School of Mathematics and Statistics - Research Publications

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Type: Conference Paper × Author: Gong, Mingming ×

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Now showing 1 - 10 of 19
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    Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples
    Xia, X ; Han, B ; Zhan, Y ; Yu, J ; Gong, M ; Gong, C ; Liu, T (IEEE, 2023-01-01)
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    Generating Dynamic Kernels via Transformers for Lane Detection
    Chen, Z ; Liu, Y ; Gong, M ; Du, B ; Qian, G ; Smith-Miles, K (IEEE, 2023-01-01)
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    Knowledge Distillation for Feature Extraction in Underwater VSLAM
    Yang, J ; Gong, M ; Nair, G ; Lee, JH ; Monty, J ; Pu, Y (IEEE, 2023-01-01)
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    Unpaired Image-to-Image Translation with Shortest Path Regularization
    Xie, S ; Xu, Y ; Gong, M ; Zhang, K (IEEE, 2023-06)
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    Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
    Zhu, A ; Ke, Q ; Gong, M ; Bailey, J (IEEE COMPUTER SOC, 2023)
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    Progressive Video Summarization via Multimodal Self-supervised Learning
    Li, H ; Ke, Q ; Gong, M ; Drummond, T (IEEE COMPUTER SOC, 2023)
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    Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
    Xu, Y ; Xie, S ; Wu, W ; Zhang, K ; Gong, M ; Batmanghelich, K (IEEE COMPUTER SOC, 2022)
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    Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint
    Guo, J ; Li, J ; Fu, H ; Gong, M ; Zhang, K ; Tao, D (IEEE COMPUTER SOC, 2022)
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    Data-Driven Approach to Multiple-Source Domain Adaptation
    Stojanov, P ; Gong, M ; Carbonell, J ; Zhang, K (PMLR, 2019)
    A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.
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    Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
    Fu, H ; Gong, M ; Wang, C ; Batmanghelich, K ; Zhang, K ; Tao, D (IEEE, 2019)
    Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an ill-posed problem, so appropriate constraints are required to obtain reasonable solutions. One of the most prominent constraints is cycle consistency, which enforces the translated image by GXY to be translated back to the input image by an inverse mapping GYX. While cycle consistency requires the simultaneous training of GXY and GY X, recent studies have shown that one-sided domain mapping can be achieved by preserving pairwise distances between images. Although cycle consistency and distance preservation successfully constrain the solution space, they overlook the special properties that simple geometric transformations do not change the semantic structure of images. Based on this special property, we develop a geometry-consistent generative adversarial network (GcGAN), which enables one-sided unsupervised domain mapping. GcGAN takes the original image and its counterpart image transformed by a predefined geometric transformation as inputs and generates two images in the new domain coupled with the corresponding geometry-consistency constraint. The geometry-consistency constraint reduces the space of possible solutions while keep the correct solutions in the search space. Quantitative and qualitative comparisons with the baseline (GAN alone) and the state-of-the-art methods including CycleGAN and DistanceGAN demonstrate the effectiveness of our method.