More Like this

1. Instance Segmentation of Neural Cells

   Yi, J. ; Wu, P. ; Jiang, M. ; Hoeppner, D. J. ; Metaxas, D. ( September 2018 , ECCV 2018 workshop)

   Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to produce wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better per- formance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods. 

   more » « less
2. PIXEL-WISE NEURAL CELL INSTANCE SEGMENTATION

   Yi, J. ; Wu, P. ; Hoeppner, D. J. ; Metaxas, D. ( April 2018 , 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   Accurate cell instance segmentation plays an important role in the study of neural cell interactions, which are critical for understanding the development of brain. These interactions are performed through the filopodia and lamellipodia of neural cells, which are extremely tiny structures and as a result render most existing instance segmentation methods powerless to precisely capture them. To solve this issue, in this paper we present a novel hierarchical neural network comprising object detection and segmentation modules. Compared to previous work, our model is able to efficiently share and make full use of the information at different levels between the two modules. Our method is simple yet powerful, and experimental results show that it captures the contours of neural cells, especially the filopodia and lamellipodia, with high accuracy, and outperforms recent state of the art by a large margin. 

   more » « less
3. GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction

   https://doi.org/10.18653/v1/2022.findings-naacl.128  

   Wang, Yiwei ; Chen, Muhao ; Zhou, Wenxuan ; Cai, Yujun ; Liang, Yuxuan ; Hooi, Bryan ( January 2022 , Findings of the Association for Computational Linguistics: NAACL 2022)

   Entity types and textual context are essential properties for sentence-level relation extraction (RE). Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence. In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance. We propose the GraphCache (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE. GraphCache aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences. The global property features act as dataset-level prior knowledge for RE, and a complement to the sentence-level features. Inspired by the classical caching technique in computer systems, we develop GraphCache to update the property representations in an online manner. Overall, GraphCache yields significant effectiveness gains on RE and enables efficient message passing across all sentences in the dataset. 

   more » « less
4. Attentive Normalization

   https://doi.org/10.1007/978-3-030-58520-4_5  

   Li, X. ; Sun, W. ; Wu, T. ( August 2020 , 16th European Conference on Computer Vision (2020))

   Vedaldi, A. (Ed.)

   In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way. The weights are learned by leveraging channel-wise feature attention. In experiments, we test the proposed AN using four representative neural architectures. In the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark. AN obtains consistent performance improvement for different neural architectures in both benchmarks with absolute increase of top-1 accuracy in ImageNet-1000 between 0.5\% and 2.7\%, and absolute increase up to 1.8\% and 2.2\% for bounding box and mask AP in MS-COCO respectively. We observe that the proposed AN provides a strong alternative to the widely used Squeeze-and-Excitation (SE) module. The source codes are publicly available at \href{https://github.com/iVMCL/AOGNet-v2}{the ImageNet Classification Repo} and \href{https://github.com/iVMCL/AttentiveNorm\_Detection}{the MS-COCO Detection and Segmentation Repo}. 

   more » « less
5. Monocularly Generated 3D High Level Semantic Model by Integrating Deep Learning Models and Traditional Vision Techniques

   https://doi.org/10.1109/IST50367.2021.9651471  

   Alsheimer, Steven ; Zhu, Zhigang ( August 2021 , 2021 IEEE International Conference on Imaging Systems and Techniques (IST))

   Scene reconstruction using Monodepth2 (Monocular Depth Inference) which provides depth maps from a single RGB camera, the outputs are filled with noise and inconsistencies. Instance segmentation using a Mask R-CNN (Region Based Convolution Neural Networks) deep model can provide object segmentation results in 2D but lacks 3D information. In this paper we propose to integrate the results of Instance segmentation via Mask R-CNN’s, CAD model Car Shape Alignment, and depth from Monodepth2 together with classical dynamic vision techniques to create a High-level Semantic Model with separability, robustness, consistency and saliency. The model is useful for both virtualized rendering, semantic augmented reality and automatic driving. Experimental results are provided to validate the approach. 

   more » « less