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Yigong Wang

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2 papers

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AAAI Conference 2021 Conference Paper

Single View Point Cloud Generation via Unified 3D Prototype

Yu Lin
Yigong Wang
Yi-Fan Li
Zhuoyi Wang
Yang Gao
Latifur Khan

As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc. , the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classification and segmentation, synthesizing point clouds remains challenging, especially from a single image. State-of-the-art (SOTA) approaches can generate a point cloud from a hidden vector, however, they treat 2D and 3D features equally and disregard the rich shape information within the 3D data. In this paper, we address this problem by integrating image features with 3D prototype features. Specifically, we propose to learn a set of 3D prototype features from a real point cloud dataset and dynamically adjust them through the training. These prototypes are then integrated with incoming image features to guide the point cloud generation process. Experimental results show that our proposed method outperforms SOTA methods on single image based 3D reconstruction tasks.

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AAAI Conference 2020 Short Paper

Few Sample Learning without Data Storage for Lifelong Stream Mining (Student Abstract)

Zhuoyi Wang
Yigong Wang
Yu Lin
Bo Dong
Hemeng Tao
Latifur Khan

Continuously mining complexity data stream has recently been attracting an increasing amount of attention, due to the rapid growth of real-world vision/signal applications such as self-driving cars and online social media messages. In this paper, we aim to address two signiﬁcant problems in the lifelong/incremental stream mining scenario: ﬁrst, how to make the learning algorithms generalize to the unseen classes only from a few labeled samples; second, is it possible to avoid storing instances from previously seen classes to solve the catastrophic forgetting problem? We introduce a novelty stream mining framework to classify the inﬁnite stream of data with different categories that occurred during different times. We apply a few-sample learning strategy to make the model recognize the novel class with limited samples; at the same time, we implement an incremental generative model to maintain old knowledge when learning new coming categories, and also avoid the violation of data privacy and memory restrictions simultaneously. We evaluate our approach in the continual class-incremental setup on the classiﬁcation tasks and ensure the sufﬁcient model capacity to accommodate for learning the new incoming categories.

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