The primary challenge in deploying Human Pose Estimation (HPE) methods in real-world applications lies in balancing computational speed, model compactness, and prediction accuracy. Existing methods achieve strong performance in one or two aspects, but usually at the expense of the remaining one. To overcome this trade-off, we propose KeyRegionPose, a novel framework that achieves high accuracy while reducing model size and computational cost. Central to our design is the Region Focus Mechanism, which enables the model to concentrate on keypoint-relevant regions rather than the entire image. During training, we generate intermediate keypoint proposals to estimate keypoint-specific areas, from which the model learns region-focused features and refines predictions. To ensure accurate keypoint localization and enhance final pose estimation performance, we introduce a Cross-Representation Consistency Loss (CRC Loss) that enforces alignment between the predicted heatmaps and the regressed keypoint coordinates. Additionally, we propose Progressive Multi-Scale Token Pruning (PMTP), a strategy that prunes irrelevant tokens across multiple feature scales to accelerate inference. KeyRegionPose achieves 76.0 AP on the COCO validation set and 75.4 AP on the test-dev set, with only 20.0 million parameters and 8.6 GFLOPs—representing a 27.3% reduction in parameter count, 21.8% decrease in GFLOPs, and a competitive result (+0.2%) over state-of-the-art lightweight HPE models.

As an important matrix factorization model, Nonnegative Matrix Factorization (NMF) has been widely used in information retrieval and data mining research. Standard Nonnegative Matrix Factorization is known to use the Frobenius norm to calculate the residual, making it sensitive to noises and outliers. It is desirable to use robust NMF models for practical applications, in which usually there are many data outliers. It has been studied that the 2,1, or 1-norm can be used for robust NMF formulations to deal with data outliers. However, these alternatives still suffer from the extreme data outliers. In this paper, we present a novel robust capped norm orthogonal Nonnegative Matrix Factorization model, which utilizes the capped norm for the objective to handle these extreme outliers. Meanwhile, we derive a new efficient optimization algorithm to solve the proposed non-convex non-smooth objective. Extensive experiments on both synthetic and real datasets show our proposed new robust NMF method consistently outperforms related approaches.

In this paper, we propose a new weakly supervised abstractive news summarization framework using pattern based approaches. Our system first generates meaningful patterns from sentences. Then, in order to precisely cluster patterns, we propose a novel semisupervised pattern learning algorithm that leverages a hand-crafted list of topic-relevant keywords, which are the only weakly supervised information used by our framework to generate aspect-oriented summarization. After that, our system generates new patterns by fusing existing patterns and selecting top ranked new patterns via the recurrent neural network language model. Finally, we introduce a new pattern based surface realization algorithm to generate abstractive summaries. Automatic and manual evaluations demonstrate the effectiveness and advantages of our new methods. Code is available at: https://github.com/jerryli1981

In Drosophila gene expression pattern research, the in situ hybridization (ISH) image has become the standard technique to visualize and study the spatial distribution of RNA. To facilitate the search and comparison of Drosophila gene expression patterns during Drosophila embryogenesis, it is highly desirable to annotate the tissue-level anatomical ontology terms for ISH images. In ISH image annotations, the image content representation is crucial to achieve satisfactory results. However, existing methods mainly focus on improving the classification algorithms and only using simple visual descriptor. If we integrate the effective local and holistic visual descriptors via proper learning method, we can achieve more accurate image annotation results than using individual visual descriptor.