38 research outputs found
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