CANet: A Context-Aware Network for Shadow Removal

Chen, Zipei; Long, Chengjiang; Zhang, Ling; Xiao, Chunxia

doi:10.1109/iccv48922.2021.00470

Cited by 80 publications

(43 citation statements)

References 39 publications

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“…Fu et al [5] regarded the shadow removal as an autoexposure fusion problem where several over-exposure images are generated first and then fused according to auto-generated fusion weights. CANet [3] absorbed the contextual matching information by transferring the non-shadow features to similar shadow patches via a contextual feature transfer mechanism. Although these methods enhance the performance of shadow removal more or less, they ignore the contradiction of color mappings between the shadow region and the non-shadow region as detailed in Sec.…”

Section: Shadow Removalmentioning

confidence: 99%

Shadow-Aware Dynamic Convolution for Shadow Removal

Xu¹,

Lin²,

Ye³

et al. 2022

Preprint

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With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC.

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Section: Shadow Removalmentioning

confidence: 99%

Shadow-Aware Dynamic Convolution for Shadow Removal

Xu¹,

Lin²,

Ye³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…CANet [8] aims to mine the contextual information of the shadowed and non-shadowed regions. It is not just removal without detection, he relies on the global luminance averaging method to achieve the detection, the image from RGB to LAB only L channel is sensitive to shadows, the L here for global averaging, you can get a kind of fuzzy shadow-free map, you can distinguish between shadow-free and shadowed areas.…”

Section: A Supervised Learning Image Shadow Removalmentioning

confidence: 99%

SpA-Former: Transformer image shadow detection and removal via spatial attention

Zhang¹,

Gu²,

Zhu³

2022

Preprint

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n this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpAformer is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/ zhangbaijin/Spatial-Transformer-shadow-removal

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“…Recently, deep learning has been applied to visual recognition (Hu, Long, and Xiao 2021), action recognition (Islam, Long, and Radke 2021), image denoising (Yu et al 2021), style transfer (Xu et al 2021), shadow removal (Wei et al 2019;Zhang et al 2020;Chen et al 2021), anomaly detection (Liu et al 2021), human motion prediction (Dang et al 2021), as well as image and video forgery detection research (Islam et al 2020). Thanks to deep learning, pedestrian trajectory prediction achieves significant progress.…”

Section: Related Workmentioning

confidence: 99%

Social Interpretable Tree for Pedestrian Trajectory Prediction

Shi

Wang

Long³

et al. 2022

AAAI

Self Cite

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Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e.g., go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in long-term prediction and different best-of-K predictions.

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CANet: A Context-Aware Network for Shadow Removal

Cited by 80 publications

References 39 publications

Shadow-Aware Dynamic Convolution for Shadow Removal

Shadow-Aware Dynamic Convolution for Shadow Removal

SpA-Former: Transformer image shadow detection and removal via spatial attention

Social Interpretable Tree for Pedestrian Trajectory Prediction

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