6 research outputs found
Learning Scene Flow With Skeleton Guidance For 3D Action Recognition
Among the existing modalities for 3D action recognition, 3D flow has been
poorly examined, although conveying rich motion information cues for human
actions. Presumably, its susceptibility to noise renders it intractable, thus
challenging the learning process within deep models. This work demonstrates the
use of 3D flow sequence by a deep spatiotemporal model and further proposes an
incremental two-level spatial attention mechanism, guided from skeleton domain,
for emphasizing motion features close to the body joint areas and according to
their informativeness. Towards this end, an extended deep skeleton model is
also introduced to learn the most discriminant action motion dynamics, so as to
estimate an informativeness score for each joint. Subsequently, a late fusion
scheme is adopted between the two models for learning the high level
cross-modal correlations. Experimental results on the currently largest and
most challenging dataset NTU RGB+D, demonstrate the effectiveness of the
proposed approach, achieving state-of-the-art results.Comment: 18 pages, 3 figures, 3 tables, conferenc
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
LGSQE: Lightweight Generated Sample Quality Evaluatoin
Despite prolific work on evaluating generative models, little research has
been done on the quality evaluation of an individual generated sample. To
address this problem, a lightweight generated sample quality evaluation (LGSQE)
method is proposed in this work. In the training stage of LGSQE, a binary
classifier is trained on real and synthetic samples, where real and synthetic
data are labeled by 0 and 1, respectively. In the inference stage, the
classifier assigns soft labels (ranging from 0 to 1) to each generated sample.
The value of soft label indicates the quality level; namely, the quality is
better if its soft label is closer to 0. LGSQE can serve as a post-processing
module for quality control. Furthermore, LGSQE can be used to evaluate the
performance of generative models, such as accuracy, AUC, precision and recall,
by aggregating sample-level quality. Experiments are conducted on CIFAR-10 and
MNIST to demonstrate that LGSQE can preserve the same performance rank order as
that predicted by the Frechet Inception Distance (FID) but with significantly
lower complexity
Statistical Attention Localization (SAL): Methodology and Application to Object Classification
A statistical attention localization (SAL) method is proposed to facilitate
the object classification task in this work. SAL consists of three steps: 1)
preliminary attention window selection via decision statistics, 2) attention
map refinement, and 3) rectangular attention region finalization. SAL computes
soft-decision scores of local squared windows and uses them to identify salient
regions in Step 1. To accommodate object of various sizes and shapes, SAL
refines the preliminary result and obtain an attention map of more flexible
shape in Step 2. Finally, SAL yields a rectangular attention region using the
refined attention map and bounding box regularization in Step 3. As an
application, we adopt E-PixelHop, which is an object classification solution
based on successive subspace learning (SSL), as the baseline. We apply SAL so
as to obtain a cropped-out and resized attention region as an alternative
input. Classification results of the whole image as well as the attention
region are ensembled to achieve the highest classification accuracy.
Experiments on the CIFAR-10 dataset are given to demonstrate the advantage of
the SAL-assisted object classification method.Comment: 11 pages, 9 figure