262 research outputs found
Self-supervised Facial Action Unit Detection with Region and Relation Learning
Facial action unit (AU) detection is a challenging task due to the scarcity
of manual annotations. Recent works on AU detection with self-supervised
learning have emerged to address this problem, aiming to learn meaningful AU
representations from numerous unlabeled data. However, most existing AU
detection works with self-supervised learning utilize global facial features
only, while AU-related properties such as locality and relevance are not fully
explored. In this paper, we propose a novel self-supervised framework for AU
detection with the region and relation learning. In particular, AU related
attention map is utilized to guide the model to focus more on AU-specific
regions to enhance the integrity of AU local features. Meanwhile, an improved
Optimal Transport (OT) algorithm is introduced to exploit the correlation
characteristics among AUs. In addition, Swin Transformer is exploited to model
the long-distance dependencies within each AU region during feature learning.
The evaluation results on BP4D and DISFA demonstrate that our proposed method
is comparable or even superior to the state-of-the-art self-supervised learning
methods and supervised AU detection methods.Comment: Accepted by ICASSP 202
Facial Action Unit Detection Using Attention and Relation Learning
Attention mechanism has recently attracted increasing attentions in the field
of facial action unit (AU) detection. By finding the region of interest of each
AU with the attention mechanism, AU-related local features can be captured.
Most of the existing attention based AU detection works use prior knowledge to
predefine fixed attentions or refine the predefined attentions within a small
range, which limits their capacity to model various AUs. In this paper, we
propose an end-to-end deep learning based attention and relation learning
framework for AU detection with only AU labels, which has not been explored
before. In particular, multi-scale features shared by each AU are learned
firstly, and then both channel-wise and spatial attentions are adaptively
learned to select and extract AU-related local features. Moreover, pixel-level
relations for AUs are further captured to refine spatial attentions so as to
extract more relevant local features. Without changing the network
architecture, our framework can be easily extended for AU intensity estimation.
Extensive experiments show that our framework (i) soundly outperforms the
state-of-the-art methods for both AU detection and AU intensity estimation on
the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can
adaptively capture the correlated regions of each AU, and (iii) also works well
under severe occlusions and large poses.Comment: This paper is accepted by IEEE Transactions on Affective Computin
An sTGC Prototype Readout System for ATLAS New-Small-Wheel Upgrade
This paper presents a readout system designed for testing the prototype of
Small-Strip Thin Gap Chamber (sTGC), which is one of the main detector
technologies used for ATLAS New-Small-Wheel Upgrade. This readout system aims
at testing one full-size sTGC quadruplet with cosmic muon triggers
Meta-analysis of the relationship between interleukin-6 levels and the prognosis and severity of acute coronary syndrome
This study aimed to explore the relationship between plasma interleukin 6 (IL-6) levels, adverse cardiovascular events, and the severity of acute coronary syndrome (ACS). A literature review was performed of studies regarding IL-6 and ACS extracted from databases including EMBASE, Cqvip, MEDLINE, Web of Knowledge, PubMed, Cochrane Library, China National Knowledge Infrastructure, and Wanfang data. The Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the literature. The literature was screened, its quality was evaluated, and relevant data were extracted for performing meta-analysis using RevMan software (version 5.3). A total of 524 studies were included in the initial survey. After several rounds of screening and analysis, six studies met the inclusion criteria and underwent meta-analysis using a fixed-effect model. Patients were divided into non-severe and severe groups based on the concentration of high-sensitivity C-reactive protein. Meta-analysis of the relationship between IL-6 and the severity of ACS showed that the plasma IL-6 level of patients in the severe group was significantly higher than that of patients in the non-severe group (p<0.00001). Additionally, patients with experience of major adverse cardiovascular events had significantly higher plasma IL-6 levels than did patients without experience of such events (p<0.00001). In summary, patients with ACS and high IL-6 levels tended to be in a critical condition, with a higher risk of adverse cardiovascular events and worse prognosis. Thus, IL-6 levels could indicate whether patients with ACS may have adverse cardiovascular events and determine the severity of ACS
Conditional Adversarial Synthesis of 3D Facial Action Units
Employing deep learning-based approaches for fine-grained facial expression
analysis, such as those involving the estimation of Action Unit (AU)
intensities, is difficult due to the lack of a large-scale dataset of real
faces with sufficiently diverse AU labels for training. In this paper, we
consider how AU-level facial image synthesis can be used to substantially
augment such a dataset. We propose an AU synthesis framework that combines the
well-known 3D Morphable Model (3DMM), which intrinsically disentangles
expression parameters from other face attributes, with models that
adversarially generate 3DMM expression parameters conditioned on given target
AU labels, in contrast to the more conventional approach of generating facial
images directly. In this way, we are able to synthesize new combinations of
expression parameters and facial images from desired AU labels. Extensive
quantitative and qualitative results on the benchmark DISFA dataset demonstrate
the effectiveness of our method on 3DMM facial expression parameter synthesis
and data augmentation for deep learning-based AU intensity estimation
Protective effects of Naringin in a rat model of spinal cord ischemia–reperfusion injury
Purpose: To evaluate the activity of naringin (NAR) in a rat model of spinal cord ischemic injury (SCII).Methods: Forty female rats were randomized into four groups: saline without occlusion (control; group I), SCII (group II), 50 mg/kg NAR (group III), or 100 mg/kg NAR (group IV) for 7 days prior to SCI insult (pre-treatment). Neurological and locomotor functions, antioxidant activity, edema and inflammatory markers were determined.Results: Pre-treatment with NAR considerably lowered the incidence of spinal edema, lipid peroxidation products, and inflammatory markers (TNF-α, NF-p65, IL-1β, and IL-6). It also successfully reverted the antioxidative activity to near-normal levels and improved locomotor function by protecting spinal tissue from oxidative damage and inflammatory insults. NAR administration effectively downregulated the protein expression of TNF-α and NF-κB p65 subunit in spinal tissue, thus confirming its antiinflammatory activity.Conclusion: The results suggests that NAR exhibits neuroprotective effects by inhibiting free radical generation and downregulating inflammatory markers in an SCI rat model.Keywords: Naringin, Spinal cord injury, Locomotor function, Edema, Oxidative stress, Inflammatio
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