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Yi Tao

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4 papers
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4

AAAI Conference 2023 Conference Paper

Dual-Domain Attention for Image Deblurring

  • Yuning Cui
  • Yi Tao
  • Wenqi Ren
  • Alois Knoll

As a long-standing and challenging task, image deblurring aims to reconstruct the latent sharp image from its degraded counterpart. In this study, to bridge the gaps between degraded/sharp image pairs in the spatial and frequency domains simultaneously, we develop the dual-domain attention mechanism for image deblurring. Self-attention is widely used in vision tasks, however, due to the quadratic complexity, it is not applicable to image deblurring with high-resolution images. To alleviate this issue, we propose a novel spatial attention module by implementing self-attention in the style of dynamic group convolution for integrating information from the local region, enhancing the representation learning capability and reducing computational burden. Regarding frequency domain learning, many frequency-based deblurring approaches either treat the spectrum as a whole or decompose frequency components in a complicated manner. In this work, we devise a frequency attention module to compactly decouple the spectrum into distinct frequency parts and accentuate the informative part with extremely lightweight learnable parameters. Finally, we incorporate attention modules into a U-shaped network. Extensive comparisons with prior arts on the common benchmarks show that our model, named Dual-domain Attention Network (DDANet), obtains comparable results with a significantly improved inference speed.

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ICLR Conference 2023 Conference Paper

Selective Frequency Network for Image Restoration

  • Yuning Cui 0001
  • Yi Tao
  • Zhenshan Bing
  • Wenqi Ren
  • Xinwei Gao
  • Xiaochun Cao
  • Kai Huang 0001
  • Alois C. Knoll

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image pairs. However, these works commonly utilize transformation tools, e.g., wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Integrating two developed modules into a U-Net backbone, the proposed Selective Frequency Network (SFNet) performs favorably against state-of-the-art algorithms on five image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, and image deraining.

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IJCAI Conference 2023 Conference Paper

Strip Attention for Image Restoration

  • Yuning Cui
  • Yi Tao
  • Luoxi Jing
  • Alois Knoll

As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing long-range dependencies, Transformer based methods have achieved promising performance on multifarious image restoration tasks. However, the canonical self-attention leads to quadratic complexity with respect to input size, hindering its further applications in image restoration. In this paper, we propose a Strip Attention Network (SANet) for image restoration to integrate information in a more efficient and effective manner. Specifically, a strip attention unit is proposed to harvest the contextual information for each pixel from its adjacent pixels in the same row or column. By employing this operation in different directions, each location can perceive information from an expanded region. Furthermore, we apply various receptive fields in different feature groups to enhance representation learning. Incorporating these designs into a U-shaped backbone, our SANet performs favorably against state-of-the-art algorithms on several image restoration tasks. The code is available at https: //github. com/c-yn/SANet.

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ICRA Conference 2021 Conference Paper

POIS: Policy-Oriented Instance Segmentation for Ambidextrous Robot Picking

  • Guangyun Xu
  • Yi Tao
  • Bowen Jiang
  • Peng Wang 0024
  • Yongkang Luo 0001
  • Jun Zhong

Robots with a parallel-jaw gripper and suction cup is an adaptive and efficient robotic picking system. This paper proposed Policy-Oriented Instance Segmentation (POIS) for ambidextrous robots. POIS can generate a pair of target masks that allows ambidextrous robots to pick in parallel. It takes a depth image and predicts initial mask, center offset, and policy confidence map through three paralleled branches. We incorporate the initial mask with center offset to obtain candidate instances, from which we select masks of target objects for policy execution (decided with policy confidence map). We also provide a dataset that contains 6k synthetic scenes and 100 real scenes for ambidextrous picking. Trained on synthetic scenes, POIS generalizes well in real scene and is capable of handling novel objects in cluttered scenes. Our dataset and video are available at https://bit.ly/3oJj8Tu.

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