In this paper, two methods for discriminative multiple instance target characterization, MI-SMF and MI-ACE, are presented. MI-SMF and MI-ACE estimate a discriminative target signature from imprecisely-labeled and mixed training data. In many applications, such as sub-pixel target detection in remotely-sensed hyperspectral imagery, accurate pixel-level labels on training data is often unavailable and infeasible to obtain. Furthermore, since sub-pixel targets are smaller in size than the resolution of a single pixel, training data is comprised only of mixed data points (in which target training points are mixtures of responses from both target and non-target classes). Results show improved, consistent performance over existing multiple instance concept learning methods on several hyperspectral sub-pixel target detection problems.

The active millimeter-wave (AMMW) scanner has been widely used for inspecting human security in public places in recent years owing to its ability to detect all kinds of objects under the clothes and be harmless to the body. However, it is really challenging to detect all concealed objects automatically and accurately due to inherent imaging noise, unknown object kind, and uncertain position. Recently, many existing methods, especially deep learning-based, have achieved good performances on concealed object detection. These methods work well for detecting a few kinds of large objects, but fail to perform on dim and incomplete hard objects. To address this task, a concealed object detection model with self-paced feature attention fusion network (SPFAFN) is proposed in this article. To be specific, the features with different scales are fused in a top-down manner to integrate details and global semantics to better detect small objects. During fusing multi-scale features, a hierarchical pyramid attention mechanism composed of channel and spatial attention is developed to perceive the object. Moreover, boosting self-paced learning is exploited to guide the model to learn hard samples that are difficultly detected. The proposed method is validated on two real-world datasets: an AMMW dataset and a publicly available passive millimeter-wave (PMMW) dataset. Experimental results demonstrate that the proposed approach is superior to the state-of-the-art methods, and achieves better performances on the two datasets with Average Precision (AP).

A multiple instance dictionary learning approach, Dictionary Learning using Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat heart rate estimation and to characterize heartbeat signatures from ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI estimates a “heartbeat concept” that represents an individual's personal ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection and heartbeat characterization as a multiple instance learning problem to address the uncertainty inherent in aligning BCG signals with ground truth during training. Experimental results show that the estimated heartbeat concept obtained by DL-FUMI is an effective heartbeat prototype and achieves superior performance over comparison algorithms.

Attention-based deep multiple-instance learning (MIL) has been applied to many machine-learning tasks with imprecise training labels. It is also appealing in hyperspectral target detection, which only requires the label of an area containing some targets, relaxing the effort of labeling the individual pixel in the scene. This article proposes an $L1$ sparsity-regularized attention multiple-instance neural network ( $L1$ -attention MINN) for hyperspectral target detection with imprecise labels that enforces the discrimination of false-positive instances from positively labeled bags. The sparsity constraint applied to the attention estimated for the positive training bags strictly complies with the definition of MIL and maintains better discriminative ability. The proposed algorithm has been evaluated on both simulated and real-field hyperspectral (subpixel) target detection tasks, where advanced performance has been achieved over the state-of-the-art comparisons, showing the effectiveness of the proposed method for target detection from imprecisely labeled hyperspectral data.

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise ( e.g. , sensor noise) and perturbations ( e.g. , body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.