Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation

Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D

Fatigue evaluation of metallic components based on chaotic characteristics of second harmonic generation signal

In the nonlinear ultrasonic technique, the nonlinear received signal, such as second harmonic generation (SHG) signal in higher harmonic experiments, is complicated and non-stationary time series which reflects the fatigue damage of metal components. To effectively evaluate the fatigue damage of metal components, especially the earlier fatigue damage, the chaos and fractal theory are proposed to analyze the received signal of higher harmonic experiments. Chaotic characteristics, for example Lyapunov exponent, correlation dimension and Kolmogorov entropy, are extracted to evaluate the fatigue damage. Experiments results indicate that chaotic characteristics can reasonably characterize and evaluate the fatigue state of beams, which the variation trend of chaotic characteristics has a close relationship with fatigue crack propagation. Furthermore, chaotic characteristics are very sensitive to earlier fatigue damage of used connecting rods, especially the Lyapunov exponent. Therefore, chaos and fractal theory could effectively extract the nonlinear received signals, and chaotic characteristics could reasonably evaluate the fatigue damage state of metal components

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and some objects sharing similar visual appearance. In contrast, we propose the first top-down unsupervised semantic segmentation framework for fine-grained segmentation in extremely complicated scenarios. Specifically, we first obtain rich high-level structured semantic concept information from large-scale vision data in a self-supervised learning manner, and use such information as a prior to discover potential semantic categories presented in target datasets. Secondly, the discovered high-level semantic categories are mapped to low-level pixel features by calculating the class activate map (CAM) with respect to certain discovered semantic representation. Lastly, the obtained CAMs serve as pseudo labels to train the segmentation module and produce the final semantic segmentation. Experimental results on multiple semantic segmentation benchmarks show that our top-down unsupervised segmentation is robust to both object-centric and scene-centric datasets under different semantic granularity levels, and outperforms all the current state-of-the-art bottom-up methods. Our code is available at \url{https://github.com/damo-cv/TransFGU}.Comment: Accepted by ECCV 2022, Oral, open-source

A Survey for Graphic Design Intelligence

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.Comment: 10 pages, 2 figure

Crosstalk of RNA methylation writers defines tumor microenvironment and alisertib resistance in breast cancer

BackgroundThe five major RNA methylation modifications (m6A, m1A, m6Am, m5C, and m7G) exert biological roles in tumorigenicity and immune response, mediated mainly by “writer” enzymes. Here, the prognostic values of the “writer” enzymes and the TCP1 role in drug resistance in breast cancer (BC) were explored for further therapeutic strategies.MethodsWe comprehensively characterized clinical, molecular, and genetic features of subtypes by consensus clustering. RNA methylation modification “Writers” and related genes_risk (RMW_risk) model for BC was constructed via a machine learning approach. Moreover, we performed a systematical analysis for characteristics of the tumor microenvironment (TME), alisertib sensitivity, and immunotherapy response. A series of experiments in vitro were carried out to assess the association of TCP1 with drug resistance.ResultsOne “writer” (RBM15B) and two related genes (TCP1 and ANKRD36) were identified for prognostic model construction, validated by GSE1456, GSE7390, and GSE20685 cohorts and our follow-up data. Based on the patterns of the genes related to prognosis, patients were classified into RMW_risk-high and RMW_risk-low subtypes. Lower RMW_Score was associated with better overall survival and the infiltration of immune cells such as memory B cells. Further analysis revealed that RMW_Score presented potential values in predicting drug sensitivity and response for chemo- and immunotherapy. In addition, TCP1 was confirmed to promote BC alisertib-resistant cell proliferation and migration in vitro.ConclusionRMW_Score could function as a robust biomarker for predicting BC patient survival and therapeutic benefits. This research revealed a potential TCP1 role regarding alisertib resistance in BC, providing new sights into more effective therapeutic plans