In this paper, we propose two novel components for improving deep salient object detection models. The first component, called saliency detection network (S-Net), introduces dense short- and long-range connections that effectively integrate multiscale features to better exploit contexts at multiple levels. Benefiting from the direct access to low- and high-level features, the S-Net can not only exploit the object context but also preserve the object boundary sharply, leading to enhanced saliency detection performance. Second, a distraction detection network (D-Net) is developed to learn to diagnose which regions of an input image are distracting and harmful for saliency prediction of the S-Net. With such distraction diagnosis, the regions that are distracting to S-Net are removed in hindsight from the input image and the resulted distraction-free image is fed to S-Net for saliency prediction. To train the D-Net, a distraction mining approach is proposed to localize the model-specific distracting regions through examining the sensitiveness of the S-Net to image regions in a principled manner. Besides, the distraction mining approach also provides a way to interpret decisions made by deep neural network (DNN) saliency detection models, which relieves the black-box issues of DNNs to some extent. Extensive experiments on seven popular benchmark datasets demonstrate the effectiveness of the combined S-Net and D-Net, which provides new state of the arts.

Class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient. Hence, these datasets have a significantly negative impact on the performance of many state of the art methods. In this paper, we propose a class imbalance loss (CI loss) to handle this problem. To distinguish imbalanced datasets in accordance with the extent of imbalance, we also define an imbalance degree that works as a decision index factor in the CI loss. Because the minority classes with fewer samples probably lose chances in descending gradient in the training process, CI loss is introduced to make these minority classes descend further than the majority classes. In view of the imbalanced distribution of data in few-shot learning, a method for generating an imbalanced few-shot learning dataset is presented in this paper. We conducted a large number of experiments in the MiniImageNet dataset, which showed the effectiveness of an algorithm for model-agnostic meta-learning (MAML) for rapid adaptation with CI loss. In the problem of detecting 15 ship categories, our loss function is transplanted to Rotational Region Convolutional Neural Network (R2CNN) detection method and Cascade network architecture and achieves higher mAP than focal loss and cross-entropy loss. In addition, Mixed National Institute of Standards and Technology dataset and Moving and Stationary Target Acquisition and Recognition dataset are sampled to imbalanced datasets to verify the effectiveness of CI loss.

Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed.

In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.

Person re-identification (Re-ID) has become a hot topic in both research and industry. We joined in a person Re-ID challenge of the First National Artificial Intelligence Challenge (China, 2019) and found some model designs and training tricks work great or not on a super big private dataset. In this paper, we propose a model that combines the most effective designs, including multi-scale, multi-branch and attention mechanism, and report training tricks that are no less or even more important in improving person Re-ID performance. We analyze four commonly used public datasets: Market1501, DukeMTMC-ReID, CUHK03, and MSMT17, and achieve the state-of-the-art performance. Besides, we analyze and confirm the effectiveness of the designs by ablation studies. We also share strategies that play a key role in the challenge and experience of model designs that do not generalize well on large datasets.