Xiaomeng Wu

Xiaomeng Wu, Yongqing Sun, Akisato Kimura et al.

Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is defined by incorporating the local pixel value differences in image reflectance into the density estimation to alleviate the adverse effects of dark lighting conditions. Over 500 images were used for evaluation, demonstrating the superiority of our approach over existing studies. It can sufficiently improve the brightness of low-light images while avoiding over-enhancement in normal-light images.

Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino

Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge preserving smoothing. Our method works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark.

Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan et al.

Similarity measures for time series are important problems for time series classification. To handle the nonlinear time distortions, Dynamic Time Warping (DTW) has been widely used. However, DTW is not learnable and suffers from a trade-off between robustness against time distortion and discriminative power. In this paper, we propose a neural network model for task-adaptive time warping. Specifically, we use the attention model, called the bipartite attention model, to develop an explicit time warping mechanism with greater distortion invariance. Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task. We also propose to induce pre-training of our model by DTW to improve the discriminative power. Extensive experiments demonstrate the superior effectiveness of our model over DTW and its state-of-the-art performance in online signature verification.

Xiaomeng Wu, Yongqing Sun, Akisato Kimura

Most recent methods of deep image enhancement can be generally classified into two types: decompose-and-enhance and illumination estimation-centric. The former is usually less efficient, and the latter is constrained by a strong assumption regarding image reflectance as the desired enhancement result. To alleviate this constraint while retaining high efficiency, we propose a novel trainable module that diversifies the conversion from the low-light image and illumination map to the enhanced image. It formulates image enhancement as a comparametric equation parameterized by a camera response function and an exposure compensation ratio. By incorporating this module in an illumination estimation-centric DNN, our method improves the flexibility of deep image enhancement, limits the computational burden to illumination estimation, and allows for fully unsupervised learning adaptable to the diverse demands of different tasks.

Cian O'Donovan, Sarp Gurakan, Ananya Karanam et al.

As AI attracts vast investment and attention, there are competing concerns about the technology's opportunities and uncertainties that blend technical and social questions. The public debate, dominated by a few powerful voices, tends to highlight extreme promises and threats. We wanted to know whether public discussions or technology companies' priorities were representative of AI researchers' opinions. Our survey of more than 4,000 AI researchers is, we think, the largest conducted to date. It was designed to understand attitudes to a variety of issues and include some comparisons with public attitudes derived from existing surveys. Most previous surveys of AI researchers have asked them for predictions of passing a technological threshold or the probabilities of some catastrophic event. These surveys mask the uncertainty and diversity that normally characterises scientific research. Our hypothesis was that the opinions of AI researchers would be markedly different from those of members of the public. While there are areas of divergence, particularly in attitudes to the technology's potential benefits, our survey shows some surprising convergence between researchers' and publics' opinions, particularly in the assessment and prioritisation of risk. Responses to an open text question 'What one thing most worries you about AI?' reveal that only 3% of AI researchers prioritise existential risks, despite the prominence given to these risks in media and policy. AI technologies and AI researchers seem to be much more 'normal' than public representations suggest. Our survey results suggest the possibility for new forms of public dialogue on AI's harms, risks and opportunities. Rather than speculating on future potential risks, policymakers and AI researchers should collaborate on understanding and mitigating the range of sociotechnical risks that are already of clear public concern.

Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan et al.

Deep time series metric learning is challenging due to the difficult trade-off between temporal invariance to nonlinear distortion and discriminative power in identifying non-matching sequences. This paper proposes a novel neural network-based approach for robust yet discriminative time series classification and verification. This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance. It is robust against not only local but also large global distortions, so that even matching pairs that do not satisfy the monotonicity, continuity, and boundary conditions can still be successfully identified. Learning of this model is further guided by dynamic time warping to impose temporal constraints for stabilized training and higher discriminative power. It can learn to augment the inter-class variation through warping, so that similar but different classes can be effectively distinguished. We experimentally demonstrate the superiority of the proposed approach over previous non-parametric and deep models by combining it with a deep online signature verification framework, after confirming its promising behavior in single-letter handwriting classification on the Unipen dataset.