Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://github.com/stellating/mvEchoSeg.

Skeleton-based action recognition is crucial for natural human-computer interaction, dynamic behavior analysis, and behavior surveillance. The key challenge is to effectively capture the intrinsic local-global clues of the activity. However, it remains challenging to efficiently leverage multidimensional information related to joints' local visual appearances, global spatial relationships, and coherent temporal cues. To address this challenge, we propose a joints-centered spatial-temporal feature-fused framework for action recognition, which exploits skeleton-based graph diffusion and convolution. Specifically, we employ Partial Differential Equation (PDE) based skeleton graph diffusion to automatically activate and diffuse the salient appearance features of joints. This approach simultaneously integrates the joints' appearance clues and their hierarchical relationships at both the super-pixel level and structure level. The diffused appearance-related features of the joints are further fused with skeleton-related spatial-temporal features, and the resulting fused features are fed into a skeleton convolution network for action recognition. Our method was extensively evaluated on two public datasets (NTU-RGBD and UWA3D), and the results demonstrate the improved accuracy and effectiveness of our approach. Our code will be public.

Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. Conventional feature descriptors primarily concentrate on discontinuity of certain differential attributes at different orders that naturally give rise to their discriminative power in depicting point, line, small patch features, etc. This paper seeks novel strategies to define generalized, user-specified features anywhere on shapes. Our new region-based feature descriptors are constructed primarily with the powerful spectral graph wavelets (SGWs) that are both multi-scale and multi-level in nature, incorporating both local (differential) and global (integral) information. To our best knowledge, this is the first attempt to organize SGWs in a hierarchical way and unite them with the bi-harmonic diffusion field towards quantitative region-based shape analysis. Furthermore, we develop a local-to-global shape feature detection framework to facilitate a host of graphics applications, including partial matching without point-wise correspondence, coarse-to-fine recognition, model recognition, etc. Through the extensive experiments and comprehensive comparisons with the state-of-the-art, our framework has exhibited many attractive advantages such as being geometry-aware, robust, discriminative, isometry-invariant, etc.