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Shiqi Lin

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AAAI Conference 2023 Conference Paper

SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

  • Shiqi Lin
  • Zhizheng Zhang
  • Xin Li
  • Zhibo Chen

Data augmentation (DA) has been extensively studied to facilitate model optimization in many tasks. Prior DA works focus on designing augmentation operations themselves, while leaving selecting suitable samples for augmentation out of consideration. This might incur visual ambiguities and further induce training biases. In this paper, we propose an effective approach, dubbed SelectAugment, to select samples for augmentation in a deterministic and online manner based on the sample contents and the network training status. To facilitate the policy learning, in each batch, we exploit the hierarchy of this task by first determining the augmentation ratio and then deciding whether to augment each training sample under this ratio. We model this process as two-step decision-making and adopt Hierarchical Reinforcement Learning (HRL) to learn the selection policy. In this way, the negative effects of the randomness in selecting samples to augment can be effectively alleviated and the effectiveness of DA is improved. Extensive experiments demonstrate that our proposed SelectAugment significantly improves various off-the-shelf DA methods on image classification and fine-grained image recognition.

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EAAI Journal 2022 Journal Article

InterpolationSLAM: An effective visual SLAM system based on interpolation network

  • Zhenkun Zhu
  • Jikai Wang
  • Meng Xu
  • Shiqi Lin
  • Zonghai Chen

Visual SLAM can be mainly divided into direct method and feature-based method, and these two methods develop relatively independently. In recent years, feature-based SLAM systems have been significantly improved by introducing more robust features, effective matching and optimization frameworks, or other sensors. The introduction of semantic information also promotes the development of direct methods. However, visual SLAM usually assumes that the system satisfies the constant velocity assumption. This assumption may lead to a poor initial pose so that the subsequent optimization falls into a local minimal. Meanwhile, large field of view changes often lead to an increase in the error of feature matching for feature-based method and large illumination changes often lead to an increase of photometric error for direct method, thus negatively influencing SLAM systems. In this paper, we mainly target at feature-based method. In detail, we focus on the number and quality of feature matches, as well as the accuracy of the initial pose, thus an interpolation mechanism for SLAM is proposed. Specifically, we introduce the interpolation network originally used to increase the number of video frames into visual SLAM. First, we point out that the traditional interpolation network evaluation metrics are not suitable for the SLAM systems, and we provide the corresponding evaluation metric. Secondly, we verify that it works both for hand-crafted and deep learning features. Thirdly, in order to verify the effectiveness and transferability of our method, we also apply our method to SLAM systems based on direct method, which proves that our method is also applicable to the direct method. Fourthly, we point out that the interpolation network effectively slows down the pose transformation of the SLAM system by inserting an intermediate frame between the previous frame and the current frame, so that the system can obtain a better initial pose based on the constant velocity assumption. This can also explain why visual-inertial systems can effectively improve the performance of visual SLAM. Finally, to ensure the efficiency of the SLAM system, we provide a turning detection module and propose a method to interpolate only at turnings. Extensive experiments and analyses verify the effectiveness and transferability of the proposed system.

Details DOI
v2026.09.13