Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains so-phisticated description of the instance's appearance, action, and relation with others. It is therefore rather difficult for a RVOS model to capture all these attributes correspondingly in the video; in fact, the model often favours more on the action- and relation-related visual attributes of the instance. This can end up with partial or even incorrect mask prediction of the target instance. We tackle this problem by taking a subject-centric short text expression from the original long text expression. The short one retains only the appearance-related information of the target instance so that we can use it to focus the model's attention on the instance's appearance. We let the model make joint predictions using both long and short text ex-pressions; and insert a long-short cross-attention module to interact the joint features and a long-short predictions intersection loss to regulate the joint predictions. Besides the improvement on the linguistic part, we also introduce a forward-backward visual consistency loss, which utilizes optical flows to warp visual features between the annotated frames and their temporal neighbors for consistency. We build our method on top of two state of the art pipelines. Extensive experiments on A2D-Sentences, Refer-YouTube-vas, JHMDB-Sentences and Refer-DAVIS17 show impres-sive improvements of our method. Code is available here.

Negative emotional states, such as anxiety and depression, pose significant challenges in contemporary society, often stemming from the stress encountered in daily activities. Stress (state or level) recognition is a crucial prerequisite for effective stress management and intervention. Presently, wearable devices have been employed to capture physiological signals and analyze stress states. However, their constant skin contact can lead to discomfort and disturbance during prolonged monitoring. In this paper, a peak attention-based multitasking framework is presented for non-contact stress recognition. The framework extracts rPPG signals from RGB facial videos, utilizing them as inputs for a novel multi-task attentional convolutional neural network for stress recognition (MTASR). It incorporates peak detection and HR estimation as auxiliary tasks to facilitate stress recognition. By leveraging multi-task learning, MTASR can utilize information related to stress physiological responses, thereby enhancing feature extraction efficiency. For stress recognition, two binary classification tasks are applied: stress state recognition and stress level recognition. The model is validated on the UBFC-Phys public dataset and demonstrates an accuracy of 94.33% for stress state recognition and 83.83% for stress level recognition. The proposed method outperforms the dataset's baseline methods and other competing approaches.

Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human facial videos and then measure multiple vital signs (e.g., heart rate, respiration frequency) from rPPG signals. Recent approaches achieve it by training deep neural networks, which normally require abundant facial videos and synchronously recorded photoplethysmography (PPG) signals for supervision. However, the collection of these annotated corpora is not easy in practice. In this paper, we introduce a novel frequency-inspired self-supervised framework that learns to estimate rPPG signals from facial videos without the need of ground truth PPG signals. Given a video sample, we first augment it into multiple positive/negative samples which contain similar/dissimilar signal frequencies to the original one. Specifically, positive samples are generated using spatial augmentation; negative samples are generated via a learnable frequency augmentation module, which performs non-linear signal frequency transformation on the input without excessively changing its visual appearance. Next, we introduce a local rPPG expert aggregation module to estimate rPPG signals from augmented samples. It encodes complementary pulsation information from different face regions and aggregates them into one rPPG prediction. Finally, we propose a series of frequency-inspired losses, i.e., frequency contrastive loss, frequency ratio consistency loss, and cross-video frequency agreement loss, for the optimization of estimated rPPG signals from multiple augmented video samples. We conduct rPPG-based heart rate, heart rate variability, and respiration frequency estimation on five standard benchmarks. The experimental results demonstrate that our method improves the state of the art by a large margin.

Negative mood states include tension, depression, anger, fatigue, and confusion, which represent the weak internal emotions of a human. Negative mood states exert adverse impact on individuals' ability to make rational decisions, which entails the practicable method of negative mood state detection. The most commonly used negative mood state detection methods are based on the psychological scale, which requires additional work and brings inconvenience to the subject in the application scenarios. To overcome this challenge, this paper proposes a novel non-contact negative mood state detection method according to the knowledge of affective computing. The POMS-net model is used to extract temporal-spatial features from visible and infrared thermal videos, and the negative mood state detection is realized using data reliability-focused multi-modal fusion. The proposed method is verified using the HDT-BR dataset collected in the aerospace medicine experiment "Earth-Star II" and the VIRI public dataset. The experimental results on the datasets verify that our method outperforms the comparison methods.

Automatic acetowhite lesion segmentation in colposcopy images (cervigrams) is essential in assisting gynecologists for the diagnosis of cervical intraepithelial neoplasia grades and cervical cancer. It can also help gynecologists determine the correct lesion areas for further pathological examination. Existing computer-aided diagnosis algorithms show poor segmentation performance because of specular reflections, insufficient training data and the inability to focus on semantically meaningful lesion parts. In this paper, a novel computer-aided diagnosis algorithm is proposed to segment acetowhite lesions in cervigrams automatically. To reduce the interference of specularities on segmentation performance, a specular reflection removal mechanism is presented to detect and inpaint these areas with precision. Moreover, we design a cervigram image classification network to classify pathology results and generate lesion attention maps, which are subsequently leveraged to guide a more accurate lesion segmentation task by the proposed lesion-aware convolutional neural network. We conducted comprehensive experiments to evaluate the proposed approaches on 3045 clinical cervigrams. Our results show that our method outperforms state-of-the-art approaches and achieves better Dice similarity coefficient and Hausdorff Distance values in acetowhite legion segmentation.

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.

Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis rate. Existing computer-assisted algorithms for analyzing cervigram images have neglected that colposcopy is a sequential and multistate process, which is unsuitable for clinical applications. In this work, we construct a cervigram-based recurrent convolutional neural network (C-RCNN) to classify different CIN grades and cervical cancer. Convolutional neural networks are leveraged to extract spatial features. We develop a sequence-encoding module to encode discriminative temporal features and a multistate-aware convolutional layer to integrate features from different states of cervigram images. To train and evaluate the performance of C-RCNN, we leveraged a dataset of 4,753 real cervigrams and obtained 96.13% test accuracy with a specificity and sensitivity of 98.22% and 95.09%, respectively. Areas under each receiver operating characteristic curves are above 0.94, proving that visual representations and sequential dynamics can be jointly and effectively optimized in the training phase. Comparative analysis demonstrated the effectiveness of the proposed C-RCNN against competing methods, showing significant improvement over only focusing on a single frame. This architecture can be extended to other applications in medical image analysis.

With the fast advances in Internet technologies, social networks have become a major platform for social interaction, lifestyle demonstration, and message dissemination. Effective community detection in social networks helps to assess public sentiment, identify community leaders, and produce personalized recommendation. While different community detection approaches have been proposed in the literature, the trust model based detection schemes model user interactions as trust transfer, which helps to capture the implicit relation in the network. Unfortunately, trust model based detection schemes face acold startproblem, i.e., they cannot accurately model newly joined users as these users have few interactions for a duration after joining the network. In this paper, we propose TLCDA, a novel trust model based community detection algorithm. By enhancing the traditional trust computation with inter-node relation strength and similarity in social networks, TLCDA detects communities through coarse-grained K-Mediods clustering. Our evaluation on real social networks shows that the communities detected by TLCDA exhibit superior preference cohesion while satisfying the topology cohesion.