Lightweight human pose estimation (HPE) has garnered significant attention due to its widespread applications in edge and mobile devices. However, lightweight human pose estimation methods demonstrate limited accuracy when detecting complex movements, thus restricting their practical utility. To address this issue, we propose MGDNet, a novel lightweight network featuring three innovative modules: the GA-Bottleneck module, the MAC-Block module, and the Dual-View Feature Enhancement Module (DV-FEM). The GA-Bottleneck module integrates ghost convolution with multi-dimensional adaptive frequency-aware attention to capture multi-scale frequency characteristics, enhancing robustness for complex actions. The MAC-Block combines multi-receptive field depth convolution with frequency domain analysis to achieve precise joint localization. The DV-FEM leverages complementary local-global information to enhance feature representation for complex actions. Extensive experiments conducted on the COCO and MPII datasets demonstrate that MGDNet achieves state-of-the-art performance among lightweight models, attaining an average precision (AP) of 73.0% on COCO val2017, which represents a 1.6% improvement over Greit-HRNet. On the MPII dataset, MGDNet achieves the highest PCKh score of 87.6%. The proposed MGDNet outperforms existing lightweight approaches by addressing the challenge of low detection accuracy in complex motion scenarios while maintaining comparable computational efficiency.

Aspect-based sentiment analysis(ABSA) is a fine-grained sentiment classification task that aims to identify the sentiment polarity of specific aspects in a sentence. Graph convolutional networks(GCN) have recently been widely applied for modeling the associations between aspects and opinion words. However, most studies treat the relationships between all words in the graph equally through dependency analysis, without considering their dependency types, which may lead to the inability to distinguish important relationships. On the other hand, dependency trees can only reveal relationships between words and cannot simulate complex sentence relationships, such as conditional, parallel, and contrast relationships, which are crucial for capturing more accurate sentiment relationships for different aspects. To address these challenges, this paper proposes a Collaborative computation model based on Dependency Types and Constituent trees(CDTC) for aspect-based sentiment analysis. Specifically, collaborative computation of dependency type graph convolutional networks and syntax encoders enhances task efficiency. Utilizing dependency types helps distinguish important relationships, while the phrase segmentation and hierarchical structure of constituent trees provide richer syntactic information. Extensive experiments conducted on three datasets demonstrate that our proposed CDTC model achieves state-of-the-art performance.

In certain specific industrial scenarios, smoking and cellphone usage are strictly prohibited behaviors. In these scenarios, it is crucial to rapidly and accurately detect smoking and cell phone usage, and promptly issue warnings, to ensure industrial safety. The detection of prohibited behaviors using computer vision has gained attention from researchers with the development of artificial intelligence. However, detecting small objects like cigarettes and cell phones in complex backgrounds and varying angles poses a challenge due to their changing shapes. In this paper, we propose a detection model called CB-YOLO, which utilizes YOLOv7 as the baseline model, for detecting smoking and mobile phone usage behavior. Firstly, we propose a new channel space pyramid network called CSPPF that pools features at different scales and introduces scale focusing on improving the perceptual ability of the network to better handle targets at various scales and locations. Secondly, we propose an enhanced feature pyramid called AWBFPN. This introduces additional learnable weight parameters to improve the model's ability to fuse multi-scale features effectively. Finally, we have also proposed a new loss function called Size-IoU. The experimental results demonstrate that our algorithm outperforms the baseline model. Specifically, it achieves a 3.9% improvement in Precision, a 2.1% improvement in Recall, a 6.4% improvement in [email protected], and a 2% improvement in [email protected]:0.95 on the Phone_check dataset. Similarly, on the Smoking dataset, our algorithm achieves a 3.3% improvement in Precision, a 1.9% improvement in Recall, a 1.7% improvement in [email protected], and a 0.3% improvement in [email protected]:0.95.

BACKGROUND: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein-protein interaction (PPI) data, computationally identifying essential proteins from protein-protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. RESULTS: In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. CONCLUSIONS: We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

We introduce a gated value network (GVN) for general multilabel classification (MLC) tasks. GVN was motivated by deep value network (DVN) that directly exploits the "compatibility" metric as the learning pursuit for MLC. Meanwhile, it further improves traditional DVN on twofold. First, GVN relaxes the complex variable optimization steps in DVN inference by incorporating a feedforward predictor for straightforward multilabel prediction. Second, GVN also introduces the gating mechanism to block confounding factors from the input data that allows more precise compatibility evaluations for data and their potential multilabels. The whole GVN framework is trained in an end-to-end manner with policy gradient approaches. We show the effectiveness and generalization of GVN on diverse learning tasks, including document classification, audio tagging, and image attribute prediction.

We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous samples that are quite different from existing labeled novel data can inevitably emerge in the testing phase. To this end, we consider augmenting an uncertainty assessment module into low-shot learning system to account into the disturbance of those out-of-distribution (OOD) samples. Once detected, these OOD samples are passed to human beings for active labeling. Due to the discrete nature of this uncertainty assessment process, the whole Human-In-the-Loop Low-shot (HILL) learning framework is not end-to-end trainable. We hence revisited the learning system from the aspect of reinforcement learning and introduced the REINFORCE algorithm to optimize model parameters via policy gradient. The whole system gains noticeable improvements over existing low-shot learning approaches.