap og teknologi "Xing, Eric P."

Viser treff 1-5 av 5

    • ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation 

      Kampffmeyer, Michael C.; Dong, Nanqing; Liang, Xiaodan; Zhang, Yujia; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-12-14)
      Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems ...

    • Dilated temporal relational adversarial network for generic video summarization 

      Zhang, Yujia; Kampffmeyer, Michael C.; Liang, Xiaodan; Zhang, Dingwen; Tan, Min; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-12)
      The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated ...

    • Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images 

      Dong, Nanqing; Kampffmeyer, Michael C.; Liang, Xiaodan; Wang, Zeya; Dai, Wei; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-09-20)
      Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and ...

    • Rethinking knowledge graph propagation for zero-shot learning 

      Kampffmeyer, Michael C.; Chen, Yinbo; Liang, Xiaodan; Wang, Hao; Zhang, Yujia; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2019)
      Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the graph structure share statistical strength allowing generalization to new classes when faced with a lack of data. However, multi-layer architectures, which are required to propagate knowledge to distant nodes in the graph, ...

    • Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio 

      Dong, Nanqing; Kampffmeyer, Michael C.; Liang, Xiaodan; Wang, Zeya; Dai, Wei; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-09-26)
      The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts ...