This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently similar underlying facial appearance, and their differences could be subtle. Secondly, facial expressions simultaneously exhibit themselves through multiple facial regions, and for recognition, a holistic approach by encoding high-order interactions among local features is required. To address these issues, this work proposes DAN with three key components: Feature Clustering Network (FCN), Multi-head Attention Network (MAN), and Attention Fusion Network (AFN). Specifically, FCN extracts robust features by adopting a large-margin learning objective to maximize class separability. In addition, MAN instantiates a number of attention heads to simultaneously attend to multiple facial areas and build attention maps on these regions. Further, AFN distracts these attentions to multiple locations before fusing the feature maps to a comprehensive one. Extensive experiments on three public datasets (including AffectNet, RAF-DB, and SFEW 2.0) verified that the proposed method consistently achieves state-of-the-art facial expression recognition performance. The DAN code is publicly available.

We address the problem of localizing waste objects from a color image and an optional depth image, which is a key perception component for robotic interaction with such objects. Specifically, our method integrates the intensity and depth information at multiple levels of spatial granularity. Firstly, a scene-level deep network produces an initial coarse segmentation, based on which we select a few potential object regions to zoom in and perform fine segmentation. The results of the above steps are further integrated into a densely connected conditional random field that learns to respect the appearance, depth, and spatial affinities with pixel-level accuracy. In addition, we create a new RGBD waste object segmentation dataset, MJU-Waste, that is made public to facilitate future research in this area. The efficacy of our method is validated on both MJU-Waste and the Trash Annotation in Context (TACO) dataset.

Recently, deep convolutional neural networks (DCNNs) have been widely adopted in scene classification for remote sensing images, yielding impressive results. One important assumption for training these models is that the image labels are free from errors. However, the mislabeling of certain images can severely degrade the performance of DCNN models. While existing methods offer some degree of mitigation, their effectiveness diminishes significantly in the presence of highly contaminated labels. This paper seeks to address this issue by proposing a robust scene classification model designed to learn from noisy labels. Firstly, a semi-supervised approach is proposed, involving bootstrapping a model that uses a cleaned training set through coarse screening of potentially noisy labels. In particular, the model is trained using consistency regularization to enhance its robustness. Secondly, the remaining images with uncertain labels are further used to increase the number of images available for training. Here, dynamic thresholding is applied to detect and correct label errors. Lastly, the images and their labels from the two steps above are jointly used for final training. The proposed method is evaluated on three widely used remote sensing datasets (AID, WHU-RS19 and UCMerced). The results demonstrate that the proposed approach outperforms competing methods, highlighting its efficacy in addressing label noise in remote sensing datasets.